EPA-460/3-74-020-B
OCTOBER 1974
IMPACT OF FUTURE USE
OF ELECTRIC CARS
IN THE LOS ANGELES REGION:
VOLUME II - TASK REPORTS
ON ELECTRIC CAR
CHARACTERIZATION
AND BASELINE PROJECTIONS
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Waste Management
Office of Mobile Source Air Pollution Control
Alternative Automotive Power Systems Division
Ann Arbor, Michigan 48105
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EPA-460/3-74-020-b
IMPACT OF FUTURE USE
OF ELECTRIC CARS
IN THE LOS ANGELES REGION:
VOLUME II - TASK REPORTS
ON ELECTRIC CAR
CHARACTERIZATION
AND BASELINE PROJECTIONS
Prepared by
W. F. Hamilton, J. C. Eisenhut,
G. M. Houser, and A. R. Sjovold
General Research Corporation
P.O. Box 3587
Santa Barbara, California 93105
Contract No. 68-01-2103
EPA Project Officer:
C. E. Pax
Prepared for
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Waste Management
Office of Mobile Source Air Pollution Control
Advanced Automotive Power Systems Development Division
Ann Arbor, Michigan 48105
October 1974
-------
This report is issued by the Environmental Protection Agency to report
technical data of interest to a limited number of readers. Copies are
available free of charge to Federal employees, current contractors and
grantees, and nonprofit organizations - as supplies permit - from the
Air Pollution Technical Information Center, Environmental Protection
Agency, Research Triangle Park, North Carolina 27711; or, for a fee,
from the National Technical Information Service, 5285 Port Royal Road,
Springfield, Virginia 22151.
This report was furnished to the U.S. Environmental Protection Agency
by General Research Corporation in fulfillment of Contract No. 68-01-2103
and has been reviewed and approved for publication by the Environmental
Protection Agency. Approval does not signify that the contents necessarily
reflect the views and policies of the agency. The material presented in
this report may be based on an extrapolation of the "State-of-the-art." Each
assumption must be carefully analyzed by the reader to assure that it is
acceptable for his purpose. Results and conclusions should be viewed
correspondingly. Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.
Publication No. EPA-460/3-74-020-b
11
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INTRODUCTION
This report is published in three volumes:
Volume 1, Executive Summary and Technical Report
Volume 2, Task Reports on Electric Car Characteristics
and Baseline Projections
Volume 3, Task Reports on Impact and Usage Analyses
Volume 1 is a comprehensive account of the effects that electric
cars would have on the air quality, energy use, and economy of the Los
Angeles region in 1980-2000. Volumes 2 and 3 contain ten individual
reports documenting the analyses on which Volume 1 is based. These
reports detail the methods, data, assumptions, calculations, and results
of the study tasks, and were originally published at the conclusion of
each task.
Task reports in Volume 2 project future characteristics of electric
cars and of the Los Angeles region in which they would be used, as follows:
1. D. Friedman and J. Andon (Mlnicars, Inc.) and W. F. Hamilton,
Characterization of Battery-Electric Cars for 1980-2000
Postulates electric vehicle performance requirements, projects
representative future battery characteristics, calculates urban
driving range versus total car weight, and estimates energy
and material requirements for selected driving ranges.
2. G. M. Houser, Population Projections for the Los Angeles Region,
1980-2000
Projects population of California's South Coast Air Basin, which
includes greater Los Angeles, by county and age group.
-------
3. W. F. Hamilton and G. M. Houser, Transportation Projections
for the Los Angeles Region. 1980-2000
Projects Los Angeles freeway and transit networks, auto
population, auto usage, auto size and age distributions, and
average fuel consumption.
4. J. Eisenhut, Economic Projections for the Los Angeles Region,
1980-2000
Projects employment and income for the South Coast Air Basin,
and the payroll and employment of businesses involved in
production, distribution, and maintenance of automobiles and
parts.
5. A. R. Sjovold, Electric Energy Projections for the Los Angeles
Region, 1980-2000
Summarizes the US energy situation as forecast in recent
studies, and in this context projects electric energy produc-
tion and consumption in the South Coast Air Basin, noting
energy available for electric car recharging and its basic
sources.
Task reports in Volume 3 project impacts due to various levels of
electric car use and investigate possible future levels of use, as follows:
6. J. R. Martinez and R. A. Nordsieck, An Approach to the Analysis
of the Air Quality Impact of Electric Vehicles
Selects the "DIFKIN" computer model and linear rollback as means
for analyzing future air quality in the South Coast Air Basin,
designates important cases for investigation, and details
required methodology.
7. J. R. Martinez and R. A. Nordsieck, Air Quality Impacts of
Electric Cars in Los Angeles
Forecasts stationary and vehicular pollutant emissions in
spatial and temporal detail, with and without electric cars,
and calculates consequent air quality levels relative to
Federal standards.
8. A. R. Sjovold, Parametric Energy, Resource, and Noise Impacts
of Electric Cars in Los Angeles
As a function of percentage electric car use, forecasts total
energy consumption and petroleum consumption in the South Coast
Air Basin through the year 2000; compares annual consumption
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and rolling inventory of key electric car materials with past
and projected US production, consumption, and reserves;
analyzes possible reductions of community noise from electric
car use.
9. J. C. Eisenhut, J. A. Cattani, and F. J. Markovich, Parametric
Economic Impacts of Electric Cars in Los Angeles
Projects life cycle costs of alternative electric cars in
comparison with conventional cars; analyzes and projects changes
in employment and payroll in industry segments impacted by
electric cars, including service stations, battery manufactur-
ing, auto parts and repairs, and auto sales; considers overall
regional and national economic impacts of electric cars.
10. W. F. Hamilton, Usage of Electric Cars in the Los Angeles
Region. 1980-2000
Analyzes 1967 data to determine distributions of daily driving
range in Los Angeles and the applicability of limited-range
electric cars; reviews market trends and estimates the potential
free-market sales of electric cars in the South Coast Air
Basin; hypothesizes particular levels of electric car use for
impact evaluations; and considers relative economic incentives
likely to be required to obtain these usages.
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TASK REPORT 1
CHARACTERIZATION OF BATTERY-ELECTRIC
CARS FOR 1980-2000
D. Friedman
J. Andon
(Minicars, Inc.)
W.F. Hamilton
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ABSTRACT
Possible battery-electric cars for 1980-2000 are characterized in
sufficient detail to support a comprehensive study of their potential
impacts if used in the Los Angeles area. The characterization is based
on assumptions that driving range should suffice for significant segments
of travel at reasonable overall cost, that improved safety will be required,
and that performance need only be sufficient to maintain traffic flow.
After a parametric analysis of weight versus range between recharges
in urban driving, specific ranges are selected, and energy and materials
requirements determined for two- and four-passenger cars using alternative
batteries representative of possible future types. Although both cars
are in the subcompact size category, the four-passenger car has freeway
capability and adequate daily range with lead-acid batteries for second-
car use, or with advanced batteries for more general use. The four-
passenger car characteristics include:
Battery Type
*
Test Weight, pounds
kilograms
Battery Weight, pounds
kilograms
Urban Driving Range, miles
kilometers
Range at 30 mph, miles
kilometers
Recharger Energy, KWH per mile
KWH per
kilometer
Lead-
Acid
3,975
1,803
1,500
681
54
87
183
295
0.79
0.40
Nickel-
Zinc
3,530
1,602
1,090
495
144
232
375
604
0.51
0.32
Zinc-
Chlorine
2,950
1,338
570
259
145
233
309
497
0.41
0.25
Lithium-
Sulfur
2,655
1,204
300
136
146
235
317
510
0.45
0.28
With 450-pound payload.
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CONTENTS
SECTION
1
2
3
4
5
PAGE
ABSTRACT
INTRODUCTION
VEHICLE PERFORMANCE
ELECTRIC VEHICLE PERFORMANCE REQUIREMENTS
3.1 Vehicle Types
3 . 2 Power
3.3 Weight
3.4 Aerodynamic Drag
3.5 Drive Line Efficiencies
3.6 Tires
3.7 Comfort and Convenience
3.8 Vehicle Space Design
3.9 Safety
DRIVING CYCLES FOR DETERMINING VEHICLE RANGE
FUTURE ELECTRIC VEHICLE BATTERIES
5.1 Lead-Acid Batteries
5.2 Nickel-Zinc Batteries
5.3 Zinc-Chlorine Batteries
5.4 Lithium-Sulfur Batteries
PARAMETRIC RANGE CALCULATION
6.1 Computer Program Verifications
6.2 Cars with Current Lead-Acid Batteries
6.3 Cars with Future Storage Batteries
6.4 Range Improvement
i
1-1
1-4
1-8
1-8
1-11
1-13
1-15
1-16
1-16
1-19
1-20
1-26
1-28
1-33
1-34
1-39
1-45
1-47
1-54
1-55
1-56
1-59
1-61
iii
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CONTENTS (Cont.)
SECTION
APPENDIX
SELECTION OF DRIVING RANGE
7.1 Lead-Acid Battery Cars
7.2 Other Battery Cars
ENERGY AND MATERIAL REQUIREMENTS
8.1 Energy Requirements
8.2 Material Requirement
COMPUTER PROGRAMS
REFERENCES
PAGE
1-64
1-64
1-70
1-72
1-72
1-76
1-81
1-107
iv
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ILLUSTRATIONS
NO. PAGE
2.1 Weight-to-Power Ratios for Various Automobiles 1-4
3.1 Simplified Theoretical Effect of Acceleration at a Traffic-
Light-Controlled Junction for Varying Green Light ("Go")
Times 1-10
3.2 Performance Curves for Vehicles with 3- and 4-mph/sec
Average Acceleration 1-12
3.3 Power Requirements for Acceleration and Hill Climbing 1-14
3.4 Electric Car Drive Train Functional Organization 1-17
3.5 Possible Two-Passenger Vehicle Configuration 1-21
3.6 Possible Spatial Arrangement of the Two-Passenger Elec-
tric Vehicle Powered by Lead-Acid Batteries 1-22
3.7 Possible Four-Passenger Vehicle Configuration 1-23
3.8 Possible Spatial Arrangement of the Four-Passenger Elec-
tric Vehicle Powered by Lead-Acid Batteries 1-24
3.9 Possible Spatial Arrangement of the Four-Passenger Elec-
tric Vehicle Powered by Lithium-Sulfur Batteries ' 1-25
4.1 Electric Vehicle Driving Cycles 1-29
4.2 Vehicle Speed Over the Federal Driving Cycle 1-31
4.3 Range Calculation Comparison of the Metropolitan Area
Driving Cycle and the Federal Driving Cycle 1-32
5.1 Specific Power Versus Specific Energy for Lead-Acid
Electric Car Batteries 1-35
5.2 Assumed Specific Power Versus Specific Energy for 1980
Lead-Acid Electric Car Batteries 1-36
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ILLUSTRATIONS (Cont.)
NO. PAGE
5.3 Assumed Life of 1980 Lead-Acid Electric Car Batteries 1-37
5.4 Typical Modified-Potential Charge Profile for Lead-Acid
Electric Vehicle Batteries 1-38
5.5 Lead-Acid Electric Vehicle Batteries, Capacity Versus
Temperature 1-40
5.6 Effect of Temperature on Lead-Acid Battery Capacity 1-40
5.7 Lead-Acid Electric Vehicle Batteries, Voltage and Current
Versus Percentage of Discharge for a 100-AH Battery Dis-
charged at 6-hour Rate at 77°F 1-41
5.8 Specific Power Versus Specific Energy Projected for
Nickel-Zinc Traction Batteries 1-42
5.9 Projected Cycle Life for Ni-Zn Electric Vehicle Battery 1-43
5.10 Projected Capacity Reduction for Ni-Zn Electric Vehicle
Battery 1-44
5.11 The EDA Zinc-Chlorine Battery System 1-47
5.12 Zinc-Chlorine Cell Discharge Curve 1-47
5.13 Specific Power Versus Specific Energy Projected for the
Zinc-Chlorine Battery System 1-48
5.14 A Conceptual Lithium-Sulfur Electric Car Battery 1-52
5.15 Power and Energy Density Curves for Various Electric
Vehicle Batteries 1-53
6.1 Urban Driving Range for Cars with Current Storage
Batteries 1-58
6.2 Urban Driving Range for Cars with Future Storage
Batteries 1-60
6.3 Constant Speed Driving Range for Cars with Future
Storage Batteries 1-62
7.1 Cumulative Frequency of Daily Auto Usage 1-65
vi
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ILLUSTRATIONS (Concl.)
NO. PAGE
7.2 Two-Passenger Car Maximum Range on the SAE Residential Driv-
ing Cycle as a Function of Lead-Acid Battery Weight and Cost 1-67
7.3 Four-Passenger Car Maximum Range on the SAE Metropolitan
Area Driving Cycle as a Function of Lead-Acid Battery
Weight and Cost 1-67
7.4 Two-Passenger Car Lead-Acid Battery Depreciation Costs
Versus Battery Weight for Average Usage 1-68
7.5 Four-Passenger Car Lead-Acid Battery Depreciation Costs
Versus Battery Weight for Average Usage 1-68
vii
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viii
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TABLES
NO.
2.1
3.1
4.1
5.1
5.2
5.3
6.1
7.1
8.1
8.2
8.3
8.4
8.5
8.6
Electric Cars of the Last Decade
Passenger Car Use
Electric Vehicle Driving Cycles
Summary Projection of Battery Characteristics
Zinc-Chlorine Battery Goals
Tentative ANL Performance Goals for Electric Automobile
Batteries;
Accessory Power Requirements
Characteristics of Selected Cars
Estimated Energy Requirements
Comparative Energy Usage of Lead-Acid Battery Cars
Battery Material Weights
Electric Motor Material Weights
Controller Material Weights
Gasoline Power Material Weights Eliminated by Conversion
PAGE
1-6
1-9
1-30
1-33
1-46
1-50
1-59
1-71
1-73
1-74
1-77
1-78
1-78
to Battery Power 1-79
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1 INTRODUCTION
Work reported here Is part of a larger study of the impacts of elec-
tric cars used for personal urban transportation. Impacts of principal
concern include effects on the mobility of drivers, on air quality, on
energy production and consumption, on resource use, and on the economy.
The focus is on impacts in the Los Angeles region in the years 1980, 1990,
and 2000.
The object of this report is to summarize electric car characteris-
tics for use in the impact calculations of the overall study. In this
limited context, car characteristics of major concern include daily driv-
ing range, acceleration and gradability, accommodations for passengers and
luggage, safety, energy consumption, and required materials and components.
Battery technology and car design thus need be pursued only to the extent
required to establish these characteristics.
The basic approach taken here is to characterize electric cars
parametrically as a function of range between recharges. Because energy
storage capability of present and future battery systems is limited, daily
range capability is critical in determining the applicability of the elec-
tric car to typical travel needs as well as its energy consumption,
materials requirements, and costs. Other characteristics of electric
cars were not given parametric treatment since an excessive number of cases
would thus require analysis.
This report begins with a brief review, in Sec. 2, of the charac-
teristics of existing gasoline cars and existing or proposed electric
cars. With this background, performance requirements and parameters
other than daily driving range are considered and selected in Sec. 3;
these include accommodations, acceleration, weight, aerodynamics, drive-
line efficiency, tire losses, and safety provisions. Section 3 also
presents basic formulas used in calculating power requirements. Next,
in Sec. 4, driving cycles for calculating power requirements and driving
1-1
-------
range between recharges are defined. Battery technology is reviewed in Sec.
5, and promising types for vehicular use are described. Daily driving range
is calculated in Sec. 6 for the different battery types as a function of
vehicle weight. Particular daily ranges for further consideration in
the impact study are selected in Sec. 7 after a brief review of typical
driving patterns and the costs of battery depreciation. For these cases,
finally, energy and materials requirements are estimated, in Sec. 8, for
the battery systems and the electric vehicles.
Since the work reported here is intended only to provide an orderly
basis for an overall study of electric car impacts, detailed design analy-
ses, innovative approaches, or even a thorough review of the literature
have not been attempted. The literature, in fact, is immense: one author-
ity has noted that "...it is almost impossible to say anything on the subject
which has not already been printed." References 1-3 will introduce the
reader concerned with further detail to almost 2,000 electric vehicle
papers, books, and articles.
Because it has been a subject of frequent concern, the competitive
position of electric cars deserves special mention here. Previous investi-
gations have often assumed, implicitly or otherwise, that electric cars
must be competitive with conventional ICE cars as to acceleration, or
accommodations, or costs, or driving range. Here, however, no such
assumptions are necessary or appropriate. The electric cars characterized
here are intended to provide maximum net benefits to broad classes of
users at given levels of battery technology. The prospective market
penetration of such cars, and the particular circumstances under which
they would actually find wide usage, are considered elsewhere in the
overall impact study.
Finally, it should be noted that this study is limited to battery-
powered electric automobiles. It does not consider other alternatives
to conventional ICE cars, such as fuel-cell electrics, hybrid-electrics,
Internal-combustion engine.
1-2
-------
steam and gas turbine cars, or ICE cars drastically redesigned for minimum
energy consumption and environmental impact.
1-3
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2 VEHICLE PERFORMANCE
Vehicles currently in use vary widely in performance. Predominant
vehicle parameters that determine performance (acceleration and top speed)
are vehicle weight, power available, aerodynamic resistance, rolling
(road) resistance, and drive ratio.
Aerodynamic resistance is not significant at speeds under 30 mph,
so it is possible to obtain a preliminary indication of a vehicle's accel-
eration and hill-climbing performance from its weight-to-power ratio.
Figure 2.1 shows curb weights and powers for a number of passenger cars,
4
mostly four-door sedans with six- or eight-cylinder engines. The lowest
weight-to-power ratio shown, indicating high performance, is 6.8 kg/kW
5000
4000
.3000
2000
1000
2500
2000
1500
1000
500
70 5040 30
10
PONT1AC FIREBIRD
5 kg/kW
CHARACTERIZED
LEAD-ACID ELECTRICS
EXISTING ELECTRICS
AMC
GM
FORD
CHRYSLER
FOREIGN
I
50
100 150
POWER, kW
200
250
50
100
150 200
POWLR, hp
250
300
Figure 2.1. Weight-to-Power Ratios for Various Automobiles
1-4
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(11.2 Ib/hp) for the 1972 Pontiac Firebird, and the highest ratio for
1C vehicles, indicating low performance, is 25 kg/kW (41 Ib/hp) for the
1972 Hondas and VW beetles.
A selected list of electric cars proposed or built in the last 10
years is presented in Table 2.1 to show representative design goals and
claims. Typical electric car weight and power parameters from Table 2.1
are plotted in Fig. 2.1 to compare their performance criteria. Most of
the electric vehicles have weight-to-power between 20 and 70 kg/kW (49
to 115 Ib/hp). Three electric vehicles (Cortina Estate Car, City Car
Pinto, and GM Electrovair) are in the 18-to-20-kg/kW (30-to-33-lb/hp)
range, which compares favorably with the majority of gasoline-powered
automobiles. These higher performance electric cars suffer from limited
range between battery charges and high cost for the battery power source.
As Fig. 2.1 indicates, most of the electric cars, especially those with
lead-acid batteries, are poor performers. The two lead-acid battery-
electric cars characterized in this report are also shown. These perfor-
mances are higher than for most of the other electric cars of Fig. 2.1,
but still lie at the lower fringe of conventional car performance.
A comparison of the estimated practical maximum energy storage
capability of gasoline with that of several different battery materials
points to the main problem of electric vehicle power sources:
Gasoline 1,130 W'hr/lb
Lead-acid battery 20 W'hr/lb
Nickel-zinc battery 50 W'hr/lb
Lithium-sulfur battery 140 W'hr/lb
Gasoline has an advantage of eight times over the highest-energy-storage
battery. Since energy storage is a direct indication of the distance an
electric vehicle can travel between battery charges, it is evident that
electric vehicle range between charges is the most important design param-
eter. Requiring enough battery energy to achieve a minimum acceptable
range between charges usually means large battery packs and allocating a
good portion of the vehicle weight to the batteries.
1-5
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TABLE 2.1
ELECTRIC CARS OF THE LAST DECADE
Vehicle
Curb Drive Motor(s)
Uelght, Ib
Comuta 1,200 Tvt> 5 hp; Series
DC
CM 512 1,250 8-1/2 hp; Series
DC (54 Ib)
Sundancer 2 1,600
FSB
Marquette 1,730 Two 4-1/2 hp
Ueatlnghouie DC (45 Ib)
Henney Kilowatt 2,135 7.1 hp; Series
Union .Electric DC
Yardney 1,600 7.1 hp; Series
DC
Allectrlt- 2,160 7.1 hp; 72V DC
Uest Penn
Power Co.
"Mini" CE 2,300 10.9 hp; DC
Motor
American Molora 1,100
and Gullon ]nd.
ESB
Renault
Rowan Klei-liu- 1,300 2 DC Compound
Alleirlrlr 11 2,300 7.1 hp ; DC
West IVim Motor
Power Co.
Sup,- 1 -IMf. I in Two '1 hp
Motif 1 A
Carwood and
Stelher 1 nd .
Con. in., 3,086 40 hp; 100V
taint.- C.ii (150 Ib)
Comet Ford .3,800 85 hp
City Car Pinto 1.200 40 hp
Mars II F.lrculr Fuel .1,640 15 lip; DC
Propulsion, [lie.
Electrovalr 3.400 100 hp; AC
CM Induction
tlectrovsn 7.100 125 hp; AC
CM Induction
All Is Chalmers 3,440
Karmann-Chla
Chryslpr-Simc.i
Klei-trl.- Fuel 1,400
Propulsion. Int:.
Fa 1.. in ... 25 hp; AC In-
l.int-.ir -Alpha diirtiun motor
Maximum Knergy Source
Speed, and Range, Miles
mph Capacity
40 Lead-add J7 (a 25 mph
48V (384 Ib)
40 Lead-acid 47 M 30 mph
84V (329 Ih)
l.ead-uc.ld 70-75 on SAE
86V (750 Ib) Resident lal
25 Lead-acid 50
72V (800 Ib)
8 KUH
40 Lead-acid 40
(800 Ib)
8 KUH
55 Silver-zinc 77
12 KUH
(240 Ib)
50 Lead-acid 50
72V (900 Ib)
9 KUH
55 Lead-acid and 100 0 40 mph
Nickel Cadmium
50 Lithium-Nickel 150 with regeneration
Fluoride (150
Ib) and Nickel
Cadmium (100 Ib)
40 Lead-acid 25-35
(72V)
4(1 Leatl-acid 100
50 Lead -acid 'if)
(900 Ib)
1
52 LeaJ-ii. 1,1
(520 Ib)
60 Nickel -Caitralun. (9.9 (' '/', oph
(900 Ih)
70 Sodliini-Sul tur
(1086 Ih)
50 Lead-add (956 Ih) J9 i« '0 mph
60-65 Lead-acid 9oV 70-120
(1700 Ib)
30 KVH
80 Silver-zinc 530V 40-80
(680 Ib)
19.5 KWH
70 Hydrogen-Oxygen Fuel 100-150
Cell 180-270 KUH
Lead -acid 120V 60 0 60 mph
(1534 Ib)
l,ead-ac Id 40
(1400 Ib)
H5 Lead-acid Cobalt 150-175
60 Lithium-nickel 75 (< 30 mph
Fluoride (360 Ib)
1-6
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Since energy storage is difficult and costly in electric cars, maxi-
mum overall efficiency is much more important than for conventional cars.
All possible parameters that have to do with power consumption—aerodynamic
drag, rolling friction losses, drive line efficiency, accessory power
requirements, etc.—must be carefully considered in the design of electric
vehicles.
1-7
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3 ELECTRIC VEHICLE PERFORMANCE REQUIREMENTS
3.1 VEHICLE TYPES
Largely because of their inherent weight and cost, electric cars
are ordinarily conceived as relatively small utilitarian vehicles. For
this impact assessment it is desirable to characterize two rather dif-
ferent alternatives in this "small and utilitarian" category. The first
presents the bare minimum in accommodations, performance, and daily range,
with limited mobility adequate only for key trips. The second, a much
larger vehicle, offers accommodations and performance approaching that of
conventional subcompact cars, together with sufficient range to provide
unimpaired mobility relative to typical daily driving patterns. The first
vehicle characterized here is capable of supporting key public needs on
roads and streets constituting feeder and collector-distributor routes,
largely used for home-to-work and family business trips. Of course, some
of these trips currently involve freeway travel but 68 percent of all
trips, as indicated on the next page, are short enough so that a lack
of freeway capability may not incur an unreasonable time penalty.
Table 3.1, from Ref. 5, shows that for many trips the seating capa-
city need not exceed two, supporting the assumption that basic daily tra-
vel may be equivalent to a to-and-from-work trip and a business-related-
to-work trip totaling 25 to 30 miles per day (10,000 miles per year). It
is estimated that the average speed for work trips (during peak hours) is
less than 25 mph, with peak speeds of 30 to 40 mph for non-arterial and
non-freeway usage. (The premise here is that the average person attempts
to find employment within a half-hour drive one-way—for a 10-mile trip,
the average velocity is thus 20 mph.)
The second vehicle characterized must satisfy the public needs for
use of arterial roads and freeways for most work, family, social, and
recreational trips—other than vacations. As such, its range must be
greater and it must provide for an occupancy of three to four. Accordingly
1-8
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TABLE 3.1
PASSENGER CAR USE'
Purpose of Travel
Earning a Living:
To and From Work
Business Related
to Work
Total
Family Business:
Medical and
Dental
Shopping
Other
Total
Educational, Civic
or Religious
Social and
Recreational :
Vacations
Visit Friends or
Relatives
Pleasure Rides
Other
Total
All Purposes
Percentage
Trips
32.3%
4.4
36.7
1.8
15.4
14.2
31.4
9.4
0.1
9.0
1.4
12.0
22.5
100.0%
Distribution
Travel
34.1%
8.0
42.1
1.6
7.6
10.4
19.6
5.0
2.5
12.2
3.1
15.5
33.3
100.0%
Average
Trip Length
One-Way
(Miles)
9.4
16.0
10.2
8.3
4.4
6.5
5.5
4.7
165.1
12.0
19.6
11.4
13.1
8.9
Average
Occupants
Per Car
1.4
1.6
1.4
2.1
2.0
1.9
2.0
2.5
3.3
2.3
2.7
2.6
2.5
1.9
it must operate at speeds of the order of at least 50 mph; in order to
retain some performance margin it should be able to achieve a top speed
of 65 mph.
1-9
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Figure 3.1 illustrates traffic flow (per lane) through an intersec-
tion for various green light ("go") times as a function of acceleration
capability in miles per hour per second. As shown, the number of vehicles
through an intersection does not increase appreciably above 3 mph/sec of
acceleration for longer "go" times. This acceleration level allows 1,050
vehicles to pass through an intersection each hour for 30-second "go"
times. Although this acceleration is significantly less than the perfor-
mance Americans currently consider acceptable, it is comparable to the
performance they normally achieve at traffic signals. The authors have
assumed that in the postulated environment of 1985 it would be tolerable
to the driver and would not significantly reduce accident avoidance capa-
bility. For comparison, the VW Beetle accelerates at an average of
Figure 3.1.
1300r
5 1200
o:
uj
Q-
i: 1100
o
1000
900
aoo
700
= 600
500
60 sec
30 set
j_
012345
ACCELERATION, mph/sec'
Simplified Theoretical Effect of Acceleration at a
Traffic-Light-Controlled Junction for Varying Green
Light ("go") Times
1-10
-------
5 mph/sec to 30 mph, while the Pinto and the Vega can achieve an average
of 6 mph/sec to 30 mph. A figure of 3 mph/sec corresponds roughly to the
performance of the '54 VW with its 30-hp engine. The point is that urban
traffic flow would not be seriously compromised by such a performance
capability, so it was chosen for the two-passenger urban vehicle.
For the four-passenger car, the ability to merge with fast-moving
freeway traffic requires somewhat higher acceleration capability. Accord-
ingly, an acceleration capability averaging 4 mph to 40 mph was selected,
with nearly 5 mph/sec to 30 mph. This, as noted above, is close to the
performance of the VW Beetle, though somewhat less than that of domestic
subcompacts.
The acceleration requirements for these two vehicles are shown in
Fig. 3.2. The 3-mph/sec average (0 to 30 mph) acceleration for the urban
car indicates that 30 mph is reached at the end of 10 seconds; the 4-mph/
sec average (0 to 40 mph) acceleration for the four-passenger vehicle
indicates 30 mph is reached in six seconds, and 40 mph is reached at the
end of 10 seconds.
3.2 POWER
Power requirements to achieve the performance of Fig. 3.2 depend on
several related factors: total vehicle weight, aerodynamic drag, tire
losses, and drive-line efficiencies. Total vehicle weight is primarily
dependent on battery weight, which, in turn, is determined by battery
performance and by design range between recharges.
Design range between recharges is treated parametrically in Sec. 6;
here we offer examples of power requirements for specific cars, and de-
scribe the methods for calculating the various factors noted above which
influence power requirements.
1-11
-------
3mph/sec, 0 TO 30 mph
5r 50 r
u 4
o> ^
0.
z" 3
o
40
30
_ 8
20
10
10 15
TIME, sec
20
u
O)
in
80
70
60
4 mph/sec, 0 TO 40 mph
50
CL
40
30
20
10
I
10 15
TIME, sec
20
25
Figure 3.2. Performance Curves for Vehicles with 3- and 4-mph/sec
Average Acceleration
-------
The highest power requirements are for cars powered by lead-acid
battery; even for very long design ranges cars powered by other battery
types are lighter. The battery output power requirements of two- and
four-passenger lead-acid battery cars are shown as examples in Fig. 3.3.
Their characteristics, as developed in subsequent sections of this paper,
are summarized as follows:
Two-Passenger Four-Passenger
Car Car
Test Weight 2,100 Ib 3,975 Ib
Aerodynamic Drag Coefficient 0.4 0.4
? o
Frontal Area 18 ft 22 ft'
Mechanical Efficiency 90% 90%
Electrical Efficiency 70% 80%
Tire Profile Low Aspect Ratio Low Aspect Ratio
Maximum Power 33 hp 85 hp
3.3 WEIGHT
Safety considerations strongly affect the electric car characteris-
tics, as does the weight of the required power source. The structure is
reinforced by an amount dependent on the weight of the power source.
Using these main vehicle criteria, the two-passenger vehicle may be con-
ceptualized as a somewhat wider and longer Honda 600, and the four-pas-
senger vehicle as a 1972 Pinto modified with lighter bumpers, seats and
other features to reduce weight without loss of crashworthiness. These
vehicles have curb weights of 1,350 and 2,100 pounds, respectively. Re-
moving their engines decreases their curb weight to 1,070 and 1,530 pounds.
Using these last two weights as the basis for our conceptual battery-
electric versions, the test weight of these two cars breaks down as
follows:
1-13
-------
60 r
7.5:?. SLOPE
5% SLOPE
.5V, SLOPE
It SLOPE (ROAD LOAD)
I
20 30
VELOCITY, mph
40
50
(a) Two-Passenger Car
3
O
o.
100
80
60
40
20
FIR
SECOND GEAR
0 10 20 30 40 50 60
VELOCITY, mph
(b) Four-Passenger Car
Figure 3.3. Power Requirements for Acceleration and Hill-Climbing
1-14
-------
Two-Passenger Four-Passenger
Car (35-mi range) Car (55-mi range)
Weight Without Power 1,100 Ib 1,625 Ib
Battery 550 Ib 1,500 Ib
Payload 300 Ib 450 Ib
Motor 117 Ib 315 Ib
Controller 33 Ib 85 Ib
Curb Weight 1,800 Ib 3,525 Ib
Total Test Weight 2,100 Ib 3,975 Ib
The weight without power is the basic vehicle weight without engine, plus
an allowance for additional structure to support the batteries. This al-
lowance was taken as 10 percent of battery weight in excess of the origi-
nal engine system weight (30 pounds for the two-passenger car and 95 pounds
for the 4-passenger car above). Recent developments have not altered the
performance which was anticipated in 1968 from the electric power-train
components for the near term (1980). DC electric motors are anticipated
to weigh 3.5 to 4 pounds per (peak) horsepower; controls weigh between 0.7
and 1.3 pounds per horsepower. Although, for peak performance capability,
lead-acid batteries might weigh 8 to 10 pounds per horsepower (a power
density of 75 to 100 W/lb), the relationship between power and energy
density suggests a minimum battery weigl
horsepower (a power density of 50 W/lb).
density suggests a minimum battery weight of about 15 pounds per peak
That the battery weights selected for these cars are heavier than
*
those required for original minimum performance goals is due to energy
considerations rather than power requirements. Whereas power available
determines vehicle acceleration, energy determines vehicle range between
charges. (Energy requirements are discussed in Sec. 8.1.)
3.4 AERODYNAMIC DRAG
The aerodynamic drag of vehicles is calculated from the following
expression:
As given by Fig. 3.2.
1-15
-------
D = 0.00119CDAV2
where C = drag coefficient
A = frontal area of the vehicle, ft
V = velocity, ft/sec
The drag coefficient for the assumed vehicles was considered to be 0.4,
which is midway between the lowest and highest drag coefficients of today's
automobiles (0.3 and 0.5).8~10
The two-passenger car was estimated to have an average body width
of 4-1/2 feet and a body height of 4 feet, resulting in a frontal area of
18 square feet. The four-passenger car was estimated to have an average
body width of 5-1/4 feet and a body height of 4-1/4 feet, resulting in a
frontal area of 22 square feet. The four-passenger car is wider than the
two-passenger car because of its greater side crush requirements.
3.5 DRIVE LINE EFFICIENCIES
Efficiencies for the drive-line are identified in Fig. 3.4. A
mechanical efficiency (which includes transmission, final drive gears,
and differentials) of 90 percent is widely accepted in present vehicle
design. Electrical efficiencies (motor and controls) of 70 and 80 per-
cent (Fig. 3.3) may be optimistic by today's practice, but should be
attainable by the 1980s. These figures do not include battery or
charger. The heavier four-passenger car has a two-speed automatic trans-
mission. By increasing motor speed and efficiency at low driving speeds,
this transmission accounts for its higher electrical efficiency of 80 percent,
3.6 TIRES
The rolling friction of tires should be held to a low value, since
it substantially affects the power needed to propel the car, particularly at
the low speeds characteristic of the vehicles described here. Recent
tire developments have greatly reduced tire-connected losses. The table
following lists power losses for several tire designs and compares their
effects on driving characteristics with a standard-design tire.
1-16
-------
ELECTRICAL DRIVE EFFICIENCY
- CHARGER -
EFFICIENCY
-BATTERY
EFFICIENCY
MECHANICAL DRIVE-
EFFICIENCY
Figure 3.4. Electric Car Drive Train Functional Organization
-------
TIRE PERFORMANCE AND HORSEPOWER LOSS AT 50 MPH
SOURCE: Goodyear Tire and Rubber Company
11
Design Rating
Standard 6.70-13
(bias ply)
Belted Radial
Special Compounding
Gauge Reduction
Reduced Deflection
Low Aspect Ratio
Total hp Loss
Ride Wear Stability Traction 2500-Pound
Vehicle
100
100
100
100
6.0
80
80
80
75
65
175
140
130
140
150
95
95
90
95
100
105
90
90
90
95
4.8
3.8
3.6
2.8
2.5
As is evident, the low aspect ratio tire would be the desired choice
for electric car tires. While ride will suffer, the limited energy of an
electric vehicle is the overriding consideration.
The rolling resistance of a car is determined by the following
12
expression:
R = (W/50)(l + 0.0014V + 0.000012V )
where R = rolling resistance, Ib
W = vehicle weight, Ib
V = vehicle velocity, ft/sec
Ninety percent of this resistance can be attributed to the tire. The re-
maining losses are due to bearing and seal friction. Therefore, when
tire resistance is lowered, the above road resistance expression can be
reduced by that part of the amount shown in the above table. For instance,
1-18
-------
the low aspect ratio tire vehicle could be considered as having a rolling
resistance of [(2.5/6.0)790% =] 46 percent of the standard bias ply tire
resistance.
3.7 COMFORT AND CONVENIENCE
Vehicle interior dimensions on today's automobiles are primarily
dictated by marketing considerations, not weight. Therefore, the vehicles
postulated in this report could be two-door coupes with easy entrance
and egress, and with leg room, head room and seating width comparable to
current full-size vehicles. The occupant would sit further aft in the
vehicle relative to the base vehicle's current seating configuration to
allow for maximum structural and interior stroke during impact.
Passenger compartment heating can require considerable power in cold
climates: up to 70 percent of propulsion energy. In the mild weather
of Los Angeles, however, sufficient heating for passenger comfort should
be derivable from power losses in the electric motor and controller.
In Los Angeles, there are no days with a minimum temperature below
freezing in the average year. The average daily minimum temperature in
the coldest month of the year, January, is 45°. Average annual heating
load is 1,451 degree-days, less than one-third that of such large cities
as St. Louis and Philadelphia.
As will be shown in Sec. 8, battery output of the four-passenger
car ranges from 0.27 to 0.35 KWH/mi in urban driving. At 80 percent effi-
ciency, motor and controller heat rejection is 0.054 to 0.07 KWH/mi, and,
at the average driving cycle speed of 24 mph, 1.3 to 1.7 kW of heat are
thus available (4,400-5,700 Btu/hr). This is over twice the heat similarly
available in the GM 512 electric car (0.66 kW, or 2,260 Btu/hr, at 30 mph
13
on a 40° day). Since the GM 512's heating was deemed adequate for pas-
senger comfort on 40°-50° overcast days, the four-passenger cars charac-
terized here should achieve equal adequacy in Los Angeles in January.
1-19
-------
A preheater operated from the power lines during recharging just
before car use might be desirable. In the GM 512, 1.2 KWH of preheating
was required on 15°-20° days. For the larger cars characterized here
for warmer climates, no more energy for preheating should be required
(1.2 KWH is less than 10 percent of recharge energy required for propul-
sion purposes in typical daily driving).
Air conditioning was not included in cars characterized here. In
1973, only 30.4 percent of US subcompacts sold were equipped with air con-
ditioners; it thus seems likely that only a small minority of motorists
would opt for air conditioners in electric cars, where it could substan-
tially reduce driving range due to its considerable power requirement.
Near the coast in the Los Angeles Basin, air conditioning is almost unnec-
essary. Inland, however, annual air conditioning loads for buildings are
about half those of such cities as St. Louis, and about equal to loads in
cities like Philadelphia. Cooling actually required for a four-passenger
subcompact is about 2.6 kW, which can be provided by a high-efficiency
1 ft
electric air conditioner requiring 1.3 kW of input power. This input
power is 15 to 20 percent of propulsion power required by the four-passen-
ger cars in urban driving, as shown in Sec. 8. Continuous use of an air
conditioner would thus decrease driving range by 25 percent or more, since
battery efficiency declines as its load is increased.
3.8 VEHICLE SPACE DESIGN
Electric vehicle space configurations comparable with the cars
characterized in this report are shown in Figs. 3.5 through 3.9. Figure
3.6 suggests a possible spatial arrangement for the two-passenger vehicle
powered by lead-acid batteries. Spaces for the passenger, batteries,
electric drive train, and crushable structure are shown. Figure 3.8 sug-
gests a configuration for the lead-acid battery-powered four-passenger
car. The lead-acid batteries are considered crushable material and are
located accordingly. Figure 3.9 shows the lithium-sulfur-battery-powered
four-passenger car. Since these batteries must not be crushed they are
1-20
-------
I
N)
Figure 3.5. Possible Two-Passenger Vehicle Configuration
-------
I
N>
SCALE
120" OVERALL LENGTH
Figure 3.6.
Possible Spatial Arrangement of the Two-Passenger Electric
Vehicle Powered by Lead-Acid Batteries
-------
I
K>
U)
Figure 3.7. Possible Four-Passenger Vehicle Configuration
-------
I
NJ
Figure 3.8. Possible Spatial Arrangement of the Four-Passenger Electric
Vehicle Powered by Lead-Acid Batteries
-------
r\j
ui
Figure 3.9. Possible Spatial Arrangement of the Four-Passenger Electric
Vehicle Powered by Lithium-Sulfur Batteries
-------
not located in the crushable part of the vehicle, thus restricting strok-
ing during an accident.
3.9 SAFETY
20 21
Other studies ' show that safety considerations strongly influence
vehicle design, particularly in mixed traffic circumstances involving sub-
stantial percentages of unequal-mass vehicles. Lighter vehicles are at a
substantial disadvantage in any collision. It has been projected that if
the driving habits, and street, road, and highway patterns do not change
(and these usually have a very long time constant for change), severe
casualties would dramatically rise to 2-1/2 times the current rate as the
percentage of small cars in the population increases, in spite of current
and proposed Federal Motor Vehicle Safety Standards.
This increase is due primarily to unequal-mass impacts, and the
statistical frequency with which such unequal-mass collisions occur. As
the small car percentage of the population increases beyond 50 percent,
the situation is alleviated. However, the trends indicate that the situa-
tion is likely to be at its worst between the 1980 and 1990 timeframe.
More and more, safety considerations dictate the physical charac-
teristics of vehicles. In particular, the structural crush and the decel-
eration distance of the occupant within the compartment (against the
restraining force of seat belts, air bags, etc.) must, in sum, consti-
tute a distance sufficient to allow the occupant to be decelerated from
22
the impact velocity within acceptable injury criteria.
The estimated usage of automobiles is approximately one hour per
day at an average speed of 25 mph. High-energy-density, high-temperature
batteries cannot be turned off and allowed to reach room temperature.
As a result of these and other design considerations, all high-temperature
cells are generally stacked and/or assembled in a single cube-like configu-
ration. This allows for good thermal and electrical connections between
1-26
-------
cells and for efficient insulation procedures to maintain battery tempera-
ture at low energy cost during non-operating storage intervals.
Having the battery all in one large box runs counter to current
design trends dictated by safety requirements. Two different kinds of
safety considerations are important here: one is occupant safety in im-
pact, and the other is occupant safety due to the secondary effects of
impact.
In order to provide enough crush stroke at the front of a small car
in high-speed impacts, it is necessary to design the car so that its
internal combustion engine is driven underneath or between the front seat
occupants. Because an ICE engine is a very rigid block of aluminum or
iron, this function is simple compared to doing the same thing with a
high-temperature-battery case. Doing so either imposes rather severe
weight penalties on the case in order to provide the necessary integrity,
or requires the safe containment of the hot cells and any other potentially
damaging battery constituents (corrosive fluids, etc.).
A review of the possibilities indicates that on a cost-effectiveness
basis there would be a preference for protecting the high-temperature
battery so as to maintain its integrity during and subsequent to impact.
Such modifications are not always consistent with current vehicle con-
figurations. As a result,.it appears that a new vehicle configuration
may be desirable to optimize the safety aspects of high-temperature-battery-
powered vehicles. For 1985 car configurations currently under study for
safety, this suggests locating the battery under the front seat occupants
and raising the height of the vehicle accordingly. This may allow some
shortening of the length of the vehicle but forces no other unacceptable
or inconsistent modifications of the vehicle. Figure 3.9 shows an alter-
native arrangement, in which required crush space is placed ahead of the
battery, which in turn is ahead of the passenger.
1-27
-------
4 DRIVING CYCLES FOR DETERMINING VEHICLE RANGE
The driving cycles used to determine the range of electric vehicles
have been evolved through the years by the SAE, beginning with (1) simple
constant-speed velocity runs, (2) constant speed with one or several
vehicle accelerations per specified distance, and finally, (3) prescribed
velocity-time cycles combining given accelerations and constant-speed
segments. At the present time two commonly used driving cycles are the
Residential and Metropolitan Area Driving Cycles listed in the SAE Hand-
23
book (J-227). These driving cycles are described in Fig. 4.1 and Table
4.1. The Residential Driving Cycle has a maximum speed of 30 mph, and the
distance per cycle traveled is 0.858 miles. The Metropolitan Area Driv-
ing Cycle has a maximum speed of 45 mph, and distance traveled per cycle
is 0.996 miles. The Federal Driving Cycle developed for exhaust emission
measurements was derived from measured driving data in the Los Angeles
0 / *? f\
area. The Federal Driving Cycle is shown in Fig. 4.2.
The Federal and SAE Metropolitan Area Driving Cycles were compared
and found to have approximately the same energy expenditure per mile.
Figure 4.3 shows a comparison of range calculations for these two driving
cycles using the same electric car. Since calculated range was nearly
the same for each cycle, either driving cycle may be used for these calcula-
tions. Because of shorter computer time, the SAE Metropolitan Area Driv-
ing Cycle was used for the four-passenger car to simulate average driving
in the Los Angeles area. For the two-passenger car, which is not intended
for high-speed highway and freeway driving, the lower speed SAE Residential
Driving Cycle was used.
1-28
-------
METROPOLITAN AREA DRIVING CYCLE
40
30
20
10
0 20
40
60 80
seconds
100 120 140
30
QL
E
o 20
U.1
UJ
a.
o
10
RESIDENTIAL DRIVING CYCLE
20 40
I
60 80
seconds
I
100 120 140
Figure 4.1. Electric Vehicle Driving Cycles
1-29
-------
TABLE A.I
ELECTRIC VEHICLE DRIVING CYCLES
Test Schedule for Residential Driving Cycle
Mode
Idle
0-30 mph
30 mph constant
30-15 mph
15 mph constant
15-30 mph
30 mph constant
30-20 mph
20-0 mph
Repeat Cycle
Test
Mode
Idle
0-30 mph
30 mph constant
30-15 mph
15 mph constant
15-45 mph
45 mph constant
45-20 mph
20-0 mph
Repeat Cycle
Average Acceleration,
mph/sec
0
2.14
0
-1.37
0
1.20
0
-1.20
-2.50
Schedule for Metropolitan
Average Acceleration,
mph/sec
0
2.14
0
-1.37
0
1.20
0
-1.19
-2.50
Time,
sec
20
14
15
11
15
12.5
46.5
8
8
Area Driving
Time,
sec
20
14
15
11
15
25
21
21
8
Cumulative Time,
sec
20
34
49
60
75
87.5
134
142
150
Cycle
Cumul a t i ve T ime ,
sec
20
34
49
60
75
100
121
142
150
1-30
-------
60
Q
LU
LU
Q-
50
40
30
20
10
0
10 12
MINUTES
14 16 18 20 22
Figure 4.2. Vehicle Speed Over the Federal Driving Cycle
-------
3000
u>
K)
6000
5000
4000
3000
2000
2500
2000
1500
1000
500
EV 140 BATTERY
3250 Ib
CM
2250 Ib BATTERY
1250 Ib BATTERY
750 Ib BATTERY
FOUR-PASSENGER CAP.
AERODYNAMIC DRAG 0.3
COEFFICIENT
FRONTAL AREA 22 ft2
MECHANICAL EFFICIENCY 90%
ELECTRICAL EFFICIENCY 70%
BELTED RADIAL TIRES
I l I
J
0
I
10
20
30
40
RANGE, mi
I
50
60
70
80
20
40
60
RANGE, km
80
100
120
Figure 4.3. Range Calculation Comparison of the Metropolitan Area
Driving Cycle and the Federal Driving Cycle
-------
5 FUTURE ELECTRIC VEHICLE BATTERIES
The capability of future electric storage batteries is the key to
the viability and utility of future electric cars. The absence of electric
cars on today's highways is directly traceable to the lack of a traction
battery with adequate energy density, cycle life, and economy.
Authoritative surveys of battery development for electric cars are
27-31
available in the literature. These, and a very limited investiga-
tion of active battery development projects, have served as the basis for
selecting and describing batteries considered here. Selected batteries
are representative of a wide range of future possibilities; a summary of
their characteristics appears in Table 5.1. Additional factors in the
selection and description of each battery appear in the following
subsections.
TABLE 5.1
SUMMARY PROJECTION OF BATTERY CHARACTERISTICS
**
Specific Energy, W-hr/lb
W-hr/kg
Specific Power, W/lb
W/kg
Approximate OEM price, dol-
lars per KWH
**
Energy Efficiency, percent
Availability
Lead-Acid
13
29
100
220
Nickel-
Zinc
44
110
70
155
Zinc-
Chlorine
70
155
60
130
Lithiugi-
Sulfur
140
310
150
330
20-25
46
1978
25-35
66
1980
10-15
70
1985
15
62
1990
t
**
Includes energy for maintaining battery operating temperature when not
in use.
*
Assumes overnight recharge, and discharge in urban driving as modeled
in Sees. 6 and 8.
>-
'Molten-electrodes.
1-33
-------
5.1 LEAD-ACID BATTERIES
This study of electric car impacts focuses on three specific future
years: 1980, 1990, and 2000. For electric car impacts to be significant
in the first year, 1980, there must be a substantial number of electric
cars on the road, implying mass production beginning at least several years
earlier, in, say, 1978. This in turn necessitates a selection of car de-
sign and a decision to proceed into production around 1975. In conse-
quence, only batteries which are already near production status are rea-
sonable candidates for the 1980 electric cars.
Among the prospects of Table 5.1, only lead-acid batteries have
reached this near-production status. A substantial lead-acid battery
capability has been developed for electric vehicles, largely to support
electric golf carts, and will be further improved in coming years.
Golf cart-type batteries are midway in cycle life and energy density
between starting-lighting-ignition (SLI) batteries, which are designed for
high energy and power density in shallow-cycle automotive use, and indus-
trial traction batteries intended for maximum deep-discharge life and
economy of delivered energy in fork lift trucks. Figure 5.1 summarizes
the electrical performance of three advanced batteries of the golf-cart
type. Each of these batteries has actually been operated in an electric
car. The Delco battery was specifically designed for the experimental
13
512 electric car built by General Motors, The other two batteries were
developed by ESB, Inc., as high-performance additions to the ESB line of
32*
golf-cart batteries, and were tested in the ESB Sundancer electric cars.
The LEV-115 battery is intended to provide a significant increase in
cycle life with modest increase in energy density. The EV-1AO battery
is intended only to provide a much larger improvement in energy density.
Whereas current golf-cart batteries commonly offer lifetimes of around
300 deep-discharge cycles, the LEV-115, in contrast, may achieve substan-
33
tially greater life.
Personal communication, ESB, Inc.
1-34
-------
" 100
10
I I I I 111
1 I I I I
1 I till
10 ion
SPECIFIC ENERGY. U-hr/lb
10uO
Figure 5.1. Specific Power Versus Specific Energy for Lead-Acid
Electric Car Batteries
The composite battery performance illustrated in Fig. 5.2, between
that of the 512 and EV-140 batteries, was chosen to characterize lead-
acid traction batteries for 1980 electric cars. This performance has been
the basis of range calculations for different battery weights to be
described in this report.
If electric cars are used as conventional automobiles, they will be
infrequently driven long distances in a single day. As a result, in most
driving days their batteries will not be fully discharged. In estimating
battery life and consequent battery depreciation costs, it is thus neces-
sary—implicitly or explicitly—to predict battery cycle life as a func-
tion of cycle depth.
Cycle life, even at a single discharge depth, is not easily or con-
fidently predictable. It is time-consuming and expensive to determine by
testing, and may require years. Moreover, it depends significantly on
1-35
-------
1000
£ 100
in
10 ion
SPECIFIC ENERGY, W-hr/lb
1000
Figure 5.2. Assumed Specific Power Versus Specific Energy for 1980
Lead-Acid Electric Car Batteries
such conditions of operation as rates of charge and discharge, amount of
overcharging, and both thermal and mechanical environments. Even for the
widely used lead-acid batteries, scarcity of published data means projec-
tions of cycle life versus cycle depth must be qualitative in derivation.
In this study, the range of possibilities assumed is shown in Fig. 5.3.
The "best" life reflects current expectations for the lower energy-density
battery designs, while the "poorest" corresponds to the very-high-energy
density designs. Thus associating the "best" life with the highest per-
formance assumed in Fig. 5.2 implies considerable optimism about improved
battery longevity by 1980. Both "best" and "poorest" lifetimes are car-
ried throughout this study to show the range of uncertainty due to cycle
life.
The energy efficiency of electric vehicle batteries may be defined
as the ratio of energy removed during discharge to energy returned during
Private communication, ESB, Inc.
1-36
-------
X
-------
and should include a modest degree of overcharge because it is beneficial
to battery longevity. In this study, charging is assumed to be accomp-
lished overnight at a tapering rate during a maximum of 8 hours, with
efficiency of 83 percent. A recommended 8-hour charging profile is shown
in Fig. 5.4. It provides a total of 197 ampere-hours to a 100-AH cell
(an overcharge of 5 to 15 percent is generally recommended), and requires
242 watt-hours in all. If the cell will deliver 200 watt-hours at a low
rate, then the charging efficiency is 83 percent.
The electrical performance of electric vehicle batteries may be
expected to decline during their lifetimes. The descriptions provided
so far characterize the battery while it is still relatively new. At the
end of its life, battery energy capacity may be reduced to 60 to 70 per-
cent of the initial rating; in fact, the end point of battery life is
usually defined in terms of a specific capacity reduction (often to 60
2.6
01 1 C
4-> 2.5
o
• 2.4
UJ
<
5 2.3
o
:>
2.2
2.1
2.QL
Figure 5.4.
C3
C_3
Q_
1/1
2345
TIME, hours
Typical Modified-Potential Charge Profile for Lead-Acid
Electric Vehicle Batteries (100-AH Cell)
1-38
-------
*
percent). Battery energy and power may also be substantially degraded
by low temperatures; in sub-zero weather, much of the battery capacity
34
will disappear, as shown for a typical lead-acid cell in Fig. 5.5.
This reduction in capacity increases at high rates of discharge, as indi-
35
cated for a different type of lead-acid cell in Fig. 5.6.
Battery electrical performance capability will also decline, of
course, during discharge in a single day's driving. The amount of this
decline depends not only on particular cell design, remaining charge,
temperature, and battery age, but also on the manner in which the battery
has been charged and discharged. No simple summary of the performance
decline is thus possible. The situation is reasonably illustrated by
Fig. 5.7, however, which shows available terminal voltage versus current
for a battery discharged at the 6-hour rate to various extents.
The loss in capability with discharge is clearly most pronounced
at high load currents, as might be demanded in electric cars for accel-
eration, hill-climbing, or maximum speed. Generally, however, the car's
motor controller will employ current-limiting circuitry to constrain maxi-
mum current, motor torque, and vehicle acceleration to moderate levels,
so reduced battery voltage will have little effect until very near total
discharge.
5.2 NICKEL-ZINC BATTERIES
The theoretical superiority of nickel-zinc alkaline batteries over
lead-acid batteries has long been recognized. Only recently, however,
have practical problems in cell construction been overcome in developmen-
tal research. Now, two separate development activities suggest that
nickel-zinc batteries will offer a viable alternative to lead-acid bat-
teries in the marketplace before the end of this decade.
*
Private communication, L. Unewehr, Scientific Research Staff, Ford Motor
Co.
1-39
-------
o
«*
Q.
100
80
60
40
20
-30
SOURCE: REF. 34
I
30
TEMPERATURE,°F
60
90
Figure 5.5. Lead-Acid Electric Vehicle Batteries, Capacity Versus
Temperature
SOURCE: REF. 35
120
OJ
« 100
Q.
t 80
1 hr
8 hr
Cxi
C3
rn
•A
\
1-hr RATE
3-hr RATE
I
40 60 80 100 120
TEMPERATURE, °F
Figure 5.6. Effect of Temperature on Lead-Acid Battery Capacity
1-40
-------
2.5r-
2.0
o
>
£ 1.5
t—
o
1.0
DISCHARGE,
SOURCE: REF. 34
I I
I
100 200 300
DISCHARGE RATE, AMPERES
.400
Figure 5.7. Lead-Acid Electric Vehicle Batteries, Voltage and Current.
Versus Percentage of Discharge for a 100-AK Battery
Discharged at 6-hour Rate at 77°F
Energy Research Corporation has developed and tested .sealed 7 and
*
25 ampere-hour nickel-zinc cells. Even in this small size, energy den-
sity is 30 watt-hours per pound and life is over 250 deep-discharge cycles.
A life improvement to 500 to 700 cycles and an energy density of over 40
watt-hours per pound (in larger cells) are current research targets. Ex-
pected costs per watt-hour are less than twice those of lead-acid batteries.
Gould, Inc., has had vented nickel-/.inc cells in laboratory sixes
under development and test for several years. Sufficient success has
been achieved that 400-ampere-hour cells have been designed and nre now
being constructed for field tests which will include vehicular use during
1974. Given satisfactory results, manufacturing is planned by the end
of 1976. After an additional two years, in 1978, improvements in cell.
Private communication, Alan Charkey, Energy Research Corporation.
v
Private communication, Mark ,1. Obert, Could, Inc.
J-4J
-------
ampere-hours and watt-hours per pound from 480 to 580, and from 40 to 50
(5-hour rate) are anticipated. Performance expected of the initial and
improved production cells is shown in Fig. 5..8.
Projected cycle life and cycle depth for the Gould production cells
are shown in Fig. 5.9. These are based on extrapolations of experimental
results shown in Fig. 5.10, and assume life ends when cell capacity has
**
fallen to 60 percent of its original value. Though these curves do not
show it, the experimental data is encouraging in that it reveals that capa-
city levels off somewhat above 50 percent after its initial decline, and
remains above 50 percent for many hundreds of cycles before further drop.
This leads to two inferences: even after very long cycle life, specific
capacity may exceed that initially provided by lead-acid batteries; and
~ 100 -
-In
10 100
SPECIFIC ENERGY, W-hr/lb
1000
Figure 5.8. Specific Power Versus Specific Energy Projected for
Nickel-Zinc Traction Batteries
**
Private communication, Claude Menard, Gould, Inc.
Private communication, Claude Menard, Gould, Inc.
1-42
-------
TOO
80
60
40
20
Figure 5.9.
400
800
1200
CYCLE LIFE
Projected Cycle Life for Ni-Zn Electric Vehicle Battery
(to 60 percent of Original Capacity)
degradation of capacity may be significantly reduced by redesign to pre-
serve the working area of the zinc electrode, which now is diminished
with use and thus reduces capacity. Overall, it seems reasonably con-
servative to forecast the availability of nickel-zinc electric vehicle
batteries with a 400-cycle life by 1980.
*
The estimated OEM price of the nickel-zinc batteries in quantity
production is around $35 per KWH. Once regular usage patterns are estab-
1
lished and volume recycling is in progress, this may fall to $25 per KWH.
Private communication, Claude Menard, Gould, Inc.
1-43
-------
100
50
1976 PRODUCTION
1007. 000
60% DOD
.307, DOD
I
400 800
CYCLE LIFE
DOD = DEPTH OF DISCHARGE
1200
100
50
197H PRODUCTION
307, DOI)
100X DOD
60X DOD
400
800
1200
CYCLE LIFi:
Figure 5.10. Projected Capacity Reduction for Ni-Zn Electric Vehicle Battery
In projecting these costs, 5-hour discharge rates are assumed. The
Gould cells are being optimized for this particular rate. Much higher
discharge rates may be obtained by appropriate cell design, but battery
costs may be more than doubled as a result. For electric cars, the lower
price is preferable as the reduction of energy density at high specific
powers is not an important sacrifice.
•
The energy efficiency of overnight recharge of the nickel-zinc trac-
tion batteries is expected to be above 80 percent. Discharge energy
efficiency is implicit in Fig. 5.8.
1-44
-------
5.3 ZINC-CHLORINE BATTERIES
A zinc-chlorine battery for automotive use is the express objective
O £
of a 5-year development program at Energy Development Associates. EDA
is a research and development partnership between wholly owned subsidiaries
of Gulf and Western Industries, Inc., and Occidental Petroleum Corporation.
The demonstrated potential of the zinc-chlorine system not only motivated
the formation of EDA, but has reportedly led to an agreement for coopera-
tion with Gould, Inc., in which Gould contributes its porous titanium tech-
nology for battery electrodes, and acquires options to produce the batteries
for stationary use by electric utilities and for standby power use in the
United States and Canada, as well as for mobile use including electric
37
cars.
An experimental zinc-chlorine battery was built and installed in a
38
Vega automobile in 1972. Though far from a production battery system,
it demonstrated important potential, including a total range of 152 miles
at a speed of 50 mph.
The development objectives of the EDA program, in Table 5.2, are
39
expected to be achieved in 1978-79. Thus it seems possible that elec-
tric cars utilizing zinc-chlorine batteries could be produced as early as
1980. This battery system represents a major advance in performance and
technology, however, and past developments in battery technology have
often progressed slower than originally anticipated. Accordingly, the
availability of zinc-chlorine batteries meeting the performance goals of
Table 5.2 is projected here for 1985 rather than 1980.
The key to the zinc-chlorine battery system is storage of chlorine
40
as chlorine hydrate. Chlorine gas under pressure, or liquid chlorine,
is corrosive, difficult, and dangerous to handle and store, but the hydrate
is very much more tractable. Usage of a hydrate store involves plumbing,
pumps, a heat exchanger, and a refrigerator in addition to the assembly
of battery plates and electrodes; thus it is truly an energy system rather
1-45
-------
TABLE 5.2
ZINC-CHLORINE BATTERY GOALS
Delivered Energy (4-hr rate) 50 KWH
Battery Weight (total system) 700 Ib (318 kg)
Charge Time (minimum) 4 hr
3 3
Volume (total system) 10 ft (0.28 m )
Cycle Life 500-5,000
Overall Energy Efficiency 70%
Peak Power (30 sec) 40 KW
Cost (estimated) $500-$750
Energy Density (4-hr rate) 70 W'hr/lb (155 W-hr/kg)
Power Density (30 sec) 57.2 W/lb (125 W/kg)
than simply a battery as generally conceived. A diagram and explanation
of system operation prepared by EDA is reproduced as Fig. 5.11. The goals
of the EDA program include a prototype 1,000-pound battery by the end of
1975. Analysis and extrapolation of laboratory data show that the eventual
result of the development program should provide the performance of Table
5.2.
A typical current-versus-voltage curve for an EDA cell is shown in
2
Fig. 5.12. In the target battery, 40 mA/cm at 2 volts corresponds to a
4-hour discharge. From this reference point and the data of Fig. 5.12,
the specific power versus specific energy as shown in Fig. 5.13 may be
J 41
computed.
To be conservative, it may be assumed that in Table 5.2 the higher
estimated cost and the lower estimated cycle life will prevail. Since
cycle life versus cycle depth is not now known, it may be simply assumed
that total energy delivered by the battery during its life is independent
of cycle depth. Thus a capability of 500 full discharges becomes equiva-
lent to one thousand 50-percent discharges, or two thousand 25-percent
discharges.
1-46
-------
STACK
lint doom* CktoiiM rttoiu*
GAS IMUMO STORE
*•*• *""*" "**""
!
5
RiCTIFItR/INVERTIR
Figure 5.11. The EDA Zim.: Chlorine Battery System
2.20
2.10
2.00
1.90
.80
1.70
1.60
I
20
I
I
I
I
40 60 80 100
CURRENT DENSITY, mA/cm2
Figure 5.12. Zinc Chlorine Cell Discharge Curve
120
140
1-47
-------
Of.
LU
o
o
1000
£ 100
10
DELCO 512
-Zn
I I I I I I 1 ll
Zn-Cl,
10 100
SPECIFIC ENERGY, W-hr/lb
I i
I il
1000
Figure 5.13. Specific Power Versus Specific Energy Projected for the
Zinc Chlorine Battery System
1-48
-------
5.4 LITHIUM-SULFUR BATTERIES
It has long been recognized that non-aqueous batteries with molten-
salt electrolytes might provide extremely high specific energy densities.
Well over a dozen research organizations have active programs in develop-
42
ment of such batteries, both in the United States and abroad.
Of the US programs, that at Argonne National Laboratory is by far
the largest. It has recently been operating at a budget level of $1,250,000
per year, with prospects for continued growth as further accomplishments
are demonstrated and as added effort is applied to alleviating US energy
problems.
The ANL program is primarily directed at batteries for utility peak-
43
shaving. Siting problems, environmental constraints, and safety chal-
lenges have for several years drastically slowed initiation of planned
construction by electric utilities, and in consequence, insufficient capa-
city for meeting peak-hour demands threatens to become commonplace. The
ANL program is to produce an electric storage battery which can economi-
cally store electric energy generated during off-peak periods, as very
late at night. By making this energy available to meet peak power demands
of the following day, requirements for total installed generating capa-
bility can be significantly reduced.
Because battery technology being developed for this application is
inherently suited for vehicular use, the ANL program includes specific
efforts to develop an electric car battery. The objectives of these
efforts include installing a vehicular battery built by a commerical
subcontractor in a test vehicle by 1980. Considering the many uncertain-
ties yet to be resolved, 1990 is probably an early year for initial pro-
duction of such autos.
Until recently, development effort was focused on batteries employ-
44-46
ing molten-lithium and molten-sulfur electrodes. Though this poten-
tially enables very high energy densities, up to 150 watt-hours per pound,
1-49
-------
it also poses difficult problems of diffusion and corrosion. These prob-
lems are dramatically reduced by use of solid electrodes, but energy den-
sity is thereby halved.
Goals in the solid-electrode ANL battery program are shown in Table
5.3. Solid lithium-aluminum and metal sulphide electrodes will be used
TABLE 5.3
TENTATIVE ANL PERFORMANCE GOALS FOR ELECTRIC AUTOMOBILE BATTERIES
(With Solid Electrodes)
Subcompact Compact
Delivered on discharge at the battery terminals.
1-50
Automobile Characteristics
Loaded Weight, kg (Ib) 800 (1,763) 1,250 (2,756)
Range, Miles 100 100
Energy Usage, KWH per mile 0.20 0.35
Battery Goals
Cost, Dollars
Weight, kg (Ib)
*
Energy Storage Capacity, KWH
Peak Power, KW
Cost/Unit Weight, $/kg ($/lb)
Specific Energy, W«hr/kg (W'hr/lb)
Specific Power, W/kg (W/lb)
Peak (15-sec bursts)
Sustained Discharge (2-hr)
Recharging Time, hr
Battery Life, yr
Cycle Life
Rate of Heat Loss, watts
600
135 (297)
20
32
4.45 (2.02)
148 (67)
237 (108)
74
5
3-5
1,000
125
800
230
35
60
3.50
152
260
76
5
3-5
1,000
150
(507)
(1.59)
(69)
(118)
-------
with an electrolyte of lithium chloride-potassium chloride eutectic,
operated at about AOO°C. A complete battery is constructed by stacking
cells and cylinders and packing the cylinders in a well-insulated box
with an electric heating system. The heating is necessary to maintain
battery operating temperature while it is standing idle; during charging
or discharging battery loss may be more than sufficient to maintain
operating temperatures, and some cooling may be needed. A complete con-
ceptual battery is illustrated in Fig. 5.14. It is much too early to pre-
dict electrical performance, cost, and life of such batteries in detail.
For the moment, it suffices to assume that the program objectives of
Table 5.3 will be met. If they are, the resultant battery will be so simi-
lar to the zinc-chlorine battery of Table 5.2 in terms of specific energy,
life, and cost that at this point it cannot be meaningfully distinguished
on performance and impact grounds.
Accordingly, only the liquid-electrode version of the lithium-sulfur
battery is considered further in this study. It is assumed to achieve
twice the specific energy goal of Table 5.3 as shown in Fig. 5.15, but
to comply with other current performance goals.
Overall energy efficiency of the molten-salt batteries is diminished
by heating requirements in a manner dependent on battery usage. Accord-
ingly, it is accounted for separately in range calculations for electric
cars in this study. Energy efficiency exclusive of heater power for the
high-temperature batteries has yet to be firmly established. At the
moment, overall charge-discharge efficiencies of 70 percent for the solid-
electrode battery and 80 percent for the more optimistic molten-lithium,
*
molten-sulfur battery will be assumed.
It should be noted that this lithium-sulfur battery is not the only
prospect for attaining energy densities of well over 100 watt-hours per
Private communication, P.A. Nelson, ANL.
1-51
-------
12 CELL STACKS; 216 CELLS; AVERAGE BATTERY VOLTAGE = 160 V
POSITIVE
BUS
COOLANT
IN
NEGATIVE
BUS
BATTERY
CASE
REMOVABLE-
INSULATOR
TOP
THERMAL
INSULATION
CELL STACK
COOLANT COIL
61 cm, COMPACT
(47 cm, SUB-COMPACT)
Cvj
i
46 cm, COMPACT
(35 cm. SUB-COMPACT)
COOLANT
OUT
RESISTANCE
HEATER ELEMENT
COOLANT
OUT
Figure 5.15. A Conceptual Lithium-Sulfur Electric Car Battery
1-52
-------
lOOOcr
£ 100
10
DELCO 512
Li-S
I I I M I I
10 100
SPECIFIC ENERGY, W-hr/lb
1000
Figure 5.15. Power and Energy Density Curves for Various Electric Vehicle
Batteries
pound. Worldwide, a clozen research programs are pursuing sodium-suifur
battery development, and four more are addressing Lithium-chlorine sys-
42
terns. The relative prospects for these different developments cannot
be conclusively appraised now. Their number and diversity, however,
clearly suggests that by 1995 battery energy densities cf 100 to .1.50
W-hr/lb may be achieved, if not by the lithium-sulfur system then by one
of the others.
1-53
-------
6 PARAMETRIC RANGE CALCULATION
Computer simulations were developed to calculate ranges between re-
charge for electric automobiles in specified driving cycles. Each simula-
tion consisted of two parts: a power demand model, to determine the elec-
tric power requirement of the electric auto for each time increment in a
given driving cycle, and a battery discharge model, to determine the pro-
gressive depletion of battery capacity in meeting these incremental power
requirements. The power demand model employed the equations developed in
Sec. 3. The battery discharge model was an approximation developed to
utilize available data.
For simplicity, a single battery discharge model was used for all
the batteries described in Sec. 5. Since relatively little data is avail-
able for advanced batteries now under development, the battery model was
simply based on the summary charts of specific power vs specific energy
for each battery presented in Sec. 5. For each time increment, the battery
model first calculated the specific power level required of the battery;
then it determined the associated specific energy level from the battery
chart; finally, it determined the fraction of battery capacity required
at this specific energy level to meet the total energy requirement of the
auto during the time increment. Battery exhaustion was assumed when the
sum of these fractional capacity reached unity. Further details are pre-
sented in the appendix.
Though this model is conceptually similar to other models which have
been verified by comparison with test data, its accuracy is not thereby
assured. The model obviously omits much desirable detail: its cutoff con-
ditions (minimum acceptable terminal voltage at given discharge current)
are implicit in the specific power and energy chart, rather than explicitly
invoked at each step of the driving cycle; furthermore, it does not allow
for battery recuperation during decelerations and stops, or for any resi-
dual energy available after the (implicit) cutoff conditions are reached.
1-54
-------
The validity of any such model can only be established by compari-
son with experimental results. For estimation of electric car range,
this is undertaken immediately. For estimation of electric car energy
consumption, it is addressed in Sec. 8.1.
6.1 COMPUTER PROGRAM VERIFICATIONS
The range calculation of the computer simulation was tested by re-
generation of published experimental results. Inputs for the simulation
were taken directly from the published data wherever possible. Where
published data was unavailable, as in the case of the battery character-
istics of the EFP test car, data from similar vehicles or components was
used.
EPA has furnished a range test of the EFP Electrosport car driven on
the Federal Driving Cycle. The range obtained was 25.0 miles. The com-
puted range was 24.7 miles, using an energy discharge curve for a LEV-115
battery from ESB and the vehicle parameters listed below:
Vehicle Weight 5500 Ib
Battery Weight 2240 Ib
Frontal Area 24 ft2
Aerodynamic Drag Coefficient 0.4
Mechanical Efficiency 90%
Electrical Efficiency 70%
Road Load Friction R = (W/50) (H-0.0014V-K).000012V2)
Where W = weight of vehicle, Ib
V = vehicle velocity, ft/sec
Another computer comparison was made using range data from the
Sundancer vehicle. The vehicle parameters that were used in this
comparison are as follows:
Vehicle Weight 2000 Ib
Battery Weight 840 and 816 < , _ , . ,
0 (batteries, respectively
Frontal Area 12 ft
for LEV-115 and EV-140
1 OJ.O <|
2
1-55
-------
Aerodynamic Drag
Coefficient 0.3
Mechanical
Efficiency 92%
Electrical J for contactor controller
Efficiency 70% and 80% and SCR controller,
( respectively.
Low Aspect Ratio
Tires
The comparison of range calculations and tests reported on the SAE
Residential Driving Cycle was as follows:
Computer Range Test Range
miles miles
LEV-115 Battery
Contactor Controller 56 55-60
SCR Controller 68 70-75
EV-1AO Battery
Contactor Controller 66 75-80
SCR Controller 79 75-80
The comparison of computer and actual tests for this vehicle is
favorable. It would be expected that the actual tests would be higher,
since there were some additional miles obtained on the vehicle when it
could not completely follow the driving cycle.
6.2 CARS WITH CURRENT LEAD-ACID BATTERIES
Range calculations were made for the two assumed vehicles using
various battery types and weights. The battery types that were used
were the LEV-115, EV-140, and Delco 512 lead-acid batteries. The power
13*
and energy density curves of these batteries are shown in Fig. 5.1.
Personal communications with ESB and Argonne National Laboratories.
1-56
-------
Range calculation results using the lead-acid batteries are shown
in Fig. 6.1. Assuming a maximum range of 35 miles between battery charges,
the two-passenger car needs about 800 pounds of LEV-115 batteries, about
600 pounds of EV-140 batteries, or about 500 pounds of Delco 512 batteries.
To obtain a 55-mile range on the four-passenger car, we need 2,300 pounds
of LEV-115 batteries, 1,800 pounds of EV-140 batteries, or 1,300 pounds
of Delco 512 batteries.
As noted in Sec. 3.5, the electrical efficiency (controller and
motor efficiency) of the four-passenger car was assumed to be higher than
that of the two-passenger car because a two-speed automatic transmission
was included to raise motor speed and efficiency during low-speed driving.
The extra complexity and cost of such a transmission was deemed unwarranted
for the two-passenger car.
The Delco 512 battery has not been developed for long life under deep
discharge conditions. It is felt that this lightweight battery could be
made acceptable, but it may lose some of its high-energy storage capa-
bility during this development towards long life. Therefore, we have
assumed that a lead-acid battery somewhere between the EV-140 and the
Delco 512 batteries can be developed for the 1980s that will have ade-
quate life in the range shown in Fig. 5.3. The remainder of our calcula-
tions for the lead-acid battery are for a battery having a power and
energy density curve between the EV-140 and Delco 512 batteries. This
assumed battery is termed the 1980 battery and its power-energy density
curve is shown in Fig. 5.2.
No allowance was made for accessory operation in these range calcu-
lations. Power requirements of basic accessories are shown in Table 6.1.
Even if operated simultaneously, a relatively infrequent condition, their
total power requirement is only 227 watts. As Sec. 8 shows, the four-
passenger electric cars require an average of about 0.3 KWH/mi in urban
driving, or 7.2 kW at the average driving cycle speed of 24 mph. The
1-57
-------
1500 r-
1000
500
4000
3000
2000
~ 1000
TWO-PASSENGER CAR
AERODYNAMIC DRAG 0.4
COEFFICIENT
FRONTAL AREA 18 ft2
MECHANICAL EFFICIENCY 90'Z
ELECTRICAL EFFICIENCY 70X
LOW ASPECT RATIO TIRES
PAYLOAD 300 Ib
EV 140 BATTERY
DELCO 512 BATTERY
SAE RESIDENTIAL DRIVING CYCLE
I
10
20
30
40
50 60
RANGE, mi
I
70
80
90
I
20
40
60
80 100
RANGE, km
120
140
100
160
3000
2500
k 2000
1500
1000
6000
- 5000
4000
^ 3000
2000
FOUR-PASSENGER CAR
AERODYNAMIC DRAG 0.4
COEFFICIENT
FRONTAL AREA 22 ft/
MECHANICAL EFFICIENCY 90'.'
ELECTRICAL EFFICIENCY 80';!
!.OW ASPECT RATIO TIRES
- PAYLOAD
EV 140 BATTERY
512 BATTERY
"ETROPCuITAN AREA DRIVING CYCLE
I I I I I
20 30 40 50 60 70
RANGE, mi
I I I I I
80
90
I
40 60 80 100 .120
RANGE, km
140
100
160
Figure 6.1. Urban Driving Range For Cars With Current Storage Batteries
1-58
-------
TABLE 6.1
ACCESSORY POWER REQUIREMENTS47 (watts)
Service Lights, High Beam 46.8
Service Lights, Rear 24
License Plate Lights 18
Windshield Wiper Motor 12
Defroster Fan Motor 24
Heater Fan Motor 24
Clock C,.u
I\.ciuj.u 60
Dash Lights 18
227.4
total accessory load would be about 3 percent of the propulsion power
requirement from the battery, and thus would reduce range by an amount
negligible in comparison with uncertainties in battery performance and
modeling.
Because of the weight of the four-passenger cars with lead-acid
batteries, power steering and power braking might be desirable options
imposing additional power requirements. If braking power were provided
by an electric pump with vacuum accumulator, about 8 watts of electric
1 ft
power would be required. Though peak power steering requirements may
reach 1 horsepower in stationary quick turns of the steering wheel, typi-
18
cal driving demands are near 0.1 horsepower, or 75 watts; together,
the average loads imposed by efficient power braking and steering subsys-
tems should be less than the accessory total of Table 6.1, with similar
implications for range.
6.3 CARS WITH FUTURE STORAGE BATTERIES
Figure 6.2 shows driving range between recharges for two- and four-
passenger electric cars with the future storage batteries described in Sec. 5.
1-59
-------
TWO-PASSENGER CAR
SAE RESIDENTIAL DRIVING CYCLE
2400
2300
2200
2100
2000
« 1900
1800
1700
0
40
80
120 160
RANGE, mi
800
600
400
-
h-
ct
CO
500
240
Figure 6.2. Urban Driving Range for Cars with Future Storage Batteries
1-60
-------
At a given battery weight, ranges of these cars in constant-speed,
level driving are considerably greater than in stop-start urban cycles.
Figure 6.3 shows the variation of range versus speed in steady driving
for cars with particular battery weights. These weights are those
selected as described in Sec. 7 to give ranges appropriate for different
patterns of urban daily driving.
It should be understood that the range calculations presented in
this report apply to a car with a fully charged new battery pack
operating in typical Los Angeles weather conditions. Moreover, the range
is calculated with the car traveling on a specific driving cycle. Once
the car cannot make a specified acceleration, the run is considered com-
plete and the range is thus determined. Typically, however, the car
could be driven slowly, without expending energy at the rate required for
the accelerations of the driving cycle, for several additional miles. If
the car is driven during colder weather (32°F instead of 80°F), the car
range could be reduced roughly 25 percent. Obviously, as the battery nears
the end of its life, acceleration and range are reduced.
6.4 RANGE IMPROVEMENT
Because of manufacturing lead times, we do not anticipate substan-
tial increases in range over those shown in Fig. 6.2. For the 1980 period,
the various schemes often suggested to improve the range of cars using
lead-acid batteries are more complex and difficult to bring to production.
As much as a 30-percent improvement in the estimated range, however, is a
clear possibility. For instance, ESB (Electric Storage Battery Inc.) has
48
been proposing a flywheel-battery combination. They believe that an
Algeir hydro-mechanical transmission is available in concept (due to the
existence of a 15-horsepower production unit) which has a speed range
continuously variable from full speed forward to full speed in reverse,
yet with very high efficiency. They propose to drive the flywheel from
batteries with a small DC motor at a maximum power level corresponding to
the average energy requirement. This would allow the batteries to operate
1-61
-------
I
o*
NJ
VEHICLE RANGE AT CONSTANT SPEEDS
TWO-PASSENGER CAR
CURB WEIGHT, Ib
TEST WEIGHT, Ib
BATTERY WEIGHT, Ib
500 i-
LEAD-
ACID
1800
2100
550
NICKEL-
ZINC
1685
1985
435
ZIN'C-
CHLORINE
1580
1880
340
LITHIUM-
SL'LFUR
1430
1730
200
400
•= 300
200
100
ZINC-CHLORINE
LITHIUM-SULFUR
10
20 30
VELOCITY, mph
40
50
ACID
CURB WEIGHT, Ib 3525
TEST WEIGHT, Ib 3975
BATTERY WEIGHT. Ib 1500
700 i-
FOUR-PASSENGER CAR
NICKEL-
ZINC
3080
3530
1090
ZINC-
CHLORINE
2500
2950
570
600
500
• 400
300
200
100
NICKEL-ZINC
ZINC-CHLORINE
1980 LEAD-AC 10
I
10 20 30 40
VELOCITY, mph
50
Figure 6.3. Constant Speed Driving Range for Cars with Future Storage
Batteries
LITHIUM-
SULFUR
2205
2655
300
60
-------
at the lowest practical power density and, therefore, the highest possible
energy density, which would extend battery life and driving range. The
flywheel would be connected to the variable-speed hydro-mechanical trans-
mission and, in turn, to the driving wheels. With such a transmission
almost any power level could be achieved for acceleration performance,
although the battery would supply power only at an average level. A fur-
ther feature of the system is that during deceleration the transmission
could regenerate energy into the flywheel. Regeneration into a battery
system is not as efficient and tends to shorten battery life due to rapid
charging. Since the system is currently in prototype, tests have not yet
been run, but computer analysis indicates a 30-percent improvement in range
will result.
Another often-discussed possibility is a hybrid-electric car which
incorporates an internal-combustion engine to supply energy for battery
recharging and propulsion. A variety of possibilities have been suggested
for hybrid combinations of different types. Most of these have emphasized
current performance requirements for the purposes of reducing engine
emissions and increasing battery range while preserving the possibilities
for all-electric operation. The two systems most frequently considered
are the parallel and series hybrids. In the former, the engine or the
motor, or both, may be used to drive the vehicle, while in the latter the
engine drives a generator which, in turn, augments battery power during
acceleration and provides some degree of recharge during operation. Both
*
systems have been extensively pursued and reported; although this
configuration is beyond the scope of this study, the following comments
are in order: possibilities exist for substantial improvement in perfor-
mance and range. They do have the disadvantages of some increased
complexity.
By Aerospace Corporation, TRW, and Minicars, among others.
1-63
-------
7 SELECTION OF DRIVING RANGE
The basic parametric car descriptions of Sec. 6 are inadequate to
support a comprehensive study of the impacts of electric car use.
Specific battery weights, car weights, and ranges must be chosen in order
to keep the impact analysis bounded to a reasonable extent.
The basic factors in range selection are patterns of use on the
one hand, and economic costs on the other. The greater the range between
recharges, the more generally useful the car will be. Long range, however,
necessitates a heavy and expensive battery which may impair car drive-
ability and increase battery depreciation costs to undesirable levels.
Concurrent with this characterization of electric cars, an analysis
of automobile usage was conducted separately within the overall impact
study. Data on actual daily driving range, however, was not available
in time for the choice of electric car range here. Accordingly, usage
49
data from the literature was employed. Though synthetic, it offers a
reasonable guide, as shown in Fig. 7.1. Especially for automobiles in
the 3000-mile-per-year (MPY) and 6000 MPY categories, increasing ranges
up to about 50 miles dramatically reduce the fraction of days on which
the driving range will be inadequate. At longer ranges, however, rela-
tively little is gained by doubling range and battery weight.
7.1 LEAD-ACID BATTERY CARS
The limitations of lead-acid storage batteries make driving range
expensive to obtain, both in amortization costs and in driveability. To
support selection of driving ranges for the lead-acid battery cars, the
following preliminary analysis was made of depreciation and energy costs
as a function of battery weight and daily range.
There are a number of factors which influence the calculation of
battery depreciation. Each is dependent upon battery design, construc-
tion, environment, and usage. Little or no reliable information is
1-64
-------
100%
O
LLJ
OC
50%
Z>
O
3000 MPY
I
25 50
MILES DRIVEN
75
100
Figure 7.1. Cumulative Frequency of Daily Auto Usage
available which would allow us to characterize the best electric vehicle
battery which might be available in the 1980s. Even to the extent that
characterizing information is available, there is little likelihood of
being able to identify usage and its relationship to depreciation. As
a result, a large amount of speculation and judgment is unavoidably
included in this discussion.
Battery life is dependent on depth of discharge, rate of discharge,
rate of charge, amount of overcharge, methods for sustaining charge, and
environmental characteristics. For instance, leaving the battery
connected in a trickle charge mode produces positive grid corrosion,
while long periods of inactivity without charging produce sulfation.
Both limit battery life.
Figure 5.3 illustrated the cycle life as a function of depth of
discharge and as a function of range utilization for the "poorest" and
1-65
-------
"best" electric vehicle battery. Actually, the "best" estimate is based
on a laminar grid battery, taking into account (by judgment) the effects
of consumer usage and charging equipment commensurate with such usage.
The "poorest" is a 1967 estimate of 1972 battery performance of the
Delco 512 type. In the remaining discussion we have utilized the "best"
curve. Figures 7.2 and 7.3 are the estimated maximum ranges for the two-
and four-passenger vehicles, respectively, as a function of battery weight.
The calculations leading to these curves are described in Sec. 6.
50
It was estimated in 1967 that the 1972 battery cost per pound to
an original equipment manufacturer would be in the 35-cent-per-pound
range. Since the retail price is 2-1/2 times the OEM price, the cost of
a replacement battery in 1973 dollars is likely to be of the order of
90 cents per pound. Figure 7.1 is an estimate of the "cumulative frequency
of daily auto usage" with three different yearly ranges. If we assumed
that battery weight determines the maximum range and that average daily
usage would be calculated by dividing the yearly usage by 365, we can
determine the daily percent utilization of range. From Fig. 5.3, we can
also determine the cycle life for various usages, and by dividing the
price of the battery by the cycle life, determine the cost per cycle,
and by dividing that by the cycle range, the cost per mile.
The assumption was made that the battery providing the best range
(EV-140) could somehow be coupled with the best (LEV-115) cycle life,
although in fact the greater ranges are associated with the poorer cycle
and vice versa. (This is the measure of optimism built into the results.)
Figure 7.4 illustrates the cost per mile for battery depreciation as a
function of battery weight for the two-passenger car for the three
different usage levels and Fig. 7.5 is the equivalent information for
the four-passenger car. It should be noted that in each case a minimum
cost per mile is achieved on the 12,000-mile-per-year usage for battery
weights providing maximum range (with high battery performance) of almost
50 miles, about 50 percent greater than average daily usage.
1-66
-------
,,
1260
900
540
180
CO
,1600i-
1200
800
400
QQ
SELECTED DESIGN
I
20
40 60
RANGE, mi
80
100
Figure 7.2. Two-Passenger Car Maximum Range on the SAE Residential
Driving Cycle as a Function of Lead-Acid Battery Weight
and Cost
2250
*1800
T3
1350
900
450
CO
2500
2000
2 1500
- 3
1000
500
0
0
SELECTED DESIGN
I
I
20 40 60
RANGE, mi
80
100
Figure 7.3. Four-Passenger Car Maximum Range on the SAE Metropolitan
Area Driving Cycle as a Function of Lead-Acid Battery
Weight and Cost
1-67
-------
0>
'E
4)
o.
c
01
u
00
o
o
a:
a.
3000 mpy
6000 mpy
12,000 mpy
I
450
BATTERY WEIGHT, 1b
900
Figure 7.4. Two-Passenger Car Lead-Acid Battery Depreciation Costs
Versus Battery Weight for Average Usage
V
1 14
I
a 12
to
O
O
01
oc
a.
oc
LJJ
<
00
10
8
0
500
3000 mpy
6000 mpy
12,000 mpv
I
1000 1500
BATTERY WEIGHT, Ib
2000
Figure 7.5. Four-Passenger Car Lead-Acid Battery Depreciation Costs
Versus Battery Weight for Average Usage
1-68
-------
As will be shown in Sec. 8, energy costs for these electric cars
are relatively low. The depreciation costs of Figs. 7.4 and 7.5 are thus
likely to dominate operating costs. Overall, then, range selection boils
down to a tradeoff between the cost of battery depreciation and the appli-
cability of the car to typical daily driving patterns. The longer-range
cars are adequate for more driving days and more usage classes in Fig.
7.1; but longer range increases battery weight, with increased battery
depreciation costs unless most of the range capability is used on the
average day.
The four-passenger car with lead-acid batteries is intended for wide
application, in the 6,000-12,000 mile per year range (the average US car
is driven about 10,000 miles per year). Figure 7.1 shows that daily
ranges of at least 50-100 miles are desirable for applicability to a
large percentage of driving days in these usage classes. Figure 7.5
shows that at 12,000 miles per year usage, depreciation costs are almost
independent of battery weight, but at 6,000 miles per year usage, they
rise substantially with battery weight as range increases from 50-75 miles
and beyond. A battery weight of 1,500 pounds, giving a range of about
55 miles, was selected for impact study. Even with the optimistic battery
life assumptions of Fig. 7.4, this results in battery depreciation costs
in the vicinity of 5 cents per mile at usages of 6,000 miles per year.
The two-passenger with lead-acid batteries is intended for very
limited application, to local driving off freeways and major highways.
Average annual usage is likely to be in the 3,000-to-6,000-mile range.
Battery weight of 550 pounds and a maximum range of 35 miles were selected
for this car. At 3,000 miles per year, this also results in 5 cents per
mile battery depreciation costs. At 6,000 miles per year, depreciation
would be less, but in either case, the depreciation cost is high con-
sidering the limited accommodations and performance offered.
1-69
-------
This preliminary review of depreciation costs and range requirements
is not, of course, intended to be definitive. Both subjects are major
topics for further analysis in this study of electric car impacts.
7.2 OTHER BATTERY CARS
For the other battery cars of Sec. 6, higher battery energy density
allowed selection of a nominal range of 145 miles. This appears to be a
reasonable minimum for general urban driving applications: it is adequate,
according to Fig. 7.1, for almost 95 percent of days for cars driven
12,000 miles per year or less, which includes some 70 percent of all cars.
For the limited-capability two-passenger cars, this may seem excessive;
but except in the case of nickel-zinc batteries, the battery packs are so
small already that there is relatively little left to be gained by further
reduction in size. In the case of the more expensive nickel-zinc battery,
driving range was reduced for the two-passenger car as far as battery power
limitations permitted. At ranges much below 100 miles, available battery
power becomes insufficient for this car to follow the SAE Residential
Driving Cycle.
Table 7.1 summarizes the weights of selected cars, together with
ranges between recharge calculated as in Sec. 6.
See Task Reports 9 and 10 (Vol. 3)
1-70
-------
TABLE 7.1
CHARACTERISTICS OF SELECTED CARS
Two-I'assenger Cars
Four-Passenger Cars
„ _ Lead- N'ickeJ- Zinc-
Battery Type Acid ?_.^ chlorine
Vehicle Curb Weight, Ib 1,800 1,085 1,580
Battery Weight, Ib 550 435 . 340
Nominal Battery Energy,
KWH 12.6 23.5 25.7
Urban Driving Range, mi 35 100 144
30 mph Range, mi 82 188 226
Lithium- Lead- Kickel- Zinc- Lithium-
Sulfur Acid Zinc Chlorine Sulfur
1,430 3,625 3,080 2,500 2,205
200 1,500 1,090 570 300
28 34.5 58.9 43.1 42
144 54 144 145 139
247 183 375 309 317
-------
8 ENERGY AND MATERIAL REQUIREMENTS
8.1 ENERGY REQUIREMENTS
As described in Sec. 6, the simulation used to determine driving
ranges of electric cars included a model of power demanded from the car
battery, and the battery discharge in providing this power. Energy sup-
plied by the battery per mile of driving is automatically calculated by
this model. Determination of overall energy required per mile of electric
car operation requires two additional steps: estimation of charging
energy which must be supplied to the battery to restore it to the fully
charged state, and estimation of the efficiency with which power-line
energy is transformed to battery charging energy.
Table 8.1 shows energy supplied by the various car batteries per
mile of driving, together with estimated power-line energy required per
mile during recharge, and consequent battery efficiency. Charger effi-
ciency, assumed to be 97 percent, is not included in the battery efficiency;
combined charger and battery efficiency is equal to the energy delivered
as a percent of power-line energy supplied. Because battery efficiency
data in Sec. 5 varies, the entries for efficiency and overall consumption
in Table 8.1 were determined by different methods for the different
batteries.
For the lead-acid battery, it was assumed that the recharging pro-
cess was 83 percent efficient, as shown in Sec. 5.1 from Fig. 5.4, and
that energy to be replaced after discharge in the driving cycle was equal
to that available at the 20-hour discharge rate. This last is a question-
able assumption: during the driving cycle, which involves periods at
relatively high power, Fig. 5.2 shows that considerably less energy is .
actually available from the battery than could be obtained at the 20-
hour rate; how much of the difference is dissipated during discharge and
how much remains stored in chemical form is the issue. Transient polari-
zation and subsequent recuperation phenomena are well known in lead-acid
1-72
-------
TABLE 8.1
ESTIMATED ENERGY REQUIREMENTS
Energy Input
to Battery
Charger,*
KWH per Mile
Two-Passenger Car
Lead-Acid
Nickel-Zinc
Zinc-Chlorine
t
Lithium-Sulfur
Four-Passenger Car
Lead- Ac id
Nickel-Zinc
Zinc-Chlorine
t
Lithium-Sulfur
0.44
0.30
0.25
0.32
0.79
0.51
0.41
0.45
Charger efficiency: 97 percent.
Battery
Efficiency,
Percent
42
62
70
54
46
66
70
80
Battery
Energy Output,
KWH per Mile
0.18
0.18
0.17
0.17
0.35
0.33
0.28
0.27
**
Calculated SAE Residential Driving Cycle for two-passenger cars, on SAE
Metropolitan Area Driving Cycle for four-passenger cars.
Charging input energy includes an allowance for maintaining battery
temperature while idle; see text.
batteries; clearly, then, some fraction of the 20-hour energy not avail-
able at higher rates remains stored in the battery after the car reaches
maximum range in the driving cycle. Since the battery discharge model is
insufficiently detailed to reveal this remaining energy, the only verifi-
cation of the accuracy of the full-discharge assumption is a comparison
with test results. Table 8.2 shows reported recharge energy per mile
for various electric cars in actual tests, as well as for the four-passen-
ger lead-acid car of this characterization. It also shows energy consump-
tion per mile per pound of car test weight, a parameter which should be
1-73
-------
TABLE 8.2
COMPARATIVE ENERGY USAGE OF LEAD-ACID BATTERY CARS
Energy Use, KWH/mi
„ Test Weight, _. , Urban ,
Car * 30 mph _ . . *
Pounds Driving
11
GM 512
37
ESB Sundancer
EFP Mars II51
52
EFP Electrosport
Four-Passenger
Characterization
1,650 0.196
2,000 0.31-0.3
4,650 0.4
5,980 0.447
3,975 0.234 0.79
Specific Energy Use,
W'hr/mi/lb
on u Urban ,
30 mph „ . . *
v Driving
0.119
0.155-0.185
0.086
0.075
0.059
0.199
SAE Metropolitan Area Driving Cycle (J 227).
approximately constant from car to car on the same driving cycle, whether
at a constant 30 mph or in a driving cycle. The specific energy consump-
tion of the characterization appears a bit low at 30 mph, but in urban
driving it is about 10 percent higher than the upper end of the range
reported for the ESB Sundancer, a car with similar total driving range,
battery performance, and high-efficiency design. The implication is that
some energy does remain in the battery which need not be replaced during
recharge, but the assumption of full replacement is not seriously in
error. Accordingly, the resultant energy consumptions are used without
further modification.
For the nickel-zinc battery, the recharge efficiency was assumed
to be the same as the lead-acid battery, 83 percent, and required re-
charge energy at the battery terminals was assumed to be that available
in a 20-hour discharge. With the battery charger efficiency of 97 per-
cent, the overall system efficiency is that shown in Table 8.2, from
1-74
-------
which the energy input figures were derived. It is much higher than
that of the lead-acid battery because performance degrades less under
increasing load, as is clear from Fig. 5.8.
For the zinc-chlorine battery described in Fig. 5.13, energy avail-
able on discharge is nearly 95% of that available at the 20-hour rate,
suggesting improved efficiency. Overall energy efficiency in vehicular
use, however, is projected at 70 percent by the developers. This figure
has been adopted in Table 8.2. How the 30-percent energy loss is allo-
cated among battery system elements has been largely documented by the
developers, as follows: 9 percent to the refrigeration required to form
chlorine hydrate during recharge; 2 percent to pumping of the electrolyte
during charge and discharge; 1 percent per day to static self-discharge;
5 percent to coulombic inefficiency. To this may be added the 5-percent
loss during discharge implicit in Fig. 5.13.
According to the developer, the lithium-sulfur battery may reach
80 percent charge-discharge efficiency. Published data on early labora-
tory charge and discharge histories suggests this optimistic goal may be
met: charge voltage is approximately 20 percent above discharge voltage
over a considerable range of charge, with 95 percent coulombic efficiency.
Energy available in discharge is relatively high, as Fig. 5.16 shows, for
a wide range of specific power levels. Heater power to maintain battery
temperature, however, exacts a considerable toll from overall system effi-
ciency. Approximately 200 watts of heater power are estimated to be re-
quired. It is assumed that this requirement is obviated by internal
losses during the hour of daily operation and the 8 hours of daily recharge,
so that only 15 hours of heater operation are required, for a total of 3
KWH per day. Since the typical daily driving distance is 30 miles, this
is a substantial quantity relative to energy delivery requirements, which
are 5.1 KWH for the two-passenger car and 8.1 KWH for the four-passenger
car. Heater energy will be supplied during battery recharge, increasing
requirements accordingly. Energy requirements shown in Table 8.2 for
lithium-sulfur batteries assume 80 percent basic efficiency, with average
daily driving and heating loads as noted above.
1-75
-------
8.2 MATERIAL REQUIREMENT
Except for the power source, the materials used for the electric
vehicles characterized in this report would be similar to those of the
present-day automobile. Thus, differences would primarily be those arising
as an electric motor, motor controls, and a battery power pack are sub-
stituted for an internal-combustion engine system. Tables 8.3, 8.4, and
8.5 give a breakdown of the materials added by each of the electric power
train components. Table 8.6 shows the materials eliminated due to removal
of the internal combustion engine system.
1-76
-------
TABLE 8.3
BATTERY MATERIAL WEIGHTS (pounds per car)
Two-Passenger Car Four-Passenger Car
Lead
Lead-Oxide
Antimony
Klectrolyte
Polypropylene
Filled Polyethylene
Epoxy
Total Weight
Nickel
Zinc Oxide
Potassium Hydroxide
Electrolyte
Polypropylene Oxide
Plastic Separators
Band and Terminals (Copper or Nickel)
Miscellaneous
Total Weight
Zinc
Chlorine
Water
Titanium
Frames, Electrodes, Mountings
Heat Exchanger (Titanium and Coolant)
Support Structure
Miscellaneous
Total Weight
Lithium
Sulfur
Electrolyte
Porous Graphite
Porous Stainless Steel
Stainless Steel Housing
Aluminum Casing
Thermal Insulation
Insulation, Connectors, Misc.
Total Weight
Lead-Acid Battery
Nickel-Zinc
Nickel)
Zinc-Chlorine
)oolant)
Lithium-Sulfur
176
180
9
156
20
7
2
550
Battery
145
130
44
39
26
13
4
34
435
Battery
38
41
119
20
20
11
11
80
340
Battery
11
45
42
35
19
40
5
11
12
200
481
489
24
426
56
20
4
1500
362
328
109
96
64
33
11
87
1090
64
69
200
34
34
17
17
135
570
17
66
63
23
29
61
7
16
18
300
1-77
-------
TABLE 8.4
ELECTRIC MOTOR MATERIAL WEIGHTS
(pounds per car)
Two-Passenger Car
TABLE 8.5
CONTROLLER MATERIAL WEIGHTS
(pounds per car)
Four-Passenger Car
Copper
Iron
Steel
Aluminum
Solder, Connectors, Misc.
Total
17.5
70.2
14.0
10.7
4.6
117.0
47.2
189.0
37.9
31.5
9.4
315.0
Copper
Steel
Aluminum
Solid State Devices
Plastics
Solder, Connectors, Misc.
Total
Two-Passenger Car
3.3
11.5
8.3
3.3
3.3
3.3
33.0
Four-Passenger Car
8.5
29.8
21.2
8.5
8.5
8.5
85.0
1-78
-------
TABLE 8.6
GASOLINE POWER MATERIAL WEIGHTS ELIMINATED BY CONVERSION TO
BATTERY POWER
(pounds per car)
Two-Passenger Car Four-Passenger Car
Steel 95 180
Iron 91 175
Aluminum A 8
Copper 6 10
Plastics 16 30
Misc. 38 72
Total 250 475
1-79
-------
1-80
-------
APPENDIX
COMPUTER PROGRAMS
A.I INTRODUCTION
Three computer programs have been developed for the purpose of per-
forming electric car parametric studies. The major parameter of interest
is the vehicle range when driven according to various set driving cycles.
The three programs differ only by virtue of the driving cycle used. The
three programs are ELCP1, ELCP2, and ELCP3. They are based on the DHEW
Federal Driving Cycle, and the Residential and Metropolitan Area Driving
Cycles of SAEJ227, respectively.
The computer programs use the driving cycle in the form of velocity
at finite time data to determine the vehicle power requirements. The
power requirements are then used to discharge the batteries with a dynamic
load profile. The driving cycle simulation is then continued until the
battery cannot fulfill the cycling power requirements.
The range of the vehicle under any particular driving cycle is
reached when the battery can no longer supply the power required to com-
plete a cycle.
A. 2 DESCRIPTION
53
tive studies. The vehicle load calculations proceed on an iteration
Calculations used in these programs are widely employed in automo-
53
tudies. The vehicle load calculations pi
period of 2 seconds. The acceleration is simply
A = DV/dt
1-81
-------
The rolling resistance R_ is given by
W/50[l + (1.4 * 10~3V) + (1.2 * 10~V)] (lb)
where V = vehicle velocity in ft/s
W = vehicle weight in lb
The air drag resistance R, is given by
R, - 0.00119A C,V2
d o d
2
where A = frontal area in ft
o
C, = drag coefficient
The acceleration resistance R. is given by
A
R. - (W/32.2)A
A
The resistance due to roadway slope R is given by
O
RS - W(A1/100)
where Al = percentage of slope
The road power is then calculated as
P = V(RR + Rd + RS + 1.1RA)
where the 1.1 factor on the acceleration load is used to approximate the
rotary acceleration load.
1-82
-------
The power required from the battery is calculated as ?„
JJ
PB-P/
-------
drag coefficient
battery weight
battery specific power versus specific energy
rolling resistance formula
accessory power
driving cycle
electrical and mechanical efficiencies
regenerative braking effects
slope effects
The output of the program includes the range in miles, running time
in hours, and the energy expended in watt-hours.
These input variables are exercised parametrically to determine
their independent and interdependent effect on vehicle range.
The program outputs in the form of vehicle ranges and power usage
may be plotted against the various input trends. This will be useful in
projecting vehicle type usage on the basis of electrical vehicle
competitiveness.
1-84
-------
COMPUTER PROGRAM SYMBOLS LIST
A vehicle acceleration, mph/s
2
AO frontal area, ft
Al slope, percentage grade
B regenerative braking factor, percent
C drag coefficient
E mechanical drive efficiency
EO electrical drive efficiency
El electrical accessory efficiency
E2 total energy regenerated
H highest power requirement
I time, seconds
J velocity at previous time interval, ft/s
K percent of battery power used/100
L data time counter
M number of data points in cycle
Ml total travel range, miles
N estimated number of .c»pr>r
-------
Computer Program Symbols List (Cont.)
VI velocity, mph
V2 velocity, km/hr
W vehicle weight, Ib
WO total energy expended, W'hr
X specific power, W/lb
Y battery energy density, W-hr/lb
Z battery power density W/lb
S number of data sets of battery
Wl battery weight, Ib
1-86
-------
ELCP1, BASIC Language Computer Program
for simulating electric vehicle performance on
the DHEW Federal driving cycle.
1-87
-------
AN EXAMPLE OF A COMPUTER RUN USING THE FEDERAL DRIVING CYCLE
ELCP1 16-.51PDT 10/02/73
'POWER REQUIREMENTS FOR FEDERAL DRIV. CYCLE
#**:::* INPUT PARAMETERS *****
DRAG CGEFF.
WEIGHT... FRONTAL AREA
POUNDS SQUARE FEET
3075 . 22
EFFICIENCIES:
MECHANICAL ELEC.DRIVE
0.9 0.8
0.3
ELEC. ACESS.
"0.8
REGEN. BRAKING POWER FACTOR = 0 %
NO SLOPES NEGCTIATF.D
BATTERY WT • ACCESSORY PCV.'ER
POUNDS ' WATTS
750 0
TOT. ENERGY
WATT-KGUUS
56C5.92
PEAK POWER
WATTS
49358.
AT TIME
SECONDS
196
RANGE
MILES
17^ 12 11
RUNNING TIME
HOURS
0.645
1-88
-------
ELCH1 II/I2//4
100 DIM /(100),Y(100)
job RL-:AD t>,wi
I 10 Re: AD W.C.AO.AI
120 READ h.EO.EI.HI
ISO PR1NI" POWER REQUIREMENTS FOR FEDERAL. DR1V. CYCLE"
160 PRINT
I/O PRINT ******INPUT PARAMETERS*****"
lc<0 HRINT
190 PRINT "WEKiHT FRONTAL AREA DRAG COErF. BATTERiC Wl."»
200 PRINT " ACCESSORY POWER"
210 PWINi "POUNDS SQUARE FEET HOUNDS"!
220 pi,! INT " WAITS"
240 f'-.'IN'l W,AO.t:.WI .HI
KtMNI " F->FICIENCIES«"
^/O PRINT "MECHANICAL El.EC.DRIVE EI.EC. ACESS."
/HO HRINT E.EO.hl
^90 PRINT
.^00 P^INl "REGEN. BRAKING POWER FACTOR = "JB;" %"
310 PRINT
.&() It- AI-0 "1UHN .JISO
J.'iO PRINT " SI.f)PES ARE NEGOTIATED"
340 un TO 4 JO
SSO PRINT " NO SLOPES NEGOTIATED"
3/0 PRINT
430 J=Wn=H=t2=0
.,40 E3=0
4'>O K-MI=0
^••0 DIM ^( 1400)
'»/0 RhA.) .Ni.M.'i I
A /S READ 0
4 lo MAT READ Z(C>.Y(G)
4bO FOR l.= I T()(M+I )
490 READ S(L)
SOO NEXT L
blO FOR 12=1 TO N
•320 FOR L= I TO (M+l) STEP 'II
10= (I.-I )
1=10+13
V-S(L)*I.4667
b60 A^ (V-.D/TI
b/0 J=V
•5HO V2=S(L)*I .609
590 R=(W/SO)*(I+.OOI4*V+.OOOOI2*V"2)
b9l R=.46*R
600 R0=.noiI9*C*AO*V~2
610 Rl=W*(Al/100)
620 R2 = (W/32.2)*A
630 IF R2<0 THEN 6SO
640 G.) \<) 670
1-89
-------
L-LGPI
I/I2//4
oSO R3=(B/IOO)*R2
6/0 PO=V*( R + RO-J-R 1 -H ,
6/2 Ir- P0<0 THHN 676
o/4 GO TO 680
JftOO/TI>
ft'JO
/IO Ir I>M ThirN /6O
/2O Ir- r'2>H '1HI-"N /4O
MO c;o To /60
/4O H-K2
/so T= I
/ftO X-P2/A1
/ /O GO^UH 9000
/HO K4=F
•too P3 = P4*WI
•(O 1 Ml =M 1 + ( V/52^0) *T 1
->IO K=K+(P2*TI /JftOO) /P3
vn U- K>=l "1HLN 920
•»r!0 NhXT L
^'00 NEXT 12
vl 0 Go TO 21 30
^:0 TO =1/3 000
;:io PRINT
^40 PRINT "*****oli iVIii *****"
yso P! .24. 4.24.5,24.
20 DATA 24.6 ,24., S, ^5. 1 ,25. 4, 2S . 4 , ^S. 2. 25 . 1 ,25.
30 DAI' A 26.26-2,2 702H«20,29.3.2^. / .,
-------
bLCPI II/I2//4
I I SO DATA 30.8..10.8, 30. 3,2 9. 9. 29.8.2V. 9. 30. .3.30.0. 31 .3.32.. 12
I 160 DATA 32.31.9,31.28.7.24.6.20.15.3. I I. 7,6.5.2.8.0.0,0.0
M /O DATA 0.0.0,0,0.0,0,0.0.0.0,0,0,0,0.0.0.0.0,0.0,0.0.0,0
I I HO DATA 0,0.0.0.0.0.0.0.0,0.0,.2,4,9.6.14.3. 16.5.20.2?.4.24.2
I 190 DATA 25.7,26.5.25.9.25.5,25,25. I.25.5.PS. 7.26,27.26.4
I,-00 DATA 24.8.22.2. 19.6.18, 17.6.18.5. 13.8.20. 1,22.5,24.9,27.8
i,-: 10 DATA 31 ,34.3.36.4. 37.8, 39. <*, 40.8, 42.43. 7. 44.9.45.8.46.5
1220 DATA 47.2,47.2.47. I .47. 1,47,47,47,47,47.4 7.47.2.47.8,48.2
1/30 DATA 48. 7,49.2.49.7.50.2.50.6.51. 1,52.2,5 1.2,53.9,54.3,54.5
1240 'UIA 54.8.54.8.54.4.54.4,54.6.54.9.55.2.55.4,55.8.55.9,56
I,'SO DATA 56.I .56.2,56.2.56.3.56.3.56.2,56.2,S6.2.56.I .56,55.7.55. 1
1/60 DATA 54.8.54.3,54. I .53.8.53. 7,53. 7.53.R.5 l.>. 54, 53.8, 53.4
12 70 DATA 53. 1 .52.8.52.2.52. I .52.51.8.51 .6,51 .5,51 .4.51 .5
1280 DATA 51.8.52,52.6.53.2.53.8,54.2.54.9,55.2,5S.5.55.7.55.5.55.2
1290 DATA 54. 7.53.9,53,51 .8.51 .4.51 .2.51 .2 . 50. -+, 40 . 8. 49.8, 49 .9
I 300 DATA 49. 7.49.S.49.4.49.3.49.2.48.9.48,47.6,46.8,45.4,44.3
1310 DATA 43.I .42.40. 7,39.5.38.36.4,34.6.33.2.32.1 .31.I.30.8
I J20 DATA 30.b,30,28.5,26,23.5,2 I . 1 , 20 , I 8. 9. I 7. 7. I 6.2, I 3.8 . I I . ">
I 130 DATA H.6,6, I.5..2.0.0,0,0,0,0.0.0.0,0.0,0.0,0,2, 7.2,10.7
1340 DATA 13.5.16.5.18.9.21,23.2,24.6,26.1,28.29.I,30.7,31.I
i iso DATA 31.8.32.5,33.2.33.9,34.2,34.7.34.3.34.1,34.5.35.2
1360 DATA 35.H.35.7.35.8,35.9.36.36.I,36.1,36.2.36.2,36.I.36
1370 DATA 35.3.34.5,33.6.31.5.28,25.3.23,20.2. 17.I 4.3, I I.2,
I 180 DATA 7.5,4,.H,0,0,0.0,0,0,2.1,6,10.6,14,16.9.20,23,24.7
1390 DATA 25.8,27.6,29.2.29.8.30,29.8.29.5.29.2.?8.7.27.5
1400 DATA 24.8,21 . 17. M. 4. 10,5.3, I .5,0,0,0.0.0,0.'), 0,0.0,0,0.0
I 410 DATA 0.0,0,0.0,.3,3,8,I 3,14.5.I 7,20.I.22.5,25.2.27.I
1-420 DATA 28.I,30.j.32.I ,33, 34. I , 35, 35. 3. 35.8. 35.9,36,36
1430 DATA 35.9, 35. 8, 35.8.35.9.35.8.35. 7. 35.5,35.4. 35.2. 35.2
1440 DATA 35.2.35.2.35.2,35.I,35,35.35,35,35,35,3S.34.9
1450 DATA 34.9.34.9.34, 33.2,31.5,29.5.27.5,25.22.18.9. 15.5, 12.5
1460 DATA 10.6.2,2.5,0.I.3.2.5.6.I.7.8,9,10,II.13.2.15.3.16.9
1470 DATA 18. 19. |,po..1.2 I.2.22.2,23.3,2.3.9,24.5.2^.I.25.1
1480 DATA 25.I .25. I .25.I .25. I.25.5,26,26.25.9.25.8.25.5.25.2
1490 DATA 25.P.25.24.H.24.5,23.5. 17.5. 12.7.5.I .8, \ 0.0.0.0.0.0
1500 DATA 0,0,0.0,0,0.0.0.0.0,0,0.0,0.0,2.5.5.3.8.5,12.?.14.9
IS 10 DATA 15. 7.16.9, I 7.1.17. I 7.S.I 8, 18.I 7.9. 17.O. I 7.2, 17. I
1520 DATA 17.1,17.2.17.1 ,1 /. I /.I 7,17.1 '7, 17 .2 . 18.2 . !-i.H.?0
1530 DAI'A 20.y.,M .21 . I ,.M .5,21 .9,22 .2 . 22 .4 . 22. 5. ,'V .4 .22. 4. 'M.5
1540 DATA .r'5. . I .2^
1580 DATA 22.9.20. <.Ih,IS.S.13.6,10.4,7.8.4.5.2.9.I.5..I
1590 DATA f.'.O.f -, O. o.O.O. O.o,'1.0, . I , .5.2. I .3.7.5. I .8. | 0 . ?, I 2.M
1600 DATA u. i. is.r. i6.M, 16. /. i6.s. i 7.5.18.8;20.20.7.22.^,25..;.-,22. i
1610 DATA 2^.2.22.8.23.5.23.22. I .21 .5. 19.8, 1 7.5. | 3.5.9.'<.6.Q.s. ,3.0..
1620 DATA 1 . i, .2.2.5.6,9.2. 12. 13. 7. I 5.6. 17.5, 19. 1. 2 I , ;-V . -». 24.2 ,25.4
1630 DATA 26. / .2 7.2 7.5.27.9,28. I ,28. 6, 28.4,2b. 3,2.8. 1 .28.27.8,
1640 DATA 2 7.2, «.-• 6. 2. 24.2 I .5. 19.6. 18. 15.6, 1 3.8. IO.S. 7.5, I.1-. I .5
1-91
-------
tLCPI II/I2//4
DATA I .5, 1 .0.2,5. 2.8. 8. IP. 5, 15.4. 17.5. 18.3, 19.20. 5.
1660 DATA 21.9.23.2,24.8.26.2,27.2.28.28.2,28.8,29.1,29
I 6 70 DATA 29, 2H. 0. 2H . /, 28. 6. PH . 5, 2H. 3, 2 /. 0,2 7. 9, 28 . 2 7. 7. 2 7 . 8 .
1680 DATA 27.8.27.8.27.M,28.28. /.2O. 7, 3O.8. 32. 12.8,33. 3 1.3.
1690 DATA 33.8,34. 1 ,3*, 14, 34, <4. J3. 9,33.^. 33.2. V.o, '<2.-i. V.
I (00 DATA 31 .9. II .-1. 31 .2. 3O. i, 30, 3O. 30, .10. 2<>.'>.S,
1710 DATA 29.2.28.9,28. 1.27.5.26.3,24.5.22.8.2 I .2. 19.8, 19.?
1720 DATA 20.2,21 .1.21. /, 22.,:. 23, 23. 6, 24 .6, 25. 2 , 26 .2 . 26. -1. 26 .*
I /30 DATA 26. /,26. /,2 /. •».2/.8,2H.2,2H. 7,2rt.o,^V,29.2,2H.:;,2^.'i
1740 DATA 2H,2 /.b,26.2,2b.3,2b.2S. I ,2S.1,2S.^).2S. 7.26.2, ^6.H,
1 /SO DATA 27.4.^8,2^.29.3.29.2.29. 1,29,28.^,28.9,28.ci,2^.4
I 760 DATA 28.j,28.>/.9,2/.4.2/.I,27.b.2/.8,28.27.9,28,28.28.I,
1 /70 DATA 28.27.8.2/.3,26.9.^6.8,26. /. 26.6. 26. 6. 26 . ^,2'-. 4 , 2S .9
I /HO DATA 2b.8,2t>.-i,2S.8.26,26.l , 2 b. b. 24 .2 .22.6.22 . 2 I .8,22,22.'?
1790 DATA 23,23.8,24. J, 24. b. 24.9. 2S. I ,2S.2,2 b. 2, 25. 3,2 ^.2,2o.2t-
IHOO DATA 25,24.9.24.8.24. /.24.5,24.5,24.9.25,24.9.24.9,24.2
1810 DATA 24.3.2b.I.25.8.25.5,24,22.20.18.9,16.6,12.6,7.6.5,I.>
1820 DATA .I,0,0,.8,4.4,9, 13.9,15.9, I 7.2,18.5,20,2!.3.22.2.23,
lr,30 DATA 24.8 ,26.2 . 27 . I .2 7. 8. 28.2 .28/3. 28 .3.28.2 . 28. 2 7. /.27.'<
1840 DATA 27,26.8.26.2,26.25.3.24.22.7,21.7.21.8,22,22.5.22.0
Irt50 DATA 23,23,23,23.22.9.23,23.5,24.2.24.9,25.1,25.3.25.9.26
I860 DATA 25.6.25.24.5.23.9,23.7,23.22. /,22.2, 21 .8,21 . H. 8. 15.
1870 DATA 11.2. / .2,3.0,0,0.0.0,0,0,0,0.0,0,0.0,0,0.0,0.0,0,0,O
I 880 DATA 0,0,0.0, 1,0.0.0.0.0. 1.5.4.6,8. II.2,14.2. I 7, 18.2. 19.9
1890 DATA 21.8.22.8.23.8.24.9.25.6.26.5.26.8.2/.2.28,28.I.28
1900 DATA 27.7.27.26.9.26.4.24.H.22.5,21.8.21.1.18.8.15. 12.9.
1910 DATA I I .1 ,10.8. 10.2.9./,9.3,9,8.y,9,9.8.9,8.7,8.3.7.2
1920 DATA 5.5,4.6.3.3. I.H.O. .3..8.2.4.4,7.3. 10.5.13.1. n.o. 14.4
1930 DATA 16,18.1, 19.H.2O.9,^I.21.I.2 I .2.2 I.6,22,22.7.2 *. 1.24.3
1U40 DATA 24.9,24.9.25,25.I.25.2,25.7.26.26.3.26./.27,27.27.
1950 DATA 26.9.26.9.26.9,26.8.26.7,26.5.26.1.25.6.25.1.23.3.
1960 DATA 22.I.20.I.IF.2,16.3,14.5,12.5.8.7.4.R..6,0.0.0,0.0.
19 10 DATA 0.0.0,0. ),0,0,0.0,0,0,.1,4.3,9.7.I 3.5,16.2,19. ^.21 .2
1980 DATA 23,23.6.23,22. 19.5, 16.6, I 1 .6.7.5.3.5..2.0,0.0,0
1900 DATA 0.0.0.0.0.0.0. I.2.5.4.a.8, 10.9.12.5, 12.H.I 3. I ,
^000 DATA 12.9,13,13.1,13.5, K.2,15.4,17,19,20.2,21.7,21.8
2010 DATA 21.8,21.9.21.4.2I.2.2M .3.21.9.21.0,2!.8.21.7.21.6.
2020 DATA 2 I.5.21.2,20.5,Io.8. 19.4,I 9.8.20,2O. I 8 .9 . I /.
2030 DATA 14.9,12,9.5,7.4.5.o.3.2.2.I,.I ,0,0,0.0,0.0,O.. /,
2040 DATA 1 . 1 , 1 . I , I . 1 , 1 . 1 . I . 2, 2. 5. 3. 8. 4.8,6 . 7.2 , 9. I O.Q . I •). ->
2050 DATA 9.5,8.4,8.1,9.7.12.5,15.18,20.3,21.3.22.22.5,23.5.
2060 DATA 24,24.3.24.6,24.2,24.23. /.23.5,23.5.23.5.23.6.23. /
2070 DATA 24,24.5.24.8,25.25.2.25.5.25.8.26.26.1.26.3,2/.2.
2080 DATA 28,28.5,28.8,29,28.8,28.2.26.5.23.1,19.6
2090 DATA 15.9.3,4, . 3,0. 0,0. 0, 0, 0,0, 0,0,0, 0, 0, 0,0, 0, •'), 0, ~i, 0
2100 DATA 0.0,0,0,0,0,0, 1 .8,5.6,9.7, 12. 14, 15.8. 17.5, 19. !•;.9
2110 DATA 20.5,21.6,22.22.4.22.4,22,21.6.21.2,21.20,19. /.18.3
2120 DATA 1 /.2,16.2,15.2,13.5. II,7.8,5,1.5,0,0.0,0.0
2130 PRINT
21 35 PHINT"OUTKJT»"
1-92
-------
ELCP1 II/I 2/74
2140 PRINT" INCREASE ESTIMATED TIME ,N.TO DETERMINE RANCH."
2145 PRINT"TIME"»TAB( 17) *"WATT-HRS"lTAB(32) t "M ILES" iTARf 49 ) I "K "
2147 PRINT I,WO.Ml,K
2 I 50 STOP
9000 REM ROUTINE FOR PIF.CEWIZE LINEAR FUNCTION
9010 REM Z IS ARRAY OF ABSCISSA VALUES I Y IS ARRAY Or ORUINVfr VAl
9020 DEF FNC(D) = Y(L)-I )+( Y (D) -Y fD-l ))*( X-Z( D- 1 ))/(/. (D) -Z I i)- I ))
9030 IF X>=Z(I) THEN 9060
9040 r=FNC(2) .
9050 GO TO 9120
9060 FOR D=2 TO O
9070 IF X>=Z(U) THEN 9100
9080 r=FNC(D)
9090 GO TO 9120
9100 NEXT D
9110 h=FNC(0)
9120 RETURN
9160 END
1-93
-------
ELCP2, BASIC Language Computer Program
for simulating electric vehicle performance
on the SAE Residential Driving Cycle.
1-94
-------
AN EXAMPLE OF A COMPUTER RUN USING THE SAE RESIDENTIAL DRIVING CYCLE
ELCP2 17:31PDT 10/03/73
POWER REQUIREMENTS FOR SAE J227 RES ID. DRIV. CYCLE
***** INPUT PARAMETERS *****
DRAG C0EFF.
' 0.4
WEIGHT
POUNDS
3030
FRONTAL AREA
SQUARE FEET
18
BATTERY WT ACCESSORY PGUER
POUNDS WATTS
1400 0
EFFICIENCIES:
MECHANICAL ELEC. DRIVE
0.9 0.7
ELEC. ACCESS.
0.8
REGEN. BRAKING POWER FACTOR = 0 Z
NC SLOPES NEGOTIATED
***** OUTPUT *****
TOT. ENERGY
l.'ATT- HOURS
1 83 95.
PEAK POWER
WATTS
34904.3
AT TIME
SECONDS
35
RANGE
MILES .
75.0514
RUNNING TIME
HOURS ...
3.64917
1-95
-------
ELCP2 I I/.I 2/74
100 DIM Z(100),Y(100)
I 10 READ B,WI
120 READ W,C,AO,AI
130 READ E,EO,E1,FI
150 PRINT " POWER REQUIREMENTS FOR SAE J227 RESID. DRIV. CYCLE"
160 PRINT
170 PRINT "***** INPUT PARAMETERS ******
180 PRINT
190 PRINT "WEIGHT -FRONTAL AREA DRAG CoEFF. BATTERY NT"»
200 PRINT " ACCESSORY POWER"
210 PRINT "POUNDS SQUARE FEET POUNDS";
220 PRINT " WATTS"
240 PRINT W,AO,C,W1,P1
250 PRINT
260 PRINT " EFFICIENCIES!"
270 PRINT "MECHANICAL ELEC. DRIVE ELEC. ACCESS."
280 PRINT E,EO,EI
290 PRINT
300 PRINT "REGEN. BRAKING POWER FACTOR ="|B|"%"
310 PRINT
320 IF A 1=0 THEN 350
330 PRINT "SLOPES ARE NEGOTIATED"
340 GO TO 370
350 PRINT "NO SLOPES NEGOTIATED"
360 PRINT
370 J=WO=H=E2=0
380 K=MI=0
390 READ N,M,TI
400 READ S
410 MAT READ Z(S),Y(S)
420 FOR I =1 To N STEP T1
430 L=(I-I)-INT((I-l)/M)*M
440 IF L<=20 THEN 540
450 IF L.<=34 THEN 560
460 IF L< = 49 THEN 580
470 IF L<=60 THEN 600
480 IF L<=75 THfc'N 620
490 IF L<-8/.5 THEN 640
500 I'r L<=I34 THEN 660
510 IF L<=I42 THEN 680
520 V1=20-(L-U2)*2.5
530 GO TO 690
540 VI =0
550 GO TO 690
560 VI=(L-20)*2.14
570 GO TO 690
580 VI=30
590 GO TO 690
600 VI=30-(L-49)*I.37
610 GO TO 690
1-96
-------
ELCP2 11/12/74
620 VI =lb
630 GO It) 690
640 Vl=lb>l.2*(L-75)
6bO GO TO 690
660 VI =30
670 GO TO 690
680 VI=30-(L-I 34)*I.2
690 V=VI*I.4667
700 A=(V-J)/T1
710 J= V
720 V2=VI*I.609
730 k=(W/50)*(I+.0014*V+.OOOOI2*V~2)
U\ R=.46*R
740 R0 = .00 I I9*C*AO*V~2
/50 RI=W*AI/IOO
/60 R2=(W/32.2)*A
7/0 IH R2<0 THEN 790
780 GO TO 810
790 R3=(b/IOO)*R2
810 PO=V*(R+RO+RI-H.I*R2)
812 IF P0H THEN 870
860 G;) TO 890
8/0 H=H2
880 T=I
890 X=P2/WI
900 GOSUB 9000
910 P4=F
92O P3 = P4*WI
930 Ml=MI+(V/5280)*TI
940 K=K+(P2*TI/3600)/P3
9bO IH K>=1 THEN 980
960 NtXT I
970 GO fo I 140
980 T0=1/3600
990 PR IN I
1000 PRINT "***** OUTPUT ******
1010 PRINT
1020 PRINT "TOT. ENERGY PEAK POWER AT TIME RANGE"!
1030 PRINT " RUNNING TIME"
1040 PRINT "WATT-HOURS WATTS SECONDS MILES"!
1050 PRINT " HOURS"
1070 PRINT WO.H.T.MI,TO
1080 GO TO I 190
1090 DATA O.b'bO
1-97
-------
ELCP2 .11/12/74
100 DATA 2 I 00..4,18,0
I 10 DATA .9,.7..8.0
130 DATA 25000,150,2
140 PRINT
1 50 PRINT"OUTPUT»"
160 PRINT"INCREASE ESTIMATED TIME ,N,TO DETERMINE RANGE."
170 PRINT"TIME"lTAB(I 7)?"WATT-HRS"lTAB(32)i»MILES"lTAB(49)«"K"
180 PRINT I,WO,Ml,K
1190 STOP
9000 REM ROUTINE FOR PIECEWISE LINEAR FUNCTION
9010 REM Z IS ARRAY OF ABSCISSA VALUES! Y IS ARRAY OF ORDINATE VALUES
9020 DEF FNC(D)=Y(D-1 )+(Y(D)-Y(D-l ))*(X-Z(D-t ))/(Z(D)-Z(D-1 ))
9030 IF X>=Z(1) THEN 9060
9040 F=FNC(2)
9050 GO TO 9120
9060 FOR D=2 TO S
9070 IF X>=Z(D) THEN 9100
9080 F=FNC(D)
9090 GO TO 9120
9100 NEXT D
9110 F=FNC(S)
9120 RETURN
9130 DATA 6
9140 DATA 0,18,35,65,114,114
9150 DATA 75.7,73.5,71.4,67.1,58.6.0
9160 END
1-98
-------
ELCP3,. RASTu Language
for simulating electric vehicle performance on
the SAE Metropolitan Area Driving Cycle.
1-99
-------
AN EXAMPLE OF A COMPUTER RUN USING THE SAE METROPOLITAN AREA DRIVING CYCLE
ELCP3 ll:43PDT 10/16/73
P0UER REQUIREMENTS FOR SAE J227 METR0. DRIV. CYCLE
INPUT PARAMETERS *****
DRAG C0EFF.
UEIGHT FRONTAL AREA
POUNDS SQUARE FEET
3075 22
EFFICIENCIES*
MECHANICAL ELEC. DRIVE
0.9 0.8
BATTERY WT- ACCESS0RY P0UER
P0UNDS WATTS
0.3 750 0
ELEC. ACCESS.
0.8
REGEN. BRAKING P0UER FACTOR « 0 Z
N0 SLOPES NEGOTIATED
***** OUTPUT *****
TOT. ENERGY
UATT-HOURS
6034.71
PEAK POWER
UATTS
33972.7
AT TIME
SECONDS
101
RANGE
MILES
18.9477
RUNNING TIME
HOURS
0.798611
1-100
-------
fcLCP3 11/12/74
100 DIM Z(100),Y(100)
I 10 READ B.WI
120 READ W.C.AO.AI
130 READ E.EO.EI ,PI
150 PRINT " POWER REQUIREMENTS FOR SAE J227 METRO. DRIV. CYCLE"
160 PRINT
170 PRINT "***** INPUT PARAMETERS ******
180 PRINT
190 PRINT -"WEIGHT FRONTAL AREA DRAG COEFF. BAITERY WT4'«
200 PRINT " ACCESSORY POWER"
210 PRINT "POUNDS SQUARE FEET POUNDS"I
220 PRINT " WATTS"
240 PRINT W.AO.C.WI.PI
2bO PRINT
260 PRINT " EFFICIENCIESi"
270 PRINT "MECHANICAL ELEC. DRIVE ELEC. ACCESS."
280 PRINT E,EO,EI
290 PRINT
300 PRINT "REGEN. BRAKING POWER FACTOR ="»b»"*"
310 PRINT
320 IF A 1=0 THF.N 350
330 PRINT "SLOPES ARE NEGOTIATED"
340 GO TO 370
350 PRINT "NO SLOPES NEGOTIATED"
360 PRINT
3/0 J=WO=H=E2=0
3bO K=MI=0
390 READ N,M,TI
400 READ S
410 MAT READ Z(S),Y(b)
420 FOR I =1 TO N STEP Tl
430 L=(I-I)-INT((1-1)/M)*M
440 IF L<=20 THEN 540
450. IF L<=34 THEN 560
460 IF L<=49 THEN 580
470 IF L<=60 THEN 600
480 IF L<=75 THEN 620
490 IF L<=100 THEN 640
500 IF L<=I2I THEN 660
510 IF L<=142 THEN 680
520 V1=20-(L-.142)*2.5
530 G:) TO 690
540 VI =0
550 GO TO 690
560 VI=(L-20)*2.14
570 GJ TO 690
580 VI=30
590 GO TO 690
600 VI=30-(L-49)*I.37
610 GO TO 690
1-101
-------
ELCP3
11/12/74
620 VI=I5
630 GO TO 690
640 Vl=lb+l .2*.(L-75)
6bO GO TO 690
660 Vl=4b
670 GO TO 690
680 VI=4b-(L-l21)*l.19
690 V=VI*I .4667
/PO A=(V-J)/TI
/!0 J=V
/20 V2=VI*I .609
730 R=(W/bO)*( I + .00!4*V+.OOOOI2*V
/3I R=.46*R
740 RO = .OOI I9*C*AO*\T2
VbO RI=W*AI/IOO
/60 R2=(W/32.2)*A
7/0 IH R2<0 'THEN /90
780 Go TO 810
790 R3=(B/IOO)*R2
810 PO=V*(R+RO+RI+I.I*R2)
812 Ih P0<0 THEN 816
814 GO TO 820
816 PO=V*R3
820 P=PO/(E*EO)+PI/EI
830 P2=P*I.3b6
840 WO=WO+P2/(3600/TI) .
8bO IP P2>H THEN 8/O
M60 GO TO 890
870 H=P2
880 T=I
bOO X=P2/WI
900 GOSUB 9000
910 P4=F
920 P3=P4*WI
930 Ml=MI+(V/b280)*T1
940 K=K+(P2*TI/3600)/P3
950 IF K>=l THEN 980
960 NEXT I
970 GO TO I 140
980 T0=1/3000
990 PRINT
1000 PRINT "***** OUTPUT *****•"
1010 PRINT
1020 PRIM
1030 PRINT
1040 PRINT
lObO PRINT
1070 PRINT
1080 GO TO
1090 DATA
2)
"TOT. ENERGY PEAK
" RUNNING TIME"
"WATT-HOURS WATTS
" HOURS"
WO.H.T.MI.TO
I 190
0,570
AT TIME
SECONDS
RANGE"!
MILES"I
1-102
-------
ELCP3 .11/12/74
100 DATA 2950,.4,22,0
I 10 DATA .9,.8,.8,0
130 DATA 25000,150,2
140 PRINT
150 PRINT"OUTPUTt"
160 PRINT"INCREASE ESTIMATED TIME ,N,TO DETERMINE RANGE."
170 PRINT"TIME»lTAB(17)t"WATT-HRS"iTAEK 32)I"MILES"|TAB(49)I"K"
180 PRINT I,WO,Ml,K
i 90 STOP
9000 REM ROUTINE FOR PIECEWISE LINEAR FUNCTION
9010 REM Z IS ARRAY OF ABSCISSA VALUES! Y IS ARRAY OF ORDINATE VALUES
9020 DEF FNC(D)=Y(D-I)+(Y(D)-Y(D-l))*(X-Z(D-I))/(Z(D)-Z(D-l))
9030 IF X>=Z(I) THEN 9060
9040 H=FNC(2)
9050 GO TO 9120
9060 FOR 0=2 TO S
9070 IF X>=Z(D) THEN 9100
9080 F=FNC(D)
9090 GO TO 9120
9100 NEXT D
9110 F=FNC(S)
9120 RETURN
9130 DATA 6
9140 DATA 0, 18,35,65,.! 14, I 14
9150 DATA 75.7,73.5,71.4,67.1,58.6, 0
9160 END
1-103
-------
TWO-PASSENGER CAR, SAE RESIDENTIAL DRIVING CYCLE
1980 Lead-
Acid Battery
Nickel-Zinc
Battery
Zinc- Chlorine
Battery
Lithium-
Sulfur
Battery
Vehicle
Weight,
Ib
1,940
2,050
2,100
2,160
2,380
1,940
2,050
2,100
2,160
2,270
1,730
1,830
1,940
2,050
2,100
1,705
1,730
1,780
1,830
Battery
Weight ,
Ib
400
500
550
600
800
400
500
550
600
700
200
300
400
500
550
175
200
250
300
Range,
mi
23.2
30.9
34.4
37.9
51.0
91.0
116.0
127.9
138.6
159.7
87.2
129.1
166.5
200.8
217.2
80.9
144.4
194.2
232.8
Running
Time,
hr
1.13
1.51
1.68
1.84
2.48
4.43
5.64
6.22
6.74
7.76
4.24
6.27
8.09
9.76
10.55
3.93
7.02
9.42
11.3
Total**
Energy
Watt-hours
4,057
5,632
6,431
7,229
10,496
15,899
21,162
23,779
26,361
31,579
13,938
21,527
29,066
36,583
40,348
13,808
24,885
34,125
41,706
Specific
Energy
W-hr/mi
175
182
187
191
206
175
182
186
190
198
160
167
175
182
186
170
172
176
180
Mileage
mi/KWH
5.72
5.49
5.35
5.24
4.86
5.72
5.48
5.38
5.26
5.06
6.26
6.00
5.73
5.49
5.38
5.87
5.80
5.68
5.56
**
Includes 300-pound payload.
Output from battery.
1-104
-------
FOUR-PASSENGER CAR, SAE METROPOLITAN DRIVING CYCLE
1980 Lead-
Acid Battery
Nickel-Zinc
Battery
Zinc-Chlorine.
Battery
Lithium-
Sulfur
Battery
Vehicle
Weight,*
Ib
2,985
3,205
3,425
3,645
3,975
2,985
3,205
3,425
3,645
3,975
3,655
2,765
2,985
3,205
2,600
2,655
2,765
2,875
Battery
Weight,
Ib
600
800
1,000
1,200
1,500
600
800
1,000
1,200
1,500
300
400
600
800
250 -
300
400
500
Range,
mi
18.4
27.4
35.4
42.9
52.9
77.8
106.7
133.3
157.8
189.9
78.5
104.7
151.5
192.9
77.2
138.6
195.9
241.3
Running
Time,
hr
0.78
1.15
1.48
1.80
2.22
3.26
4.47
5.57
6.60
7.94
3.28
4.38
6.34
8.07
3.24
5.80
8.19
10.09
Total
Energy**
Watt-hours
5,295
8,276
11,303
14,391
19,076
22,103
32,148
42,404
52,768
68,308
20,395
27,998
43,083
58,117
20,838
37,881
55,235
70,008
Specific
Energy
W-hr/mi
288
302
319
335
361
284
301
318
334.
360
260
267
284
301
27C
273
282
290
Mileage
ni/KWH
3.47
3.31
3.13
2.98
2.77
3.52
3.32
i.14
2.99
2.78
3.85
3.74
3.52
3.32
3.70
3.66
3.55
3.45
Includes 450-pound payload.
*
Output from battery.
1-105
-------
TWO-PASSENGER CAR
Vehicle Test Weight, Ib
Battery Weight, Ib
Charging Energy, KWH
Battery Energy Available,
KWH
Range + Energy, mi @ KWH
SAE Residential Cycle
5 mph
15 mph
30 mph
45 mph
1980
Lead-Acid
Battery
2,100
550
15.7
12.6
35 @ 6.4
194 fl 12.3
158 @ 11.3
62 (? 9.2
37.7 @ 7.1
Nickel-Zinc Zinc-Chlorine Lithium-Sulfur
Battery Battery Battery
1,985 1,880
435 340
29.2 31.9
23.5 25.7
99.7 @ 17.7 144 @ 24.5
392 @ 23.5 453 « 25.7
322
-------
REFERENCES
1. S. Appelt, Electric Vehicles, A Bibliography. 1928-1966, Bonneville
Power Administration, Portland, Oregon.
2. E. S. Starkman, "Prospects of Electrical Power for Vehicles," SAE
Paper 680541, August 12, 1968.
3. An Evaluation of Alternative Power Sources for Low-Emission
Automobiles, Committee on Motor Vehicle Emissions, National Academy
of Sciences, Washington, 1973.
4. Automotive Industries, March 15, 1972.
5. Automobile Facts, 1972, Motor Vehicle Manufacturers Association,
1972.
6. "An Electric Automobile Power Plant Survey," Address given at
Institute of Electrical and Electronic Engineers National Convention,
New York, N.Y., March 18, 1968.
7. Subpanel Reports to the Panel on Electrically Powered Vehicles,
Part 2, "The Automobile and Air Pollution," US Department of
Commerce, Washington, D.C., December 1968.
8. R. G. S. White, "Rating Scale Estimates Automobile Drag Coefficient,"
SAE Journal, Vol. 77, No. 6, July 1969.
9. D. M. Tenniswood and H. A. Graetzel, "Minimum Road Load for Electric
Cars," SAE Paper No. 670177, January 1967.
10. R. S. McKee, et al., "Sundancer: A Test Bed Electric Vehicle,"
SAE Paper No. 720188, January 1972.
11. D. M. Tenniswood and H. A. Graetzel, "Minimum Road Load for Electric
Cars", SAE Paper No. 670177, January 1967.
12. NAPCA Vehicle Design Goals. Revision A, November 30, 1970; Revision B,
National Air Pollution Control Agency, Ann Arbor, Michigan,
February 11, 1971.
13. J. J. Gumbleton, et al., Special Purpose Urban Cars. SAE Paper
690461, May 1969.
1-107
-------
REFERENCES (Cont.)
14. US Bureau of the Census, Statistical Abstract of the United States;
1973. Washington, D.C., 1973.
15. The Weather Handbook. Conway Publishing Co., Atlanta, 1963.
16. Calculated from air conditioner sales by model and sales by model
in Automotive News 1973 Almanac.
17. Handbook of Air Conditioning, Heating and Ventilating, 2nd Edition,
Industrial Press, New York, 1965.
18. L. R. Foote, et al., Electric Vehicle Systems Study, Report No.
SR-73-132, Scientific Research Staff, Ford Motor Co., Detroit,
October 25, 1973.
19. P. A. Nelson, et al., ANL High-Energy Batteries for Electric Vehicles.
presented at the 3rd International Electric Vehicle Symposium,
Washington, February 1974.
20. Subcompact Car Crashworthiness Proposal. Minicars Inc., National
Highway Traffic Safety Administration Contract HS 113-3-746, February
1973.
21. Research Safety Vehicle Proposal. Minicars, Inc., National Highway
Traffic Safety Administration Contract HS-4-00844; July 1973.
22. Subcompact Car Driver Restraint System Proposal, Minicars, Inc.
(Contract No. HS-113-3-742), June 1973.
23. Electric Vehicle Test Procedure - SAE J 227, SAE Handbook, 1973,
(p. 939).
24. "Federal Driving Cycle" Federal Register, November 10, 1970, or
SAE Handbook. 1973, pp 927-8.
25. D. M. league, Los Angeles Traffic Pattern Survey, SAE Paper No. 171,
August 1957.
26. G. C. Hass , et al., Laboratory Simulation of Driving Conditions in
the Los Angeles Area, SAE Paper No. 660546, August 1966.
27. E. David, M. Obert, Electric Vehicles; Challenge to the Battery
Manufacturer, presented to 3rd International Electric Vehicle
Symposium, Washington, D.C. February 1974.
28. H. J. Schwartz, Electric Vehicle Battery Research and Development,
NASA TM X-71471, presented at Electrochemical Society Meeting,
Boston, Mass., October 7-11, 1973.
1-108
-------
REFERENCES (Cont.)
29. J. H. B. George, Electrochemical Power Sources for Electric Highway
Vehicles. C-74692, Arthur D. Little, Inc., Cambridge, Mass., June
1972.
30. D. V. Ragone, Review of Battery Systems for Electrically Powered
Vehicles. SAE Paper 680453, May 1968.
31. An Evaluation of Alternative Power Sources for Low-Emission
Automobiles, Report of the Panel on Alternate Power Sources to the
Committee on Motor Vehicle Emissions, National Academy of Sciences,
Washington, April 1973, pp. 65-86.
32. R. S. McKee, et al., Sundancer; A Test Bed Electric Vehicle, SAE
Paper 720188, January 1972.
33. L. R. Foote, et al., Electric Vehicle Systems Study, Scientific
Research Staff, Ford Motor Co., Report SR-73-132, October 1973,
p. 116.
34. G. A. Mueller, The Gould Battery Handbook. Gould, Inc., Mendota
Heights, Minnesota, 1973, p. 355.
35. C. L. Montell, Batteries and Energy Systems. McGraw-Hill Book Co.,
New York, 1970, p. 124.
36. The Zinc-Chloride Battery, Energy Development Associates, Madison
Heights, Michigan (undated).
37. Advanced Battery Technology. Vol. 10, No. 4, April 1974.
38. C. J. Amato, A Zinc-Chloride Battery—The Missing Link to a Practical
Electric Car, SAE Paper 730248, January 1973.
39. Reference 27, Fig. 5.
40. P. C. Symons, Batteries for Practical Electric Cars, SAE Paper
730253, January 1973.
41. P. C. Symons, Performance of Zinc Chloride Batteries, Presented at
3rd International Electric Vehicle Symposium, Washington,
February 1974.
42. P. A. Nelson, et al., The Need for Development of High-Energy
Batteries for Electric Automobiles. Argonne National Laboratory,
January 1974, Appendix Table VIII.
43. Reference 42, Sec. III.
1-109
-------
REFERENCES (Cont.)
AA. E. J. Cairns, et al., Development of High-Energy Batteries for
Electric Vehicles. Progress Report for July 1970-June 1971, Argonne
National Laboratories #7888, July 1971.
A5. E. J. Cairns, et al., Development of High-Energy Batteries for
Electric Vehicles. Progress Report for February 1972-July 1972,
Argonne National Laboratories #7953, August 1972.
A6. E. J. Cairns, et al., Development of High-Energy Batteries for
Electric Vehicles. Progress Report for August 1972-June 1973,
Argonne National Laboratories #7998, February 1973.
A7. Flywheel Feasibility Study and Demonstration, LMSC-D007915, Lockheed
Missiles and Space Co., Sunnyvale, 30 April 1971.
A8. G. Kugler, Electric Vehicle Hybrid Power Train. ESB, Inc., 1972.
A9. S. J. Kalish, The Potential Market for On-the-Road Electric Vehi-
cles, Electric Vehicle Council/Copper Development Association, New
York, May 1971.
50. C. W. King, Comparisons of Alternate 40-hp Power Trains, A Specific
Analysis of Electric Component Weight and Price, Delco-Remy Division,
Dayton, Ohio, 1967.
51. J. E. Greene, An Experimental Evaluation of the MARS II Electric
Automobile. CAL No. VJ-2623-K-1, Cornell Aeronautical Laboratory,
Buffalo, N.Y., February 1969.
52. Laboratory Report prepared for Electric Fuel Propulsion, Inc.,
March 30, 1972, by W. F. Volk, J. R. Miller Test Center, Dana
Corporation.
53. H. H. Hwang and Paul C. Yuen, "A Feasibility Study of the Electric
Car for Hawaii", Department of Electrical Engineering, University
of Hawaii, Second International Electric Vehicle Symposium, November
1971.
5A. D. F. Taylor and E. G. Siwek, The Dynamic Characterization of Lead-
Acid Batteries for Vehicle Applications, General Electric Company,
Research and Development Center, SAE Paper No. 730252.
1-110
-------
TASK REPORT 2
POPULATION PROJECTIONS
FOR THE LOS ANGELES REGION, 1980-2000
G.M. Houser
-------
PREFACE
This is the first task report in a series projecting baseline
conditions for a study of electric car impact. The complete series
comprises:
Task Report 2 Population Projections for the Los Angeles
Region, 1980-2000
Task Report 3 Transportation Projections for the Los
Angeles Region, 1980-2000
Task Report 4 Economic Projections for the Los Angeles
Region, 1980-2000
Task Report 5 Electric Energy Projections for the Los
Angeles Region, 1980-2000
The projections in these reports are to support a comprehensive analysis
of the impacts of electric cars. Thus it is changes relative to the base-
line projections, rather than the absolute projections themselves, which
are ultimately most important. Largely for this reason, detailed fore-
casts are neither needed nor justified here. Instead, projections are
based on straightforward extrapolation of existing trends, making maximum
use of existing forecasts and analyses in the literature.
Projections of the sort offered in these reports generally cover a
range of possible futures reflecting a range of assumptions about future
rates of change. Here, however, only a single baseline case can usefully
be offered. The study of electric car impacts to be supported by this
baseline is Itself a multi-dimensional parametric analysis. Overall study
resources are insufficient to pursue this parametric Impact analysis for
more than one baseline.
-------
The key assumption guiding the selection of the baseline in these
reports is that the future of Southern California will be characterized
by much slower growth than in the past, with an attendant higher quality
of life for residents than otherwise would be possible. This is very
much in accord with the current wish of the public as manifested in the
1972 referendum on the Coastal Zones Protection Act, under which planning
for and protection of the California coastline is now in progress; the
"Mammoth Decision" by the California Supreme Court requiring environmental
impact statements for private as well as public projects, and the subse-
quent concurrence in this decision by the California Legislature; and the
Clean Air Act Amendments of 1970, together with subsequent court interpre-
tations, which reflect public desire for obtaining and maintaining excel-
lent air quality in both urban and rural areas. It is also in accord with
current population trends, wherein the rates of natural increase and immi-
gration are much less than in the past.
In particular, this baseline projection assumes that there will be
no dramatic alterations in long-established underlying factors in regional
development. Thus it does not anticipate such dramatic technological
breakthroughs as efficient conversion of solar to electric power or wide-
scale deployment of personal rapid transit or dual-mode transportation
systems. Similarly, it anticipates no dramatic sacrifice of economic and
social patterns to environmental goals, such as would occur if no further
construction of electric power facilities were to be allowed, or if drastic
gasoline rationing were put into effect (as has been proposed in recent
EPA rule-making required by current Federal air quality legislation).
ii
-------
CONTENTS
SECTION PAGE
1
2
PREFACE
INTRODUCTION
PAST AND PROJECTED POPULATION
2.1 South Coast Air Basin Population, 1950-1970
2.2 Projections for 1980, 1990, and 2000
i
2-1
2-5
2-5
2-7
3 ALTERNATIVE POPULATION FORECASTS 2-15
APPENDIX A TOTAL POPULATION FOR 1970 TO 2000 BY REGIONAL
STATISTICAL AREA 2-22
REFERENCES 2-23
iii
-------
ILLUSTRATIONS
NO. PAGE
1.1 RSA Boundary Map 2-2
2.1 Projected Population by County 2-8
2.2 Population Age Distribution 2-10
2.3 Age Distribution Profile, Los Angeles County 2-12
2.4 Age Distribution Profile, Orange County 2-12
2.5 Age Distribution Profile, Riverside County 2-13
2.6 Age Distribution Profile, San Bernardino County 2-13
2.7 Age Distribution Profile, Santa Barbara County 2-14
2.8 Age Distribution Profile, Ventura County 2-14
3.1 Alternative Population Projections, Los Angeles County 2-18
3.2 Alternative Population Projections, Orange County 2-19
3.3 Alternative Population Projections, Riverside County 2-20
3.4 Alternative Population Projections, San Bernardino County 2-20
3.5 Alternative Population Projections, Santa Barbara County ' 2-21
3.6 Alternative Population Projections, Ventura County 2-21
-------
TABLES
NO. PAGE
1.1 Percentages of County Populations in South Coast Air Basin 2-3
1.2 Comparison of Projections 2-4
2.1 Population by County 2-5
2.2 Percentage of SPAR o—''-ion in Each Bouncy 2-6
2.3 Projected Populations 2-7
2.4 Future Growth Rates, 1970 to 2000 2-9
2.5 Population by Age Group ° ir
3.1 Alternative Population Forecasts 2-17
vii
-------
1 INTRODUCTION
The purpose of this report is to present population projections for
the South Coast Air Basin (SCAB) for the years 1980, 1990, and 2000.
These data serve as the basis for the energy, transportation, and economy
projections prepared under contract to the Environmental Protection
Agency for a study of the impact of the electric automobile in the Basin.
The South Coast Air Basin consists of all of Orange and Ventura
Counties and parts of Los Angeles, Riverside, San Bernardino, and Santa
Barbara Counties. With the exception of Santa Barbara, all of these
counties are members of the Southern California Association of Governments
(SCAG).
SCAG has subdivided the member counties into Regional Statistical
Areas (RSAs). The boundaries of these RSAs coincide with the boundaries
of the Air Basin and are shown in Fig. 1.1. To determine population for
the SCAB area it was necessary only to delete the population in the RSAs
outside the boundary from whole-countywide data. Projections for each
RSA are shown in the appendix. For Santa Barbara County (not a member of
SCAG), study area data was extrapolated from figures provided by the
Santa Barbara County Planning Department.
Scaling factors used to adjust whole-county data to the study area
are shown in Table 1.1. These factors represent the percentage of total
county population in the study area.
Time and resources did not permit a detailed demographic study.
The data in this report was drawn and extrapolated from published sources,
specifically forecasts and projections which were obtained from State,
County, and private agencies. These agencies Include the California
1 23
Department of Finance, Southern California Association of Governments, '
4 5
Southern California Edision, Wells Fargo Bank, Los Angeles Regional
Transportation Study (LARTS), United California Bank, Security Pacific
2-1
-------
N>
ro
Figure 1.1. RSA Boundary Map
-------
TABLE 1.1
PERCENTAGES OF COUNTY POPULATIONS IN SOUTH COAST AIR BASIN
County
Los Angeles
Orange
Riverside
San Bernardino
Santa Barbara
Ventura
1950
99
100
72
88
64
100
Actual
1960
99
100
70
84
55
100
1970
98
100
71
83
60
100
1980
1
98
100
72
83
60
100
Projected
1990
98
100
72
84
60
100
2000
98
100
72
85
60
100
Q
National Bank, and the planning departments of the counties. A review of
these publications clearly indicates that the rate of natural increase and
rate of immigration are decreasing each year. As a result, the use of
reduced population projections for planning purposes is increasing rapidly.
This is demonstrated in Table 1.2, which shows that population projections
prepared by SCAG (and officially accepted by its member counties) were
consistently lower each year than those prepared the previous year for
the same time periods. (These figures are presented for comparison pur-
poses only, and therefore have not been adjusted to fit the Air Basin.
Santa Barbara County, not a member of SCAG, is not included.)
*
SCAG has recently declared that its Series D forecast is too high
and should be replaced by a revised lower forecast as soon as possible.
The new forecast will be a combined Series D and E. This forecast applies
the Series D factors to all areas in the counties which are outside the
critical Air Basin and the Series E to all areas inside the Basin. The
* «
Essentially the same as Department of Finance Series D-150 shown in Table 3.1,
in Sec. 3 of this report.
2-3
-------
TABLE 1.2
COMPARISON OF PROJECTIONS
Date of Projection
19 709
197110
19732
1980
13,062,000
11,634,300
11,070,070
Projection
1990
15,748,000
13,900,000
12,205,160
2000
__
16,062,500
13,164,730
Department of Finance is also currently preparing a combination D/E pro-
jection for the area.
Additionally, in the Basin, the Environmental Protection Agency,
the State Water Resources Control Board, and local water quality control
boards require that Series E forecasts be used exclusively in planning
and facilities grants.
On the basis of the above, we have concluded that the most reason-
able choice of projections for this study is the Department of Finance
Series E. For the South Coast Air Basin, this projection is identical
to the DOF Series D/E.
Section 2 of this report presents these projections as well as
brief historical data for the area. Section 3 contains alternative
population forecasts.
2-4
-------
2 PAST AND PROJECTED POPULATION
2.1 SOUTH COAST AIR BASIN POPULATION, 1950-1970
In 1950 the total population of the Basin was 4,900,518. Eighty-
five percent of this total regional population resided in Los Angeles
County. San Bernardino ranked second with 5 percent, and Orange third
with 4 percent. Riverside, Ventura, and Santa Barbara contained only 3,
2, and 1 percent, respectively.
While the proportion of population to the total of the region
remained almost constant in the outlying counties for two decades, changes
in Los Angles and Orange County were more pronounced. By 1960, Los
Angeles had dropped to 78 percent, and by 1970 to 71 percent. Orange
County, however, continued to increase to 9 percent of the region in 1960
and 14 percent in 1970. These figures are presented in Tables 2.1 and 2.2.
Los Angeles County experienced substantial and almost continuous
growth in the first 70 years of the century. This growth rate has slowed
TABLE 2.1
POPULATION BY COUNTY (SCAB ADJUSTED)
County 1950 1960 1970
Los Angeles 4,135,687 5,986,771 6,866,566
Orange 216,224 703,925 1,419,200
Riverside 123,046 215,191 322,766
San Bernardino 248,142 423,591 555,519
Santa Barbara 62,832 93,255 154,920
Ventura 114,647 199,138 375,600
Totals 4,900,578 7,621,871 9,694,571
2-5
-------
TABLE 2.2
PERCENTAGE OF SCAB POPULATION IN EACH COUNTY
County
Los Angeles
Orange
Riverside
San Bernardino
Santa Barbara
Ventura
1950
85
4
3
5
1
2
1960
78
9
3
6
1
3
1970
71
14
3
6
2
4
dramatically in recent yeatfs. The County population decreased more than
70,000 between 1970 and 1972. As a result, the L.A. County Planning
Department has recently made a substantial downward revision of planning
figures from 8,700,000 to 7,700,000 for 1990.
Orange County has experienced very rapid growth in the past 22 years.
During the 1950-1960 period, it was the fastest growing county not only
in Southern California, but also in the entire United States. Once pri-
marily an agricultural area, it is now the second most populous county in
California. Much of Orange County's growth has been through immigration
from adjoining counties.
Riverside and San Bernardino Counties as a whole have been growing
at a slower rate. Most of the growth in these counties, however, has
been in the areas which are part of the South Coast Air Basin. The
mountains and desert areas not included in SCAB are growing at a much
slower rate.
Population growth in Ventura County, the second fastest growing
county in the 1960s, has been extensive and is attributed mostly to im-
migration.
2-6
-------
Santa Barbara County grew rapidly In the period from 1960 to 1970,
but has experienced a substantial slowdown In recent years. As In San
Bernardino and Riverside, the portion of the County In the South Coast
Air Basin has grown at a faster rate than that outside the boundaries.
2.2 PROJECTIONS FOR 1980, 1990, AND 2000
Population projections for the study were developed using Department
of Finance Series E-0 projections. Series E-0 projections incorporate a
fertility rate of 2.1 births per woman and zero net immigration to the
state. Some migration between the counties has been incorporated by the
Department of Finance which utilizes forecasts provided by individual
county planning groups wherever possible.
The SCAB area population is projected to reach 12.4 million by the
year 2000. These projections are shown in Table 2.3 (1970 is included for
comparison) and graphed in Fig. 2.1. In the period 1980 to 1990 the net
increase is expected to be approximately 973,844 and thereafter will slow
to 784,914 in the period 1990 to 2000.
TABLE 2.3
PROJECTED POPULATIONS (SCAB ADJUSTED)
County
Los Angeles
Orange
Riverside
San Bernardino
Santa Barbara
Ventura
TOTALS
1970
6,866,566
1,419,200
322,766
555,519
154,920
375,600
9,694,571
1980
7,179,578
1,774,000
370,440
635,780
179,640
488,400
10,627,838
1990
7,514,150
2,122,500
408,384
730,128
208,020
618,500
11,601,682
2000
7,757,680
2,408,300
434,016
812,260
233,340
741,000
12,386,596
2-7
-------
10'r-
10
LA COUNTY S
*»•
ORANGE COUNTY
SAN BERNARDINO COUNTY
RIVERSIDE COUNTY
SANTA BARBARA COUNTY
I I
1970 1980 1990 2000
Figure 2.1. Projected Population by County
2-8
-------
As stated earlier, the rate of growth will vary from county to
county. The projected compound annual growth rates for the years 1970
to 2000 are shown in Table 2.4.
Projections of age distribution are shown in Fig. 2.2 and Table
is
2.5. Figure 2.2 clearly shows a substantial drop in population under 18
years of age and an increase in the 18 to 65 age group. The percentage
of people in the over-65 age group for the area remains almost constant.
Profiles of age distribution by county are shown in Figs. 2.3
through 2.8. Changes are less noticeable for Los Angeles, Riverside,
and San Bernardino Counties, with those counties containing a larger per-
centage of the over-65 age group and less marked changes between the
TABLE 2.4
FUTURE GROWTH RATES, 1970 TO 2000
County Projected Annual Growth Rate
Los Angeles 0.38%
Orange 1.78%
Riverside 1.00%
San Bernardino 1.47%
Santa Barbara 1.37%
Ventura 2.29%
These projections were developed for SCAB from whole-county age distri-
bution data provided by the Department of Finance.
2-9
-------
70
60
50
40
o
OC
30
20
10
c-g
Cvj
18-65
UNDER 18
OVER 65
Ol I I J
1970 1980 1990 2000
Figure 2.2. Population Age Distribution
2-10
-------
TABLE 2.5
POPULATION BY AGE GROUP
1980
3,108,879
6,536,673
982,220
1990
3,358,800
7,121,554
1,121,288
2000
3,330,528
7,939,840
1,116,313
1970
Under 18 3,245,843
18-64 5,571,451
Over 64 877,277
under-18 and 18-to-65 group. In Orange, Santa Barbara, and Ventura Coun-
ties, however, the drop in the under-18 age group and increase in the 18-
to-65 groups is much more pronounced. Increases in the 18-to-65 age group,
of course, will have the greatest impact on the demand for automobiles
and/or public transportation.
2-11
-------
1970
'///////////////A
^////////////TTTTA
1980
1990 V///////////////A
2000 W7//////////////A
UNDER 18
18-65
OVER 65
Figure 2.3. Age Distribution Profile, Los Angeles County
""I V//////////////A
i™ I V////////////////A
"*l V77//////////////A
2000'"~
| | UNDER 18
f7V] 18-65
OVER 65
Figure 2. A. Age Distribution Profile, Orange County
2-12
-------
1970
V/////////////A
V//////////////A
1980
1990
y///////////////.
2000
UNDER 18
EZ3 18-65
HHiOVER 65
Figure 2.5. Age Distribution Profile, Riverside County
1970
1980
1990
2000
UNDER 18
18-65
OVER 65
Figure 2.6. Age Distribution Profile, San Bernardino County
2-13
-------
1970
V/////////////777A
1980
1990
2000
V/////////////////A
| 1 UNDER 18
CZZJ 18-65
f-:::H OVER 65
Figure 2.7. Age Distribution Profile, Santa Barbara County
UNDER 18
18-65
OVER 65
Figure 2.8. Age Distribution Profile, Ventura County
1970
1980
1990
2000
'///////////////.
'/////////////////,
'/////////////////.
y /////////////////.
'.-,':•; '•'•':•
5
i
:z
2-14
-------
3 ALTERNATIVE POPULATION FORECASTS
Currently available forecasts were obtained from various State,
County, and private agencies. The most complete of these data have been
scaled down to fit the study area and are tabulated in Table 3.1 and
graphed in Figs. 3.1-3.6. They represent different assumptions about
future growth and include Department of Finance Series D-150 and Series
E-0, SCAG combination D/E and D-dispersed, Southern California Edison,
and individual county forecasts.
The DOF Series D-150 assumes 2.45 births per woman and net annual
state migration of 150,000. Series E-0, as described earlier, projects
2.1 births per woman and zero state migration.
SCAG D-Dispersed modifies DOF Series D projections to locate popula-
tion in the suburbs rather than the central city. This concept includes:
• Establishment of new towns and employment centers in outlying
areas, linked to the rest of the region by high-speed ground
transportation
• Limited protection of agricultural areas
• Development of the Palmdale Intercontinental Airport
• Protection of coastline and mountain areas
• Improvement of air and water quality
• Some central city renewal, maintaining the same net density
As illustrated in the following charts, the SCAG D dispersed pro-
jection is lower than the DOF Series D in Los Angeles, Orange, and Ventura
Counties and higher in Riverside and San Bernardino Counties. SCAG D/E
is the same as the projection being used in this study (Table 2.3), since
it incorporates Series D for areas outside the Air Basin and Series E for
areas inside.
2-15
-------
Southern California Edison projections include the assumptions that
the average birth rate is approaching Series E and that net annual migra-
tion will reach 100,000 for the State per year by 1980 and remain at that
rate thereafter. SCE develops projections for whole counties by extrapolat-
ing from projections of the portion of the county in its service area.
Wells Fargo projections are much closer to Series E than to Series
D. These projections, available only for 1980, differ from the others in
that their projections for Los Angeles, and Ventura are much lower, while
Riverside, Orange, Santa Barbara, and San Bernardino are higher.
2-16
-------
TABLE 3.1
ALTERNATIVE POPULATION FORECASTS
Year
-
9
I!
0
TCTAL
1
9
9
0
TOTAL
2
0
0
0
TOTAL
County l>- 1 y.<
Los Angeles 7,jOt>, >>'
Riverside . •<<•/',....;
San Bernardino (.m.'w*
Santa Barbara isi.yu
Ventura '.."». :.
ii.i-is.j;
Los Angeles i(, ViO,-.,:-:
Orange 2 , 445 , 300
Riverside .S?2,Hf>4
San Bernardino 894
Santa Barbara 2.39, 90 /
Ventura 902, JOu
13.494.S61
Los Angeles 9,41j,()'ih
Orange 2, Oil/, t-;)i)
Riverside 6 .H, _•••-.
San Bernardino l.K-.. .:>•;
Santa Barbara .'»'•. , ;.
Ventura 1 ,:•'/< l ':;'',
1 5, 600, <>•(.!
SCAU D
Oi^perued
'.302,430
1,843,800
559,711
910,227
—
:4 1.220
---
.I.U71.839
2.565,680
864,079
1,097,981
—
789,820
—
6,815,061
i', US. 220
1,139,040
1.411,808
: 093,500
.__
Southern
California
Edison
7,281,400
1.937,500
398,088
661,080
188,400
535.000
11,001,468
8,009,540
2,372,600
464,832
787,080
230,040
770,000
12,634,092
DOF Series E
7,215,753
1,774,000
370.467
635,780
173.562
488,400
10,657,962
7,530,288
2,122,500
409,576
730,128
208,020
618,500
11,619,012
7,767,251
2,408,300
437,371
812,260
233,340
741,000
12,399,522
County
1,905,057
439,531
690,560
191,943
799,500
7,546,000
2,240,386
550,670
894,264
241,331
799,500
12.272,151
2,560,386
641,282
1,104,150
281,306
1 027,600
2-17
-------
10
c
a
O.
O
Q-
1950
1960
I
1970 1980
YEAR
1990
2000
Figure 3.1. Alternative Population Projections, Los Angeles County
2-18
-------
3200
2800
2400
2000
c
-------
leoor-
1200 -
oo
O
o
O-
800 -
400 -
1950
1960
1970 1980
YEAR
1990
2000
Figure 3.3. Alternative Population Projections, Riverside County
1200
CO
O
V3 800
o
400
(X
o
Q.
0
-o
M-
DOF SERIES D-l 50
E-0
SCAG COMBINATION D/E
1
I
I
1
1950 1960 1970 1980 1990 2000
YEAR
Figure 3.4. Alternative Population Projections, San Bernardino County
2-20
-------
400
oo
o
oo
:D
o
o
I—I
£
Q.
O
Q.
200
00
CSI
E
I
I
1950 1960
1970 1980
YEAR
1990
2000
Figure 3.5. Alternative Population Projections, Santa Barbara County
1400
1200
' 800
o
<:
o.
o
Q.
400
I
1950
1960
1970 1980
YEAR
1990
2000
Figure 3.6. Alternative Population Projections, Ventura County
2-21
-------
APPENDIX A
TOTAL POPULATION FOR 1970 TO 2000 BY REGIONAL STATISTICAL AREA
N i iralu- r
RSA NAMF.
1970
1980
1440
1
'2
.1
4
5
h
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
l.OSPDRS
O.IAIVEN
OXNCAMK
MKPSIMI
TH SOAKS
FILP.ISUI
ilAI.ABAS
NEWHAL1.
I.ANCAST
1'Al.MDAI.
S C MTS
SW SKV
BURBANK
SANKERN
MALIBU
SMON1CA
WCENTRL
SO BAY
PALVRDS
L BEACH
ECENTRL
NOR-WHI
LA CBD
GLENDAL
WSANCAB
ESANGAB
POMONA
WESTEND
EASTEND
MTS-SB
BAKER
BARSTOW
TWPALMS
NEEDLES
J-BUPK
A-FULTN
H-ANAHM
I-N CST
F-C CST
D-S CST
B-CANYN
G-S ANA
C-TRABU
E-TORO
JURUPA
RVRSIDE
FERRIS
HEMET
MURRIET
PASS
IDYW1LD
PALM SP
COACHEL
CHUCKAW
IMPERAL
SCAC
175
112,165
136,540
67,756
51,542
10,229
18,935
47,242
51,446
31,429
2,013
539,935
264,922
267,158
11,709
304,300
934,831
531,138
413,510
435,416
828,311
592,502
90,416
412,626
667,492
441,043
149,654
233,386
312,097
20,374
9,700
76,701
24,103
5,872
160,903
170,787
307,729
240,377
161,253
38,834
34,390
266,278
18,306
21,529
37,095
221,619
22,564
34,368
12,001
26,852
3,048
48,586
38,411
16,397
74,492
10,052,689
375
141,824
173,145
84 , 320
79,220
12,515
27,065
66,584
69,301
41,046
2,013
553,720
266,607
281,544
13,098
321,563
981,753
541,652
427,295
451,783
843,190
610,948
94,781
429,889
684,755
458,306
161,360
264,631
352,072
2Jj467
10,596
81,559
26,586
6,266
176,275
200,249
329,456
273,603
238,721
79,500
57,103
308,333
47,768
59,721
40,169
249,593
25,343
39,423
13,292
28,438
3,640
56,225
41,880
17,490
79,951
10,949,000
J7S
1 82 , 1 ')4
204,461
100, J1H
1 11,354
1 7,324
35,261
85,360
86,713
50^606
2,013
577,276
275,146
295,884
14,462 .
338,288
1,038,425
555,992
440,948
467,831
867,433
632,796
98,874
446,614
706,260
478,446
175,013
305,295
403,571
22,812
11,941
86,722
29,167
6,884
190,212
228,133
357,340
302,881
315,396
114,353
75,221
354,336
79,834
101,543
41,641
278,263
27,453
43,643
15,078
30,646
4,484
63,390
44,510
19,078
85,555
11.930,500
375
234,290
240,64 1
112,553
129,699
26,450
37,964
92,579
93,932
54,817
2,013
591,410
289,280
301,595
15,868
358,141
1,066,997
573,634
447,362
485,473
896,005
654,454
100,280
460,748
720,394
492,580
182,232
339,883
444,277
24,515
13,644
90,128
32,573
7,689
207,057
244,978
391,123
319,726
365,829
131,198
92,066
404,769
113,617
135,326
42,641
298,819
29,461
45,651
16,578
32,146
4,984
67,203
46,010
20,070
88,063
12,711,792
2-22
-------
REFERENCES
1. California Population 1971, California Department of Finance, May
1972; with accompanying undated computer printouts entitled Civilian
Population.
2. Population Growth Analysis, Southern California Association of
Governments, April 5, 1973.
3. Progress Report: Growth Forecast Revision, Southern California
Association of Governments, September 12, 1973.
4. System Forecasts 1973-1995, Southern California Edison, February 1973.
5. Moving Ahead, Wells Fargo Looks at Southern California, Wells Fargo
Bank, June 1973.
6. LARTS Annual Report, 1971/72, Los Angeles Regional Transportation
Study, July 1972.
7. 1973 Forecast, United California Bank, November 10, 1972.
8. The Southern California Report, Security Pacific National Bank, March
1970.
9. Annual Report, 1971, Southern California Association of Governments,
April 1971.
10. Regional Development Guide, Southern California Association of
Governments, April 1972.
11. Projected Population Growth, Los Angeles Regional County Planning
Commission, February 9, 1973.
2-23
-------
TASK REPORT 3
TRANSPORTATION PROJECTIONS
FOR THE LOS ANGELES REGION, 1980-2000
W.F. Hamilton
G.M. Houser
-------
SUMMARY
Transportation projections are developed for California's South
Coast Air Basin, which includes greater Los Angles, based on existing
analyses and forecasts and the assumption that there will be no dramatic
changes in existing trends such as would result from stringent gasoline
rationing. The projections foresee much slower growth of the freeway
system, from 677 route miles in 1972 to 885 miles in 1990, but no significant
diversion of area travel to public transit. Auto ownership is projected
'to increase from 0.52 to 0.61 cars per person from 1970 to 2000, with
total autos increasing from 5 to 7.6 million. Daily vehicle mileage will
rise slightly more than proportionately, reaching 168 million miles in
1980 and 228 million miles in 2000, with a slight increase in the fraction
of miles driven on freeways. The average auto will be driven 10,600 miles
per year, or 30 miles per day, by 2000, only 9 percent more than at
present. By 1985, compact and subcompact autos are projected to capture
two-thirds of the new car market, four times the share of standard autos,
and average new-car fuel economy will increase 50 percent by 1984 and
100 percent by 2000. Total auto fuel consumption in the Air Basin is
consequently projected to decrease slowly until the late 1990's.
-------
CONTENTS
SECTION
PAGE
SUMMARY
1 INTRODUCTION
2 FREEWAYS
3 RAPID TRANSIT
4 AUTO AVAILABILITY
5 TRAVEL CHARACTERISTICS
6 AUTO AGE AND CLASS
6.1 Auto Age Distribution and Sales
6.2 Auto Market Shares and Populations by Class
7 AUTO FUEL CONSUMPTION
i
3-1
3-6
3-10
3-15
3-22
3-33
3-33
3-42
3-47
APPENDIX AUTO SALES PROJECTION BASED ON NATIONAL SURVIVAL
TABLES
REFERENCES
3-59
3-63
iii
-------
ILLUSTRATIONS
NO.
1.1
2.1
3.1
4.1
4.2
5.1
5.2
6.1
/" f\
O . <-
6.3
6.4
6.5
6.6
6.7
6.8
7.1
7.2
7.3
7.4
The LARTS Study Area
Freeway Routes in the South Coast Air Basin
Los Angeles Rapid Transit — Proposed Routes
Basic Automobile Availability
Automobile Population in the South Coast Air Basin
Distribution of Trip Times
Average Annual Passenger Car Mileage Versus Time
Survival Rates for US Autos
Relation of New Car Registrations to Total Car Population
Projected Automobile Survival Rates
Annual Automobile Sales, South Coast Air Basin
Distribution of Vehicles with Age, South Coast Air Basin
US Auto Market Share by Class
Projected Auto Market Share by Class for the South Coast
Air Basin
Projected Auto Population Share by Class for the South
Coast Air Basin
Automobile Weight by Class
Sales Weighted Fuel Economy Versus Model Year
National Average Fuel Economy Versus Calendar Year
Projected Auto Fuel Economy
PAGE
3-2
3-8
3-12
3-16
3-18
3-26
3-30
3-34
3-34
3-36
3-37
3-41
3-43
3-46
3-46
3-48
3-49
3-51
3-53
-------
Illustrations (Cont.)
NO. __ PAGE
7.5 Projected Average Auto Fuel Economy for the South Coast
Air Basin 3-57
7.6 Projected Total Auto Fuel Use for the South Coast Air Basin 3-57
vi
-------
TABLES
NO.
1.1
2.1
.3.1
4.1
4.2
4.3
4.4
5.1
5.2
5.3
5.4
5.5
6.1
6.2
7.1
A-l
A-2
Summary Results, LARTS Base Year Reports
Freeway Route Mileage in the South Coast Air Basin
Summary of the 1973 Rapid Transit Recommendation for Los
Angeles
Automobiles per Person, South Coast Air Basin
Automobile Population of the South Coast Air Basin
Automobiles Available by Dwelling Unit Type
Autos Available by Household Car Ownership Class
Comparison of Trip Production Model Runs, LARTS
Daily Vehicle Trips by Originating Household Types
Comparison of Network Model Runs - LARTS
Implications of Trip Production and Network Model Runs -
LARTS
Baseline Auto Travel Projections South Coast Air Basin
Comparison of Auto Population Projections for the South
Coast Air Basin
Distribution of Vehicles with Age, South Coast Air Basin
Automobile Usage Versus Age, California
Automobile Survival Factors
Auto Population Annual Growth Factors South Coast Air
PAGE
3-4
3-7
3-11
3-16
3-19
3-20
3-21
3-23
3-24
3-27
3-29
3-32
3-39
3-40
3-56
3-60
Basin 3-61
vii
-------
1 INTRODUCTION
This is the second in a series of reports projecting baseline condi-
tions (in the absence of electric cars) for use in a study of the impacts
of future electric car use. It follows assumptions and data presented in
the first report of the series, Task Report 2, Population Projections for
the Los Angeles Region. 1980-2000.
The basic source of transportation information for the projection
of this baseline is the Los Angeles Regional Transportation Study (LARTS).
A continuing effort, LARTS has developed the definitive transportation
data base for the region, and is currently in the process of modeling
the future on this basis.
LARTS was established in January 1960 to deal with the transportation
planning of the greater Los Angeles area, a region of some 9,000 square
miles comprising 122 cities and parts of five Southern California counties.
The LARTS study area is shown in Fig. 1.1, along with the South Coast Air
Basin. The Basin and the LARTS region are nearly coincident; they differ
primarily in that the Air Basin includes the Santa Barbara area and a
sparsely populated portion of Riverside County, but does not include the
Lancaster/Palmdale environs of Los Angeles County north of the San Gabriel
Mountains. Well over 90 percent of the combined population is included
in both of them.
LARTS began its studies and analyses in 1960 with the compilation
of data from various sources. This included a home interview survey at
a small number of dwellings (2,700 in all), a larger postcard question-
naire with 300,000 responses, a land use survey, material from the 1960
census, and others. In the effort to make maximum use of existing data,
". . . sampling of travel characteristics was minimized." The LARTS Base
Year Report 1960 is the definitive initial presentation of the regional
transportation situation.
3-1
-------
N>
SAN LUIS 08ISPO
KERN
-COUNTY BOUNDARY
• SOUTH COAST AIR BASIN BOUNDARY LINE
•LARTSBOUNDARY
SAN BERNARDINO
RIVERSIDE
Figure 1.1. The LARTS Study Area
-------
In 1967, LARTS greatly extended and improved its data base with a
home interview survey of over 30,000 dwelling units. The results of this
survey have been published as LARTS Base Year Report: 1967 Origin-
Destination Survey. In many respects, they may be compared directly with
the results of previous years, as in Table 1.1.
The 1967 survey is particularly useful for forecasts because it
reflects an increased scope for LARTS. In the years since 1960, the
Southern California Association of Governments (SCAG) had been founded
as the review agent for comprehensive planning in the greater Los Angeles
area. LARTS became the technical study arm of the transportation arm of
SCAG, and as such became more involved with overall regional planning
problems, as opposed to automobile transportation alone.
LARTS is presently engaged in developing transportation plans for
1990, together with detailed forecasts describing their prospective per-
formance and cost. Only interim results are now available, however, since
both plans and analyses remain in a state of flux. Substantial reasons
for this state of flux may well be found in two major changes appearing
in Los Angeles since work began on the 1967 survey. These changes are
virtual cessation of population growth, and unprecedented popular concern
for maintenance or improvement of environmental quality.
LARTS has been most cooperative in supplying working papers, reports,
advice, and commentary in support of the development of baseline trans-
portation projections reported here. It must be emphasized, nevertheless,
that these projections are the responsibility of the authors.
The objective of this report is to draw together baseline projec-
tions for transportation systems of Los Angeles, with emphasis on charac-
teristics particularly relevant to the impact of electric car usage and
utility. It begins with a consideration of the basic facilities for the
future: freeways available and their distribution among households of the
3-3
-------
TABLE 1.1
SUMMARY RESULTS, LARTS BASE YEAR REPORTS
1960 1967
Study Area, square miles 9,000 9,000
Population 7,597,000 9,008,400
Automobiles 3,437,000 3,930,200
Trucks 409,000 391,500
Weekday Person Trips ~ 22,189,500
Weekday Driver Trips 12,261,164 15,773,538
Weekday Bus Passenger Trips 700,000 490,900
Weekday Freeway Vehicle Miles 20,273,640
Weekday Total Vehicle Miles** 88,078,391
Housing Units 2,644,000 3,078,200
Apartment Units 568,000 986,400
Percent Units With: No Car 16.7 14.7
One Car 51.8 48.5
Two Cars 27.4 30.8
More Than Two Cars 4.1 6.0
Median Household Income, dollars per year 6,900 7,818
Median Age 30.6 27.8
Computed; "counted or estimated" figures within 10 percent.
**
Computed.
3-4
-------
Basin. It then considers future travel characteristics, with particular
concern for trends in automobile movement and daily usage of automobiles.
Finally, it addresses the probable mix of different kinds of automobiles
in the future automobile population, and projects on this basis their
requirements for gasoline.
3-5
-------
2 FREEWAYS
As the data in Table 1.1 makes clear, the Los Angeles region is
highly automobile-oriented. The modal split—the fraction of trips taken
by public transit—was only 3 percent in 1967, after many years of decline,
Though the majority of automotive trips have been and will continue
to be made in the street system of the region, the freeway network plays
an increasingly important role in both personal and vehicle movement.
Los Angeles has, in fact, long been generally regarded as the stereotype
of the "freeway city." The freeway system was inaugurated in 1940 with
the opening of the Arroyo Seco Parkway (now the Pasadena Freeway), a six-
mile, six-lane, six-million-dollar harbinger of the future. Subsequent
expansion brought the total freeway system in the South Coast Air Basin
to 677 route miles, as broken down by county in Table 2.1 and mapped in
Fig. 2.1. In 1959, the California State Legislature had designated
almost 1,600 miles of route in the LARTS area as part of the California
Freeway and Expressway System. In recent years, however, public sentiment
has come to favor much less freeway construction. In consequence, it now
seems unlikely that a substantial portion of as-yet-incomplete segments
of this System will be brought to freeway or expressway standards in this
century.
Present network modeling at LARTS assumes that only about 200 miles
*
of freeway route will be added in the SCAB area by 1990. This mileage
includes completion of the Foothill Freeway traversing the region from
west to east, construction of the Century Freeway from the ocean to
Interstate 605, construction of a missing segment of Interstate 15 on a
north-south alignment through the eastern part of the Basin, and
completion of a number of smaller segments.
Whether much of this additional route will actually be completed is
open to speculation. At the moment, major parts of the addition—the
Century Freeway, for example—are embroiled in litigation, the results
of which cannot be confidently predicted.
*
SCAB - South Coast Air Basin.
3-6
-------
TABLE 2.1
FREEWAY ROUTE MILEAGE
IN THE SOUTH COAST AIR BASIN
County
Los Angeles
Orange
Ventura
San Bernardino
Riverside
Santa Barbara
Freeway
1972
349
109
75
68
46
30
Miles
1990
419
124
105
114
81
42
Increase,
Percent
20
14
40
6S
76
40
Annual Growth
Rate, Percent
1.0
.7
1.9
2.9
3.2
2.4
SCAB Total
677
885
31
1.5
-------
oo
-- COUNTY LINE
— SCAB BOUNDARY
• 1972 FREEWAYS
••1990 FREEWAYS
-- OTHER HIGHWAYS
0 10 ?0 30
STATUTE MILES
Figure 2.1. Freeway Routes in the South Coast Air Basin
-------
Nevertheless, we have assumed that additional freeways modeled for
1990 by LARTS will be completed by that year. This represents, after all,
a major reduction in planned route additions from that contemplated only
a few years ago. The location of this additional route is shown in
Fig. 2.1, and tabulated by County for the SCAB area in Table 2.1.
It is noteworthy, in Table 2.1, that the result of these prospective
route additions is an average annual growth rate of 1.5 percent for free-
way mileage in the SCAB. This amounts to a growth more rapid than that
for the SCAB population, which is forecasted to grow at an average rate of
0.8 percent in the same period. On a county basis, rate of population
growth in these projections exceeds rate of freeway route mileage growth
only in Orange County, where relatively rapid population growth (1.3 percent
per year) is in prospect, but only minor freeway additions are expected.
Route mileage, of course, is only one indicator of freeway availa-
bility. Probably more significant is total lane mileage, but current and
prospective lane mileages for the SCAB are not conveniently available.
Continuing expansion in number of lanes over existing freeway routes is,
however, in progress now and contemplated for the future.
Overall, then, it appears that freeway capacity in the Air Basin
will expand somewhat more rapidly than population during the remainder
of this century.
3-9
-------
3 RAPID TRANSIT
Though transit presently accounts for a very small portion of
travel in the South Coast Air Basin, this was not always the case. Street
cars and inter-urban electric railways provided comprehensive service to
transit patrons before World War I. Their routes extended from San
Bernardino to Santa Monica, and from the top of Mount Lowe to Balboa Bay,
comprising twice as many route miles as the freeway system of the 1960s.
But the electric street railways could not provide service competi-
tive with the automobile, and as automobile popularity grew, the trolley
cars were additionally penalized by increasing numbers of at-grade cross-
ings. By 1967, only motor coaches were providing transit service in the
area to only 3 percent of total trips taken.
Private transit companies were consolidated in 1958, when the Los
Angeles Metropolitan Transit Authority acquired and joined the separate
systems into a single bus system. The MTA was subsequently transferred,
in 1964, into the Southern California Rapid Transit District, which was
created to raise funds from property or sales taxes to finance a rapid
transit system. In 1968, an 89-mile, $2.5 billion rail rapid transit
system was submitted to the voters. It received 45 percent of the vote,
whereas 60 percent was required for approval.
Additional sources of funds have subsequently been developed in
both state and federal government, and a new plan for rapid transit has
been prepared. This plan was published in July 1973, in a volume titled
Rapid Transit for Los Angeles; Summary Report of Consultants' Recommenda-
tions. Major points of this recommendation are summarized in Table 3.1,
while the recommended route system is shown in Fig. 3.1.
The current recommendation is for a system including 116 route
miles of generic "mass rapid transit," plus 24 miles of exclusive busways.
The mass rapid transit routes are to be served by vehicles operated in
3-10
-------
TABLE 3.1
SUMMARY OF THE 1973 RAPID TRANSIT RECOMMENDATION FOR LOS ANGELES
Mass Rapid Transit
Route System
Stations
Line Capacity
Maximum Speed
Exclusive Busways
Route System
Line Capacity
Maximum Speed
Surface Bus System
Costs
Capital Cost
1972 Prices
Over 12-year Acquisition
Annual Operating Cost
Rapid Transit
Surface Busses
Annual User Charges (fares)
Estimated 1990 Patronage
Weekday Rapid Transit Riders
Weekday Surface Bus Riders
Peak One-way Volume
116 mi
62
24,000 pass./hr (seated)
80 mph
24 mi
10,000 pass./hr
60 mph
2,740 buses
$3.5 billion
6.6 billion
$226 million
285 million
$237 million
1.05 million
0.875 million
40,000 pass./hr
3-11
-------
u>
LEGEND:
D 11111
W!
CM
Initial Mass Rapid
Transit System
Initial Exclusive Lane
Busway
Stations
Total Regional System
Figure 3.1. Los Angeles Rapid Transit—Proposed Routes
-------
trains, like the BART system in the San Francisco Bay area, but their
design has not been "finalized."
The rapid transit system of Fig. 3.1 serves Los Angeles County only.
Patronage estimates for this area were made in conjunction with overall
LARTS forecasts and modeling of 1990 trip production and automobile usage.
In 1990, LARTS forecasts 29.7 million daily trips for Los Angeles County,
of which all but a small portion are south of the San Gabriel Mountains,
in the area served by the recommended transit system. Thus the estimated
1990 rapid transit patronage of 1.05 million represents a rapid transit
modal split of only 3.5 percent. An additional 0.875 million riders per
day are expected on the bus system, leading to a 6.5 percent modal split
for the total transit system. This must be regarded as a high estimate,
since a good many of the bus passengers are using the bus to gain access
to the rapid transit system, and thus are being counted twice in the
6.5 percent figure.
The diversion of travelers from auto to the recommended rapid transit
is relatively small overall: 2.4 percent of all automobile travelers in
Los Angeles County. For trips to and from the Central Business District
of Los Angeles, however, the diversion rate is over ten times higher.
Such key areas, however, do not always play a role of increasing prominence
in Los Angeles: the number of persons visiting the central business
district in a day, has actually declined overall during the last 50 years,
despite concurrent growth in total regional population from slightly over
one million persons to nearly ten million persons.
Final planning of the recommended transit system proposal is now
in progress, with the prospect that it will be submitted to Los Angeles
County voters in November 1974, for approval of necessary local taxes.
Whatever the election outcome, it seems unlikely that any mass rapid
transit more expensive and effective than that of the current recommendation
will be built in Los Angeles by 1990. Thus the patronage estimates of the
3-13
-------
recommendation are unlikely to be exceeded, and in consequence we may
conclude that for purposes of this study, baseline transit use will be
under 6 percent of total trips and consequently must be regarded as
negligible in Its impact on overall automobile movement. Only in the event
of dramatic changes explicitly excluded from the baseline, such as stringent
gasoline rationing, is it likely that rapid transit would serve as much
as 10 percent of Los Angeles' daily travel.
3-14
-------
4 AUTO AVAILABILITY
The automobile population for the Los Angeles region has been fore-
cast for 1990 by the Los Angeles Regional Transportation Study. The
forecast is documented in LARTS Technical Work Paper No. 1: Trip Genera-
tion Analysis Report, September 1, 1971. Additional detail is available
in computer tabulations provided by LARTS.
The LARTS 1990 projections are based on extensive data gained in
the 1967 home interview survey. They employed multiple linear regression
in order to project automobile availability as an intermediate step in
projecting future trips.
Projections for electric car impact analysis have been developed
simply by extension of the LARTS results to the geographical area of the
South Coast Air Basin, with adjustment for the different population
projection adopted in the impact study.
The basic result taken from LARTS Is the number of automobiles per
person in the Los Angeles region. Since LARTS projected specifically to
the year 1990 alone, figures for 1980 and 2000 were obtained by linear
extrapolation, using 1972 actual automobile registrations, plus the popu-
lation projections of this study. The result is shown in Fig. 4.1,
which presents comparable automobile availability rates for California
and for the US. Evidently, the projection calls for a leveling off in
automobiles available per person, and the implication of past trends is
that the high availability rates of the South Coast Air Basin will even-
tually be equalled in California and the US, which have demonstrated more
rapid rates of growth since 1950. Automobile ownership rates for portions
of the South Coast Air Basin by county are tabulated in Table 4.1.
The automobile availability data of Fig. 4.1 and Table 4.1 was com-
bined with the population projections of this study to arrive at projections
of total automobile population in the South Coast Air Basin. This total
and its breakdown by county, is shown in Fig. 4.2 and tabulated in Table 4.2.
3-15
-------
o.
a:
SCAB ^
3
CALIFORNIA
UNITED STATES
1
1940
Figure
1
1950
4.1.
1
I
I960' 1970
YEAR
Basic Automobile
1 i
1 980 1 990
Availability
I
2000
TABLE 4.1
AUTOMOBILES PER PERSON, SOUTH COAST AIR BASIN
County
Los Angeles
Orange
Riverside
San Bernardino
Santa Barbara
Ventura
SCAB Total
Actual
1950
.41
.431
.386
.368
.417
.366
.409
1960
.459
.440
.411
.407
.44
.386
.451
1970
.522
.527
.495
.48
.51
.481
.519
Projected
1980
.559
.547
.525
.53
.538
.51
.551
1990
.59
.565
.558
.56
.568
.538
.579
2000
.63
.584
.588
.60
.592
.565
.613
3-16
-------
Since the LARTS projections are based on detailed data about past
and future residences, it is possible to break down the automobile popula-
tion not only according to geographical area, but according to type of
dwelling and automobiles available per household. This is done, for the
LARTS area, in Tables 4.3 and 4.4. Since the SCAB area differs only in
minor respects from that of LARTS, the percentages in these Tables from
the LARTS area may be applied directly to the SCAB area with reasonable
accuracy.
3-17
-------
CO
O
O
TOTAL SCAB
VENTURA
RIVERSIDE
SANTA BARBARA
1960
1970 1980
YEAR
1990 2000
Figure 4.2. Automobile Population in the South Coast Air Basin
3-18
-------
TABLE 4.2
AUTOMOBILE POPULATION OF THE SOUTH COAST AIR BASIN
!
County
Los Angeles
Orange
San Bernardino
Santa Barbara
Riverside
Ventura
Total SCAB Area
10-year annual
growth rate, percent
1950
1,705,694
93,106
91,342
26,180
47,442
41,930
2,005,694
Actual
1960
2,747,570
309,392
172,391
41,171
88,508
76,852
3,435,884
5.5
1970 1980
3,626,450
748,217
265,492
78,975
160,523
4,033,605
970,378
339,726
93,425
194,495
180,746 1 249,084
5,060,403 5,880,713
3.9 1.5
Projected
1990
4,442,869
1,199,212
411,312
118,155
228,543
332,753
6,732,844
1.3
2000
4,893,368
1,406,447
486,940
138,137
257,174
418,665
7,600,731
1.2
u>
Source: California Department of Motor Vehicles
-------
u>
NJ
O
TABLE 4.3
AUTOMOBILES AVAILABLE BY DWELLING UNIT TYPE
(In thousands)
County
Los Angeles
Orange
Ventura
San Bernardino
Riverside
Total
'
Percent
Single
2,091
453
125
204
125
2,998
69
1967
Mult.
1,064
165
32
36
25
1,322
31
Total
3,155
619
158
240
150
4,322
100
Single
2,833
823
343
399
231
4,629
62
1990
Mult.
1,978
504
141
107
79
2,809
38
Total
4,811
1,327
484
506
310
7,438
100
Source: LARTS Interim Model Runs, Tabs 400260-4, 400504-7, 400603
-------
TABLE 4.4
AUTOS AVAILABLE BY HOUSEHOLD CAR OWNERSHIP CLASS
(In thousands)
County
Los Angeles
Orange
Ventura
San Bernardino
Riverside
Total
Percent
1967
1 car 2+
957 2
132
36
63
43
1,231 3
28
cars
,198
487
122
177
107
,091
72
1990
1 car
1,353
308
106
105
76
1,948
26
2+ cars
3,458
1,019
378
401
234
5,490
74
Source: LARTS Interim Model Runs, Tabs 400260-4, 400504-7, 400603
3-21
-------
5 TRAVEL CHARACTERISTICS
Eventually, definitive travel forecasts for the Los Angeles area
will be produced by LARTS. At the moment, travel forecasting is not com-
plete, but interim data is available. From this data, detailed by computer
models which regenerate 1967 conditions and forecast 1990 conditions,
travel characteristics for 1980, 1990, and 2000 must be developed for
electric car impact analysis. Results of the first step in computer
modeling, projections of household characteristics and automobile availa-
bility, have been summarized already, as in Tables 4.3 and 4.4. The next
modeling step utilizes these results to estimate trip production. The
results of separate trip production model runs for 1967 and 1990 appear
in Table 5.1.
Comparison of the 1967 and 1990 model output in Table 5.1 shows a
tremendous growth in daily person trips and vehicle trips—over 100 percent.
During the same time, the population increase in these models is only
50 percent. The difference is accounted for by major increases in daily
trips per vehicle and daily trips per person.
Considerable data on the source of the vehicle trips in Table 5.1
is available from interim LARTS computer tabulations, as summarized in
Table 5.2. The 1990 LARTS projections in Table 5.2 for person-trips
rather than vehicle-trips; for comparability with the 1967 data, they have
been scaled by the average vehicle occupancy of the area in 1967, 1,407
persons per vehicle. The absolute figures in the table apply, of course,
to the LARTS area and to its 1990 population forecast, both of which differ
from those used in this study. Nevertheless, because of the great simi-
larities between the two situations, the percentage distributions at the
bottom of Table 5.2 may be applied in this study with reasonable confidence.
They show, for example, that in 1967 and 1990 over two-thirds of all
vehicle trips will originate at dwelling units having two or more cars and
that the fraction of such trips originating from multiple-unit dwellings
will increase from 25 to 31 percent between 1967 and 1990.
3-22
-------
TABLE 5.1
COMPARISON OF TRIP PRODUCTION MODEL RUNS, LARTS
1967 1990
Population 9,019,184 13,446,007
Vehicles 4,479,000 7,437,000
Person Trips Per Day 22,272,000 48,554,752
Vehicle Trips Per Day 15,583,000 34,509,000
Vehicle Trips Per Day
Per Vehicle 3.5 4.6
Per Person 1.7 2.6
Vehicles Per Person .50 .55
Person Trips Per Day
Per Person 2.5 3.6
Trips Per Vehicle At
I car households 3.9 5.2
2+ car households " 3.5 4.3
Source: LARTS Interim Model Pvuns,
Tabs 400260-4, 400504-7, 400603
3-23
-------
u>
I
N>
TABLE 5.2
DAILY VEHICLE TRIPS BY ORIGINATING HOUSEHOLD TYPES
(In thousands)
COUNTY
Los Angeles
Orange
Ventura
San Bernardino
Riverside
Totals
Totals, percent
DWELLING
UNIT TYPE
single
multiple
single
multiple
single
multiple
single
multiple
single
multiple
single
multiple
overall
single
multiple
all
1 car
2,167
1,496
343
202
119
34
231
50
141
33
3,001
1,815
4,816
62
38
31
1967
2+ cars
5,367
2,032
1,382
421
375
111
594
85
308
54
8,026
2,703
10,729
75
25
69
total
7,534
3,528
1,725
623
494
145
825
135
449
87
11,027
4,518
15,545
71
29
100
1 car
3,725
3,009
966
776
450
180
527
134
308
102
5,776
4,201
10,177
59
41
30
1990
2+ cars
9,239
4,543
3,203
1,533
1,363
692
1,599
309
792
241
16,196
7,318
23,514
69
31
70
total
12,964
7,552
4,169
2,309
1,813
872
2,126
443
1,100
343
22,172
11,519
33,691
66
34
100
Source: LARTS Interim model runs, Tabs 400260-4, 400504-7
400603. 1990 tabulations of person trips reduced
by factor of 1.407 to approximate vehicle trips.
-------
The next step in travel modeling is to distribute the trips of
Table 5.1 and 5.2 by application of a gravity model, i.e., to determine
where on the map the various trips go. In the course of this process,
distributions of trip times by trip are necessarily produced. The results
for several trip types in 1990 are shown in Fig. 5.1, along with a summary
of home-to-work trips observed in the 1967 origin-destination survey. For
1990, as may be expected, it may be seen that work trips are generally
substantially longer than the average of all trips, and that shopping
trips are substantially shorter. That the 1967 work trips are longer
in time than the 1990 work trips is probably the consequence of a different
definition. The discrepancy, an almost-constant eight minutes regardless
of total trip time, may be due to a portal-to-portal definition of trip
time in the survey, which would include parking and walking times.
It is noteworthy that trips in the Los Angeles area are not substan-
tially longer in duration than elsewhere in the nation. According to
the 1963 Census of Transportation, 77 percent of all workers traveled less
than 35 minutes to work, as compared to 76 percent from the 1967 Los
Angeles 0-D survey.
Once trips have been distributed by the gravity model, they are
assigned to specific routes in a detailed network model of the transporta-
tion system. The results of the assignment process, summarized in
Table 5.3, show a great deal about the travel distances and speeds asso-
ciated with different categories of trips. They also show what portion of
trips and travel miles are on city streets as opposed to freeways.
Though these are interim model results, they suggest that vehicular
travel conditions in Los Angeles are not to change greatly by 1990. The
speeds in Table 5.3 change relatively little during this period, reflecting
the prospect envisioned previously in this report that roadway capacity
will keep up with travel demand, so that congestion levels will remain
about as they are now.
3-25
-------
100,
80
c
<3J
20
HOME TO SHOPPING^, —- —
1990 X
/ ALL-1990
* X
HOME TO WORK
1967
HOME TO WORK
1990
(SOURCES: LARTS TAB 400312,
1967 0-D SURVEY REPORT)
10
20 30 40
TRIP TIME, minutes
50
60
Figure 5.1. Distribution of Trip Times
3-26
-------
TABLE 5.3
COMPARISON OF NETWORK MODEL RUNS - LARTS
1967
MODEL RESULT BY TRIP TYPE
1990
MODEL RESULT 3Y TRIP TYPE
TRIP TYPE CHARACTERISTICS
Vehicle Trips, Thousands
Percent
Avg. Distance, Miles
Avg. Speed, mph
Streets
Freeway
Freeway Use Trips
Percent of Total
Avg. Distance, mi.
Fwy. Distance, mi.
Street Distance, mi.
Freeway Vehicle Miles, percent
Home-
Work
4,151
20
8.9
28.1
2A.1
36.9
37.7
15.4
10.4
5.0
41
All
Work
5,977
29*
8.3
28.0
24.0
36.9
36.0
15.3
10.3
5.0
41
Non-
Work
14,287
71
4.7
34.3
29.1
54.4
21.0
14.1
9.7
4.4
32
All
Internal
20,264
100
5.8
32.4
27.6
49.2
25.5
14.9
10.1
4.8
36
Home-
Work
6,689
20
10.8
29.7
24.7
36.5
40.1
19.7
15.6
4.1
53
All
Work
9,890
29
10.2
29.5
24.5
36.5
38.5
19.5
15.5
3.9
52
Non-
Work
23,642
71
6.1
34.7
28.6
46.0
25.3
19.7
16.0
3.7
46
All
Internal
33,532
100
/.3
33.2
27.4
43.2
29.2
19.6
15.7
3.8
49
u>
NJ
includes home-to-work
Source: LARTS Network Model Runs, Tabs 601978,
601979, 450268, 450269
-------
It should be noted that the total number of vehicle trips shown
for 1967 in Table 5.3 is much greater than that from the 1967 survey
reported in Table 1.1. It is also much greater than the total shown in
Table 5.1, which comes from a trip production model closely approximating
the survey result. The reason for this discrepancy is that the survey
and model results were scaled up by approximately one-third in the network
run, primarily to give screen line volumes in accord with traffic counts
made at the time of the 1967 survey. This is clearly a major adjustment,
leaving the various 1967 survey and model results in some doubt; under-
reporting of travel in the survey is the most likely explanation.
To proceed with travel forecasting for electric car impact analysis,
it is necessary to select either the 1967 description of Table 5.1 or that
of Table 5.3 as a basis for viewing growth projected for 1990. It is most
conservative to assume that the network results of Table 5.3 are more
realistic than those which would result with the smaller number of trips
of Table 5.1. This assumption minimizes the projected increase in vehicle
movement within the LARTS area between 1967 and 1990. Nevertheless, as
Table 5.4 demonstrates, the implications of the comparison of network
runs is that there will be substantial increases in individual vehicle
usage and in travel by individuals.
The consequences of these increases would be considerable, both
in contribution of conventional automobiles to air pollution, and in the
utility of electric vehicles for the increased number of longer trips on
the average day. It appears, however, that though the increases may be
implied by the LARTS multiple regression analyses, they are not suggested
by other past experience. Figure 5.2 displays the long-term national
trend in annual automobile use, as reported in Highway Statistics, the
annual publication of the Federal Highway Administration. Also shown in
Fig. 5.2 are recent figures for California, and the figures implicit in
the LARTS 1967 and 1990 network model runs. Three points are immediately
evident in Fig. 5.2:
3-28
-------
TABLE 5.4
IMPLICATIONS OF TRIP PRODUCTION AND
NETWORK MODEL RUNS - LARTS
WEEKDAY TRAVEL CHARACTERISTIC
Total Vehicle-Miles, Thousands
Total Vehicle-Minutes, Thousands
*
Total Vehicle-Trips, Thousands
**
Vehicles, Thousands
**
Persons, Thousands
Miles Per Vehicle
Minutes Per Vehicle
Trips Per Vehicle
Miles Per Person
Minutes Per Person
Trips Per Person
Miles Per Trip
Minutes Per Trip
1967
123,503
231,392
20,399
4,479
9,019
27.6
51.7
4.6
13.7
25.7
2.3
6.1
11.3
1990
262,828
476,009
33,932
7,437
13,446
35.3
64.0
4.6
19.5
35.4
2.5
7.7
14.0
Percent
Increase
113
106
66
66
49
28
24
0
67
38
10
26
24
Source: Network Tabs 601978-9, 450268-9
**
Source: Trip Production Tabs 400260-4, 400504-7, 400603
3-29
-------
u>
U)
o
14,000
uo
CM
12,000
^10,000
8000
6000
LARTS MODEL RUNS/'
LONG TERM TREND
ENTIRE UNITED STATES CALIFORNIA ONLY
I
1930
1940
1950
1960 1970
YEAR
1980
1990
2000
Figure 5.2 Average Annual Passenger Car Mileage Versus Time
-------
• Except for dislocations due to World War II, national average
automobile mileage per year has risen very slowly over the
past 35 years. From 1940 to 1970, the total increase in
annual automobile mileage was less than 10 percent.
• In recent years, California automobiles have been driven
less, on the average, than those in the nation as a whole.
• The LARTS models envision a very rapid growth in Los Angeles
automobile usage, at a rate over three times that of the
long-term national trend.
It is true, of course, that for the last eight years, automobile
use nationally has grown as rapidly as the LARTS projections. In view
of the thirty preceding years of much lower growth, however, together
with the depressing effects of fuel shortages and environmental controls,
we believe that continued growth according to the LARTS models is
unrealistic.
Accordingly, we have adopted the projections shown in Table 5.5, which
are much closer to the national trend. The basis for these projections
is the national trend of Fig. 5.2, which leads immediately to the daily
vehicle mile projections. The daily trips per vehicle follow immediately
from Table 5.4. The total daily vehicle miles follow from the daily
averages and the automobile population projections of Table 4.1. The
daily minutes of use per vehicle follows from the average speed in the
LARTS 1990 model run.
The percentage of vehicle miles on freeways is less easily derived.
Since the number of trips per vehicle is being held constant, the average
trip in 1990 according to the baseline projection will be only slightly
longer than in 1967. Thus the model run increase in trip distance, a
disproportionate portion of which is by freeway, is inappropriate. The
baseline projection of Table 5.5 assumes a n^ch lesser growth rate of
freeway use, equal to the rate of growth in j:he average trip distance.
3-31
-------
TABLE 5.5
BASELINE AUTO TRAVEL PROJECTIONS
SOUTH COAST AIR BASIN
Daily Vehicle Miles, millions
Percent on Freeways
Percent on Streets
Daily Miles Per Vehicle
Daily Trips Per Vehicle
Daily Minutes Per Vehicle
Miles Per Trip
Minutes Per Trip
Average Speed, mph
1980
167
39
61
28.3
4.6
53
6.15
11.5
32.0
1990
196
42
58
29.2
4.6
54.7
6.35
11.9
32.0
2000
228
45
55
30.0
4.6
56.2
6.52
12.2
32.0
3-32
-------
6 AUTO AGE AND CLASS
In Sec. 4, the total number of automobiles for the South Coast Air
Basin was projected from area population and forecast automobile ownership
rates. The result is shown in Fig. 4.2. Important changes are in progress
in the automobile population, however, as emission control and safety
requirements are being introduced, and as buyer preference shifts from
the larger to the smaller classes of automobiles. The projections
developed in this section describe the changing future mix of automobiles
by age and class, providing the necessary basis for projecting future
overall emissions and fuel consumption, and for changes in them as increas-
ing numbers of electric cars are introduced.
6.1 AUTO AGE DISTRIBUTION AND SALES
Survival rates for US automobiles from several different model
years are shown in Fig. 6.1. These rates, drawn from actual registration
data, are vital in translating market share and sales trend data into
total auto population characteristics at any single time. But the rates
of Fig. 6.1 are not immediately applicable to the South Coast Air Basin,
for two reasons. First, they are obviously erroneous in showing number of
survivors for 1962 and 1959 models to be larger after two years of use
than after one. Second, they are drawn from data for the entire US which
on the average involve higher levels of use, more difficult environmental
conditions, and consequently shorter life than in Southern California.
The first of these difficulties may be removed by a minor adjustment of
the questionable curves of Fig. 6.1, as suggested by the 1967 model
curve; but the second is not so simply handled.
When the average car lasts longer, as in California, lower sales
are required relative to a given automobile population in order to maintain
that population constant by replacing scrapped cars. This illustrated
in Fig. 6.2, which shows the proportion of new car registrations to
total car population for the US and for California alone.
3-33
-------
1.0
0.8
19
I 0.6
2
0.4
0.2
1962 MODELS)
SOURCE: AUTOMOBILE NEWS ALMANAC 1972
I I 1 I
J
16
10
12
14
AGE, YEARS
Figure 6.1. Survival Rates for US Autos
a
s
o
h-
O
UNITED STATES
O (-
O .
«z 8
SOURCES: AUTOMOTIVE NEWS ALMANAC - 1972
HIGHWAY STATISTICS, SUMMARY TO
1965, AND SUBSEQUENT ANNUAL ISSUES
I
I
I
1962 1964 1966 1968
YEAR
1970
Figure 6.2. Relation of New Car Registrations to Total Car Population
3-34
-------
There Is an additional factor, of course, in the relation of regis-
trations to population: the rate of growth of car population. Independent
of the longevity of automobiles, a rapid growth rate requires higher new
car additions to the population than a lower growth rate. During the
latter part of the period illustrated in Fig. 6.2, however, the growth
rates for California and US populations were almost equal. Accordingly,
the difference between the two curves of the figure is a significant
Indicator of longer automobile life in California.
The sales rates relative to car population in Fig. 6.2 for recent
years range from 14 to 17 percent higher in the US than in California.
If anything, these figures underestimate the difference between California
and the US, because in previous years California did grow faster than the
nation in automobile population, and consequently may require replacement
of relatively fewer elderly cars than does the US generally. Accordingly,
we have assumed a difference of 18 percent for adjustment of national
survival times.
Figure 6.3 shows the adopted projection for automobile survival
rates in both the US and the South Coast Air Basin. The US rates were
obtained by replotting average data from Fig. 6.1. The California rates
in Fig. 6.3 follow immediately from the assumption that California
automobiles survive 18 percent longer than US automobiles in general.
The adequacy and accuracy of this assumption rests on its simultaneous
compatibility with actual sales rates of automobiles in California and
with the projections of automobile population for the South Coast Air
Basin appearing in Table 4.2. These projections are independent, of course,
since they were derived from the LARTS forecasts of automobile availa-
bility in Table 4.1, together with the population forecasts of this impact
study.
Automobile sales for the South Coast Air Basin are shown in Fig. 6.4.
Actual sales data for the Air Basin were not available, so estimates
3-35
-------
u>
100
80
-o
•*»•
o
UJ
a.
a:
o
a;
^
to
60
40
20
L
0
I
8
AUTO AGE, US
I I
10
12
14
16
8 10
AUTO AGE, SCAB
12
14
16
18
Figure 6.3. Projected Automobile Survival Rates
-------
1000
900
800
GO
00
CO
o
700
600
500
400
COMPUTED FROM NATIONAL
SURVIVAL TABLE AND SCAB
POPULATION PROJECTION
51% OF CALIFORNIA
NEW CAR REGISTRATIONS
I
1960
1970
1980
YEAR
1990
CNI
Ml
•"»•
ADOPTED
PROJECTION
2000
Figure 6.4. Annual Automobile Sales, South Coast Air Basin
3-37
-------
were obtained by taking'51 percent of total car new car registrations in
California, as shown. In 1970, 51 percent of all automobiles in California
were in the Air Basin.
The upper line on Fig. 6.4 shows a computed automobile sales rate
necessary to support the independent population projection of Table 4.2,
assuming survival rates according to the US curve of Fig. 6.3. Computation
of this sales rate is described in the appendix. It is obviously much
higher than past sales in Fig. 6.4, further substantiating the observation
that California automobiles last considerably longer than automobiles in
the entire US. Accordingly, another projection method is needed.
The adopted projection of Fig. 6.4 was based on estimated SCAB
sales in preceding years and estimated future sales to meet the SCAB auto
population forecast of Table 4.2. When combined with the California
survival rate of Fig. 6.3, it results in a total automobile population
which is in excellent agreement with the independent projection; a com-
parison appears in Table 6.1. As described in the next section, this
population projection has been used in converting market share percentages
by automobile class into total population percentages by class. The
projection of Table 4.2, however, remains basic, so resultant population
percentages are applicable to the left-hand population totals of
Table 6.1 rather than those on the right.
The sales projections of Fig. 6.4 and the California survival rates
of Fig. 6.3 may be combined to determine the distribution of SCAB automobiles
according to age. This is done, on a percentage basis, in Table 6.2, and
plotted in Fig. 6.5. In Table 6.2, only relative sales rates are required,
since only percentage distributions are sought. The relative sales figures
are increased uniformly from a nominal value of one for the sales year
of the oldest car in the population, at the percentage rate implicit in
Fig. 6.4.
3-38
-------
TABLE 6.1
COMPARISON OF AUTO POPULATION PROJECTIONS FOR
THE SOUTH COAST AIR BASIN
Year
1980
1990
2000
Computed from
Auto Availability
and Population
Projection
5,880
6,733
7,600
Computed from
Auto Survival and
Sales Projections
6,046
6,955
7,740
Discrepancy
2.0%
3.2%
1.8*
3-39
-------
uj TABLE 6.2
0 DISTRIBUTION OF VEHICLES WITH AGE, SOUTH COAST AIR BASIN
Vehicle
Age
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Relative
Sales
1.772
1.724
1.676
1.628
1.580
1.531
1.483
1.435
1.386
1.338
1.290
1.241
1.193
1.145
1.097
1.048
1.0
Survival
Rate
0.990
.985
.970
.952
.935
.909
.87
.82
.759
.668
.551
.428
.319
.22
.149
.09
.04
Survivors
1.754
1.698
1.626
1.550
1.477
1.392
1.290
1.177
1.016
.894
.711
.531
.381
.252
.163
.094
.04
Percent of
Total
10.9
10.6
10.1
9.7
9.2
8.7
8.0
7.3
6.3
5.6
4.4
3.3
2.3
1.6
1.0
.6
.2
Cumulative
Percent
10.9
21.5
31.6
41.3
50.5
59.2
67.2
74.5
80.8
86.4
90.8
94.1
96.4
98.0
99.0
99.6
99.8
Total: 16.046
-------
12
10
o
LU
O.
. 6
X
UJ
10
AGE, YEARS
15 20
Figure 6.5. Distribution of Vehicles with Age, South Coast Air Basin
3-41
-------
The distribution of vehicles with age (Fig. 6.5) was compared with
a standard distribution used In California. This distribution appears In
a California air quality manual and Is In general use In determining emis-
sions factors In a given year appropriate to a realistic mix of vehicle
2
ages. The standard distribution Is so nearly Identical to that of
Fig. 6.5 that It Is not practical to plot them separately on the same
figure. Thus the adopted sales projection and survival rates seem fully
corroborated.
6.2 AUTO MARKET SHARES AND POPULATIONS BY CLASS
There has been a drastic shift In the last 15 years In the nature
of automobiles sold in the US. In 1958, 90 percent of the US auto market
was taken by standard-size automobiles. But by 1972, in the bellwether
Los Angeles area, sales of standard cars had precipitously declined to
25 percent of the market and they have continued to slide subsequently.
Projection of this trend into the future is important partly to establish
the baseline auto world on which electric cars will Impact, and partly
to show the kind of market in which electric cars must compete.
Figure 6.6 presents actual and projected shares of the US auto
market by class. Though definitions of the classes seem to change with
time, they may be described in recent years as follows:
Class Price Weight, pounds Cylinders
Subcompact <$2285 up to 2,600 four
Compact <$2800 2,601-3,200 six (with eight-cycle
options)
Intermediates <$3498 3,201-4,000 six and eight
Standard <$3500 4,001 and up eight
Specialty "Sports" models encompassing entire range.
At the left of Fig. 6.6 are actual market shares. Those for 1964
through 1972 were taken directly from annual issues of Automotive News
Almanac; earlier data from the same sources were supplied by Dr. Joseph
3-42
-------
PROJECTED
AUTO CLASS
• STANDARD
D INTtRMEIilATt.
• COMPACT
O 5,U
A SP
1970 1980
YEAR
1990
2000
Figure 6.6. US Auto Market Share by Class
3-43
-------
Meltzer of the Aerospace Corporation. The rapid decline in popularity
of the standard automobile is evident in Fig. 6.6, as is the very rapid
rise of subcompact popularity.
The market share projections at the right of Fig. 6.6 are basically
extrapolations of the existing trends. But trend extrapolation, if carried
too far, leads to market shares below zero or over 100 percent, so some
limitation or reversal of trend must be introduced. The limitations
appearing in Fig. 6.6 are necessarily subjective assumptions, since
detailed forecasts are beyond the scope of this study. They reflect
the following major prospects:
• That dwindling supplies of fuel and rising prices will
increasingly favor smaller, more economical automobiles
• That stringent exhaust emission controls and noise emission
controls will make "muscle" cars more difficult and less
rewarding to produce and operate
• That automotive safety requirements will increasingly
encourage smaller cars
In the past, of course, there has been some talk of elimination of
smaller cars in the interests of Improved safety. At present, however,
national emphasis in automotive safety research is moving rapidly towards
the problem of smaller cars, both because the public has demonstrated
Increasing desire for small cars, and because the national energy situation
could be much improved with lighter, more efficient automobiles. There
is an inherent safety problem when large and small cars mingle, as at
present on US streets and freeways. But the mix is shifting; and whereas
it might once have seemed sensible to eliminate a few hazardous little
cars, it may soon seem much more desirable to eliminate a few menacing
large cars.
3-44
-------
The projections of Fig. 6.6 approach ultimate values which are fore-
seeable now in view of the above considerations and existing trends. In
the further future, of course, other problems, issues, and trends will
doubtless develop which will further alter market shares. It is not possi-
ble, however, to delve into such prospects within the scope of this study.
In consequence, the market shares are projected at a constant level after
1985.
Because historical data for market shares in the South Coast Air
Basin are not readily available, projections for the Basin must be
developed from the national situation of Fig. 6.6. This was accomplished
as shown in Fig. 6.7, simply by extrapolating from existing actual market
shares for 1972 to the same ultimate market shares of Fig. 6.6.
Looking to 1985 and beyond, when further shifts in market share
cannot be anticipated by this study, there is no solid ground for assuming
different market shares in Los Angeles and,the nation. The underlying
assumption in Fig. 6.7 is that the SCAB auto market has been perhaps
five years ahead of the national trend of Fig. 6.6, but is eventually
going to go no further than elsewhere in the nation.
Given the sales projections of Fig. 6.4 for the South Coast Air
Basin, the market shares of Fig. 6.5, and the survival rates of Fig. 6.3,
it is possible to project the mix of autos in the Basin for future study
years. The result is shown in Fig. 6.8. Some further assumptions,
however, are necessary, since as Table 6.2 shows, 20 percent of automobiles
in 1980 California will be pre-1972 models, for which market shares are
not presented in Fig. 6.7. To obtain necessary estimates, the extrapola-
tions of Fig. 6.7 were simply continued backward several years. This is
not necessarily an accurate procedure but since it only affects a minor
fraction of the total vehicle population in 1980, the expense of obtaining
additional regional data seemed unjustified.
3-45
-------
UJ
o:
UJ
v:
a:
SUBCOMPACT
irt
to
50
2000
40
o
u_
o
I—
UJ
CE:
30
20
10
SUBCOMPACT,
INTERMEDIATE
SPECIALTY
I
1970 1980 1990
YEAR
2000
Figure 6.7. Projected Auto Market Share by Class
for the South Coast Air Basin
Figure 6.8. Projected Auto Population Share by
Class for the South Coast Air Basin
-------
7 AUTO FUEL CONSUMPTION
In the first approximation, fuel requirements for automobiles have
been proportional to vehicle weight. According to a summary from the
Motor Vehicles Manufacturers' Associate
road load and acceleration is given by
3
Motor Vehicles Manufacturers' Association, horsepower required to meet
Horsepower required - KjWV + K.,DAeV3 + K-jVWC
where K., K«, and K, are constants
W is car weight
V is car speed
D is vehicle drag coefficient
A is frontal area of the car
e is air density
C is acceleration rate
Where speeds are not excessive and stops are frequent, the first
and last terms of this expression dominate required energy production in
a given driving cycle, and both are directly proportional to vehicle
weight. Weight correlation with fuel economy is shown by an EPA study
of dynamometer tests of automobiles of various model years on the Federal
4
Driving Cycles.
The general trend to increased average weights for major classes of
automobiles is shown in Fig. 7.1. This trend has not been compensated
fully by shifts in market preference towards the compacts. Moreover,
there have been automobile efficiency sacrifices due to Increased use of
air conditioning and to pollutant emission controls. In consequence,
fuel economy persistently declined until the 1975 model year, as Fig. 7.2
shows. Points in Fig. 7.2 are based on EPA measurements of fuel economy
by market year on the Federal Driving Cycle, weighted for the sales mix
of each year since 1958.
3-47
-------
5000 r-
SOURCE:
PRIVATE COMMUNICATION, DR. JOSEPH MELTZER,
AEROSPACE CORPORATION
4000
o
Q.
3000
INTERMEDIATE
COMPACT
2000 -
SUBCOMPACT
I
1955
1960
1965
YEAR
1970
1975
Figure 7.1. Automobile Weight by Class
3-48
-------
16
O 15
O
u
U 14
u.
Q 13
uj
K
I
$2 12
LU
2
CO
3 11
10
I i i
I i I I r i
il
1973 DATA
ARE ESTIMATED
SOURCE: REF. 5 AND US EPA
I
I I
I I
I I
1958 1960 1962 1964 1966 1968 1970 1972 1974
YEAR
Figure 7.2. Sales Weighted Fuel Economy Versus Model Year
3-49
-------
Fuel economy for new cars Is not the same as for all cars on the
road in a given year, nor is fuel economy in the Federal Cycle identical
with average fuel economy in actual use. Figure 7.3 shows national average
fuel economy as reported by DOT from automobile miles driven and gallons
of fuel consumed, in comparison with fuel economy calculated for the actual
mix of cars on the road based on Federal Emission Cycle measurements.
The national average mileage is consistently about 6 percent higher than
that calculated from Federal Emission Cycle measurements.
For future electric cars, it is important to estimate gasoline
mileage for conventional cars which the electric may replace. This is
not easily done, however, partly due to the current spate of gasoline
shortages and price increases, partly due to pending legislation which
may directly influence fuel economy, and partly due to research programs
intended to make major improvements in auto efficiency.
A joint DOT/EPA program initiated in 1973 has as its objective a
30-percent decrease in fuel consumption (equivalent to a 43-percent
increase in fuel economy), suitable for mass production automobiles of
1980. The objective is particularly impressive because it is sought with-
out sacrifice in performance, appearance, available space, safety standards,
emission standards, and noise standards, and without undue increase in
cost.
Legislation is currently being formulated which will deal directly
with automobile fuel economy. To date, proposals have included setting
of standards and imposition of taxes and penalties, in addition to further
supporting research. The EPA Administrator was reported to favor Congress-
ional action for a minimum new-car average of 13.5 miles per gallon by
1977, with increases to follow in subsequent years. A bill introduced
in the House of Representatives by Charles Vanik of Ohio would impose
taxes up to $770 on the purchase of automobiles providing less than 20
o
miles per gallon in 1981. A bill introduced by Senator Rollings called
for a standard of fuel economy effective in 1978 designed to achieve a
3-50
-------
e>
o.
5
O
O
u
ut
15
14
jjj 13
u.
UJ
O
oc
ui
12
11
2 10
SOURCE: REF. 5
O DOT DATA. CALENDAR
YEAR BASIS (CY)
A EPA/FTP DATA, MODEL
YEAR BASIS (MY)
I
2
1966 1967 1968 1969 1970 1971 1972 1973 CY
1966 1967 1968 1969 1970 1971 1972 1973 MY
Figure 7.3. National Average Fuel Economy Versus Calendar Year
3-51
-------
25-percent increase relative to model year 1972 automobiles, coupled with
a graduated fee paid at the time of vehicle purchase which reaches zero
only for vehicles achieving fuel economy 35-percent better than the
standard (69 percent better than 1972 autos). A bill subsequently passed
by the Senate adopts as an objective a 50-percent increase in fuel
9
economy for 1984 automobiles.
In the longer term, there seems little doubt that even greater
fuel economy can be achieved. The Mercedes 1973 diesel automobile, for
example, delivered 85 percent better fuel economy in EPA tests than the
corresponding gasoline-fueled Mercedes car of similar test weight, appear-
ance, and accommodations (there are, of course, differences between the
two cars in acceleration, top speed, maintenance requirements, and so on).
As an alternative to more efficient power plants, automobile size may be
simply reduced. The 1974 Honda automobile, for example, delivered 29.1
miles per gallon in EPA tests on the Federal Driving Cycle, well over
100 percent better than the fuel economy of the average automobile in
1974.10
The various economic, legislative, and technological factors which
will determine future automobile fuel consumption have obviously not yet
stabilized. Nevertheless, a nominal projection is required for purposes
of this study. The various considerations noted above and this nominal
projection are illustrated in Fig. 7.4.
The dashed line in Fig. 7,4, the projection adopted for the study,
assumes that a 50-percent increase in fuel economy of the average new
car will be achieved by 1984, and that subsequent Improvements at a slower
rate will yield a 100-percent overall improvement by the year 2000. This
is in line with the proposal of the EPA Administrator and the current
Senate bill (Circles 1 and 2 in Fig. 7.4), and relatively less optimistic
than either the standards and incentives proposed by Representative Vanik
and Senator Rollings (Circles 3 and 4 in the figure), or the DOT/EPA
research program goal (Circle 5).
3-52
-------
25 i-
20
en
Q.
15
10
VARIOUS PROPOSALS
AVERAGE OF US CARS ON THE ROAD
f© /\
3) / STUDY PROJECTION
2) ' FOR NEW CARS
AVERAGE OF NEW CARS SOLD
I
I
I
I
J
1930 1940 1950
1960 1970
YEAR
1980 1990 2000
U)
I
CO
Figure 7.4. Projected Auto Fuel Economy
-------
Also shown in Fig. 7.4, for reference, are the national average
fuel economics reported annually in Highway Statistics by DOT, and the
sales-weighted model year gasoline mileages of Fig. 7.2 (adjusted upward
6 percent for comparability with the DOT figures, as Fig. 7.3 shows to be
appropriate). In comparison with their history of persistent decline,
the nominal projection of the study obviously constitutes a dramatic
switch to rapid improvement in gasoline mileage. As such, it seems
relatively optimistic, even though it falls considerably below at least
some current research objectives and legislative proposals.
Though the fuel economy projection of Fig. 7.4 is national in scope,
it may be applied without modification to the Los Angeles area, because
average fuel economy in California, and presumably in Southern California,
apparently differs very little from the national figure. From data in the
1971 edition of Highway Statistics, for example, California vehicles
travel 118 billion miles on 9.817 billion gallons of fuel, for an overall
average of 12.02 miles per gallon. On the same basis, US vehicles travel
1,186 billion miles on 97.5 billion gallons of fuel for an average of
12.16 miles per gallon. Trucks and diesel fuel are included, so these
values are somewhat lower than for passenger cars, but they are directly
comparable since truck activity in California is in proportion to that
nationally. (10.7 percent of California motor vehicles are trucks, versus
11.0 percent nationally; California's use of special vehicular fuels—
diesel, propane, etc.—is 6.7 percent of its total fuel use, versus
7.8 percent nationally,) Thus the overall conclusion must be that California
autos get about the same gasoline mileage as autos nationally.
The projection of Fig. 7.4 does not specify what combination of
weight decrease and propulsive efficiency increase will be adopted to
achieve the indicated overall increase in fuel economy. Figures 6.6
and 6.7 of course, already envision a considerable further swing to the
subcompact and compact class of autos, but class weights themselves are
continually changing so this cannot serve as a ready basis for further
3-54
-------
projections. If Individual class weights stayed fixed at the most recent
levels shown in Fig. 7.3, and if fuel usage remained proportional to car
weight, then the increasing numbers of compact cars would reduce average
car weight and Increase average miles per gallon by only 10 percent, much
less than the 100 percent projected for 2000 in Fig. 7.4. Prospects are
that there will be not only a significant improvement in mechanical effi-
ciency, but significant reductions in class weights as well, quite
possibly with some sacrifice in accommodations and performance. Thus as
the conventional auto becomes progressively more desirable on energy
grounds, it is likely to compromise in some of these other areas.
The fuel economy projection of Fig. 7.4 is for future new cars.
To estimate average fuel economy for all cars operated in the South Coast
Air Basin, it is necessary to average together the different fuel uses
of each different model year likely to be on the road in a given year,
weighted according to probable usage. Table 7.1 presents the necessary
usage data, together with the age distribution of cars on the road shown
in Fig. 6.5. The overall result of the averaging process is shown in
Fig. 7.5. The rise in average auto fuel economy here is delayed considerably
relative to that of Fig. 7.4 on account of the sizeable admixture of
older, less economical cars.
As shown in Fig. 5.2, the average annual .usage of automobiles is
trending slowly upward. Moreover, the number of automobiles in the South
Coast Air Basin is expected to increase considerably, as shown in
Table 4.2. In combination with projected average fuel economy of Fig. 7.5,
these factors result in total annual auto fuel usage for the Basin as
shown in Fig. 7.6. Total fuel use has been rising rapidly, but is being
arrested now by the trend to smaller cars, and is projected to be turned
around by projected rapid Improvement in average auto economy. By the
end of the century, however, when projected rates of fuel economy Increase
are dropping, total fuel use will tend to rise again as more cars are
added to the total Basin auto population.
3-55
-------
TABLE 7.1
AUTOMOBILE USAGE VERSUS AGE, CALIFORNIA
AGE, YEARS
1
2
3
4
5
6
7
8
9
10
11 and up
PERCENT OF
ALL CARS
10.8
10.5
10.2
9.8
9.3
8.8
8.1
7.2
6.2
5.1
13.0
PERCENT OF
ALL AUTO MILES
19.9
16.7
13.7
11.5
9.5
7.5
5.2
4.4
3.4
2.6
5.6
Source: Air Quality Manual, Vol. II, "Motor Vehicle Emission Factors
for Estimates of Highway Impact and Air Quality," FHWA-RD-72-34,
Federal Highway Administration, Washington, D.C., April 1972.
3-56
-------
25
20
cn
a.
S 15
10
1
1970
I
I
1980 1990
YEAR
2000
Figure 7.5. Projected Average Auto Fuel Economy for the South Coast
Air Basin
.
< 5
LU 3
d.
GO
§
o
oo
o
1
1970
1980
1990
2000
YEAR
Figure 7.6. Projected Total Auto Fuel Use for the South Coast Air Basin
3-57
-------
3-58
-------
APPENDIX
AUTO SALES PROJECTION BASED ON NATIONAL SURVIVAL TABLES
The projected auto population of the South Coast Air Basin is
shown in Fig. 4.2. To forecast sales necessary to support this growth,
we must determine, at each sales year, what number of the previous year's
autos in use will be scrapped. This can be readily accomplished with
satisfactory accuracy by assuming a constant auto sales growth per year,
and using the auto survival rates of Table A-l. Table A-l is derived
from Fig. 6.3, which shows actual national survival rates for several
auto model years.
We adopt the following nomenclature:
S = sales in model year
s, = fraction of any model year sales surviving after
k years of use
P = auto population surviving at year n from previous
year's sales
g = annual sales growth factor
Then we have
P - s, S . + s0S 0 -I- s0S , . . . = s. S ,
n 1 n-1 2 n-2 3 n-3 *?* k n-k
But because S = gS , , This can be rewritten as
n n—i
Pn - K*- + a/' + . . . ).. - so
k
3-59
-------
TABLE A-l
AUTOMOBILE SURVIVAL FACTORS
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Survival Factor
1.00
.99
.98
.963
.94
.915
.87
.81
.73
.60
.46
.32.
.21
.13
.07
0.0
3-60
-------
The previous year's auto population may be written as
r. V n-k-1 _ -1 v~» n-k
So £Skg = So8
So the annual growth factor for auto population is simply g , as is to
be expected.
With the survival rates of Table A-l, the value of S correspond-
ing to a given P and g can be found by substitution in the above.
This enables forecasting sales rates required to support the SCAB auto
population projections of Fig. 4.2. These are reproduced, with annual
growth factors, in Table A-2. Since the rate is not quite constant,
different calculations of S may be made with the growth year taken at
the beginning of each coming decade, with results plotted in Fig. 6.4.
TABLE A-2
AUTO POPULATION ANNUAL GROWTH FACTORS
SOUTH COAST AIR BASIN
Year
1950
1960
1970
1980
1990
2000
Auto Population
2,005,000
3,435,000
5,060,000
5,880,000
6,732,000
7,600,000
Annual Growth Factor
1.055
1.039
1.015
1,0.13
1.012
-------
3-62
-------
REFERENCES
1. Statistical Abstract of the United States - 1967, US Department of
Commerce, Washington, D.C.
2. J. L. Beaton, et al., Air Quality Manual. Vol. II, "Motor Vehicle
Emission Factors for Estimates of Highway Impact on Air Quality,"
Federal Highway Administration Report FHWA-RD-72-34, US Department
of Transportation, Washington, D.C., April 1972.
3. Automobile Fuel Economy, The Motor Vehicle Manufacturers' Association
of the United States, Detroit, Michigan, 21 September 1973.
4. Fuel Economy and Emission Control, US Environmental Protection Agency,
Office of Water Programs, Mobile Source Pollution Control Program,
November 1972.
5. A Report on Automobile Fuel Economy, Office of Air and Water Programs,
US Environmental Protection Agency, October 1973.
6. A Study of Technological Improvements in Automobile Fuel Consumption
2nd Bi-Monthly Progress Review Presentation, Arthur D. Little, Inc.,
Cambridge, Mass., 30 October 1973.
7. The Wall Street Journal, 17 January 1974.
8. Automotive Research and Development and Fuel Economy, Hearings
before the Senate Commerce Committee, Serial 93-41, US Government
Printing Office, Washington, D.C.
9. "National Fuels and Energy Conservation Act of 1973," S.2176, 93rd
Congress, 1st Session, 10 December 1973.
10. "Gas Mileage Data for 1974 Cars," released to the press 18 September
1973, by Russell E. Train, Administrator, US Environmental Protection
Agency, Washington, D.C.
3-63
-------
3-64
-------
TASK REPORT 4
ECONOMIC PROJECTIONS
FOR THE LOS ANGELES REGION, 1980-2000
J. Eisenhut
-------
ABSTRACT
Business activity in the South Coast Air Basin (Greater Los Angeles)
is examined to determine what industry sectors might be affected if elec-
tric cars are used in the area. There are 16 industry sectors comprising
3.4 percent of the area's employment and 3.6 percent of the area's payroll.
The industries are grouped in the general areas of vehicle and vehicle
parts manufacturing, petroleum distribution, and the sales and repair of
automobiles. Historical trends in the employment, payroll, and number of
these firms are extrapolated to the year 2000. These extrapolations are
a basis for Task Report 9 (Vol. 3) which shows the relative magnitude of
changes induced by various levels of electric car use.
-------
CONTENTS
SECTION ; PAGE
ABSTRACT i
1 ,, INTRODUCTION 4-1
2 SOUTH COAST AREA EMPLOYMENT 4~2
3 TOTAL PERSON INCOME 4-4
4 BUSINESS IMPACTS 4-9
APPENDIX A EMPLOYMENT AND PAYROLL DATA 4-19
APPENDIX B NUMBER OF FIRMS SUBJECT TO ELECTRIC CAR IMPACTS 4-29
APPENDIX C BASELINE PROJECTIONS FOR IMPACTED INDUSTRY SECTORS 4-37
REFERENCES 4-53
ill
-------
ILLUSTRATIONS
NO. . PAGE
3.1 Consumer Price Index for Public Transportation in Los Angeles 4-6
3.2 Consumer Price Index for Private Transportation in Los
Angeles 4-6
C.I Automobile Population in the South Coast Air Basin, by
County 4-41
C.2 SIC 3691: Storage Battery Manufacturing 4-42
C.3 SIC 3717: Motor Vehicle and Motor Vehicle Parts
Manufacturing 4-43
C.4 SIC 5012: Motor Vehicle Distribution 4-44
C.5 SIC 5013: Automotive Parts Distribution 4-45
C.6 SIC 5014: Tire Distribution 4-46
C.7. SIC 5092: Petroleum Distribution 4-47
C.8 SIC 5511 and 5521: Car Dealers 4-48
C.9 SIC 5531: Auto Supply Stores 4-49
C.10 SIC 5541: Service Stations 4-50
C.ll SIC 7534: Tire Retreading Shops 4-51
C.12 SIC 7538 and 7539: Auto Repair Shops 4-52
-------
TABLES
NO. : PAGE
2.1 South Coast Area Employment 4-2
3.1 Consumer Price Indices, Los Angeles-Orange Counties 4-5
3.2 Average Personal Income 4-7
3.3 Total Personal Income 4-7
4.1 Businesses Subject to Electric Car Impacts 4-10
4.2 Relative Importance of Auto-Related Activity 4-12
4.3 Total 1971 Employment, Payroll, and Number of Firms 4-13
4.4 Total US Activity of selected Industries 4-13
4.5 Projected Economic Activity Subject to Electric Car Impact 4-15
4.6 Projected Economic Activity Subject to Electric Car Impact 4-16
4.7 Projected Economic Activity Subject to Electric Car Impact 4-17
A.I Industrial Classification Listing 4-20
A.2 1951 Employment and Payroll Subject to Electric Car Impact 4-21
A.3 1956 Employment and Payroll Subject to Electric Car Impact 4-21
A.4 1962 Employment and Payroll Subject to Electric Car Impact 4-22
A.5 1965 Employment and Payroll Subject to Electric Car Impact 4-23
A.6 1967 Employment and Payroll Subject to Electric Car Impact 4-24
A.7 1969 Employment and Payroll Subject to Electric Car Impact 4-25
A.8 1971 Employment and Payroll Subject to Electric Car Impact 4-26
A.9 1972 Employment and Payroll Subject to Electric Car Impact 4-27
vii
-------
Tables (Cont.)
NO. PAGE
B.I Industrial Classification Listing 4-30
B.2 Number of Firms Subject to Electric Car Impact 4-31
B.3 Number of Firms Subject to Electric Car Impact 4-32
B.4 Number of Firms Subject to Electric Car Impact 4-33
B.5 Number of Firms Subject to Electric Car Impact 4-33
B.6 Number of Firms Subject to Electric Car Impact 4-34
B.7 Number of Firms Subject to Electric Car Impact 4-34
B.8 Number of Firms Subject to Electric Car Impact 4-35
C.I Automobile Population of the South Coast Air Basin 4-40
viii
-------
1 INTRODUCTION
This is the third in a series of reports projecting baseline con-
ditions (in the absence of electric cars) for use in a study of the im-
pacts of future electric car use. It follows assumptions and data pre-
sented in the first report of the series, Task Report 2, Population Pro-
jections for the Los Angeles Region. 1980-2000.
This economic baseline projection is organized into three sections.
Section 2 lists employment levels in the South Coast Air Basin. Section 3
details total personal income and discusses the regional product. The
area's business establishments, employment, and payroll which are subject
to electric car production and use are listed in Sec. 4. The detail
behind the development of Sec. 4 is available in the appendixes.
In drawing on existing forecasts, the baseline economic projections
set forth in Sees. 2 and 3 take population as one primary variable. Popu-
lation projections were developed from official sources in Task Report 2.
These projections are used in adjusting other projections and forecasts,
which generally were based on varying population forecasts. In addition,
factors based on the population projections are used to allocate appro-
priate fractions of whole-county data to those county portions included
in the South Coast Air Basin (SCAB).
4-1
-------
2 SOUTH COAST AREA EMPLOYMENT
A Department of Commerce study was the source of past and projected
employment data. The study presented economic activity for all states and
Standard Metropolitan Statistical Areas (SMSAs) by decade intervals from
1950 through 2000. Included were projections of population, employment,
and personal income, the latter projection being used in Sec. 3. Popula-
tion projections in the Commerce Study were based upon the high growth
assumptions of Series C, and consequently it was necessary to adjust them
according to the population projections presented in Task Report 2. Also
the data, which is organized by SMSAs, was adjusted to conform to the SCAB
boundaries. The results of this exercise are shown in Table 2.1.
The historical employment trends in Ref. 1 were based on employment
covered by Unemployment Insurance and then adjusted to include all civi-
lian employment. The projected trends assume an unemployment rate of 4
percent, and regional employment to population ratios moving toward the
national average. While a 4 percent unemployment rate is probably opti-
mistic, it is consistent with the rate for the first half of 1973, and
TABLE 2.1
SOUTH COAST AREA EMPLOYMENT (Thousands)1
1950 1959 1970 1980 1990 2000
Los Angeles 1,612 2,394 3,147 3,175 3,162 3,340
Orange 78 254 475 585 743 915
Riverside and
San Bernardino
Santa Barbara
Ventura
SCAB Total
126
25
43
1,884
230
37
74
2,989
274
60
115
4,071
364
65
146
4,335
411
77
186
4,579
449
91
230
5,025
4-2
-------
It conforms to the announced employment goals of the federal government.
A South Coast Association of Governments study projected an employment
figure about 15 percent higher for the year 2000. This study simply
assumed a constant employment to population ratio, however, failing to
account for industry or national trends as did Ref. 1.
4-3
-------
3 TOTAL PERSONAL INCOME
To present a more meaningful comparison of trends in personal income,
all dollar values have been adjusted to 1972 dollars. Presenting "constant"
rather than "current" dollars enables one to compare growth patterns
exclusive of inflationary effects. Normal practice calls for the use of
the consumer price index as a price deflator to show the constant buying
power of personal income. We, however, are specifically concerned with
the effects of cost variations of private transportation and the impacts
of these variations upon personal and business income. Thus, the Private
Transportation Index will be used throughout this report as the price
deflator utilized in calculating the "constant" or 1972 purchasing power
of any dollar amounts. The makeup of this index is approximately one-
third auto purchases, one-third auto services, and one-third petroleum
and parts cost. This makeup avoids any bias which might be caused by a
rapid increase in price of any one sector. Table 3.1 contains unpublished
Consumer Price Indices for Transportation for Los Angeles-Orange Counties.
Figures 3.1 and 3.2 further present this data.
We have used Ref. 1 as the source of projected personal income data.
The data is scaled to conform to our projected SCAB population. In addi-
tion, the values which were presented in current dollars were deflated
to constant 1972 dollars with the use of the Private Transportation Price
Index. Table 3.2 shows actual and projected average personal income,
and Table 3.3 shows actual and projected total personal income. The
source of the actual personal income data is an unpublished document
from the Bureau of Economic Analysis.
Personal income is defined to include income from all sources
Including labor, proprietors, and property income and transfer payments,
but excludes personal contributions for social security insurance. Total
personal income (Table 3.3) is related to Gross Regional Product (GRP)
and is a reasonable surrogate for use as an indicator of regional economic
activity. Total personal income differs from GRP in that it includes
4-4
-------
TABLE 3.1
CONSUMER PRICE INDICES, LOS ANGELES-ORANGE COUNTIES
Private Public
Transportation Transportation Transportation
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
54.5
56.9
62.7
65.2
64.6
64.2
65.2
67.9
69.6
73.2
73.6
76.0
78.7
78.7
81.7
83.5
83.4
85.0
87.7
90.1
93.2
97.7
100.0
58.4
61.5
66.2
68.5
67.2
66.2
67.4
70.4
72.0
76.1
75.2
76.1
78.7
78.7
81.8
83.7
84.0
85.0
87.6
90.1
93.2
97.8
100.0
38.0
38.0
46.8
50.5
52.6
54.9
55.8
56.9
59.1
59.7
67.6
79.0
81.3
81.2
81.2
81.4
82.1
86.1
90.2
91.5
94.4
97.3
100.0
Source: Bureau of Labor Statistics, unpublished data.
4-5
-------
1972-100
I960
1965
1970
Figure 3.1. Consumer Price Index for Public Transportation in Los Angeles
(1972=100)
1972=100r
80
70
60
30
1950
Figure 3.2. Consumer Price Index for Private Transportation in Los Angeles
(1972=100)
4-6
-------
TABLE 3.2
AVERAGE PERSONAL INCOME (1972 dollars)
Los Angeles
Orange
Riverside and
San Bernardino
Santa Barbara
Ventura
1950
3,159
2,572
2,299
3,267
2,602
Actual
1959
3,880
3,450
2,871
3,468
3,103
1970
5,196
4,326
3,496
3,680
3,153
Projected
1980
7,069
5,987
5,000
5,055
4,601
1990
8,838
7,669
6,582
6,719
6,104
2000
11,678
10,294
9,079
9,374
8,481
Bureau of Economic Research, Total Personal Income, Table 5.0.
TABLE 3.3
TOTAL PERSONAL INCOME (1972 $ millions)
Los Angeles
Orange
Riverside and
San Bernardino
Santa Barbara
Ventura
SCAB Total
1950
13,826
690
897
217
317
15,947
Actual
1962
25,181
3,017
2,022
427
713
31,360
1970
36,258
6,101
3,225
634
1,405
47,623
Projected
1980
51,006
10,621
5,049
909
2,245
69,830
1990
66,551
16,273
7,471
1,398
3,772
95,465
2000
90,706
24,789
11,330
2,184
6,284
135,293
Bureau of Economic Analysis, Total Personal Income, Table 5.0.
4-7
-------
transfer payments and does not include retained corporate profits and
corporate taxes. This difference does not inhibit its usefulness as an
2
economic indicator.
4-8
-------
4 BUSINESS IMPACTS
This section examines the business sectors which may be impacted
by lead-acid battery car production or use. For the impacted sectors,
data are presented on employment, payroll, and number of firms, and are
examined to show the relative importance of the industry within the
basin. Various hypotheses are utilized in projecting the data through
2000. It should be re-emphasized that these projections are largely
simple extensions of existing trends and are made to provide a basis for
Task Report 9, which shows the magnitude of the changes induced by electric
car use.
Table 4.1 shows the results of a thorough search of all Industry
3
classification descriptions. This search included all Standard Industrial
Classification (SIC) groupings. The detail available at the four-digit
SIC level groups can be distinguished by a very specific type of activity.
Not all automotive-related industries were selected, only those where there
was a possibility of some electric car impacts. Note that electrical manu-
facturing sectors, not necessarily automobile-related, were included
because they would be impacted by the regional manufacture or assembly
of electric cars. Also included are lead mining and manufacturing, on
a national scale only, because of the possible increased demand for lead-
acid batteries should electric cars become plentiful. Among those indus-
tries omitted were those relating to the supply of material for projected
but undeveloped battery types (e.g., lithium-sulfur). The SIC listings
which cover the processing of these materials contains too many other
materials to be useful.
For each of these industries, SCAB data were obtained on employment,
payroll, and number of firms for several years. County Business Patterns
4
(CBP) was used as the data source because it contains this data on a
four-digit SIC level. The data is based on Unemployment Insurance coverage
data and thus has some gaps in self-employment and government employment.
These gaps affect the total employment figures, but not the SICs in which
4-9
-------
TABLE 4.1
BUSINESSES SUBJECT TO ELECTRIC CAR IMPACTS'
Standard
Industrial
Classification
(SIC)
1031
3332
3621
3622
3691
3717
4911
5012
5013
5014
5092
5511
5521
5531
5541
7534
7538
7539
Description
Lead and Zinc Ore Mining
Lead Smelting and Refining
Electrical Motors and Generators Manufacturing
Electrical Industrial Controls Manufacturing
Storage Battery Manufacturing
Motor Vehicle and Motor Vehicle Parts Manufacturing
Electric Services
Motor Vehicle - Wholesale Distribution
Automotive Parts - Wholesale Distribution
Tires - Wholesale Distribution
Petroleum - Wholesale Distribution
New and Used Car Dealers
Used Car Dealers
Auto Supply Stores
Service Stations
Tire Retreading Shops
Automotive Repair Shops
Specialized Auto Repair Shops
4-10
-------
we are interested. Also, CBP is the only general, long-term data available
for four-digit SIC levels. The data were adjusted to the boundaries of the
SCAB and to 1972 dollars. The resulting tables are contained in Appendixes
A and B. Lead Mining and Refining is not included in the appendixes
because there is no activity in the SCAB. These are included in national
data shown further along in Table 4.4. Electric Services, which are
affected by electric car use, was not included in the CBP due to confi-
dentiality. Data for 1972 was obtained through surveys of the two major
electrical utilities.
Table 4.2 shows these industries grouped together into related clus-
ters, and the relative importance of each cluster to the basin's economy.
Some clusters are more susceptible to electric car impacts than others.
For example, petroleum sales, which are 1 percent of SCAB's employment
and 4.1 percent of its business units, would obviously be affected by
electric car usage. It is not so apparent that Vehicle Distribution and
sales would be affected as, of course, cars would still be sold. Table
4.3 shows the SCAB employment data obtained from the CBP. As mentioned,
CBP is not all-inclusive and thus there is some disparity with the employ-
ment as listed in Table 2.1. Since Table 2.1 better represents total
employment (e.g., includes government, self-employment, and agriculture),
it was used in calculating the relative importance of battery car related
employment. The payroll calculations are based on the average salary
implicit in Table 4.3 but scaled to total employment.
For additional reference, Table 4.4 shows the nation-wide activity
of the manufacturing concerns. The impacts upon refining and manufactur-
ing are less geographically restricted than are the impacts upon service
and trade Industries. Employment in the three electrical manufacturing
industries considered is 3 percent of the US electrical manufacturing
employment, and SCAB auto manufacturing employment is 3 percent of US
auto manufacturing employment.
4-11
-------
I
H«
ro
TABLE 4.2
RELATIVE IMPORTANCE OF AUTO-RELATED ACTIVITY
(SCAB 1971)
Vehicle & Parts Mfg.
(SIC 3717)
Petroleum—Wholesale &
Retail Sales
(SIC 5092 & 5541)
Auto Parts & Supplies
(SIC 5013, 5014, & 5531)
Auto Repair
(SIC 7534, 7538, 6, 7539)
Vehicle Distribution
(SIC 5012)
Vehicle Sales
(SIC 5511 & 5521)
Battery & Motor Mfg.
(SIC 3621, 3622, & 3691)
Emp loyment
19,210
39,703
22,606
9,448
5,606
37,679
4,910
Percent of
Area Total
Employment
0.5
1.0
0.6
0.2
0.1
0.9
0.1
3.4
Payroll,
$ million
233.5
192.7
175.4
65.0
57.2
362.2
44.5
Percent of
Area Total
Payroll
0.7
0.6
0.6
0.2
0.2
1.2
0.1
3.6
Number
of
Firms
62
6,694
1,938
2,444
147
1,182
65
Percent of
Area Firms
0.0
4.1
1.2
1.5
0.1
0.7
0.1
7.7
-------
TABLE 4.3
TOTAL* 1971 EMPLOYMENT, PAYROLL, AND NUMBER OF FIRMS4
Los Angeles
Orange
Riverside
San Bernardino
Santa Barbara
Ventura
SCAB Total
*
Data excludes
Employment
2,326,207
336,344
63,340
102,298
36,233
98,609
2,963,031
Payroll,
$ million Firms
18,482.5 123,760
2,482.5 19,595
401.6 5,100
700.0 7,836
250.6 2,816
403.3 4,843
22,720.5 163,950
government and self-employed persons.
TABLE 4,
4
TOTAL US ACTIVITY OF SELECTED INDUSTRIES (1971)
4
S'lC Employment
1031 9,412
(Lead Mining)
3332 3,672
(Lead
Smelting)
3621 93,064
(Motor Manuf.)
3622 47,941
(Electrical
Controls
Manuf.)
3691 20,432
(Battery
Manuf.)
3717 746,929
(Automobile
Manuf.)
SCAB Employment
as % of US
0
0
3.0
3.0
3.0
3.0
Payroll4 SCAB Payroll
(1972 $M) as % of US
79.3 0
31.8. 0
767.3 3.3
407.4 3.3
182.4 3.3
7,976.0 3.0
Firms4
94
24
396
536
221
1,945
4-13
-------
The historical data gathered in Appendixes A and B were extended
throughout the year 2000. Tables 4.5-4.7 summarize the results of these
extrapolations to the years 1980, 1990, and 2000. These projections are
used in Task Report 9 which deals with the variations in economic trends
caused by electric car usage.
Since these projections are used to show the relative importance of
changes in automobile-related activity in the SCAB, the ratio of histori-
cal industry sector data to SCAB automobile population levels was deter-
mined. Using least squares regression techniques, some curve (linear or
power) was fit and this ratio was extrapolated. Then, again using the
**
auto population projections found in this study, the ratio was recon-
verted to an absolute level of anticipated business activity. Thus these
economic projections are consistent with the level of automobile activity
anticipated elsewhere in this study. Appendix C contains the curves used
in these baseline projections as well as a discussion of data limitations
and a brief rationale for each of the industry projections.
Electrical manufacturing (SIC 3621 and 3622) is the exception to
this, trend extrapolation and are not shown in Appendix C. Neither of
these activities is directly related to current automobile activity. In
addition, activity in these industries is more dependent on national
trends than are the other more regional service industries. Thus projec-
tions for these industries were simply taken from a Department of Commerce
publication.
A check for reasonableness was made by taking the average salary in
1972 (Table A.9) and in 2000 (Table 4.7) and computing the annual compound
The transportation projections arrived at in Task Report 3 showed that
the number of miles per car per year is fairly constant and thus number
of cars is also a reasonable indicator of total miles driven.
The pertinent projections from Task Report 3 are reproduced in Appendix C.
4-14
-------
TABLE 4.5
PROJECTED ECONOMIC ACTIVITY SUBJECT TO ELECTRIC CAR IMPACT (1980)
SIC
3621
(Motor Manuf.)
3622
(Electrical
Controls
Manuf . )
3691
(Battery Manuf.)
3717
(Automobile
Manuf . )
5012
(Wholesale
Vehicle Dist.)
5013
(Wholesale
Parts Dist.)
5014
(Wholesale Tire
Dist.)
5092
(Wholesale
Petroleum Dist.)
5511 & 5521
(Retail Vehicle
Sales)
5531
(Auto Supply
Stores)
5541
(Service
Stations)
7534
(Tire Retreading
Shops)
7538 & 7539
(Auto Repair
Shops)
Employment
3,314
2,484
2,235
21,090
10,000
15,289
2,646
3,646
41,164
8,820
39,400
470
10,584
Payroll,
1972 $ millions
30.0
22.3
24.1
339.2
105.8
129.4
22.6
45.3
394.0
70.5
182.3
2.9
80.5
Number of
Firms
48
44
22
223
162
1,000
182
270
1,058
1,058
6,175
41
2,470
4-15
-------
TABLE 4.6
PROJECTED ECONOMIC ACTIVITY SUBJECT TO ELECTRIC CAR IMPACT (1990)
SIC
3621
(Motor Manuf.)
3622
(Electrical
Controls
Manuf.)
3691
(Battery Manuf.)
3717
(Automobile
Manuf.
5012
(Wholesale
Vehicle Dist.)
5013
(Wholesale
Parts Dist.)
5014
(Wholesale
Tire Dist.)
5092
(Wholesale
Petroleum Dist.)
5511 & 5521
(Retail Vehicle
Sales)
5531
(Auto Supply
Stores)
5541
(Service
Stations)
Employment
3,512
2,732
2,895
21,568
15,485
18,851
3,366
3,366
40,397
10,166
43,090
Payroll,
1972 $ millions
31.8
24.5
34.3
399.4
168.3
175.0
28.2
57.9
424.1
85.5
208.7
Number of
Firms
51
48
22
289
153
1,009
222
309
942
1,212
5,386
7534 451 2.7 27
(Tire Retreading
Shops)
7538 & 7539) 12,793 104.4 2,558
(Auto Repair
Shops)
4-16
-------
TABLE 4.7
PROJECTED ECONOMIC ACTIVITY SUBJECT TO ELECTRIC CAR IMPACT (2000)
SIC
3621
(Motor Manuf.)
3622
(Electrical
Controls
Manuf.)
3691
(Battery Manuf.)
3717
(Automobile
Manuf.)
5012
(Wholesale
Vehicle Dist.)
5013
(Wholesale
Parts Dist.)
5014
5092
(Wholesale
Petroleum Dist.)
5511 & 5521
(Retail Vehicle
Sales)
5531
(Auto Supply
Stores)
5541
(Service
Stations)
7534
(Tire Retreading
Shops)
7538 & 7539
Employment
3,723
3,005
3,800
21,300
22,800
22,422
4,560
3,420
41,800
11,550
46,360
456
15,200
Payroll,
1972 $ millions
33.7
27.0
45.6
511.2
250.8
224.2
35.7
64.6
455.0
104.9
237.1
2.3
129.2
Number of
Firms
54
53
22
364
137
988
258
350
798
1,368
4,560
23
2,584
(Auto Repair
Shops)
4-17
-------
growth rate for each industry. The rates ranged from plus three to minus
one, and the mode was about one. The highest rate, for motor vehicle
manufacturing, was 0.4% below the 3.6% annual productivity gains for that
industry sector over the past 13 years. Other unpublished, non-releas-
able productivity data obtained from the Bureau of Labor Statistics showed
projected salary gains trailed productivity gains by about half. This
seems reasonable when noting that the service sector is less heavily
unionized than is motor vehicle manufacturing. The annual compound salary
increase, in 1972 dollars, is 2% for the total activity projected.
4-18
-------
APPENDIX A
EMPLOYMENT AND PAYROLL DATA
4-19
-------
TABLE A.I
INDUSTRIAL CLASSIFICATION LISTING3
Standard Industrial _ . ...
Classification (SIC) Description
3621 Electrical Motors and Generators Manufacturing
3622 Electrical Industrial Controls Manufacturing
3691 Storage Battery Manufacturing
3717 Motor Vehicle and Motor Vehicle Parts Manufac-
turing
5012 Motor Vehicle—Wholesale Distribution
5013 Automotive Parts—Wholesale Distribution
5014 Tires—Wholesale Distribution
5092 Petroleum—Wholesale Distribution
5511 New and Used Car Dealers
5521 Used Car Dealers
5531 Auto Supply Stores
5541 Service Stations
7534 Tire Retreading Shops
7538 Automotive Repair Shops
7539 Specialized Auto Repair Shops
4-20
-------
TABLE A.2
1951 EMPLOYMENT AND PAYROLL SUBJECT TO ELECTRIC CAR IMPACT
SIC
5511
5521
5531
5541
Los Angelf
Employment
19,066
1,658
3,581
13,657
is
SM
140.8
10.0
18.9
50.9
Orange
Employment
1,116
19
202
648
$M
7.3
0.2
1.0
2.1
Riverside
Employment
495
27
63
362
San Bernardino
$H
2.9
0.2
0.3
1.3
Employment
856
48
165
684
SM
5.9
0.2
0.8
2.4
Santa Barbara
Employment
345
19
60
241
$M
2.1
0.2
0.3
0.8
Ventura
Employment
579
26
67
251
SCAB Total
SM
3.6
0.2
0.3
0.8
Efflp loyrnen t
22,457
1,797
4.138
15,543
SM
162.6
11.0
21.6
58.3
Normalized to 1972 dollars.
TABLE A.3
1956 EMPLOYMENT AND PAYROLL SUBJECT TO ELECTRIC CAR IMPACT
Los Angeles
SIC
5511
5521
5531
5541
Employment
23,948
2,133
2,624
18,946
SM
193.2
13.5
23.4
79.8
Orange
Employment
1,662 ,
109
176
1,381
Riverside
SM
12.0
0.6
1.0
5.6
Employment
536
34
76
616
SM
5.3
0.1
0.4
2.4
San Bernardino
Employment
916
128
214
1,278
SM
8.9
0.7
1.2
5.2
Santa Barbara
Employment
357
19
62
278
SM
2.4
0.1
0.3
1.0
Ventura
Employment
784
55
97
438
SCAB Total
SM
5.2
0.3
0.6
1.6
Employment
28,203
2,478
3,244
22,937
SM
227.0
15.3
26.9
95.6
*-
I
N>
Normalized to 1972 dollars.
-------
to
TABLE A.4
*
1962 EMPLOYMENT AND PAYROLL SUBJECT TO ELECTRIC CAR IMPACT
Los Angeles
SIC
3621
3622
3691
3717
5012
5013
5014
5092
5511
5521
5531
5541
7534
7538
7539
Employment
2.559
451
687
16,180
2,065
7,726
1,527
3,583
21,837
2,220
4,076
20,024
500
4,066
1,238
$M
19.7
2.8
5.0
139.0
16.9
54.8
13.3
28.8
188.1
15.6
26.3
93.8
3.3
25.8
7.9
Orange
Employment
891
352
219
2.505
171
407
2,343
389
123
Riverside
$M
7.5
—
2.4
2.0
21.3
1.0
2.7
9.9
2.5
1.4
Employment
89
79
833
67
147
806
144
28
SM
0.5
0.5
6.5
0.4
0.8
3.3
0.8
0.1
San Bernardino
Employment
255
.
149
1,301
113
240
1,668
76
275
35
SM
1.5
1.0
10.2
0.6
1.4
6.4
0.5
1.5
0.1
Santa Barbara
Employment
33
26
400
22
74
400
67
SM
0.3
0.3
3.3
0.1
0.5
1.5
0.4
Ventura
Employment
•
92
48
783
76
117
706
78
31
SCAB Total
SM
0.6
0.1
5.8
0.5
0.8
2.7
4.2
0.1
Employment
3,450
451
687
16,180
2,065
8,547
1.527
4,104
27,659
2,669
5.061
25,947
576
5,019
1.424
SM
27.2
2.8
5.0
139.0
16.9
60.1
13.3
32.7
235.2
18.2
32.5
117.6
3.8
35.2
9.6
Normalized to 1972 dollars.
-------
TABLE A.5
1965 EMPLOYMENT AND PAYROLL SUBJECT TO ELECTRIC CAR IMPACT
Los Angeles
SIC
3621
3622
f691
3717
5012
5013
5014
5092
7534
7538
7539
Employment
2,276
945
611
17,086
2,973
8,379
1,349
3,162
470
3,989
1,483
SM
17.0
6.7
4.9
150.0
30.0
61.1
10.5
27.1
~t.lt
24.5
9.5
Orange Riverside San Bernardino Santa Barbara Ventura SCAB Total
Employment $M Employment SM Employment $M Employment $M Employment $.M Employment
996
482
294
205
618
202
—
519
136
8.4
4.1
2.9
1.9
4.3 267
1.6 65
—
3.2 185
0.9 96
3,272
945
!,093
17,380
3,178
1.6 52 0.3 9,316
1,349
0.4 213 1.5 55 0.5 73 0.6 3,770
57 0.3 — 527
1.0 340 1.9 73 0.4 119 0.8 5,225
0.3 57 0.3 19 0.1 20 0.1 1,811
SM
25.4
6.7
9.0
152.9
31.9
67.3
10.5
31.7
3.7
31.8
11.2
Normalized to 1972 dollars.
N>
U>
-------
N>
•C-
TABLE A.6
1967 EMPLOYMENT AND PAYROLL* SUBJECT TO ELECTRIC CAR IMPACT
Los Angeles
SIC
3621
3622
3691
3717
5012
5013
5014
5092
5511
5521
5531
5541
7534
7538
7539
Employment
2,856
2,427
1,079
17,698
3,321
3,898
1,257
3,340
27.060
1 ,584
5,244
24,117
461
3,739
1,378
$M
24.1
19.6
9.1
149.8
30.9
68.0
10.5
30.2
7.52.0
11.9
37.5
110.8
3.4
24.8
9.6
Orange
Employment
1,557
654
278
617
97
257
3,717
169
607
4,510
37
523
174
Rtverside
SM
13.4
—
5.8
2.7
4.1
0.7
2.6
36.7
1.1
4.4
20.0
0.2
3.5
1.3
Employment
—
263
78
1,147
58
261
1,225
208
30
SM
—
1.6
0.5
9.4
0.2
1.6
4.7
1.3
0.2
San Bernardino
Employment
--.
—
__-
9.3
373
256
1,867
140
311
2,165
53
310
73
SM
—
—
0.8
2.6
2.1
15.3
0.7
1.9
8.2
0.4
1.9
0.5
Santa Barbara
Employment
—
—
__-
50
51
581
86
637
88
23
SM
— -
— .
—
0.4
0.5
4.7
0.6
2.1
0.5
0.1
Ventura
Employment
-._-
114
88
988
109
203
1,160
112
13
SCAB Total
SM
—
_-_
___
...
o.e
---
0.7
8.0
0.7
1.3
4.4
0.8
0.1
Employment
4,413
2,427
1,733
17,698
3,692
10,315
1,354
4,079
35,360
2,060
6.712
33,814
551
4,980
1,691
$M
37.5
19.6
14.9
149.8
34.4
77.5
11.2
36.6
326.1
14.6
47.3
150.2
4.0
32.8
11.8
Normalized to 1972 dollars.
-------
TABLE A.7
*
1969 EMPLOYMENT AND PAYROLL SUBJECT TO ELECTRIC CAR IMPACT
Los Angeles
SIC
3621
3622
3691
3717
5012
5013
5014
5092
7534
7538
7539
Employment
2,747
2,303
683
18,612
4,303
10,804
2.170
3,391
483
4,156
2,540
SM
26.5
20.2
5.7
170.6
42.3
85.4
19.1
32.5
3.6
28.4
18.8
Orange
Employment
1,270
905
266
367
714
100
—
—
616
314
Riverside
SM Employment
11.8
7.8
2.4
4.4
5.1 345
0.7
82
4.1 1?0
2.4 61
SM
-__
2.1
0.5
-T-
1.0
0.4
San Bernardino
Employmen t
96
94
445
217
330
93
SM
0.8
0.7
3.2
1.9
2.0
0.6
Santa Barbara
Employment
—
62
137
92
28
SM
—
0.4
1.2
0.7
0.2
Ventura
Employment
169
81
130
41
SCAB Total
$M
1.1
0.4
0.5
0.2
Employment
4,017
2,303
1,588
18,974
4,764
12,539
2,270
3,908
483
5,494
3,077
SM
38.3
20.2
13.5
173.8
47.4
97.3
19.8
36.5
3.6
36.7
22.6
Normalized to 1972 dollars.
i
fO
l/l
-------
Is)
TABLE A.8
1971 EMPLOYMENT AND PAYROLL* SUBJECT TO ELECTRIC CAR IMPACT
Los Angeles
SIC
3621
3622
3691
3717
5012
5013
5014
5092
5511
5521
5531
5541
7534
7538
7539
Employment
1,992
1,577
615
18,328
5,090
10,231
1,931
3,699
25,889
1,250
6,096
23,964
465
3,928
2,824
SM
18.1
14.4
5.3
225.6
52.3
81.3
17.5
35.7
254.7
9.0
46.2
105.4
3.4
27.2
20.4
Orange
Employment
1.026
681
1 ,018
680
314
825
87
289
5,143
135
1,214
5,860
45
681
382
Riverside
SM
9.3
5.9
9.7
6.0
3.3
5.7
0.7
2.3
51.4
0.9
8.6
24.7
0.3
4.7
2.8
Employment
- —
— _
—
102
391
—
66
1,310
_._
338
1,325
—
232
94
SM
™
—
0.8
2.4
0.4
11.8
2.4
5.1
1.3
0.5
San Bernardino
Employment
150
202
100
441
239
1,980
68
454
2,185
—
354
117
SM
— _
„-
1.2
1.9
0.8
3.3
2.2
17.4
0.5
3.0
8.4
2.2
0.7
Santa Barbara
Employment
109
61
61
559
—
140
677
92
37
SM
0.9
—
0.5
0.5
5.1
0.9
2.4
0.2
0.2
Ventura
Employment
170
141
1,271
74
227
1,197
143
54
SCAB Total
SM
—
__.
1.2
1.0
11.0
0.4
1.7
4.6
0.8
0.3
Employment
3,127
2,258
1,783
19,210
5,606
12.119
2,018
4,495
36,152
1,527
8,469
35,208
510
5,430
3,508
SM
28.3
20.3
16.2
233.5
57.2
94.4
18.2
42.1
351.4
10.8
62.8
150.6
3.7
36.4
24.9
Normalized to 1972 dollars.
-------
TABLE A.9
1972 EMPLOYMENT AND PAYROLL* SUBJECT TO ELECTRIC CAR IMPACT
SIC
3621
3622
3691
3717
**
5012
5013
5014
5042
5511
5521
5531
5541
7534
75 IK
7519
L
os Angeles
Employment SM
1
1
16
21
5
10
1
3
26
1
6
24
tt
)
,960
,466
604
, 13!
,81,7
,763
, 300
.895
,46(1
,358
,2K)
,058
,941
425
,014
,''4:,
18. 1
1 l.i
161 .6
232.0
60. h
85.5
IK. |
35.7
271.1,
10.3
47. 7
109. H
3.2
28. 7
2 '.6
tiraiij;!- Rivet •; i
Employment ?M Ei^p 1 •''ynvnt
961 9.S
71,', i, .;•
441 9.5
514 3.7
1,395 15.4 till
245 <. 7
498 7. '/' is i
1»1 II. K
VI 2 2.V nit
5.i4(i 58.2 !.(»<•
198 1.4 17
1 , 1 89 8.6 1 S'l
6,197 24.4 1.314
51 0.4
823 6.1 20;
VH i.i. 88
•1r San Bernard i no
SM Kmp 1 oyraen t $M
—
1.0 1 , 146 11.4
108 0.9
:.-i 52.: ).:
.-.
ii . 4 277 2 . 4
12.4 1 , '1 ] i, I 7 . H
0.2 11" 0.8
2.0 481 i.!
5. i 2.021) 8.1
._
!.i )4B 2.1
.'!.i 1 IS ;).s
Santa Barbara
Ventur
Employment $N Eraplo>-ment.
—
/'i
.._
72
„.
70
58 I
32
1 4 .'.
074
...
113
4l)
--
0.
—
0.
--
0.
5.
0.
!.
2
--
0.
-
0
-
5
-
h
5
•>
0
4
-
7
"*
—
66t>
,„
189
—
116
1 , 330
44
29)
1,250
...
122
64
.1 SCAB Total
SM
—
7.0
l.i
—
0.9
12.6
0.4
2.2
4.H
—
0.8
0.4
Employment
2,921
2,235
1,545
16,850
25,454
6,166
12,464
1,996
4,315
37,020
1,720
8,524
36,396
476
5,632
3,753
$M
27.6
19.6
15.3
165. 3
269.6
65.2
101.5
19.1
42.9
378.6
13.3
65.2
154.8
3.6
39.7
30.2
**
Normalized to 1972 dollars.
Employment data was provided by Southern California Edison and the Los Angeles Department of Water
and Power (for power only) . Salary was estimated to be proportional to the salary of the total
utilities sector.
J>
ro
-------
APPENDIX B
NUMBER OF FIRMS SUBJECT TO ELECTRIC CAR IMPACTS
4-29
-------
TABLE B.I
INDUSTRIAL CLASSIFICATION LISTING3
Standard Industrial _ ...
Classification (SIC) Description
3621 Electrical Motors and Generators Manufacturing
3622 Electrical Industrial Controls Manufacturing
3691 Storage Battery Manufacturing
3717 Motor Vehicle and Motor Vehicle Parts
Manufacturing
5012 Motor Vehicle—Wholesale Distribution
5013 Automotive Parts—Wholesale Distribution
5014 Tires—Wholesale Distribution
5092 Petroleum—Wholesale Distribution
5511 New and Used Car Dealers
5521 Used Car Dealers
5531 Auto Supply Stores
5541 Service Stations
7534 Tire Retreading Shops
7538 Automotive Repair Shops
7539 Specialized Auto Repair Shops
4-30
-------
TABLE B.2
NUMBER OF FIRMS SUBJECT TO ELECTRIC CAR IMPACT (1951)
SIC
5511
5521
5531
5541
Los
Angeles
604
275
371
3337
Orange
66
5
30
210
Riverside
39
5
12
124
San
Bernardino
62
14
24
224
Santa
Barbara
24
1
8
78
Ventura
47
5
11
101
SCAB
Total
847
305
456
4074
NUMBER OF FIRMS SUBJECT TO ELECTRIC CAR IMPACT (1956)
SIC
5511
5521
5531
5541
Los
Angeles
595
509
355
4083
Orange
72
29
29
392
Riverside
40
8
13
185
San
Bernardino
65
39
34
351
Santa
Barbara
19
17
8
84
Ventura
47
14
11
130
SCAB
Total
838
616
450
5225
4-31
-------
TABLE B.3
NUMBER OF FIRMS SUBJECT TO ELECTRIC CAR IMPACT (1962)
3621
3622
3691
3717
5012
5013
5014
5092
5511
5521
5531
5541
7534
7538
7539
30
23
25
51
118
672
74
97
576
485
442
4568
88
1203
309
7
2
6
36
23
88
54
78
622
129
29
1
10
24
42
13
26
219
46
8
31
26
50
38
41
445
15
107
13
7
10
15
8
13
107
23
10
19
41
15
20
176
28
11
37
23
27
52
124
766
74
199
812
613
620
6137
103
1536
370
4-32
-------
SIC
TABLE B.A
NUMBER OF FIRMS SUBJECT TO ELECTRIC CAR IMPACT (1965)
Los n ...... San Santa „ SCAB
, Orange Riverside „ ., „ . Ventura _ .. .
Angeles ° Bernardino Barbara Total
3621
3622
3691
3717
5012
5013
5014
5092
7534
7538
7539
SIC
3621
3622
3691
3717
5012
5013
5014
5092
5511
5521
5531
5541
7534
7538
7539
31
34
18
151
150
791
79
74
92
1200
416
NUMBER
Los
Angeles
35
42
17
175
126
760
80
67
574
343
493
4596
75
1064
415
11
4
7
14
75
28
164
50
OF FIRMS
Orange
10
4
12
82
14
24
96
64
91
888
11
163
65
4
14
23
54
8
TABLE
SUBJECT TO
Riverside
19
21
43
13
33
291
50
11
31
9
115
18
B.5
ELECTRIC
San
Bernardino
4
43
26
61
33
56
476
8
100
17
9
11
29
10
CAR IMPACT
Santa
Barbara
7
12
18
13
129
28
8
15
20
45
13
(1967)
Ventura
15
22
40
15
31
244
38
11
42
34
22
162
164
904
79
187
101
1607
515
SCAB
Total
45
42
21
175
142
926
94
172
832
468
717
6624
94
1443
517
4-33
-------
TABLE B.6
NUMBER OF FIRMS SUBJECT TO ELECTRIC CAR IMPACT (1969)
,,T_ Los „ .. San Santa „ ^ SCAB
SIC , Orange Riverside „ .. _ _. „„ Ventura _, .. .
Angeles e Bernardino Barbara Total
3621
3622
3691
3717
5012
5013
5014
5092
7534
7538
7539
STC
3621
3622
3691
3717
5012
5013
5014
5092
5511
5521
5531
5541
7534
7538
7539
35
26
15
179
130
778
125
141
58
.1100
628
NUMBER
Los
Angeles
34
30
14
34
125
771
121
137
560
257
520
4334
55
1069
671
8
5
14
16
82
16
28
166
101
OF FIRMS
Orange
8
li
6
22
13
92
15
31
123
36
143
952
10
183
118
20
23
50
23
2
4
5
54
29
100
32
TABLE B.7
SUBJECT TO ELECTRIC
„. ., San
Riverside ,
Bernardino
4
22
21
43
40
282
56
33
1
6
5
51
27
62
27
74
476
104
38
2
8
15
26
15
CAR IMPACT
Santa
Barbara
2
9
13
19
14
139
28
14
---
5
25
22
45
20
(1971)
Ventura
26
25
43
12
40
257
44
21
45
26
22
197
156
967
141
258
58
1487
819
SCAB
Total
44
41
21
62
147
971
136
254
850
332
831
6440
65
1484
895
4-34
-------
TABLE B.8
NUMBER OF FIRMS SUBJECT TO ELECTRIC CAR IMPACT (1972)
SIC
3621
3622
3691
3717
5012
5013
5014
5092
5511
5521
5531
5541
7534
7538
7539
Los
Angeles
33
29
14
177
127
749
121
134
568
247
551
4229
48
1074
667
Orange
9
11
6
18
12
99
15
32
132
39
138
969
11
199
120
Riverside
4
6
24
18
44
7
40
278
60
31
San
Bernardino
2
4
51
25
61
33
78
460
107
37
Santa
Barbara
3
2
11
12
20
7
14
138
32
17
Ventura
26
21
41
12
45
258
42
22
SCAB
Total
45
40
22
201
149
960
136
242
866
345
1211
6332
59
1514
894
4-35
-------
APPENDIX C
BASELINE PROJECTIONS FOR IMPACTED INDUSTRY SECTORS
The following is a brief rationale for most of the curve fits.
This documentation deals with topics such as the industry relationship
to the automobile industry, business characteristics (selling or manu-
facturing), and growth patterns. In many of these, the reader should
recall that miles per year per car is fairly constant overtime. Thus
*
the number of autos (Table C.I and Fig. C.I) is a reasonable indicator
when looking at a service or auto usage connected industry, as it is a
substitute for total miles.
The data used in these projections are given at the four-digit SIC
level of detail. Data are not available at finer detail except through
area survey. While a four-digit SIC code is narrowly defined there are
inclusions which are not germane to this study. For example, tire stores
may feature an expanding array of spare parts and repair services not
related to tires. This would result in distorted employment projections.
These data deficiencies are not a severe limitation on the outcome of
the projections.
C.I SIC 3691 (STORAGE BATTERY—FIG. C.2)
A spokesman for the Lead Industries Association indicated that
batteries are generally manufactured in the region where they are solid,
as lead is less bulky to ship than are batteries. Thus this is a good
indicator of the regional activity. The projected payroll trend is curved
because the straight line projection gave an unreasonably high annual
average salary increase. The curves for employment and number of firms
were both excellent statistical fits.
Figures and tables for this appendix are given following the text.
4-37
-------
C.2 SIC 3717 (AUTOMOBILE MANUFACTURING—FIG. C.3)
Here, payroll and employment are best related to the number of new
autos. The number of firms Is more stable, and is related to the total
auto population. The first two curves are the best statistical fit and
the employment curve is adjusted upward slightly as it approaches zero.
The steep increase in payroll is plausible given the strength of the UAW,
and the annual average salary increase here closely approximates the his-
torical productivity growth of auto manufacturing.
C.3 SIC 5012 (VEHICLE DISTRIBUTION—FIG. C.4)
The statistical fit here is excellent. It is reasonable that the
number of firms per auto should be decreasing, indicating a higher volume
per distributor, while employment per auto is fairly constant.
C.4 SIC 5013 (PARTS DISTRIBUTION—FIG. C.5)
Again, there is a good statistical fit. It is reasonable to assume
a higher volume per firm.
C.5 SIC 5014 (TIRE DISTRIBUTION—FIG. C.6)
The statistical fit here for payroll is poor. The curve was drawn
with a slightly flatter slope than the statistical fit indicated. It
seems reasonable that employment should climb slightly as tire stores
become less specialized and sell more tire related items. Also, it seems
reasonable that a recent increase in tire competition and in the number
of high volume company stores should cause a drop in firms per auto.
C.6 SIC 5092 (PETROLEUM DISTRIBUTION—FIG. C.7)
The statistical fit here for payroll and number of firms is poor.
However, they seem reasonable when considering that the service area of
a bulk plant distributor is controlled by the major oil companies.
C.7 SIC 5511 AND 5521 (CAR DEALERS—FIG. C.8)
The statistical fit is good for employment and number of firms;
less so for payroll. The decline here in number of firms, and to a lesser
4-38
-------
extent, payroll, should not be contrasted with the situation in motor
vehicle distribution. Motor vehicle distributors are franchised whole-
salers while car dealers include used car dealers; a less stable industry.
C.8 SIC 5531 (AUTO SUPPLY STORES—FIG. C.9)
The statistical fit here is only fair but acceptable without any
adjustment of the curves.
C.9 SIC 5541 (SERVICE STATIONS—FIG. C.10)
The fit for payroll and employment is fair. Due to the small average
employment of service stations, it is reasonable that employment should
decline with the number of stations.
C.10 SIC 7534 (TIRE RETREADING—FIG. C.ll)
Curves were found with a good statistical fit for all three indica-
tors. The number of firms, however, was projected to reach zero and so
that curve was redrawn in a less statistically sound but more logical
form.
C.ll SIC 7538 AND 7539 (AUTO REPAIR SHOPS—FIG. C.12)
The statistical fit here is only fair, but it seems reasonable that
shops should increase both in volume and in employment as cars become
increasingly complex. These two SIC categories were combined as they
are related and the statistical fit is better when they are combined.
Increasing employment is a logical result of the increasing complexity
of cars.
4-39
-------
*>
o
TABLE C.I
AUTOMOBILE POPULATION OF THE SOUTH COAST AIR BASIN
County
Los Angeles
Orange
San Bernardino
Santa Barbara
Riverside
Ventura
Total SCAB Area
10-Year Annual
Growth Rate,
Percent
1950
1,705,694
93,106
91,342
26,180
47,442
41,930
2,005,694
Actual
1960
2,747,570
309,392
172,391
41,171
88,508
76,852
3,435,884
5.5
1970
3,626,450
748,217
265,492
78,975
160,523
180,746
5,060,403
3.9
1980
4,033,605
970,378
339,726
93,425
194,495
249,084
5,880,713
1.5
Projected
1990
4,442,869
1,199,212
411,312
118,155
228,543
332,753
6,732,844
1.3
2000
4,893,368
1,406,447
486,940
138,137
257,174
418,665
7,600,731
1.2
Source: California Department of Motor Vehicles.
-------
TOTAL SCAB
CO
i
o
SAN BERNARDINO
VENTURA
RIVERSIDE
]SANTA BARBARA
1960
1970
1980
1990
2000
Figure C.I. Automobile Population in the South Coast Air Basin, by County
4-41
-------
30
20
10
NUMBER OF FIRMS (ABSOLUTE)
60
50
40
30
20
PAYROLL (DOLLARS PER 10 AUTOS IN 1972 DOLLARS)
10
EMPLOYMENT (PER 10,000 AUTOS)
1960
1970
1980
1990
2000
Figure C.2. SIC 3691: Storage Battery Manufacturing
4-42
-------
40
30
10
NUMULR OF FIRMS (PLR 1,000,000 AUTOS)
70
60
50
40
30
PAYROLL (DOLLARS PER 10 NLW AUTOS IN 1972 DOLLARS)
40
30
20
EMPLOYMENT PER 1,000 NEW AUTOS)
1960
1970
1990
2000
Figure C.3. SIC 3717: Motor Vehicle and Motor Vehicle Parts Manufacturing
4-43
-------
40
30
NUMBER OF FIRMS
(PER 100,000 NEW AUTOS)
<=>
<=>
20
40
30
PAYROLL (DOLLARS PER AUTO
IN 1972 DOLLARS)
20
10
1970
EMPLOYMENT (PER 1,000 AUTOS)
1980
1990
2000
Figure C.4. SIC 5012: Motor Vehicle Distribution
4-44
-------
30
20
NUMBER OF FIRMS (PER 100,000 AUTOS)
10
30
20
EMPLOYMENT (PER 10,000 AUTOS)
PAYROLL (DOLLARS PER AUTO IN 1972 DOLLARS)
10
1960
1970
1980
1990
2000
Figure C.5. SIC 5013: Automotive Parts Distribution
4-45
-------
50
40
30
20
PAYROLL (DOLLARS PER 10 AUTOS IN 1972 DOLLARS)
NUMBER OF FIRMS (PER 1,000,000 AUTOS;
10
EMPLOYMENT (PER 10,000 AUTOS)
1960
1970
1980
1990
2000
Figure C.6. SIC 5014: Tire Distribution
4-46
-------
90
80
PAYROLL •
(DOLLARS PER 10 AUTOS IN 1972 DOLLARS)
70
60
50
NUMBER OF FIRMS (PER 1,000,000 AUTOS)
40
30
20
10
EMPLOYMENT (PER 10,000 AUTOS)
1960
1970
1980
1990
2000
Figure C.7. SIC 5092: Petroleum Distribution
4-47
-------
90
80
70
60
50
40
30
20
10
PAYROLL (DOLLARS PER AUTO
IN 1972 DOLLARS)
NUMBER OF FIRMS
(PER 100,000 AUTOS)
0
1950
I
(PER 1,000 AUTOS)
I I I
1960 1970 1980 1990 2000
Figure C.8. SIC 5511 and 5521: Car Dealers
4-48
-------
30
20
NUMBER OF FIRMS
(PER 100,000 AUTOS)
10
20
10
EMPLOYMENT
(PER 10,000 AUTOS)
PAYROLL (DOLLARS PER AUTO
IN 1972 DOLLARS)
1950
1960
1970
.1980
1990
2000
Figure C.9. SIC 5531: Auto Supply Stores
4-49
-------
80
70
EMPLOYMENT
(PER 10,000 AUTOS)
60
50
40
30
20
:-AVROLL (DOLLARS PER AUTO
IN 1972 DOLLARS)
OF FIRMS
(PER 10,000 AUTOS)
1950
1960
1990
2000
Figure C.10. SIC 5541: Service Stations
4-50
-------
20
10
PAYROLL (DOLLARS PER 10 AUTOS z
IN 1972 DOLLARS)
30 i-
20
10
NUMBER OF FIRMS
(PER 1,000,000 AUTOS)
EMPLOYMENT
(PER 100,000 AUTOS)
1960
1970
1980
1990
2000
Figure C.ll. SIC 7534: Tire Retreading Shops
4-51
-------
60
r*
50
40
NUMBER OF FIRMS
(PER 100,000 AUTOS)
30
20
10
EMPLOYMENT
(PER 10,000 AUTOS)
PAYROLL (DOLLARS PER AUTO IN
1972 DOLLARS)
1960
1970
1980
1990
2000
Figure C.12. SIC 7533 and 7539: Auto Repair Shops
4-52
-------
REFERENCES
1. OBERS Projections: Economic Activity in the U.S., U.S. Department
of Commerce, Bureau of Economic Analysis, Washington, D.C., 1972.
2. H. Noarse, Regional Economics, McGraw-Hill, 1968.
3. Standard Industrial Classification Manual, Office of Management and
Budget, 1972.
4. County Business Patterns, California Department of Commerce, 1951,
1957, 1962, 1965, 1967, 1969, and 1972.
5. U.S. Industrial Outlook 1971, U.S. Department of Commerce, 1971.
6. Indices of Output Per Man-Hour: Selected Industries, 1947-1970.
Bureau of Labor Statistics Bulletin 1962, 1971.
4-53
-------
4-54
-------
TASK REPORT 5
ELECTRIC ENERGY PROJECTIONS
FOR THE LOS ANGELES REGION, 1980-2000
A.R. Sjovoid
-------
ABSTRACT
Electric energy consumption in Southern California and the South
Coast Air Basin is expected to grow at slower rates in the future than
have been experienced in the past. Per capita consumption is expected
to grow at an average annual rate of 4.3 percent. Adding the 0.5 percent-
per-year expected population growth rate (which is lower than earlier
projections), overall electric consumption is expected to grow at an average
annual rate of 4.8 percent. Much electric energy in the South Coast Air
Basin will be generated by fossil-fueled power plants—primarily fuel
oils—until the year 2000 when nuclear fission powered facilities should
constitute a significant fraction of total generating capacity. The
growth in electric energy consumption will probably be accompanied by
rising prices, the net rise depending on basic fuel scarcities and whatever
inflation persists.
Available off-peak generating capacity within the South Coast Air
Basin should be ample now and in the future to recharge in excess of a
million electric cars daily, and so additional generating capacity will
probably not be required. However, off-peak generation will during peak
demand seasons most likely be associated with the burning of additional
fuel oil in the older plants within the basin; the newer out-of-basin
fossil plants and nuclear plants will be base-loaded but will provide
some of the off-peak recharge energy during off-peak seasons.
-------
CONTENTS
SECTION PAGE
ABSTRACT i
1 INTRODUCTION 5-1
2 FORECASTS OF TOTAL US ENERGY SUPPLY AND DEMAND 5-2
2.1 Forecast of US Energy Demand 5-3
2.2 Forecast of US Energy Supplies 5-5
2.3 Power Plant Efficiency Trends 5-10
2.4 Estimated Future Prices of Energy Sources 5-14
2.5 Clean Power Generation Technology 5-16
3 BASELINE ENERGY FORECASTS FOR SOUTHERN
CALIFORNIA AND THE SOUTH COAST AIR BASIN 5-22
3.1 Energy Supply and Demand in Southern
California 5-23
3.2 Electrical Energy and Power in the South
Coast Air Basin 5-27
4 AVAILABILITY OF ELECTRIC POWER AND ENERGY FOR
ELECTRIC CARS 5-42
REFERENCES 5-47
iii
-------
ILLUSTRATIONS
NO. PAGE
2.1 Projected US Total Energy Demand 5-4
2.2 Projected Primary Energy Supply for Total US Compared
with Projected Demand 5-6
2.3 Historical Trend of Overall Thermal Efficiency of Power
Generation by Fossil Fuel Plants 5-11
2.4 Estimated Trends for Efficiency of Electric Power Genera-
tion from Fossil Fuels, 1972-1990 5-13
2.5 Projected Average Prices of Primary Energy Sources,
Case II Conditions 5-15
3.1 Primary Energy Supply and Demand, Southern California 5-24
3.2 Energy Demand by End Uses, Southern California 5-24
3.3 Electrical Energy Consumption, Southern California by
Users (SRI) 5-26
3.4 Electrical Energy Production, Southern California by
Energy Sources (SRI) 5-26
3.5 SCE and LADWP Electrical Energy Consumption 5-28
3.6 Per Capita Consumption of Electrical Energy SRI Total
California and SCE 5-30
3.7 Peak Demand, MW, SCE and LADWP and (G/P/B) 5-30
3.8 Forecast Energy Consumption in the South Coast Air Basin 5-31
3.9 Forecast Peak Demand in the South Coast Air Basin 5-31
3.10 Power Capability, SCE Plus LADWP and (Glendale/Burbank/
Pasadena) 5-33
3.11 Power Capability. SCE Plus LADWP and (Glendale/Burbank/
Pasadena) 5-34
-------
ILLUSTRATIONS (Cont.)
NO. • PAGE
3.12 Hourly Demand, 1973 5-37
3.13 Profile of Hourly Demands with Projected Supply, South
Coast Air Basin 5-38
4.1 Potential Electrical Energy Available for Electric Car
Recharge 5-42
4.2 Forecast of Power Plant Fuel Prices 5-46
4.3 Forecast of Electrical Energy Consumer Prices 5-46
vi
-------
TABLES
NO. PAGE
2.1 Average Required "Prices" for Oil and Gas 5-9
2.2 Estimates on Availability of Commercial Technology for
Energy Conversion 5-11
2.3 Poll of Experts on SO- Removal Technology 5-18
2.4 Sulfur Dioxide Removal Systems at US Steam-Electric Plants 5-19
2.5 US Residual Fuel Oil Sulfur Content 5-21
3.1 Southern California Energy Demand 5-23
vii
-------
1 INTRODUCTION
This is the fourth in a series of reports projecting baseline condi-
tions (in the absence of electric cars) for use in a study of the impacts
of future electric car use. It follows assumptions and data presented in
the first report of the series, Task Report 2, Population Projections for
the Los Angeles Region, 1980-2000.
A significant shift from gasoline to electrically powered cars may
cause an equally significant impact upon the patterns of energy consumption.
To adequately assess the significance of such an impact it is first neces-
sary to establish a set of baseline conditions regarding energy supply
and demand that would exist if electric cars are not introduced.
Although the primary focus of the study is the South Coast Air Basin,
the future patterns of energy supply and demand in the Air Basin will be
influenced by overall energy policies pursued at the National level. The
presently forecast shortages in domestic crude oil and natural gas supplies
are causing a fundamental reassessment of the Nation's energy supply and
demand functions. Accordingly, we first focus our attention on the National
energy forecasts to determine the overall constraints and conditions within
which the Air Basin regional supply and demand relationships are resolved.
Additionally, these constraints and conditions include considerations of
the technologic and economic limitations attendant to the development of
environmentally clean energy sources.
In this paper we establish both National and South Coast Air Basin
energy forecasts with compatible fundamental assumptions regarding popula-
tion growth.
5-1
-------
2 FORECASTS OF TOTAL US ENERGY SUPPLY AND DEMAND
There is at present a considerable focus on forecasting the future
energy needs of the nation. It appears that for the next few years, the
US may have to rely to a significant degree on foreign sources of crude
oil to fuel our economy. A greater reliance on imported sources arouses
concern about its dependability and would undoubtedly entail undue con-
sequences with regard to our overall diplomatic strength in the world
and our balance of payments in world trade. This reliance on foreign
sources was not foreseen even as late as 1964 based on authoritative
forecasts made at that time. It is only within the last decade that a
trend toward an increasing rate of energy consumption has brought us to
the present state of our energy supply problems.
Thus, there is some evidence that we should view such long term
forecasts with some caution as to their absolute accuracies. However,
for the study of electric car impacts, our need is for a representative
energy forecast to establish a baseline from which to measure impacts
on energy supply and demand. Consequently, small inaccuracies or pertur-
bations in the forecast conditions should not unduly affect the relative
accuracies of estimated impacts.
There have been several recent analyses of our current problems
and our expectations regarding future energy supplies and demands. One
of the most recent and more thorough efforts was reported in "U. S.
Energy Outlook" prepared by a special committee of the National
2
Petroleum Council in December 1972. Another equally comprehensive
effort was the analysis by the Inter Technology Corporation for the
National Science Foundation reported in November 1971. Still other
analyses have contributed insights to particular aspects or reduced
scopes of the problem such as the analysis by Stanford Research
/
Institute (SRI) on "Meeting California's Energy Requirements, 1975-2000"
and the Rand Corporation on California's future electric supply and
demand. For the purpose of establishing a National baseline energy
forecast we have relied most strongly on the report by the National
?
Petroleum Council (NPC).
5-2
-------
2.1 FORECAST OF US ENERGY DEMAND
One of the initial steps of the NPC analysis was to forecast in
some detail the demand for energy for the year 1985 with an additional
but less detailed forecast for the year 2000. Figure 2.1 presents the
NPC predictions of future energy demand for the three alternatives labeled
"high," "intermediate," and "low." Their forecasts are based on the
following four variables which they deemed to be the most significant
long range determinants of energy demand: (1) economic activity as
characterized by GNP, (2) cost of energy (including cost-induced effi-
ciency improvements), (3) population and (4) environmental controls.
The three cases then are the result of a set of high and low projections
for each of the four variables with the intermediate case representing
something in between. With regard to the population variable, the three
cases are associated with official US Census series projections C, D,
and E as noted in the figure. The energy demand forecasts assume that
there will be no substantial changes in the living habits of the US
population and do not anticipate reduced energy consumption because of
supply limitations or political decisions to regulate or allocate energy
*
consumption. In general, the forecasts assume that growth in economic
*
The NPC forecast did not foresee the "crises" spawned by the Mid-East
conflict, although in overview it warned of the potential for such a
condition. It is difficult to assess the impact of the Mid-East spawned
oil embargo on the US level of demand in the year 1985, the focus of the
NPC forecast. The embargo may affect several of the assumptions under-
lying the NPC forecast. First, we should note that the embargo has
forced almost instant fuel rationing in various degrees throughout the
free world. Since unilateral responses by the impacted nations to make
themselves independent of such actions will take several years to imple-
ment, a sustained embargo would necessarily force a rationing condition
for some time which most likely will result in long-lived modifications
to energy consumption habits. Second, we should note that the embargo
disrupted the energy supply and demand relationships throughout the World
with the net effect of increasing the posted prices of crude oil in inter-
national trade. Thus, the future prices (or costs) of energy may take a
different path than forecast by the NPC. Third, to the degree that imme-
diate reductions in the availability of energy may affect the growth in
the National economy there may result a delay or deferment in the GNP
growth rate envisioned by the NPC.
5-3
-------
250 x 10
15
200
150
CJ3
cc
100
•=C
I—
O
50
1970
/
/
(SRI)
/ *HIGH
/ / (SERIES C)
* INTERMEDIATE
(SERIES D)
LOW (SERIES E)
(SRI ALTERNATE
PROJECTION)
US ENERGY OUTLOOK, NATIONAL
PETROLEUM COUNCIL, DEC, 1972
I
1980 1990
YEAR
2000
Figure 2.1. Projected U.S. Total Energy Demand
5-4
-------
activity, and achievement of social goals such as full employment would
be seriously impeded if energy consumption were arbitrarily curtailed.
Figure 2.1 also presents two alternative forecasts made by Stanford
Research Institute (SRI) pursuant to their study of California's energy
supply and demand problems. The high curve by SRI assumes that energy
consumption will expand at a constant 4.5 percent per year to the end of
the century. The low curve has made allowance for a lower growth in
population plus allowances for the costs of environmental cleanup. SRI
believes that the low projection should prove to be the more realistic
proj ection.
Consistent with our analysis of population growth, we have chosen
the "low" demand case derived by the NPC study as the baseline for the
US. This is supported to some degree, by the analyses of SRI except that
they are somewhat different in the near term.
2.2 FORECAST OF US ENERGY SUPPLIES
Four scenarios for the development of future domestic energy
supplies were explored in the NPC study. The four cases ranged from a
level where the maximum economically feasible expansion of future energy
sources was anticipated to the case represented by a continuation of
present policies. The four cases are depicted in Figs. 2.2(a-d) which
represent correspondingly the maximum expansion down to the lowest expan-
sion. Superimposed on each figure is the low-demand case from Fig. 2.1.
The cross hatched regions represent the shortfall in domestic supplies
and it is assumed that this deficiency will be met by imported energy
sources, primarily foreign crude oil.
The four cases were developed by analyzing the current state of
consumption relative to proven reserves, current prices, and a range of
economic incentives by which production of each of the primary energy
5-5
-------
Ln
I
200 x 1015,-
NUCLEAR
HYDROELECTRIC
GEOTHERMAL
200 x 10 i-
HYDROELECTRIC
GEOTHERMAL
i
1980
1990
2000
1970
1980
1990
2000
a. NPC Case I
b. NPC Case II
Figure 2.2. Projected Primary Energy Supply for Total U.S. Compared
With Projected Demand (Ref. 2)
-------
200 x 10
15
CD
ce
100
0
1970
NUCLEAR
HYDROELECTRIC +
GEOTHERMAL
200 X 1010,-
1980
1990
2000
HYDROELECTRIC
GEOTHERMAL
2
o
o
1980
1990
2000
c. NPC Case III
d. NPC Case IV
Figure 2.2(Cont.)
-------
sources could be stimulated. The forecasts are all tempered with judgments
reflecting the physical constraints on resource availability.
In all cases, nuclear power is forecast to become the major source
of primary energy by the year 2000. Natural gas reserves are particularly
2 3
low and there seems to be agreement among most energy observers ' that
it is presently underpriced. A significant increase in natural gas
prices is anticipated which should stimulate the discovery of new reserves;
however, the new reserves will not be instrumental in alleviating the
near term shortages. If new incentives for gas exploration are not
advanced (Case IV), then gas will decline relatively and absolutely as
a significant primary energy source. In any case, gas supplies are not
expected to expand greatly and in terms of its user priorities will be
reserved mainly for residential uses and little used in fueling electric
power generation in the future. In all four cases, new energy forms or
sources such as coal gassification or exploitation of oil shales are
expected to add very little to total energy supply through 1985; by the
year 2000 shale oil is expected to provide between 2 and 3 percent of
total US energy with coal gassification expected to contribute 5 percent.
A significant difference among the four cases of supply is in the
quantities of oil and gas supplies and their sensitivities to prices.
For example, in 1985 there is almost a 2-to-l difference in the sum of
oil and gas supplies between Cases I and IV (oil + gas = 69 * 10 Btu
for Case I and 38 x lO Btu for Case IV). Table 2.1 presents the esti-
mated future prices of gas and oil for each case deemed adequate to call
forth the necessary exploration and drilling activity to produce the
corresponding level of supply. These estimates of supply are the result
of careful calculations by the NPC of economic equilibrium conditions and
it is significant to note that the NPC estimates that a 27-percent dif-
ference in oil prices between Cases I and IV will result in a 330-percent
difference in the corresponding drilling rates. This difference in
exploration activity is responsible for the 2-to-l difference in oil and
gas supply between Cases I and IV.
5-8
-------
TABLE 2.1
AVERAGE REQUIRED "PRICES" FOR OIL AND GAS
(1970 CONSTANT DOLLARS)
Case 1
Case II
Caselll
Case IV
Oil ($/BBL.)
3.18
3.18
3.18
3.18
1970
6.69
6.18
6.60
5.28
1985
Gas (d/MCF)
17.1
17.1
17.1
17.1
1970
43.6
39.8
53.0*
38.7
1985
"If prices for gas discovered prior to 1971 were held at current
levels, new gas would cost over 75d/MCF
If we examine the four cases of supply as contrasted with forecast
demand in Figs. 2.2(a-d), we find that they present a wide range of
impacts on the necessary imports of fuels. Case I sets into motion a
set of conditions that begins to sharply overshoot forecast demand by
1982; oil imports are minimized. Case II represents a more measured
response to the near term shortage without result in sharp overshoot;
required oil imports are not much greater than for Case I. Cases III and
IV fail to solve both the near term and long term energy supply problems
and commit the nation to a significant steady or even increasing reliance
on foreign sources. Accordingly, we have chosen Case II supply conditions
as most in accord with recently stated national energy policy for a steady
expansion of energy supply while minimizing the risks of relying too
heavily on imported sources. Case II also promises to return the Nation
to a condition of self-sufficiency in energy by the year 1986. Case II
also assumes that a quicker solution is found to problems in fabricating
and installing nuclear power plants than is presently available.
5-9
-------
2.3 POWER PLANT EFFICIENCY TRENDS
Because all four cases of energy supply forecast a heavy reliance
on nuclear energy, which is programmed solely for electric energy produc-
tion, an important factor in overall energy balances is the thermal effi-
ciency with which basic energy sources are converted to electric energy,
including both nuclear and fossil fuel sources. Among the factors in-
volved in the design of a new plant affecting its design efficiency are
the magnitude of the increased capital investment necessary to build
more efficient equipment, the annual fueling and maintenance costs and,
of course, the existing level of technology. Figure 2.3 shows the his-
torical trend in average thermal efficiency of fossil fueled electric
power generation in the United States. The figure shows that in the
decade between 1951 and 1961 there was a steady increase in the overall
thermal efficiency which has since remained relatively constant for the
past decade near the 1961 level. Although the major fraction of power
generation through this period has been by conventional fossil-steam
plants, the period from about 1965 to the present has witnessed a rapid
rate of increase in installed new capacity with nuclear and internal com-
bustion (1C) source facilities. Both nuclear and 1C facilities operate
at significantly lower efficiencies than conventional steam plants. New
plants listed under fossil fuel fired capacity include 1C sources which
tend to offset any gains in efficiency in new conventional steam plants.
Furthermore, the shift to nuclear plants reduces the requirement to build
new and more efficient conventional steam plants thereby helping to account
for the slow rate of improvement in average fossil fuel plant efficiency
evidenced in the decade 1961 to 1971.
Beyond 1971, the efficiency of fossil fuel plants is expected to
further improve as new technology is incorporated in the mix of generation
2
capacity. Table 2.2 shows a 1972 projection of the expected efficiencies
for the most promising new technologies in power generation for selected
years and the time they are projected to become available. The effect of
these new technologies on the expected future trend of overall average
generation efficiency of fossil fuel plants in the US is depicted in
5-10
-------
0.4
0.3
0.2
0.1
AVERAGE FOR US
£(ELECTRICAL ENERGY OUT)
E(CHEMICAL ENERGY IN)
0
1950
1960
1970
1980
YEAR
Figure 2.3. Historical Trend of Overall Thermal Efficiency of Power
Generation by Fossil Fuel Plants 6
TABLE 2.2
ESTIMATES ON AVAILABILITY OF COMMERCIAL
TECHNOLOGY FOR ENERGY CONVERSION
Electrical
Thermal Efficiency
(Percent)
20-25
50-52
55-60
40-45
40
45
48
40
45
40
45
48
Stand-Alone MHD
MHD-Topped Power Plant
MHD-Topped Power Plant
Fuel Cells Using Reformed Methane
*
Combined Cycle Using Clean Fossil Fuels
Combined Cycle* Using Clean Fossil Fuels
Combined Cycle* Using Clean Fossil Fuels
*
Fixed-Bed Gassification of Coal and Combined Cycle
Fixed-Bed Cassification of Coal and Combined Cycle*
*
Fluid-Bed Cassification of Coal and Combined Cycle
Fluid-Bed Cassification of Coal and Combined Cycle*
Fluid-Bed Cassification of Coal and Combined Cycle*
Fluid-Bed Combustion Coal or Residual Oil—
Rankine Cycle
Thermionic Topping Fossel-Fuel Power Plants
Gas Turbine-Brayton Cycle (Clean Fossil Fuels)
Gas Turbine-Brayton Cycle (Clean Fossil Fuels)
Gas Turbine-Brayton Cycle (Clean Fossil Fuels)
38-41
45
28
34
38
When
Available
1980
1985
1995
1976
1972
1978
1985
1975
1978
1982
1988
1992
1980
1985
1972
1978
1985
Brayton-Rankine
5-11
-------
2
Fig. 2.4 which indicates an overall improvement of 8 percent by the year
1990 when compared to present efficiency. Also shown are the expected
efficiency trends for each year for newly installed conventional fossil
fuel plants, the best available combined cycle plants, and the average
of the cumulative capacity of installed combined cycle plants. The indi-
cated improvement in efficiency averaged over all fossil fuel plants
implies that a significant fraction of new, more efficient fossil plants
will be installed either as net additions or replacement of old plants.
This situation is expected to occur in conjunction with the very rapid
buildup in nuclear plant capacity.
Among the factors that might affect this forecast trend in overall
generation efficiency of fossil fuel plants is a significant departure
from the forecast fossil fuel costs. Several possibilities
present themselves. Extremely costly fossil fuels will focus a great
deal of attention on generation efficiency. Improvements in overall
efficiencies beyond that forecast in Fig. 2.4 would require a greater
installation rate of new, more efficient plants such as the combined
cycle plants. This result could in turn occur only if older plant
were written off at a faster rate with a corresponding increase in the
rate of capital investment in new plants or if the additional new plant
capacity were to supplant some of the increase forecast for nuclear
generation capacity. Since it is expected that nuclear plant overall
energy costs will already be less than fossil fuel plants, the latter
result is not likely. An additional possibility, if fossil fuel costs
should be higher than forecast, would be a reduction in the expansion of
new fossil fuel generation capacity to be made up by an even greater
expansion in nuclear capacity. Although there will be an incentive to
move in this direction any significant increase in the planned expansion
of nuclear plant must contend with many other factors that are not yet
clearly resolved. These include, environmental factors of radioactive
waste disposal, emergency core cooling processes, and nuclear plant siting,
and economic factors dealing primarily with the capital intensiveness of
5-12
-------
0.30
0.35
O
0.40
0.45
0.50
11,000
10,000
9000
ili 8000
7000
6000
5000
TREND LINE FOR
NATIONAL HEAT RATE
FOR FOSSIL FUELS
AVERAGE OF ALL FOSSIL-
FUEL RANKINE CYCLES
INSTALLED IN THAT YEAR
HEAT RATE FOR ALL
INSTALLED COMBINED
CYCLES
BEST COMBINED CYCLE
AVAILABLE IN THAT YEAR
I
I
1975 1980 1985
YEAR
1990
Figure 2.4. Estimated Trends for Efficiency of Electric Power Generation
from Fossil Fuels, 1972-199Q2
5-13
-------
nuclear plants. Regarding this latter factor, a commitment to more nuclear
plants in response to high fossil fuel costs would undoubtedly be based on
beliefs that such high fuel costs would prevail for a long time, at least
for a significant fraction of the typical economic life of a nuclear plant.
2.4 ESTIMATED FUTURE PRICES OF ENERGY SOURCES
A primary focus of the NPC analyses was the determination of the
economic incentives in terms of future market prices necessary to stimu-
late an expansion of a particular energy source. The estimated future
average prices of primary fuels corresponding to Case II supply conditions
are depicted in Fig. 2.5. These are basic prices in constant 1970 dollars
for conditions at the wellhead or the mine and so do not include costs for
cleaning the fuels such as desulfurization costs.
We have assumed a uniform increase in basic energy prices between 1970
and 1985. A result of the Arab oil embargo has been to cause profound
changes in the posted prices and actual market prices of crude oil in
international trade. A news release on January 1, 1974' announced the
following posted prices:
Indonesia $10.80/bbl
Libya 18.76
Nigeria 14.69
Bolivia 16.00
Venezuela 14.08
Market prices typically run about 70 percent of posted prices except that
"buy back" oil (oil actually owned by the producing country) is nearer the
posted price (94 percent in the case of Saudi Arabia). It is not clear
what the magnitude of these international prices will be on domestic crude
oil prices or how long such elevated prices will persist. If they should
prevail for any length of time, there will undoubtedly result such a
significant and rapid increase in exploration activity that over supply
and eventual price depression would occur and we would expect the NPC
equilibrium price estimates for 1985 to remain reasonably valid. However,
we would also expect the unforeseen rapid rise to stimulate competition
of alternative technologies such as synthetic crude oil and gas production
from shale oil and coal much earlier than anticipated by the NPC. Thus,
the fractions of total US supply in the future from these sources may be
slightly greater than anticipated by the NPC. However, the NPC estimates
that under non-emergency conditions the maximum feasible rate of shale
oil production in 1985 would be 750,000 bbls/day compared to the 400,000
bbls/day assumed for Case II (these amounts are 1.4 and 0.8 percent
respectively of total 1985 US energy supply).
5-14
-------
1.!
3
co
C£ _J
LlJ O
0. O
OO O
cz r^
O I—
C-J O
o;
o.
i.oo
0.50
'TOP OF BAND FOR UNDERGROUND MINES
BOTTOM OF BAND FOR SURFACE MINES
Csl
t-^
CSI
•SJ-
CRUDE OIL (DOMESTIC)
0 WELLHEAD
@ WELLHEAD
COAL @ MINE*
NUCLEAR, TOTAL
FUEL CYCLE
1970
1980
1990
YEAR
2000
Figure 2.5. Projected Average Prices of Primary Energy Sources, Case II
Conditions
5-15
-------
Examining Fig. 2.5, we find that crude oil is expected to double
by 1985 reaching approximately $1.05 per million Btu or approximately
$6.00 per barrel. The average price of natural gas, our cleanest energy
source environmentally, is still significantly underpriced relative to
crude oil. However, it is expected that new discoveries would require
prices of $0.78 to $0.72 per million Btu according to the NPC analysis.
The prices shown for nuclear fuel are somewhat deceptive in that
this primary source is destined entirely for electric power production
and requires a higher proportion of capital costs than for ordinary
fossil fuel fired plants. SRI has estimated a 1980 "break even" price
of fossil fuels in competition with nuclear fuels for power generation
at $0.42 per million Btu, of which $0.20 is estimated for the nuclear
fuel cycle. Thus, the forecast prices of crude oil would certainly allow
a favorable competition for the expansion of nuclear fuel sources.
However, present prices set by the AEC for separative work in enriching
uranium are based on extremely cheap electrical energy. It is antici-
pated that future requirements for separative work will require additional
gaseous diffusion plants. These may be provided within the private sector
and in that case could double the present cost of a separative work unit
Q
according to a recent analysis. A doubling of enrichment services would
raise the total nuclear fuel cycle costs from $0.20 to $0.26 per million
Btu, but would still allow nuclear power to maintain its competitive
position.
2.5 CLEAN POWER GENERATION TECHNOLOGY
By the year 2000, it is expected that nuclear energy will have
assumed the major fraction of the burden of electrical power generation.
However, in the intervening time span great reliance will be based on
fossil fuels for energizing steam-electric power plants. As natural gas,
availability for power generation becomes reduced, the fossil fuel burden
will fall most heavily on fuel oils and coal. Many of the available oil
and coal sources contain excessive sulfur and could not be burned directly
without violating air quality standards or adding cleanup equipment.
5-16
-------
In the near term the use o'f coal for power generation will depend
on the success with which the development of stack gas sulfur removal
o
technology is met. In a recent report, the results of a poll of
experts by the Delphi Technique concerning the expectations for sulfur
dioxide removal technology were published and are here reproduced in
Table 2.3. Six categories of sulfur oxide removal equipment were con-
sidered: lime/limestone scrubbing, sodium sulfite-bisulfite scrubbing,
catalytic oxidation, double alkali scrubbing, magnesia scrubbing, and others,
Essentially, the panel was asked when they thought each of these pro-
cesses would reach demonstrated reliability (one year operation) of 10,
50, and 90 percent of time on-stream. The results at the end of the
second round shown in the table indicate the panel considered lime/
limestone scrubbing and magnesia scrubbing the processes likely to be
available first and double alkali scrubbing the one that would be demon-
strated last.
Furthermore, although the poll results indicate that it will be
3 or 4 years before we can expect confident sulfur dioxide removal
techniques, we can reasonably assume that by 1980 sulfur dioxide removal
from stack gases should not be a problem. Further corroboration is
evidenced by the present activity in installation of S0_ removal equip-
2
ment as shown in Table 2.4.
In the longer term, other technologies will likely become available
as means to cleaning up coal. Coal gasification can be thought of as
a clean up technology. However, the practical application of this
technology involves more than an alternative cleanup method; it allows
the energy of the coal to be transformed to a more transportable commodity
and thus its advantage may be in exploiting the more remote coal fields
in the Western United States.
5-17
-------
TABLE 2.3
POLL OF EXPERTS ON S02 REMOVAL TECHNOLOGY
Process
Lime/Limestone Scrubbing
Double Alkali Scrubbing
Magnesia Scrubbing
Sodium Sulfite-Bisulfite
Scrubbing with By-product
Recovery
Catalytic Oxidation
On-Stream Factor Year Anticipated
10%
50
90
10
50
90
10
50
90
10
50
90
10
50
90
1973
1975
1976
1975
1976
1978
1973
1974
1976
1974
1975
1976
1973
1974
1977
5-18
-------
TABLE 2.4
SULFUR DIOXIDE REMOVAL SYSTEMS AT US STEAM-ELECTRIC PLANTS
2*
Unit
Power Station
Size
(MW)
Designer S02
System
New or
Retrofit
Scheduled
Start-Up
Anticipated Efficiency
SC>2 Removal
Limestone Scrubbing: 44
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
Union Electric Co., Meramec No. 2
Kansas Power & Light, Lawrence Station No. 4
Kansas Power & Light, Lawrence Station No. 5
Kansas City Power & Light, Hawthorne Station No. 3
Kansas City Power & Light, Hawthorne Station No. 4
Kansas City Power & Light, Lacygue Station
Detroit Edison Co., St. Clair Station No. 3
Detroit Edison Co. , River Rouge Station No. 1
Commonwealth Edison Co. , Will County Station No. 1
Northern States Power Co., Sherburne County
Station Minu. No. 1
Arizona Public Service, Chella Station Co.
Tennessee' Valley Authority, Widow's Creek
Station No. 8
Duquesne Light Co., Phillips Station
Louisville Gas .&. Electric Co., Paddy's Run Station
City of Key West, Stock Island1'
Union Electric Co. , Meramec No. 1
140
125
430
100
100
800
180
265
175
700
115
550
100
70
37
125
Combustion Engineer
Combustion Engineer
Combustion Engineer
Combustion Engineer
Combustion Engineer
Babcock & Wilcox
Peabody
Peabody
Babcock & Wilcox
Combustion Engineer
Research Cottrell
Undecided
Chemico
Combustion Engineer
Zurn
Combustion Engineer
R
R
N
R
R
N
R
R
R
N
R
R
R
R
N
R
September 1968
December 1968
December 1971
Late 1972
Late 1972
Late 1972
Late 1972
Late 1972
February 1972
1976
December 1973
1974-1975
March 1973
Mid-Late 1972
Early 1972
Spring 1973
Operated at 73Z Efficiency
During EPA Test
Operated at 73Z Efficiency
During EPA Test
Will Start 65Z & Be Upgraded
to 83Z
Guaranteed 70Z
Guaranteed 70Z
80Z as Target
90Z as Target
90Z as Target
Guaranteed 80Z
Guaranteed 80Z
Guaranteed 80Z
Guaranteed 85Z Removal
80Z as Target
Sodium Hydroxide Scrubbing Installation:
1. Nevada Power Co., Reed Gardner Station
Magnesium Oxide Scrubbing Installations:
1. Boston Edison Co., Mystic Station No. 6
2. Potomac Electric Power, Dickerson No. 3
Catalytic Oxidation:
1. Illinois Power, Wood River
tt
250 Combustion Equipment
Associates
150 Chemico
195 Chemico
100 Monsanto
1973
February 1972
Early 1974
June 1972
Guaranteed 90Z SC>2 While
Burning 1ZS Coal
90Z Target
90Z
Guaranteed 85Z SC>2 Removal
tt
Federal Register, Vol. 37, No. 55 (March 21, 1972), p. 5768, updated.
Now abandoned.
Oil-fired plants (remainder are coal-fired).
Partial EPA funding.
-------
The cost of stack gas cleanup of coal fired plants can vary signifi-
cantly depending primarily on whether the cleanup equipment is part of
a new installation or retrofitted to an existing one.
Domestic and foreign crude oils are expected to supply a significant
fraction of the energy for power generation. Crude oils can vary signifi-
cantly in their sulfur content and 65 percent of the current level of
domestic supply has 0.5 percent or less sulfur content. However, much
of the crude supply is fed to refineries which are operated to yield a
high percentage of jet, gasoline and distillate fuels which leaves most
of the sulfur burden with the residual fuel oils, the main source of all
heating oils for power stations. Presently, the free world refineries
produce 27.8 percent of their output as residual fuel oil whereas the US
refineries only produce 6.8 percent of their output as residual fuel oil.
Table 2.5 presents a breakdown of the sulfur content of the current supply
of residual fuel oil by region of the country. It is to be noted that
the Pacific Coast supplies are predominantly high sulfur content oils.
The average sulfur content of all imported oil in 1971 was in the
range of 2.4 to 2.6 percent. There may exist some opportunities to
allocate low-sulfur oils directly to fuel power plants, but due to blend-
ings that occur in our crude oil distribution systems, the benefits of
low sulfur sources are not always preserved. Thus, it appears that most
fuel oils will have to undergo desulfurization before they can be utilized
in the clean generation of power. It has been estimated that the addi-
tional cost of desulfurizing a barrel of oil will be approximately $0.90
for the case of hydrodesulfurization based on planned operations in
*
Venezuela. The premium prices now being paid for low-sulfur oil (up to
$18.00 per barrel) would make desulfurization at this price economically
feasible. Assuming that desulfurization equipment can be constructed „
with sufficient capability, we foresee that low sulfur oil should be in
ready supply by 1980 and beyond.
This is the price in Venezuela. The energy requirement for the desul-
furization process could not be readily determined.
5-20
-------
TABLE 2.5
US RESIDUAL FUEL OIL SULFUR CONTENT10
(Current supply in thousands of barrels per day)
Sulfur Content, percent
East Coast
Gulf States
Central States
Pacific Coast
<.7 .7-1.0
2.5
9.1 13.0
24.0 35.4
5.4 22.3
1.0-1.5
-
42.0
52.8
14.2
1.5-2.0
6.0
11.0
0.5
67.3
2.0-3.0
42.9
25.6
69.9
18.1
>3.0
-
6.0
5.8
11.8
In the longer term it is expected that the developing technologies
of coal gasification and the manufacture of synthetic crude oil will be
able to produce clean sources from high sulfur content feed stocks.
5-21
-------
3 BASELINE ENERGY FORECASTS FOR SOUTHERN CALIFORNIA AND THE
SOUTH COAST AIR BASIN
Efforts similar to that of the National Petroleum Council have
recently been made in studying the energy supply and demand relationships
for California. Noteworthy are the studies by Rand Corporation and the
Stanford Research Institute (SRI). The public utilities in the State
also have made many planning and forecast studies which have become input
to many of these studies. To develop forecasts of future energy supply
and demand for the South Coast Air Basin, we have relied most heavily
on the SRI study and the planning studies made by the Southern Cali-
11 12
fornia Edison Company (SCE) ' and the Los Angeles Department of Water
and Power (LADWP).13
The SRI study, in addition to forecasting overall California energy
needs, presented results by Northern and Southern divisions of the State.
The Southern division was chosen as that area composed of the service
areas of SCE, LADWP, the San Diego Gas and Electric Company, the Imperial
Irrigation District and the small municipal power systems of Glendale,
Pasadena, and Burbank (G/P/B). With minor corrections the SCE, LADWP, and
G/P/B composite service area is almost congruent with the South Coast Air
Basin and comprises about 70 percent of SRl's Southern California division.
While SRI made quite detailed comprehensive forecasts of energy
supply and demand, the detailed planning forecasts of the utilities were
concerned with sources of electric power supply for only the next ten years.
*
The Rand Corporation study was found less useful to our study since it
dealt solely with electric power demand forecasts. The assessment of
electric car impact on energy resources will require careful considera-
tion of the substitutability of basic energy resources and the Rand study
did not couch their analyses of demand within a total energy supply and
demand for California, whereas the-SRI study did. Furthermore, the SRi
study conveniently divided the State into Northern and Southern halves
which helped us in our task of developing a South Coast Air Basin fore-
cast. Nonetheless, comparisons with the Rand forecasts are offered
where appropriate.
5-22
-------
Consequently, we have put together the forecast data from SRI, SCE, and
LADWP to help establish a picture of the future energy needs of the South
Coast Air Basin.
3.1 ENERGY SUPPLY AND DEMAND IN SOUTHERN CALIFORNIA
Figure 3.1 presents the SRI forecast of primary energy supply and
demand for the Southern California region while Table 3.1 shows the rela-
tive fractions of supply from each energy source in percent. Consistent
with the National picture, natural gas is expected to first decline abso-
lutely and then recover to a steady level of supply equal to its 1970
level. Nuclear energy is not expected to be a significant source until
the late 1980s. The energy source labeled coal represents the contribu-
tion expected from the plants either completed or under construction in
the Nevada desert and the four-corners region of the Southwest. Through-
out the period of interest, oil is expected to be the major energy source.
TABLE 3.1
SOUTHERN CALIFORNIA ENERGY DEMAND4
(In percent by source)
Oil
Gas
Nuclear
*
Coal
Other
1970
55.
41.
.8
1.5
1.7 -
1980
65.1
22.4
3.3
7.1
2.1
1990
58.8
19.1
13.4
6.6
2.1
2000
46.
15.8
31.5
4.8
1.9
*
The SRI figures have been modified to account for Southern California
energy consumption from the Four-Corners coal fired plants.
5-23
-------
10 X 1015r-
0 ._
1970
OTHER
NUCLEAR
1980 1990
YEAR
2000
Figure 3.1. Primary Energy Supply and Demand, Southern California (SRI)
10 x 10
,15
NUMBERS IN PARENTHESES ARE
AVERAGE ANNUAL GROWTH RATES
(1970-2000)
ELECTRICITY (6.4%)
RAW MATERIALS (2.1%)
1970
1980 1990
YEAR
2000
Figure 3.2. Energy Demand by end uses, Southern California (SRI)
5-24
-------
Figure 3.2 shows, for the same region, SRI's estimates of how the
energy demand will appear in terms of end uses with corresponding average
annual growth rates for the period 1970 to 2000. "Gas" in this context
is meant to represent the end use of gas for residential, commercial, and
industrial uses. Transport demand represents mostly trucks, autos, and
aircraft and is expected to grow moderately. Demand for energy to generate
electricity is expected to grow rapidly through the year 2000.
Comparing Figs. 3.1 and 3.2, we note that the demand for energy to
generate electricity everywhere exceeds the available nuclear supply.
Furthermore, end uses of gas (present policy is to accord first priority
to residential needs) will leave little gas available for fueling power
plants. Thus, the energy demand for power generation will require
significant amounts of oil in addition to the contribution of the coal
fired plants.
Figure 3.3 presents SRI's estimates of how the annual electrical
energy supply (in KWH electrical) will be consumed by major customer
classes; industrial, commercial, and residential. All three classes are
expected to grow at nearly equal rates (6.1 percent through 1985 and
4.7 percent thereafter) and represent nearly equal fractions of the total
*
consumption.
Comparing SRI's year 2000 forecast for California (Fig. 3.3 is for
Southern California only) with the Rand forecast, we find that the SRI
9
estimate for electric energy production is 651. x 10 KWH in the year
9
2000 while the Rand estimate for their base case is 747 x 10 KWH in the
year 2000. However, the Rand calculation assumes a population for
California at that time of 33.54 x 10 , while SRI has estimated 27.525 x K
If we scale the Rand electric energy, production estimate by the population
*
The Rand report estimates electrical demand annual growth rates for all
of California over the period of 1970 to 2000 of: industrial 4.0 percent,
commercial 7.5 percent, and residential 4.4 percent for their base case
conditions.
5-25
-------
5 x 10" r
1970
1980 1990
YEAR
2000
Figure 3.3. Electrical Energy Consumption, Southern California by Users (SRI)
5 x 10'
§
oc
Q.
>*
u
a
UJ
5 ?
1970
1980 1990
YEAR
2000
Figure 3.4. Electrical Energy Production, Southern California by Energy
Sources (SRI)
5-26
-------
ratio (27.5/33.5), the Rand value would be 612 x 109 KWH or within 7 percent
of the SRI value. Under varying assumptions of lower growth rates and
increased electricity prices, Rand forecasted considerable reductions in
electric power demand relative to their base case. However, the Rand
study assumed that feasible alternative energy sources could be utilized
at lower cost than would be entailed under increased electricity prices.
The present situation is one where prices of other basic energy sources
are rapidly increasing. Thus, substitutions of sources as envisioned by
Rand may not materialize as readily.
Figure 3.4 depicts how the annual electrical energy consumption is
to be met in terms of the primary energy sources fueling the power plants.
The contributions of each primary energy source have been calculated with
due consideration of the varying efficiencies attendant to the various
conversion processes and the likelihood that the sources will be either
base loaded or peak loaded. These SRI estimates dealt strictly with energy
production that occurs within Southern California to which we have added
the expected amount to be derived from the coal fired plants. Thus, the
total production shown is more than the consumption (Fig. 3.3); however,
the Southern California region has in the past been the beneficiary of
excess power capability from Northern California and the Bonneville Power
Administration which they expect to repay in the future which may account
for the excess production. It should also be noted that hydro-electric,
gas, and geothermal sources are not expected to figure significantly in
year 2000 power sources.
3.2 ELECTRICAL ENERGY AND POWER IN THE SOUTH COAST AIR BASIN
Figure 3.5 presents a comparison of electircal energy consumption
11 13
forecasts between the sum of SCE and LADWP and the SRI Southern
California total less a constant percentage fraction representing San
Diego Gas and Electric and the Imperial Irrigation District. As noted
on the figure, we estimate that the South Coast Air Basin represents
about 95 percent of the aggregate service area of SCE, LADWP, and G/P/B.
5-27
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4 x TO11 ,-
o
CJ
5 2
o; t
UJ
0
1970
ESTIMATE BASED ON SRI FORECAST
V
SCE + LADWP
NOTE: SCAB - 0.95 (SCE+ LADWP)
I
I
1980 1990
YEAR
2000
Figure 3.5. SCE and LADWP Electrical Energy Consumption
5-28
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The SCE forecast is based on a population growth represented by
Series E fertility and a steady ne;t migration of 100,000 to the State
of California in 1980 and thereafter. SRI's forecast assumes an average
annual Statewide growth of 250,000 people per year which in effect
produces very nearly the same population forecast as Series E fertility
and 100,000 net migration. The LADWP did not state the underlying
assumptions of their population forecasts, but since their service
area is primarily the incorporated limits of the City of Los Angeles,
there is little expectation of any radical growth patterns developing.
Figure 3.5 indicates fair agreement between SRI and Utility forecasts;
consequently, the Southern California overview of energy supply and
demand presented in the few previous figures fairly depicts the situation
we may expect for the South Coast Air Basin (recall that the South Coast
Air Basin represents close to 70 percent of the SRI Southern California
region).
The growth in per capita electrical demand (in annual KWH per person)
imputed by dividing for each year overall consumption by population was
calculated from the SRI, SCE and Rand forecasts and compared as shown in
Fig. 3.6.
Figure 3.7 presents the forecast of peak demand in MW as determined
from the forecasts of SCE, LADWP and an allowance estimated for G/P/B.
Also shown is an estimate based on the SRI Southern California forecast
which is in good agreement with the utility forecasts. The curves indi-
cate a five-fold increase in peak demand between 1970 and 2000.
Because the fundamental assumption for population growth for this
study is based on a Series E fertility and no net migration to or from
the South Coast Air Basin, the energy consumption curve of Fig. 3.5 and
the peak demand curve of Fig. 3.7 have been rescaled to reflect these study
^ *
assumptions. The results are depicted in Figs. 3.8 and 3.9 for electrical
energy consumption and peak demand respectively. Also, the factor of
95 percent representing the fraction of SCE, LADWP and G/P/B service area
5-29
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25 x 10V
RAND
(BASE CASE TOTAL CALIF.)
SRI (TOTAL CALIF.)
SCE
1970
2000
Figure 3.6. Per Capita Consumption of Electrical Energy SRI Total
California and SCE
60 x 10"
50
30
20
1970
SCE * LAOUP + (B/P/B)
ESTIMATE BASED ON SRI
SOURCE: UTILITY FORECASTS OF
SCE AND LAOWP, ESTIMATED FOR
G/P/B, BASED ON SRI TABLE C-12
1990
2000
YEAR
Figure 3.7. Peak Demand, MW, SCE and LADWP and (G/P/B)
5-30
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3 x 10
11
o
t—I
I—
Q-
I *
O
oc.
\—
(_>
SCAB (BASED ON 95% OF SCE +
LADWP + G/P/B CONSUMPTION
CORRECTED TO SERIES E, NO
NET MIGRATION POPULATION
FORECAST)
0
1970
1980
1990
2000
YEAR
Figure 3.8. Forecast Energy Consumption in the South Coast Air Basin
60 x 10 r-
S 40
o.
S 20
CO
0
1970
SCAB (BASED ON 95% OF SCE +
LADWP + G/P/B CONSUMPTION
CORRECTED TO SERIES E, NO
NET MIGRATION POPULATION
FORECAST)
_^ I I
1980
1990
2000
YEAR
Figure 3.9. Forecast Peak Demand in the South Coast Air Basin
5-31
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within the South Coast Air Basin has been included. The net result is
to decrease the values of each curve by about 15 percent in the year 2000
with lesser changes in the intervening years.
3.2.1 Forecast Power Capabilities for South Coast Air Basin
The necessary increases in power generation facilities have been
carefully planned by the utilities out to the next ten years. This plan-
ning data for SCE and LADWP have been combined (with the assumption that
the existing generation capabilities of G/P/B remain constant) to develop
an estimate of the expected sources of power generation for the next ten
years. The results of this exercise are shown in Fig. 3.10 (not corrected
to SCAB growth). Also shown is the forecast peak demand of Fig. 3.7
multiplied by 1.23 to allow for reserve capacity. As previously noted,
nuclear power does not figure significantly in the near term sources.
There is some expansion in coal fired sources representing the phasing of
additional generating units as they become available at the existing remote
sites. The growth in hydro-electric capability represents mostly the addi-
tion of pumped storage facilities and capabilities due to integration of
facilities with the State Water Project. The remaining major fraction of
generation capability is represented by facilities fired by oil (or gas
when it is available). The category "Other" represents primarily firm
purchase from northern sources.
Beyond the next ten years we have relied upon the SRI forecasts for
Southern California generation capability. These estimates have been
coupled with the utility planning data to derive a composite forecast for
generation capability of the SCE, LADWP, and G/P/B service area (not
corrected to SCAB growth) to the year 2000 as shown in Fig. 3.11. Again,
we have bounded the total capacity requirement by scaling the peak demand
of Fig.. 3.7 by a factor of 1.23. By the year 2000, nuclear energy is
4
SRI assumes that power generation facilities will run with a reserve
capacity of 18.6 percent. This factor gives good agreement with the
Rand model which scales capacity from electric energy consumption assum-
ing 30 percent for maintenance, outage, and contingency.
5-32
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45 x 1(T
40
35
30
_
-------
I
u>
100 x 10J,-
PLANNED ADDITIONS
(SCE + LADWP + G/P/B)
1980
Csl
^fr
BASED
ON SRI
FORECASTS
1990
2000
YEAR
Figure 3.11. Power Capability, SCE Plus LADWP and (Glendale/Burbank/Pasadena)
-------
expected to provide the major fraction of electrical generation capacity.
In terms of electrical energy production (annual KWH) nuclear sources
will be even more significant since they are expected to be base loaded.
Because of the uncertainty in future gas supplies and the consumer priori-
ties accorded them, gas is not expected to be a significant fuel source.
Thus oil fired plants continue as significant power sources throughout
the forecast period.
The implied expansion in fossil fueled generation capacity in the
Air Basin involves no new power plant sites. Instead the new capacity
is to be derived by either providing additional generating units at
existing sites or by retrofitting existing plants with combined cycle
capability.
As is apparent in Fig. 3.11, the planned additions based on the
SCE and LADWP planning studies produce a faster expansion than may be
necessary when compared to the scaled (x 1.23) peak demand forecast.
We would assume that the difference may be accounted for in allowances
for schedule shippage plus the fact that there is likely to be a buildup
in the utilization of new plant after it first comes on line. Also,
overall transmission losses may increase slightly due to increased
utilization of the remote coal fired plants.
3.2.2 Hourly Power Demand Profiles
Of significant importance to the feasibility of electric car use
and its ultimate public acceptance is the ease or difficulty with which
it can be re-energized. A battery powered vehicle will have to be
recharged daily and it is anticipated that one feasible recharge routine
would rely on the potential power availability during the typical early
morning off-peak hours. Depending on the amount of ordinarily unused
off-peak energy available for a given level of electric car use, there
may or may not exist a requirement for additional power generation
facilities. The capability to readily use this potential off-peak
5-35
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energy depends further on details of the electric power service for
individual households.
To determine the likely potential for available off-peak power for
the purposes of electric car re-charge, we first examined the typical
hourly demand profiles experienced by SCE and LADWP. Figure 3.12a pre-
sents the power demands by hour of the day in terms of the percent of
the yearly peak demand which occurs typically sometime in August.
Figure 3.12a is the peak demand case for August while Fig. 3.12b depicts
the case for April, a typical off-peak month. The hourly demand profiles
for SCE and LADWP compare favorably and we have arbitrarily chosen the
SCE profile for the month of August as representative of the situation
to be expected in the future. (Utility planning detail is insufficient
to allow one to deduce likely shifts in future hourly demand profiles.)
The SCE peak month profile of hourly demand (Fig. 3.12a) has been
scaled by the forecast peak demand for SCAB (Fig. 3.9) for the selected
years 1980, 1990, and 2000. For each of these years we have also scaled
the projected power capability curves of Fig. 3.11 to show how this hourly
demand will be met. The results are shown in Figs. 3.13(a-c) for the
three years 1980, 1990, and 2000, respectively. In each case, we sought
to determine which of the available power sources would provide the base
loads (used continuously with allowances only for maintenance and contin-
gencies) and which would be associated with the peak loads. We also show
in Fig. 3.13 hourly profiles for an average Monday and an average
Saturday for an off-peak month, May, as experienced by LADWP.
Nuclear power generation is economically most efficient when
utilized for base loads and present utility planning is based on that
criterion. Although in the past many hydro-electric plants were eco-
nomically justified on the basis of meeting peak needs, it does repre-
sent a clean energy source and under present constraints will probably
5-36
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£ 100
>-
_i
o:
UJ
>-
U,
O
I—
Z
UJ
O
UJ
Q_
Q
SCE
LADWP\
^ 0
o
0600
I
1200
HOUR
1800
2400
a. Peak Day In Peak Month
S 100
>-
_J
o:
UJ
>-
u.
O
I—
•z:
UJ
o
UJ
Q.
Q
UJ
o
I
0600
1200
HOUR
1800
2400
b. Typical Day In Off-Peak Month
Figure 3.12. Hourly Demand, 1973
5-37
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1001
S
5
21,600 KM
PEAK DAY (AUGUST)
(SCE)
\\
\V AVG. MONDAY
NX (MAY)
AVG. SATURDAY
(MAY)
HYDROELECTRIC
6:00 AM 13:00 NOON 6:00 PM
a) 1980
12:00 MIDNIGHT
loot
2
u.
O
£
\ f ^ X
V^ >r . , /^\\
—^x^^r^y—^>: •> ^ \>
PEAK DAY (AUGUST)
(SCE)
/ ' COAL S GAS
> -/
AVG. MONDAY
\X (MAY)
- MK. SATURDAY
(MAY)
NUCLEAR
HYDRO
6:00 AM
12:00 NOON 6:00 PM
12:00 MIDNIGHT
b) 1990
100% r- • 50,000 M«
DAY (AUGUST)
NUCLEAP
(SCE)
->
\\ AVG. MONDAY
NX (MAY)
AVG. SATURDAY
(MAY)
| HYDROELECTRIC |
6:00 AM
12:00 NOON 6:00 Pll
12:00 MIDNIGHT
c) 2000
Figure 3.13. Profile of Hourly Demands with Projected Supply, South Coast
Air Basin
5-38
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be used for other than just peak loading. Gas, as one of our cleanest
sources, will be used as available. Coal, which in our case represents
power stations in Nevada and the Four Corners, will likely be preferred
to oil burning in the Air Basin provided the coal stations can meet the
air quality standards of their respective states. All oil burning repre-
sents the fueling of power plants within the Air Basin and it seems likely
that in meeting air quality standards the burning of oil would be minimized.
Furthermore, oil will be the most expensive energy source.
14
This general assessment is corroborated in a recent paper by
Eugene N. Cramer of SCE wherein he indicated that newer fossil fueled
and nuclear plants will be base loaded with older fossil fueled plants
next to be brought on line with gas turbine operations last to be used
in meeting peak demands. For the Los Angeles region, newer fossil fueled
plants are really represented by the coal plants in the desert and at the
Four-Corners region of the Southwest. Older fossil fueled plants are
primarily those in the Los Angeles Basin.
Although in the very near term, prior to 1980, there may be some
problems with sufficient supplies of low sulfur oils, we have previously
noted that by 1980 de-sulfurized fuel oils should be available.
Overall generation efficiency for plants in the South Coast Air
Basin is expected to remain constant at around 36 percent to 1983.
12
Detailed planning data for Southern California Edison indicates that
approximately 1669 megawatts of combined cycle capacity will be installed
by that time. Although the combined cycle capability is expected to
*
It is difficult to accurately estimate just how much hydro-electric
generation will be allocated to peak demands. Total hydro capacity
includes power dams in the Sierra Nevada mountains, with variable water*
conditions throughout the year, and pumped storage capabilities as part
of the State water project. The depiction of constant generation through-
out the day may be over simplified, but because of the minor contribution
of hydro in the future this simplification does not unduly impact the
analysis.
5-39
-------
operate at generally higher efficiencies, there will not be enough of
it to significantly change the overall efficiency for SCE's operation.
Beyond 1983, our baseline forecast shows little change in the generating
capacity of fossil fuel plant (depicted by oil, gas and coal in Fig. 3.11)
with most of the projected increase in demand to be served by nuclear
power plants. Thus, except for modernizing and adding combined cycle
capacity to older plant, we expect little change in the overall generating
efficiency of fossil fuel plants out to the year 2000. Furthermore, since
combined cycle efficiency will be better than conventional steam plants
and fossil fuel costs will be a significant factor in overall generating
costs, we would expect combined cycle plants to be brought on-line in
serving peak demands before conventional steam plants. The last compon-
ent of generating capacity to be used in peak demands will be isolated gas
turbines and other internal combustion powered generators owing to their
poorer thermal efficiencies.
Figures 3.13(a-c) indicate that oil will most likely be used to
satisfy peak demands except for low demand days in low demand seasons
(e.g., a typical Saturday in May). If the available capacity during normal
off-peak periods is to be used to recharge electric cars, the sources to
be used at any given time will vary between low and high demand seasons
and with the changing proportions in the mix of sources over the forecast
period. In 1980 additional off-peak generation for recharging electric
cars will come from oil-fired plants throughout most of the entire year.
By 1990 substantial off-peak generation can come from coal- and gas-fired
plants during low demand seasons, but during the peak seasons, off-peak
generation will come largely from oil-fired plants. By the year 2000,
there will be sufficient nuclear capacity such that it can provide addi-
tional off-peak generation during low demand seasons and a very slight
amount in the peak season. Otherwise substantial off-peak generation in^
the peak season will be met first with coal and gas and finally oil if
needed.
5-40
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The advent of significant electric car usage utilizing the early
morning periods for recharge may virtually eliminate the distinction
between peak and off-peak demands. Such a condition could conceivably
alter utility planning considerations with respect to how and when indi-
vidual power plants are used. A less pronounced off-peak period could
economically justify a greater utilization of nuclear plants. Since the
forecast nuclear capacity is already planned to be base loaded additional
nuclear plants would have to be constructed. This, in turn, would imply
that presently existing technical and environmental problems be speedily
resolved, significant increases occur in the requirement of capital invest-
ment rates, and a faster than normal write-off of older facilities be
pursued. Although such an expansion of nuclear power, in conjunction w±th
electric car use may significantly relieve air pollution, there would
likely be attendant additional dollar costs due to the greater rate of
expansion of capital costs.
5-41
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4 AVAILABILITY OF ELECTRIC POWER AND ENERGY FOR ELECTRIC CARS
The total additional potential energy production per day, due
to utilizing off-peak capacity, will depend on many factors such as
required downtime for routine maintenance and reserve for repairs.
Presently, the utilities recognize August as the month of greatest
demands and they tend to schedule maintenance periods around the
peak demand month. For example, SCE estimates that, for adverse
water conditions, the margin of available capacity over peak
demand (month of August) may be as low as 13 percent.
We have made calculations based on the Figs. 3.13(a-c)
of the potential electrical energy available during the off-peak
pc-rlod assuming that generation facilities could be run at 85 percent
of peak demand. The results of this calculation are presented in
Fig. 4.1 which shows daily off-peak kilowatt-hours that potentially
could be used for electric car recharge for each year of the forecast
15 x 10
7
10
-------
period. The curve Is based on the peak demand forecast scaled to the
South Coast Air Basin study region (see Fig. 3.9).
The magnitude of the available off-peak energy indicated in Fig.
4.1 is quite significant. Electric cars may be expected to achieve
energy consumption rates of 0.4 to 1.0 KWH (at the point of recharge)
per mile of travel. Thus, a 10-KWH expenditure, for example, may
produce 10 to 25 miles of travel. The 1980 recharge capability from
Fig. 4.1 is pproximately 50 x 10 KWH per day which could allow as many
as 5 x 10 cars a 10-KWH recharge. All electric losses for recharge must
of course be accounted for and our assumption of an 85 percent off-peak
load may be too generous. Nonetheless, the calculation indicates that
there is the recharge potential to accommodate on the order of a million
or so electric cars in the Air Basin without requiring additional
generation facilities.
The ability of individual householders to conveniently recharge an
electric car may depend on the characteristics of the electric service
provided the homes. Most modern garages of single family residences will
have at least one convenience outlet on a 15-ampere, 110-volt circuit and
under some circumstances may have outlets on circuits of greater capacity
such as for a dryer or large power tools. Assuming that at least 1.2 kW
could be delivered over an 8-hour period from an ordinary convenience
outlet, approximately 10 KWH of recharge (no allowance has been made for
recharge losses) could be obtained which as we have already noted, can
represent a significant daily mileage.
However, there are many apartment complexes that may have no garages
at all while apartments in general have fewer garage spaces provided than
apartment dwellers have cars. Of those apartments with garages, many are
neither required to have nor are built with convenience outlets.
Although the National Electrical Code, 1968, does not require garages of
single family dwelling units to have such outlets, it does require that
a special branch circuit be installed for a laundry machine, which quite
often is located in the garage in the Los Angeles region.
5-43
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According to data published in 1965 for the Nation as a whole by
the Department of Housing and Urban Development, 45 percent of homes of
less than $9,000 value had garages, with corresponding percentages of
66 percent and 78 percent for homes of $15,000 and $20,000 (or greater)
value, respectively. New homes constructed in that year had significantly
fewer garages and a greater proportion of carports. It is probably even
less likely that carports as compared to garages would have convenience
outlets.
New residential construction, both nationally and in Southern
17 18
California, has shifted predominantly to multi-family structures. '
Garage facilities in Southern California communities for multi-family
units may consist of detached car stalls or simply paved parking areas.
Consequently, such new construction may have little or no built-in capa-
bility for electric car recharge and if desired, would have to be supplied
on a retro-fit basis. Apartment complexes without garages or stalls may
in practice preclude the utilization of electric cars unless economically
feasible recharge systems could be provided on a retro-fit basis.
Although population is forecast to grow more slowly through the
next several decades, the rate of formation of new households will remain
at generally higher rates of growth than the population itself. Accord-
ing to the SCE forecast, they expect to increase the number of resi-
dential customers by a factor of 1.65 between 1970 and 1995. Thus, a
significant fraction of future customers will be housed in residential
units yet to be built. Without the introduction of electric cars, we
foresee no conditions that will necessitate a fundamental change in the
building and electrical codes that would be inherently useful to electric
cars.
r
A signicant shift to elecrric cars with routine recharging during
the present off-peak period may cause utilities to readjust price schedules
for various users especially with respect to peak and off-peak rates.
5-44
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However, in the absence of foreseeable shifts in demand the present trend
is not to alter the basic structure of the price schedules. Indeed, the
SRI study believes that the fact that rate structures allow large users
to pay lower average unit costs than small users is a phenomenon existing
literally in every sector of our economy and consequently they foresee
no marked changes in this phenomenon that are likely to occur. On the
other hand, the SRI study does foresee a steady but slow rise in energy
costs throughout the forecast period. Figure 4.2 presents curves showing
the SRI forecast for future prices of conventional fuels for power plants
in units of dollars per million Btu's assuming 3 percent per year infla-
tion. For reference in the figure, a curve of the wellhead price of crude
oil in constant 1970 dollars from the NPC study is also shown. The circled
point shown for the year 1971 is the average cost of all conventional fuels
sold to California generating plants. Based on these forecast prices for
fuels in combination with the expected costs for nuclear generated power,
SRI forecast the retail prices of electrical energy in cents per KWH out
*
to the year 2000. Figure 4.3 presents these forecasts for residential
and industrial users which are the highest and lowest prices of the large
consumers respectively. Again, the 1971 average price for all users is
shown by a solid dot.
Since the SRI forecasts on fuel costs are not in basic disagreement
with the NPC forecasts, we have chosen to use their derived electrical
energy price forecasts for our baseline condition.
*
Due to the current high prices of imported oil that electric utilities
must pay to fill our their fuel requirements, the average cost of elec-
tricity in California is rising rapidly and under Public Utility Com-
mission I'M'! :•'•; ».hese costs can fairly easily be passed on to the customer.
Should inte .1 national oil prices stabilize in the future as expected by
the NL'C IP ••••-• fsul, we would expect utility rates to accurately reflect
that condi'. Lou as well which correspond to the estimates presented in
Figs. 4.2 and 4.3.
5-45
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3r-
Of CO
LU
Z *0
a: zs
o u-
00
SRI AT POWER PLANT CALIF. PROJECT^
(INFLATION 63S/YR)
0
1970
CRUDE OIL AT WELLHEAD
US ENERGY OUTLOOK
(CONSTANT 1970 DOLLARS
AVERAGE PRICE OF ALL FUEL SOLD
•FOR GENERATION IN CALIF. (EDISON
ELECTRIC INSTITUTE, YEAR BOOK 1971]
I I
1980
1990
2000
YEAR
Figure 4.2. Forecast of Power Plant Fuel Prices
Br-
OL
LLJ
-------
REFERENCES
1. Energy R&D and National Progress, prepared for the Interdepart-
mental Energy Study by the Energy Study Group under the direction
of Ali Bulent Cambel, June 1964.
2. US Energy Outlook. A report of the National Petroleum Council's
Committee on the US Energy Outlook, December 1972.
3. The US Energy Problem. Inter Technology Corporation, Report C 645,
November 1971.(prepared for the National Science Foundation).
4. Meeting California's Energy Requirements. 1974-2000, Stanford
Research Institute, May 1973.
5. California's Electricity Quandry: Estimating Future Demand,
Rand, September 1972.
6. Edison Electric Institute Statistical Yearbook of the Electric
Utility Industry for 1971, Edison Electric Institute, October,
1972.
7. Los Angeles Times, January 1, 1974
8. Stephen R. Boyle, The Dollar and Energy Economics of the Gaseous
Diffusion Enrichment Process, General Research Corporation,
IM-1784, March 1973.
9. Weekly Energy Report. Vol. 1, No. 27, August 13, 1973.
10. Study of the Future Supply of Low Sulfur Oil for Electrical
Utilities. Hitman Associates Inc., February 1972.
11. System Forecasts. 1973-1995, Southern California Edison Company,
February 1973.
12. Assorted Planning Documents of the Southern California Edison
Company, transmitted privately to GRC, October 1, 1973.
13. Assorted Planning Documents of the Los Angeles Department of
Water and Power, transmitted privately to GRC, September 14, 1973.
*
14. Eugene N. Cramer, Advanced Batteries and Energy Storage, proceedings
of Advances in Battery Technology Symposium, Electrochemical Society,
Inc., December 1, 1972.
5-47
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15. The Automobile and Air Pollution; A Program for Progress. Part II.
US Department of Commerce, December 1967.
16. Annual Report. 1965. US Department of Housing and Urban Development.
17. US Statistical Abstract, 1972.
18. Monthly Report of Building Permit Activity in the Cities and
Counties of California. Security Pacific Bank, August, 1972
through June 1973.
5-48
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TECHNICAL REPORT DATA
(Please read'Instructions on the reverse before completing)
1. REPORT NO.
EPA-460/3-74-020-b
3. RECIPIENT'S ACCESSION NO.
PB 238 878/AS
4. TITLE AND SUBTITLE
Impact of Future Use of Electric Cars in The Los Angeles
Region: Volume II -Task Reports on Electric Car Char-
acterization and Baseline Projections
5. REPORT DATE
October 1974
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
W.F. Hamilton, J.C. Eisenhut,
G.M. Houser and A.R. Sjovold
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
General Research Corp.
P.O. Box 3587
Santa Barbara, California 93105
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-01-2103
12. SPONSORING AGENCY NAME AND ADDRESS
U.S. Environmental Protection Agency
Office of Air and Waste Management
Office of Mobile Source Air Pollution Control
Alternative Automotive Power Systems Div.
13. TYPE OF REPORT AND PERIOD COVERED
Final T?pnm-f 1
14. SPONSOFlING AGENCY CODE
Arbor
"" /•am';
15. SUPPLEMENTARY NOTES
16. ABSTRACT
Impacts of the use of electric cars in the Los Angeles region in
1980-2000 were projected for four-passenger subcompact electric cars using
lead-acid and advanced batteries, with urban driving ranges of about 55
and 140 miles, respectively. Data from Los Angeles travel surveys shows
that such cars could replace 17 to 74 percent of future Los Angeles autos
with little sacrifice of urban driving. Adequate raw materials and night-
time recharging power should be available for such use in the Los Angeles
region. Air quality improvements due to the electric cars would be minor
because conventional automobile emissions are being drastically reduced.
The electric cars would save little energy overall, as compared to conventional
subcompacts, but would save a considerable amount of petroleum if they were
recharged from the nuclear power plants that are planned. The electric
subcompacts would be 20-60% more expensive overall than conventional subcompacts
until battery development significantly reduces battery depreciation costs.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS C. COSATI Field/Group
Electric Vehicles
Air Pollution
Batteries
Conservation
13 B
18. DISTRIBUTION STATEMENT
Release unlimited
19. SECURITY CLASS (This Report)
Unclassified
21. NO. OF PAGES
358
20. SECURITY CLASS (TMspage)
Unclassified
22. PRICE
$9.50
EPA Form 2220-1 (9-73)
5-49
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