Emission Adjustments for Onroad
Vehicles in MOVES4
£% United States
Environmental Protect
Agency
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Emission Adjustments for Onroad
This technical report does not necessarily represent final EPA decisions
or positions. It is intended to present technical analysis of issues using
data that are currently available. The purpose in the release of such
reports is to facilitate the exchange of technical information and to
inform the public of technical developments.
Vehicles in MOVES4
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
NOTICE
4>EPA
United States
Environmental Protection
Agency
EPA-420-R-23-021
August 2023
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Table of Contents
1. Introduction .7
2. Temperature Adjustments .8
2.1. Data Sources for Gasoline Temperature Effects 8
2.2. Temperature Effects on Gasoline Start Emissions 10
2.2.1. THC and CO Start Emissions for Gasoline-Fueled Vehicles 11
2.2.2. Temperature Effects on Gasoline NOx Start Emissions 20
2.2.3. Temperature Effects on Gasoline PM2.5Start Emissions 23
2.3. Temperature Effects on Running Exhaust Emissions from Gasoline Vehicles 32
2.3.1. THC, CO and NOx Running Exhaust Temperature Effects 32
2.3.2. PM2.5 Running Exhaust Temperature Effects 32
2.4. Temperature Effects on Diesel Vehicles 36
2.4.1. THC, CO, and NOx Temperature Effects for Diesel Vehicles 36
2.4.2. PM Temperature Effects for Diesel Vehicles 39
2.4.3. HD Diesel NOx Temperature Effects for Model Years 2027 and Later 40
2.5. Temperature Effects on Compressed Natural Gas Vehicles 41
2.6. Temperature Effects on Start Energy Consumption 41
2.7. Temperature Adjustments for Electric and Fuel-Cell Vehicles 43
2.8. Conclusions and Future Research 45
3. Humidity Adjustments 47
3.1. Humidity Adjustment Equation 47
3.2. Future Research 48
4. Air Conditioning Adjustments .49
4.1. Air Conditioning Effects Data 49
4.2. Air Conditioning Effects on Emissions and Energy 51
4.2.1. Full A/C Adjustments for THC, CO and NOx Emissions 51
4.2.2. Full A/C Adjustments for Energy Consumption 52
4.3. Adjustments to Air Conditioning Effects 52
4.4. Conclusions and Future Research 53
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5. Inspection and Maintenance Programs 54
5.1. Overview of Exhaust Inspection & Maintenance in MOVES 54
5.2. Development of MOVES l/M Factors 55
5.2.1. Inspection & Maintenance in MOBILE6 58
5.3. I/M Compliance Factors 58
5.4. Default l/M Program Descriptions (IMCoverage) 59
5.5. Future Research 64
6. Electric Vehicle Charging and Battery Efficiency .....66
6.1. MOVES Design and Implementation 66
6.2. Data Analysis and Literature Review 67
6.2.1. Charging Efficiency 67
6.2.2. Battery Efficiency 68
6.2.3. Conclusion 69
7. Averaging, Banking and Trading with Electric Vehicles ...71
7.1. ABT Impacts for Criteria Pollutants 71
7.2. ABT Impacts for Energy Consumption and C02 73
8. References 76
Appendix A Derivation of Temperature, Humidity and Meteorology Calculations 82
Al. Data Sets and Quality Control 82
A2. County Temperature Assignment 83
A3. Temperature Recalculation 84
A4. Relative Humidity Recalculation 85
A5. Calculation of 10 Year Averages 85
A6. Calculation of Specific Humidity 86
A7. Calculation of Heat Index 87
Appendix B OTAQ Light-duty gasoline 2012 Cold Temperature Program .88
Appendix C Air Conditioning Analysis Vehicle Sample 89
Appendix D Consistency of MOVES EVTemperature Adjustment with Other Sources.. ..............91
Dl. North American Transit Bus Study 91
D2. Japanese Passenger Car Study 92
D3. Canadian Passenger Car Study 92
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D4. Conclusion 93
Appendix E Vehicles in the 2021 ORD Cold-temperature Program ......94
Appendix F Model-Fitting Information for Analysis of Fuel-Injection Technology 95
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List of Acronyms
AAA American Automobile Association
A/C air conditioning
ABT averaging, banking and trading
ACCF air conditioning correction factor
ASM Acceleration Simulation Mode
CO Carbon Monoxide
CDB county database
CF critical flow factor coefficient
CFR Code of Federal Regulations
CNG Compressed Natural Gas
CV coefficients of variation
DPF diesel particulate filter
ECCC Environment and Climate Change Canada
EPA U.S. Environmental Protection Agency
EV Electric Vehicle
E85 gasoline containing 70-85 percent ethanol by volume
F Fahrenheit
FTP Federal Test Procedure
GDI Gasoline Direct Injection
GHG Greenhouse Gases
GVWR Gross Vehicle Weight Rating
HC hydrocarbons
HP horsepower
ICE Internal Combustion Engine
l/M Inspection and Maintenance program
IM240 Inspection and Maintenance roadside vehicle driving schedule
KCVES Kansas City Light-Duty Vehicle Emissions Study
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kW
Kilowatt
LA-92
California dynamometer driving schedule for light-duty vehicles
LDT
Light-Duty Truck
LDV
Light-Duty Vehicle
LHDT
Light Heavy-Duty Truck
LLDT
Light Light-Duty Truck
MDPV
Medium-Duty Passenger Vehicle
M0BILE6
EPA Highway Vehicle Emission Factor Model, Version 6
MOVES
Motor Vehicle Emission Simulator Model
MPGe
Miles Per Gallon Equivalent
MSAT
Mobile Source Air Toxics rules
MSOD
Mobile Source Observation Database
NEI
National Emission Inventory
NMHC
Non-Methane Hydrocarbons
NMOG
Non-Methane Organic Gases
NMIM
National Mobile Inventory Model
NOx
Oxides of Nitrogen
OBD
On-Board Diagnostics
ORD
Office of Research and Development
OTAQ
Office of Transportation and Air Quality
PFI
Port Fuel Injection
PM
Particulate Matter
RIA
Regulatory Impact Analysis
SFTP
Supplemental Federal Test Procedure
SIP
state implementation plan
SRC
selective reduction catalysts
STP
scaled tractive power
SwRI
Southwest Research Institute
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THC
Total Hydrocarbons
US06
A drive cycle that is part of the SFTP
VIN
Vehicle Identification Number
VOC
Volatile Organic Compound
VSP
vehicle specific power
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1. Introduction
The United States Environmental Protection Agency's Motor Vehicle Emission Simulator—commonly
referred to as MOVES—is a set of modeling tools for estimating air pollution emissions produced by
onroad (highway) and nonroad mobile sources. MOVES estimates the emissions of greenhouse gases
(GHGs), criteria pollutants, and selected air toxics. The MOVES model is currently the official model for
use for state implementation plan (SIP) submissions to EPA and for transportation conformity analyses
outside of California. The model is also the primary modeling tool to estimate the impact of mobile
source regulations on emission inventories.
MOVES calculates emission inventories by multiplying emission rates by the appropriate emission-
related activity, applying correction and adjustment factors as needed to simulate specific situations,
and then adding up the emissions from all sources and regions. The highway vehicle emission rates in
the MOVES model represent emissions under a single (base) scenario of conditions for temperature,
humidity, air conditioning load and fuel properties. MOVES is designed to adjust these base emission
rates to reflect the conditions for the location and time specified by the user. MOVES also includes the
flexibility to adjust the base emission rates to reflect the effects of local Inspection and Maintenance
(l/M) programs. This report describes how these adjustments for temperature, humidity, l/M, and air
conditioning were derived. Adjustments for fuel properties are addressed in a separate report.1
This report describes MOVES adjustments that affect running exhaust, start exhaust, and extended
idling exhaust emissions for Total Hydrocarbons (THC), carbon monoxide (CO), nitrogen oxides (NOx),
fine particulate matter (PM2.5) and energy consumption. The temperature effects that impact these
pollutants, also affect the pollutants that are calculated from these pollutants in MOVES, such as volatile
organic compounds (VOC)2 and individual toxics such as benzene3 (chained to THC), N02 (chained to
NOx)4-5, elemental carbon (chained to PM2.5)2, and C02 emissions (chained to energy).6 The definitions of
these pollutants and the relationship to the primary pollutants are discussed in the cited MOVES
reports. The crankcase emission processes4,5are chained to running exhaust, engine start, and extended
idling exhaust emissions, and thus are similarly affected by the temperature adjustments described in
this report. The impact of fuels, temperatures, and l/M programs on vapor venting, permeation, and
liquid leaks is addressed in a separate report on evaporative emissions.7
For MOVES4, this report was updated to account for adjustments needed to model energy consumption
from battery and fuel-cell electric vehicles. This includes new temperature adjustments as described in
Section 2.7, and adjustments to account for electric vehicle charging and battery efficiency as described
in Section 6. Also, in Section 7, we describe adjustments to energy consumption and hydrocarbon and
NOx emissions from internal combustion engine vehicles that account for the averaging, banking, and
trading provisions of light-duty regulations in the context of a growing population of electric vehicles.
We updated the temperature correction for HD diesel vehicles of model year 2027 and later (Section
2.4.3). We also updated the humidity adjustments for NOx emissions (Section 3) and reorganized the
l/M section (Section 5).
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2. Temperature Adjustments
Emission rates in MOVES are adjusted by the ambient temperature to account for temperature effects
that impact emissions such as inefficient oxidation of emissions at cool catalyst temperatures and
additional fuel needed to start an engine at cold temperatures. In MOVES, exhaust emissions are
adjusted relative to their base rates at 75 degrees Fahrenheit based on two considerations:
1. Ambient temperature
2. The latent engine heat from a previous trip, applied as an adjustment based on the length of
time the vehicle has parked since operating (soak time).
This report describes the adjustment based on ambient temperature. Soak time and start emissions are
addressed in the light-duty4 and heavy-duty5 emission rates reports.
This report addresses temperature sensitivity of emissions from gasoline vehicles in Sections 2.1 through
2.3. Although the gasoline temperature effects are developed based on emissions data from light-duty
gasoline vehicles, they are applied to all gasoline vehicles in MOVES, including motorcycles, heavy-duty
gasoline vehicles, and light-duty vehicles fueled on ethanol-gasoline blends.
Section 2.4 discusses the temperature effects derived for diesel vehicles. The data used to derive diesel
temperature effects is based on light-duty diesel vehicles but are applied to all diesel vehicles in MOVES
due to a lack of temperature effect data on heavy-duty diesel vehicles. The diesel temperature effects
are also applied to CNG buses as discussed in Section 2.5.
Section 2.6 discusses the temperature effects for energy consumption for all non-electric vehicle types
in MOVES. These effects are applied only to vehicle starts.
Section 2.7 describes temperature effects on energy consumption from battery and fuel-cell electric
vehicles.
2.1.Data Sources for Gasoline Temperature Effects
To determine the impact of ambient temperature on running emissions, our analysis included the Bag 2
emissions of Federal Test Procedure (FTP) tests as well as US06 tests (without engine starts).
For start emissions, measurements from both the Federal FTP and California Unified Cycle (3-phase / 3-
bag tests) were used. Within each test cycle, the first and third phases are identical driving cycles, but
the first phase begins with a cold-start (cold engine and emission control equipment) while the third
phase begins with a hot-start (relatively warm engine and control equipment). The difference between
Bag 1 and Bag 3 (in grams) are the emissions attributed to the cold start of the vehicle.
The data used in these analyses are from the following sources:
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Table 2-1 Summary of Data Sources
Data Source
Test
Temperatures Tested (deg. F)
# of Vehicles
MY Range
MSOD
FTP +
15-110
Hundreds
Pre-2005
ORD (2002)
FTP, IM240
-20, 0, 20, 40, 75
5
1987-2001
MSAT
FTP
0, 20, 75
4
2005
OTAQ
FTP, US06
0, 20, 75
9
2006, 2010
ORD (2021)
FTP
20, 71
3
2014-2015
• MSOD - EPA's Mobile Source Observation Database (MSOD) as of April 27, 2005. EPA has
acquired data representing emissions measurements over various cycles (often the FTP) on
tens of thousands of vehicles under various conditions. EPA has stored those test results in its
Mobile Source Observational Database (MSOD).8
For the data stored in MSOD, we limited our analysis to those tests for which vehicles were tested at
two or more temperatures. The subset of tests meeting this criterion covered a temperature range from
15 to 110°F. Note that the results acquired from MSOD were collected in aggregate or "bag" modes.
• ORD (2002) - The EPA Office of Research and Development (ORD) contracted (through the
Clean Air Vehicle Technology Center, Inc.) the testing of five cars (model years 1987 through
2001). Those vehicles were tested using both the FTP and the IM240 cycles under controlled
conditions at temperatures of: 75, 40, 20, 0 and -20-F.9
• MSATProgram - Under a contract with EPA, the Southwest Research Institute (SwRI) tested
four Tier 2 vehicles (2005 model year car and light-duty trucks) over the FTP under controlled
conditions at temperatures of: 75, 20, and 0-F. This program was used in the Regulatory
Impact Analysis of Final Rule: Control of Hazardous Air Pollutants from Mobile Sources10,
which is referred to as MSAT-2 in this report to distinguish it from an earlier mobile source air
toxics (MSAT) rulemaking.11 The MSAT-2 rule required Tier 2 vehicles to meet a non-methane
hydrocarbon (NMHC) standard on the FTP cycle of 0.3 g/milefor light-duty vehicles (<6,000
lbs) beginning phase-in for model year 2010 vehicles.12
OTAQCold Temperature Program (2012) - EPA's Office of Transportation and Air Quality (OTAQ)
contracted the testing of nine Tier 2 vehicles (2006 and 2010 model year car and light-duty trucks). Eight
of the nine vehicles were Mobile Source Air Toxics (MSAT-2) rule compliant. Vehicles were tested on the
FTP and US06 under controlled conditions 75, 20, and O^F. Information on the tested vehicles is
summarized in 0 . Note that for the estimation of the THC and CO cold start effects the two GDI vehicles
were excluded from the analysis.
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ORD (2021) - A recent program was conducted under the auspices of the Office of Research and
Development (ORD). In this project, emissions were measured on three vehicles equipped with gasoline
direct injection (GDI). All three vehicles are passenger cars, including a Ford Fusion, Honda Accord and
Volkswagen Jetta, all in model year 2015. One of the vehicles is naturally aspirated, and the others
turbocharged. Mileage at test ranged from 9,000 to 13,000 miles. Emissions were measured on chassis
dynamometers over two test cycles, the Federal Test Procedure (FTP) and the US06. For the FTP, results
are available by phase. Emissions were measured on a single fuel, a "winter E10" at two temperature
levels, 20 and 71°F. A variety of pollutants were measured, including the gaseous criteria pollutants and
particulate matter. Particulate matter, as PM2.5, was measured gravimetrically on three replicate filters
in a heated box and with sample flows drawn from a constant-volume sampler (CVS). Replicate
measurements were also collected from each filter holder.
2.2.Temperature Effects on Gasoline Start Emissions
When a vehicle engine is started, emissions can be higher than during normal operation due to the
relatively cold temperature of the emissions control system. As these systems warm up to their ideal
operating temperature, emissions from the vehicle can be dramatically reduced. The cold start effect
can vary by pollutant, temperature, and vehicle technology.
The effects of ambient temperature on THC, CO, and NOx start emissions were developed using the
following approach:
• No adjustment for temperatures higher than 75°F. 75°F is the midpoint of the allowable
temperature range (68°F-86°F) per the FTP.
• Additive adjustments for temperatures below 75°F. These adjustments are added to the
emissions that would occur at 75°F.
• Calculate the adjustments as either polynomial (Equation 2-1) or log-linear (Equation 2-2)
functions, depending on model year group and pollutant:
Additive Grams = A*(Temp-75) + B*(Temp-75)2 Equation 2-1
Additive Grams = Be A* + c Equation 2-2
This approach provides a value of zero change for the additive adjustment at 75°F (i.e., the temperature
of the federal FTP test). The coefficients, A and B, for the adjustment equations are stored in the
StartTempAdjustment table. This table contains temperature effect coefficients for each model year
group, operating mode, and pollutant.
In MOVES, the temperature effects for older model year groups use polynomial function (Equation 2-1)
and more recent model year vehicles use log-linear function (Equation 2-2). The data processing and the
model fitting process differed for the polynomial and log-linear fits, and each is described separately
below.
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2.2.1. THC and CO Start Emissions for Gasoline-Fueled Vehicles
In developing temperature adjustments for THC and CO start emissions, both polynomial and log-linear
regression models were used to fit the data. Data anomalies were resolved by combining two or more
model year groups to obtain a larger dataset, or by removing anomalous data points. We also
distinguish temperature effects between pre-MSAT-2 (Mobile Source Air Toxics) and MSAT-2 compliant
vehicles, which began phase-in starting in 2010. The MSAT-2 rule included the first regulation on low
temperature (20°F) non-methane hydrocarbon (NMHC) emissions for light-duty and some medium-duty
gasoline vehicles.12
Polynomial Fits
The coefficients for THC emissions for pre-2006 gasoline vehicles and CO emissions for pre-2001
gasoline vehicles were calculated with polynomial fits to data processed in the following steps. First, the
cold start emissions (grams/start) were calculated as the difference between Bag 1 and Bag 3 emissions
for each relevant vehicle test in the MSOD, ORD and MSAT data. Next, the cold start emissions were
stratified by model year groups. The data was initially grouped according to the following model year
groups:
• 1960 to 1980
• 1981 to 1982
• 1983 to 1985
• 1986 to 1989
• 1990 to 1993
• 1994 to 1999
• 2000 to 2005
Then, the mean emissions at 75°F were subtracted from the mean emissions at the other temperatures
to determine the change in emissions as functions of ambient temperature. Then, we modeled the
changes in cold-start emissions as a polynomial function of temperature minus 75°F. The additive
adjustments are set equal to zero for temperatures higher than 75°F. Thus, we did not use the changes
in emissions from temperature above the FTP temperature range (68^ to 862F). The model year groups
were aggregated to larger intervals when the less aggregated groups yielded non-intuitive results (e.g.,
older model year group had lower cold start emissions).
Table 2-2 summarizes the coefficients used with Equation 2-1 (polynomial) to estimate additive start
temperature adjustments for older model year gasoline vehicles.
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Table 2-2 Polynomial Model Coefficients for CO Temperature Effects for 2000 Model Year and Earlier Gasoline
Vehicles and THC Temperature Effects for 2005 and Earlier Gasoline Vehicles
CO
THC
Model Year Group
A
B
A
B
Pre-1981
-4.677
-0.631
1981-1982
-4.631
-0.414
1983-1985
-4.244
-0.361
1986-1989
0.002
1986-2000
0.023
1990-2005
0.003
The THC test data for the 1986-1989, and 1990-2005 model year groups included the ORD program
vehicles that were tested at an ambient temperature of -20°F. However, when this ultra-low
temperature data was included, the "best fit" THC regression curves (linear, quadratic, and cubic) all
exhibited poor fits for temperatures from zero through 20°F. We removed the five ORD vehicle tests
conducted at -20°F, which improved the estimate of the cold-start THC emissions in the more common
0° F to 20°F range. Therefore, the coefficients in MOVES are based on the changes in cold-start
emissions for temperatures from zero through 75°. However, these coefficients are applied to all
ambient temperatures below 75°F in MOVES.
For CO, the temperature effect developed based on the 1994-2000 model year vehicles was applied to
all model years from 1986-2000, because including 1986-1993 model year vehicles in the analysis
resulted in cases where older model years were modeled with substantially lower CO emissions than
newer model years. Note that the base CO emission rates still vary across this model year range.
To adapt the additive ambient temperature adjustments to account for intermediate soak times, the A
and B coefficients for start operating modes other than cold starts were reduced by multiplying by a
factor equal to the ratio between emissions at the desired soak time and the cold start emissions for
catalyst equipped vehicles as used in MOBILE6.13 These factors are summarized in Table 2-3.
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Table 2-3 Soak Time Multipliers for Additive Start Temperature Effects
Operating Mode ID
Nominal SoakTime (min)
THC
CO
NOx
108
720
1
1
1
107
540
0.908778
0.91377
1.053118
106
240
0.733962
0.79137
1.117624
105
105
0.64496
0.72996
1.128799
104
75
0.599625
0.6285
1.129778
103
45
0.444825
0.44136
1.02786
102
18
0.208548
0.199678
0.58398
101
3
0.037593
0.035422
0.20508
Log-linear Fits
In estimating the THC temperature effect for model years 2006 and later and the CO temperature effect
for model years 2001 and later3, data from ORD, MSAT and OTAQ cold temperature programs'5 were
used to fit regression models. We used linear mixed models, with both continuous and categorical
variables, to fit to the logarithm of the start emissions. Second-order polynomial models exhibited non-
intuitive behaviors (e.g., negative values, non-monotonically increasing emissions). Thus, we chose to fit
the data with log-linear models because they provide monotonically increasing emissions at colder
temperatures and can model the strong curvature evident in the cold start data (See Figure 2-1 and
Figure 2-2).
The model parameters were fit using linear mixed models using the function Ime within the R statistical
package nlme,14 Using random effects for vehicle, and the test temperature as a fixed effect, we
accounted for the paired test design of the data set, yielding robust temperature effect estimates for
the entire data set (e.g., not all vehicles were tested at the same set of temperatures which is evident at
-20°F in Figure 2-1).
The linear mixed model had the following form:
aThe CO temperature effects for 2001-2005 model years were estimated using the log-linear fit because the temperature
correction for these model years in previous versions of MOVES caused the model to estimate cold start CO emissions that
were unrealistically high relative to older model year vehicles.
b We excluded the two GDI vehicles from the OTAQ cold temperature program from the model fit because they were not
deemed representative of the predominate technology in the 2010 vehicle fleet. In addition, they were believed to be
transitional GDI technologies that were not necessarily representative of future GDI technology.
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l°g(y) = oc + ^ ¦ Temp + Veh
Where:
Equation 2-3
y = start emissions (grams)
Temp = temperature in Fahrenheit
Veh = random effect for each individual vehicle
The mean model simply removes the random vehicle effects:
log(y) = oc + /?! ¦ Temp Equation 2-4
We then converted the mean logarithmic model to real-space, yielding:
y = eK+PiTemp Equation 2-5
We then normalized to degrees below 75°F, by setting T' = 75 — Temp , and substituting Temp =
75 — T' into the above equation and rearranging. This yields the equations:
y _ ea:+Pi(75-T') Equation 2-6
y = eoc+75'ft ePi(~T) Equation 2-7
y = ea:+75-ft eft(Temp-75) Equation 2-8
Then setting A = /?1; and B= ea+7S'^1, B is essentially the 'Base Cold Start' at 75°F, with units of (g/start).
The eA(TemP-75) term is a multiplier which increases the cold start at temperatures below 75°F.
To convert the model to an additive adjustment, we calculated the additive difference from the cold
start: y - y(75) = BeA(TemP-75) — B. This model form can be used in the current MOVES temperature
calculator for THC and CO, by setting C = -B, yielding Equation 2-2:
Additive Grams = Be A*0.90).
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Table 2-4 Fixed Effects for the Initial CO Model Fit to Data from 2001+ Model Year Vehicles from the ORD, MSAT
and Cold Temperature Programs (13 vehicles, 95 observations)
Value
Std. Error
DF
t-value
p-value
Intercept (ai)
3.5502
0.1433
80
24.8
2.8E-39
Temperature (Pi)
-0.0380
0.0022
80
-17.5
4.3E-29
pre-MSAT (a2)
0.7378
0.2066
11
3.6
0.0044
Temperature (Pi) x pre-MSAT (a2)
-0.0003
0.0032
80
-0.1
0.9225
Because there was not a significant temperature effect between the pre- and post-MSAT-2 vehicles, we
estimated the temperature effect (Pi) from a model fit where the pre-MSAT-2 and post-MSAT-2 vehicles
are pooled together as shown in Table 2-5.
Table 2-5 Fixed Effects for the Final CO Model Fit to Data from 2001+ Model Year Vehicles from the ORD, MSAT
and Cold Temperature Programs (13 vehicles, 95 observations)
Value
Std.Error
DF
t-value
p-value
Intercept (ai)
0.6914
0.1400
81
4.94
4.1E-06
Temperature (Pi)
-0.038
0.0016
81
-24.08
1.1E-38
pre-MSAT (a2)
0.7284
0.1815
11
4.01
0.0020
The data along with the final model fits are displayed in Figure 2-1. The MSAT-2 compliant group (2010+)
has significantly lower base cold start (coefficient a), which causes the emissions to be lower across all
temperatures for the newer model year vehicles. The CO model coefficients in the form of Equation 2-2
for use in MOVES are provided in Table 2-8. The 2009 and 2013 model year B values are derived from
the linear mixed model for the pre-MSAT-2 and the MSAT-2 compliant groups, respectively. The 2010
through 2012 model year B values are derived by linearly interpolating the 2009 and 2013 values.
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0 25 50 75
Degrees (F)
Figure 2-1 FTP CO Start Emissions with Log-linear Model Fit
For THC emissions, a statistically significant difference was detected in the log-linear temperature effect
((Bi) between the pre-MSAT-2 and MSAT-2 compliant vehicles as shown in Table 2-6 (p-value of the
Temperature x pre-MSAT term is much smaller than 0.05).
Table 2-6. Fixed Effects for the Final THC Model Fit to Data from 2006+ Model Year Vehicles from the MSAT
Program and the Cold Temperature Program (11 vehicles, 69 observations)
Value
Std. Error
DF
t-value
p-value
Intercept (ai)
1.8613
0.1321
56
14.1
4.6E-20
Temperature (|3i)
-0.0394
0.0011
56
-34.6
1.7E-39
pre-MSAT (a2)
0.7503
0.2254
9
3.3
0.0088
Temperature (|3i) x pre-MSAT (a2)
-0.0111
0.0021
56
-5.2
2.7E-06
The THC model fit to the cold start emissions data is graphed in Figure 2-2. As shown, the pre-MSAT-2
cold start emissions for THC are much more sensitive to cold temperature than the MSAT-2 compliant
vehicles.
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1
\
\
\
\
\
\
Program
MSAT Vehicle Data 2010+
V
\
• pre-MSAT Vehicle Data 2006-2009
V
Model
\ •
— Model Fit 2010+
\
%
%
V
V
t-Model Fit 2006-2009
*
o v
Xj •
8 ^—I
** "¦ ^ —
5
0 25 50 75
Degrees (F)
Figure 2-2 FTP THC Start Emissions with Log-linear Model Fit
The differences in the THC cold start temperature effect represent the impact of the Mobile Source Air
Toxic (MSAT-2) rule. The MSAT-2 rule included a limit on low temperature (20°F) non-methane
hydrocarbon (NMHC) emissions for light-duty and some medium-duty gasoline-fueled vehicles.12
Specifically:
• For passenger cars (LDVs) and for the light light-duty trucks (LLDTs) (i.e., those with GVWR up to
6,000 pounds), the composite (combined cold start and hot running) FTP NMHC emissions should not
exceed 0.3 grams per mile.
• For light heavy-duty trucks (LHDTs) (those with GVWR from 6,001 up to 8,500 pounds) and for
medium-duty passenger vehicles (MDPVs), the composite FTP NMHC emissions should not exceed 0.5
grams per mile.
These cold weather standards are phased-in beginning with the 2010 model year, as shown in Table 2-7.
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Table 2-7 Phase-in of Vehicles Meeting Cold Weather THC Standard
Model Year
LDVs / LLDTs
LHDTs / MDPVs
2010
25%
0%
2011
50%
0%
2012
75%
25%
2013
100%
50%
2014
100%
75%
2015
100%
100%
For the phase-in years, the coefficients for the THC temperature effect equation in the
startTempAdjustment table were adjusted linearly according to the light-duty vehicle phase-in. Equation
2-9 shows how the temperature effect is calculated for a model year 2010 LDV, where A2010 is the 2010
emissions rate:
^2010 — -^2009(1 0.25)+ ^2013(0-25) Equation 2-9
With this approach, the log-linear temperature effect (coefficient A) for THC emissions is reduced from
2009 to 2013 while the base 75° F THC cold start (coefficient B) is relatively constant.
Within the current MOVES design, temperature effects are applied by fuel types and model year
vehicles, but not by regulatory class (e.g., LHDTs/MDPVs). As such, the light-duty rates, including the
light-duty MSAT-2 phase in are applied to all the gasoline-fueled vehicles in MOVES. No data on
LHDTs/MDPVs or heavy-duty temperature effects were available to assess this approach.
Table 2-8 summarizes the coefficients used with Equation 2-2 (log-linear) to estimate additive start
temperature adjustments for more recent model year gasoline vehicles.
Table 2-8. Coefficients Used for Log-linear Temperature Effect Equation for All Gasoline Source Types
CO
THC
Model Year Group
A
B
C
A
B
C
2001-2009
-0(V,x
4 1 V.
-4 1 V.
2006-2009
-0.051
0.308
-0.308
2010
-0.038
3.601
-3.601
-0.048
0.315
-0.315
2011
-0.038
3.066
-3.066
-0.045
0.322
-0.322
2012
-0.038
2.531
-2.531
-0.042
0.329
-0.329
2013 & later
-0.038
1.996
-1.996
-0.039
0.336
-0.336
Figure 2-3 and Figure 2-4 graphically compare all the cold start temperature effects for gasoline vehicles
by model year groups in MOVES for CO and THC, respectively. These include both the polynomial fits
and the log-linear curve fits to the data.
18
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300
(/>
E
CO
L—
O)
0)200
>
T3
CO
O100
o
Pre 1981
— MY81_82
MY83_85
— MY86_05
MY06_09
— MY10
MY11
— MY12
MY13 50
20 40 60
Temperature (deg F)
Note: In MOVES, "MY13_50" applies to all model years 2013-2060.
Figure 2-3 CO Additive Cold Start Temperature Effects for Gasoline Vehicles by Model Year Groups
19
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40-
£
CD
O)30
-------
Table 2-9. Average Incremental Cold Start NOx Emissions by Temperature for Gasoline Vehicles Calculated from
the MSOD, ORD and MSAT Programs
Delta
Temp F
NOx (grams)
-20
1.201
0
1.227
19.4
0.202
20.7
0.089
22.4
-0.155
31
-0.007
40
0.876
48.8
0.127
49.8
0.333
51
0.325
54.2
0.438
76.3
0
95.3
0.225
97.1
0.37
105.8
0.543
Using the data above, we fit a linear regression to the emission averages for temperatures of 76.3°F and
lower and obtained the following fit:
NOx temperature additive adjustment = A * (Temp - 75) Equation 2-10
Where:
A = -0.009
R2 = 0.61
Although the value of R2 is not as high as for the THC and CO regression equations, the fit is statistically
significant.
Note that Equation 2-10 predicts a decrease in cold-start NOx emissions for temperatures greater than
75°F, while the data in Table 2-9 indicates an increase in cold-start NOx emissions as the ambient
temperature rises above 90°F. The increase is small and may be an artifact of how these data were
analyzed, since only a subset of vehicles were measured above 75°F. Therefore, as with the other
21
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temperature adjustments, we have set the NOxadditive adjustment to zero in MOVES for temperatures
higher than 75°F.
In addition, we investigated whether different NOx temperature correction is needed for vehicles
subject to the MSAT-2 rule. Figure 2-5 shows a comparison between NOx start emissions data from
OTAQ Cold Temperature Program, including both the port-fuel injection (PFI) and gasoline-direct
injection (GDI) 2006-2010 model year vehicles, and the emissions predicted using temperature effects
calculated from the MY2005-and-earlier vehicles. Because start emissions compose such a small
percentage of total NOx emissions, the differences between the MOVES temperature effects and the
NOx data from the OTAQ Cold Temperature Program were considered negligible. Thus, we applied the
NOx temperature adjustment estimated in Equation 2-10 for all model years.
0 20 75
Temperature (degF)
Figure 2-5 FTP Start NOx Emissions, Bag 1 - Bag 3, Model Years 2006-2010
To adapt the additive adjustments for intermediate soak times, the A coefficients for start operating
modes other than cold starts were adjusted by multiplying by a factor equal to the ratio between
emissions at the desired soak time and the cold start emissions for catalyst equipped vehicles as used in
MOBILE6 and summarized in Table 2-3.
22
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2.2.3. Temperature Effects on Gasoline PM2.5 Start Emissions
The temperature effects for particulate matter emissions from gasoline engines were obtained from the
Kansas City Light-Duty Vehicle Emissions Study (KCVES)15, conducted between 2004 and 2005. The
KCVES measured emissions from 496 vehicles collected in the full sample, with 42 vehicles sampled in
both the winter and summer phases of the program. The EPA conducted an analysis of the temperature
effects of gasoline vehicles from the KCVES by estimating the temperature effect on PM emissions from
34 paired vehicle tests that were sampled in both winter and summer ambient conditions (10 paired
vehicle tests were removed due to missing values and/or too small temperature differences between
the phases) as described in the EPA report15 and subsequent analysis.16
The analysis of the KCVES data indicated that ambient temperature affects for start PM emissions is best
modeled by (log-linear) multiplicative adjustments of the form:
Equation 2-11
Multiplicative Factor = eA*(72-TemP)
Where:
Temp = Temperature
A = log-linear temperature effect A = 0.0463 for cold starts from the KCVES analysis1516
The log-linear temperature effect of 0.0463 is used in MOVES for gasoline vehicles of model year 2009
and earlier (i.e., vehicles not affected by the MSAT-2 requirements).
The MSAT-2 rule (signed February 9, 2007) does not explicitly limit cold weather emissions of particulate
matter (PM). However, the Regulatory Impact Analysis (RIA) document that accompanied the rule10
noted there is a strong linear correlation between NMHC and PM2.5 emissions based on the MSAT
program discussed in Section 2.1. That correlation is illustrated in Figure 2-6 (reproduced from that RIA)
as the logarithm of the Bag-1 PM2.5 versus the logarithm of the Bag-1 NMHC (for various Tier-2 vehicles).
23
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CN _
^ _
E
CT)
CL
X
CD _
V O A
V o
V
4- V
$
# ¥
+
%
+
Oi
CD
m
oo
V v
A o
-3
-2
-1
0
1
Bag 1 NHMC - ln(g/mi)
Plot Icons are Vehicle-Specific
Figure 2-6 FTP Bag 1 PM and FTP Bag 1 NMHC for Tier 2 Vehicles
Therefore, the limitation on cold weather THC (or NMHC) emissions is expected to result in a
proportional reduction in cold weather PM2.5 emissions. In the MSAT-2 RIA (Table 2.1.-9), EPA estimated
that this requirement would result in a 30 percent reduction of VOC emissions at 202F. Applying the
same analytical approach that was used in the RIA means that a 30 percent reduction in VOC emissions
would correspond to a 30 percent reduction in PM emissions at 20° F (for Tier 2 cars and trucks).
Applying the 30 percent reduction for vehicles affected by the MSAT-2 requirements to the temperature
effects calculated for the fully phased-in (2015+) MSAT-2 vehicles implies a PM increase as the
temperature decreases from 72° to 20° F of:
Multiplicative Factor at 20'F for MSAT-2 Vehicles = o.7*e00463*(72 20) Equation 2-12
= 7.8
24
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Using Equation 2-12 with the MSAT-2 phase-in schedule from Table 2-7 leads to the following
(multiplicative) increases as the temperature decreases from 72° to 20° F:
Table 2-10 Multiplicative Increase in Cold Start PM2.5 from 72° to 20° Fahrenheit for Gasoline Vehicles
Model Year
LDVs / LLDTs
LHDTs / MDPVs
2008
11.1
11.1
2009
11.1
11.1
2010
10.3
11.1
2011
9.4
11.1
2012
8.6
10.3
2013
7.8
9.4
2014
7.8
8.6
2015+
7.8
7.8
Solving for the corresponding log-linear terms gives us these "A" values:
Table 2-11 Log-linear Temperature Effect for Start PM2.5 Emissions (Coefficient A) for Gasoline Vehicles
Model Year
LDVs / LLDTs
LHDTs / MDPVs
2008
0.0463
0.0463
2009
0.0463
0.0463
2010
0.0448
0.0463
2011
0.0432
0.0463
2012
0.0414
0.0448
2013
0.0394
0.0432
2014
0.0394
0.0414
2015+
0.0394
0.0394
We confirmed this theoretically derived temperature effect for MSAT-2 compliant vehicles by comparing
it to data from the OTAQ Cold Temp Study, which includes only the MY 2010 PFI vehicles(See Appendix
B) The temperature effect developed for MOVES fits this data well, as shown in Figure 2-7. Note, as
discussed in the light-duty report, we significantly updated the start PM2.5 emission rates to account for
GDI vehicles in MOVES3 and made additional minor updates in MOVES4,4, but we did not revisit the
temperature effects for start emissions.
25
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1 1 1—
0 20 75
Temperature (degF)
Figure 2-7. FTP PM2.5 Start Emissions, MSAT-2 Compliant Vehicles (7 PFI Vehicles, 40 Tests with Nonzero PM
Measurements on E10 Fuel) from OTAQ Cold Temperature Program
Figure 2-8 presents the light-duty multiplicative temperature effects using the coefficient from Table
2-11, and the model form of Equation 2-11.
26
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— Pre2010
— MY2010
— MY2011
— MY2012
— MY201 3
20 40
Temperature (deg F)
Note: In MOVES, "MY2013_2050" applies to all model years 2013-2060.
Figure 2-8. PM Start Exhaust Emissions Effect for Gasoline Light-Duty Vehicles in MOVES
Because the PM2.5 speciation profile for gasoline vehicles did not change significantly between the
winter and summer rounds of the KCVES,17 we apply the same temperature adjustment to each
component of the PM emissions, including elemental carbon, organic carbon, sulfate, and other species.
The PM start temperature adjustment does not vary with soak time since it is multiplicative.
Effect of Fuel-Injection Technology on Temperature Effects for PM Start Emissions
The adjustment for start emissions described above represents only vehicles equipped with fuel-
injection technologies prevalent in 2005, presumably port fuel injection (PFI). Since then, an alternate
technology, "gasoline direct injection" (GDI), has entered the market and come to represent a major
market share.
This development raises the question as to whether vehicles with GDI would respond differently to cold
ambient temperatures than those equipped with PFI. To investigate this question, we combined two
datasets, OTAQ (2012) and ORD (2021), which gives a vehicle sample that includes both technologies.
Our analysis, explained below, found that a single logarithmic slope term (or rate constant), as in
Equation 2-11 above, can be appropriately used as the basis for a temperature adjustment to represent
fleets including both PFI and GDI-equipped vehicles.
27
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As detailed in Appendix B , the ORD dataset includes three vehicles, all equipped with GDI. The OTAQ
data includes nine vehicles, of which two are GDI-equipped. Combining the two samples gives a total of
12 vehicles, with five GDI-equipped and seven PFI-equipped. This sample enables an analysis designed
to test the hypothesis that the trend in PM2.5 with ambient temperature might differ between GDI and
PFI.
For this purpose, we used results from the cold-start phase of the FTP cycle (Bag 1). Figure 2-9 shows
logarithmically transformed PM, as mg/mi (InPM) vs. temperature for all 12 vehicles, with those from
the recent ORD project distinguished with the prefix "ORD_" and those from the older OTAQ program
identified with the prefix "OTAQ_". In this figure, the view is restricted to the temperatures between 20
and 75°F, despite the fact that some vehicles in the OTAQ program were measured at 0°F. This analysis
focused on the question of whether the temperature trend differs between PFI and GDI over this
temperature range. A linear trendline is imposed on each panel, which reflects an assumption that the
emissions trend is log-linear over this temperature range.
28
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ORD_Accord ORD_Fusion ORD_Jetta
S OTAQ_Lucerne OTAQ_Mazda6 OTAQ_Passat
temperature
o ln(PM Rate) Regression
Figure 2-9. InPM: logarithmically transformed FTP Phase-1 emissions (mg/mi) vs. temperature, by vehicle.
For a more focused comparison of the two fuel-injection technologies, Figure 2-10 shows the data
grouped by vehicle and paneled by fuel injection. As a body of data, the GDI data sits higher, with the
exception of the ORD Accord, with has the lowest emissions at warm temperature and an apparently
steeper trend. With the exception of this vehicle, the two bodies of data have similar slopes.
29
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injection = GDI
injection = PFI
o
0
8
o
20 30 40
50 60 70 20 30 40
50 60 70
temperature
vehicle
o ORD_Accord o ORD_Fusion o ORD_Jetta
o OTAQ_Passat o OTAQ_STS o OTAQ_Accord
o OTAQ_Forte o OTAQ_Gallant o OTAQ_Lucerne
o OTAQ_Mazda6 OTAQ_Patriot OTAQ_Santa Fe
Figure 2-10. InPM: logarithmically transformed FTP Phase-1 emissions (mg/mi) vs. temperature,
by vehicle and fuel-injection technology.
This body of data is sufficient to fit a model to test the hypothesis that the two fuel-injection
technologies could have different (logarithmic) trends with temperature over the range of 20-70°F. The
mixed-factor 'random coefficients' model includes 'fixed' effects for temperature and fuel injection, as
well as 'random' intercepts and slopes for each vehicle.
InPM = natural-log transformed PM emissions (mg/mi), for a given replicate for a given vehicle,
T = soak temperature (°F), treated as a continuous variable,
60 = a fixed intercept term, reflecting averaging across all vehicles,
61 = a fixed slope term, reflecting averaging across all vehicles,
62 = a dummy variable indicating fuel- injection technology (0 if PFI, 1 = GDI),
63 = an fixed intercept increment representing the effect of fuel injection,
64 = a fixed slope increment representing the effect of fuel injection.
bo,v = a "random" increment in the intercept with respect to 60, for vehicle v, e.g., the individual
intercept for vehicle v is 60 + b0, v.
bi,v = a "random" increment in the slope with respect to 61, for vehicle v, e.g., the individual
slope for vehicle v is 61 + bi/V.
InPM^ = /?„ + PiT + p2p3 + p2p4T + b0>v + blvT
+ £v,r
Equation 2-13
Where:
30
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sr = residual error variance for replicate r.
Accordingly, when 62 = 0, the model for PFI vehicles = 60 + 61T, and
when 62 = 1, the model for GDI vehicles = (60 + 63) + (61+ 64)T
As Figure 2-9 and Figure 2-10 suggest, the model fits individual trends (intercepts and slopes) for each
vehicle and treats the trends for the vehicles as representing random variation around a mean "fleet"
trend.
For this dataset, the random component of the best-fit model contains 14 covariance parameters,
including two variances for the random intercepts and slopes, that describe the variance among
vehicles, plus individual error variances for each of the 12 vehicles.
The solution for the fixed-effects in the best-fit model is shown in Table 2-12. Additional model-fitting
information, including the solution for the random effects, is presented in Appendix F.
Table 2-12. Fixed-Effects Solution for the Best-fit Temperature-effects Model.
Effect
Fuel Injection
Estimate
Standard
Error
DF
t value
•+0
A
L_
Q_
Intercept (60)
3.3669
0.1301
12.5
25.88
<.0001
Temperature T (61)
-0.03078
0.003357
14.6
-9.17
<.0001
Fuel injection (63)
GDI (62 = 1)
1.1018
0.1833
9.78
6.01
0.0001
Fuel injection
PFI (62 = 0)
0
Temperature x Injection (64)
GDI (62 = 1)
-0.00563
0.004951
12.7
-1.14
0.2763
Temperature x Injection
PFI (62 = 0)
0
The initial question in model fitting is whether the interaction term for temperature and fuel injection
(64) is significant and improves model fit. If this term were significant, it would indicate that the
logarithmic slope for GDI-equipped vehicles differed from that for PFI-equipped vehicles. As the table
shows, the value for this coefficient is small relative to 61 and its own standard error, resulting in a small
t statistic and correspondingly large and insignificant p-value. The model fitting thus indicates that both
GDI and PFI equipped vehicles can be modeled with the same slope term.
However, the intercept increment for GDI is highly significant, indicating that two logarithmic trends
exist for GDI- and PFI-equipped vehicles. These trends have different intercepts but the same slope, i.e.,
they are parallel, but with the GDI trend sitting higher. If the slope increment for GDI (1.1) is reverse
transformed, exp(l.l) = 3.00. This indicates that in this vehicle sample, the PM Phase-1 emissions are
31
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three times higher for GDI-equipped than for PFI-equipped vehicles over the measured temperature
range.
The overall conclusion from this analysis is that a single logarithmic slope term (or rate constant), as in
Equation 2-11 above, can be appropriately used as the basis for a temperature adjustment to represent
fleets including both PFI and GDI-equipped vehicles.
2.3.Temperature Effects on Running Exhaust Emissions from Gasoline
Vehicles
While MOVES is designed to model the impact of ambient temperature on running exhaust emissions,
current data suggests that there is little effect of temperature on THC, CO, NOx or PM. The sections
below discuss the relevant data and analysis for gaseous pollutants and for particulate matter.
2.3.1. TIIC, CO and NOx Running Exhaust Temperature Effects
We examined the same data described above for starts to evaluate potential running temperature
effects. These test data suggest that there is very little effect of temperature on running emissions of
THC, CO, or NOx. Regression analyses found that the coefficients (slopes) were not statistically
significant (that is, the slopes were not distinguishable from zero). This contrasts with the significant
temperature effect in THC, CO, and NOx Bag 2 of the Kansas City Light-Duty Vehicle Emissions Study
(KCVES) with higher emissions at colder temperatures.15 As discussed for PM emissions in the next
subsection, we attribute the temperature effect on THC, CO, and NOx emissions observed in the KCVES
to the short duration and mild acceleration of Bag 1 of the LA-92 driving cycle, such that the vehicles had
not fully reached hot-stabilized condition by the beginning of Bag 2.
As an additional test, we examined a set of continuous data collected on the IM240 cycle in the Chicago
l/M program. To avoid potential confounding due to variable levels of conditioning vehicles experienced
in the queues at the l/M stations, we only used the second IM240s when back-to-back IM240s were
performed, and for single IM240s, we examined only the final 120 seconds of full duration IM240s.
Based on this analysis, we found no evidence of a temperature effect for THC, CO, and NOx between 5
and 95°F.
Because most of the data sets evaluated did not find a significant temperature effect, and the
temperature effect observed in the KCVES is attributed to the test conditions not achieving hot-
stabilized running conditions, we do not model temperature effects for THC, CO, and NOx in MOVES for
running exhaust for all gasoline vehicles. In MOVES, these effects are coded using polynomial functions
as multiplicative adjustments. Therefore, in MOVES, we set all of those adjustments equal to 1.0.
2.3.2. PM2.5 Running Exhaust Temperature Effects
The initial analysis of the Kansas City Light-Duty Vehicle Emissions Study (KCVES) data1516 indicated that
significant ambient temperature effects existed for both start (Bagl-Bag3) and running (Bag 2) PM
emissions on the LA-92 cyclec. Thus, MOVES2010 applied a temperature effect for running emissions for
c The temperature effects in MOVES2010 and MOVES2014 for pre-2004 vehicles were substantial. Emissions increased by a
factor of 10 between ambient temperatures of 72°F and 0°F.
32
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all model year vehicles based on the Bag 2 measurements from paired vehicles tests conducted in the
winter and summer of the KCVES.
For MOVES2014, we updated the PM temperature effect for running emissions for Tier 2 and later
model year vehicles (2004+) based on data from the 2012 Cold Temperature Program (documented in
Appendix B). Experimental data collected in the 2012 OTAQ program involved measurement of PM
emissions on both the FTP (by phase) and the US06 cycles at temperatures of 0, 20 and 75°F of Tier 2
and MSAT-2-compliant vehicles and PFI and GDI (See Appendix B). The results from these programs are
plotted against temperature in Figure 2-11. We also fit log-linear models to the data and found the
effect of temperature was not statistically significant on either cycle. Based on these results, we
removed the temperature effect for Tier 2 vehicles (model year 2004 and later) in MOVES2014.
0 .04 -
. .0.03 "
3
cn
c
o
'(/}
— 0.02 -
£
LU
0.01 -
Figure 2-11. Hot-running PM Emissions Measured on Two Cycles (FTP Bag 2, US06) on MSAT-2 Compliant MY
2010 Gasoline Vehicles, Reported as Grams/cycle
These results contrast with the significant PM running temperature effect detected for Bag 2 emissions
in the KCVES. Upon further analysis of the PM emissions from the KCVES study, we determined that
muchd of the temperature effect observed in the KCVES Bag 2 emissions was due to the short duration
and relatively mild accelerations of the cold-start phase of the LA92 cycle, which is only 310 sec (1.18 mi)
in length. We note that the PM temperature effect was much larger at the beginning of Bag 2 than at
d We believe that the small, but statistically significant temperature effect that persists at the end of Bag 2, even after 1,025
seconds (17 minutes) of operation on the LA-92 in KCVES may be an artifact of this particular study, because this persistent
temperature effect on hot-stabilized running emissions was not observed in other studies.
FTP
US06
20
75 0
Temperature (degF)
20
75
33
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the end. In contrast, the cold-start phase of the FTP, used in the Cold Temperature Program is 505
seconds (3.59 miles) in length.
For MOVES3, we conducted a literature review from other studies that measured particulate matter
emissions from gasoline vehicles including model years before 2004 at different ambient temperatures.
The results are summarized in Table 2-13.
34
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Table 2-13. Literature Review of Temperature Effects on Running PM2.5 emissions from Gasoline Vehicles
Study
Vehicles and Test conditions
Findings on PM2.5 emissions
Measurements of
Exhaust Particulate
Matter Emissions
from In-Use Light-
Duty Motor Vehicles
in the Denver,
Colorado Area1819
71 light-duty gasoline vehicles from model
year 1970 to 1996 tested in the summer of
1996 and winter of 1997 on a chassis
dynamometer using bag 2 of the FTP
driving schedule.
Linear mixed model was fit and
no significant temperature
effect was observe.
Comprehensive
particle
characterization of
modern gasoline and
diesel passenger cars
at low ambient
temperature20
Two Euro-3 (apply to 2000 -2004 model
year vehicles) port-injection gasoline
vehicles (Renault Megane and Alfa 406 TS)
Tested +23, -7 and -20' C on a chassis
dynamometer on the common Artemis
driving cycle (CADC), after warmed up on
50-minute IUFC15 driving cycle.
No temperature effect observed
on running emissions.
Characterization of
Metals Emitted from
Motor Vehicles21
Emission rates derived from PM2.5
concentrations measured at the entrance
and exit concentration of the Howell
tunnel in Milwaukee, Wl in the summer of
2000 and the winter of 2000-2001. Light-
duty vehicles constituted between 90.6
percent to 93.9 percent of the vehicle
fleet, with 6.1 percent to 9.4 percent
heavy-duty trucks.
Chemical mass balance methods were
used to estimate the contribution of
tunnel emission rates to gasoline tailpipe,
diesel tailpipe, brake wear, resuspended
road dust, and tire wear emissions.
Carbonaceous PM2.s(EC+OC)
emission rates (mg/km) were
significantly lower (49-51
percent) in the winter than the
summer. Gasoline tailpipe
emissions are estimated to be
the largest contributors to EC
and OC emissions3; more than
diesel tailpipe, brake wear, and
resuspended road dust.
The winter tests had
comparable or larger PM
measurements of inorganic ions
and metals (including Na and CI)
presumably due to road salt in
the winter.
a Gasoline tailpipe and tire wear are combined together because they
lave similar source profiles.
However, gasoline tailpipe emissions in MOVES contribute a much larger share of PM2.5 emission rates
(and thus EC and OC) than brake wear emissions.22
The result of the literature review (Table 2-13) suggested no temperature effects on PM exhaust
emissions, even for model year vehicles similar to the years measured in the KCVES. Thus, we now
believe the significant running PM temperature effect in KCVES was an artifact of the measurement
35
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conditions of the study, including the short Bag 1 of the LA-92 cycle. Therefore, starting with MOVE3, we
have removed the running temperature effect for exhaust particulate matter emissions for all model
year light-duty gasoline vehicles.
2.4.Temperature Effects on Diesel Vehicles
With the exception of projections for 2027 and later HD NOx effects (see Section 2.4.3), the data used to
evaluate and estimate temperature effects on diesel vehicles were limited to laboratory tests on pre-
2007 model year light-duty diesel vehicles. From this analysis, MOVES models a temperature effect only
for THC start emissions. The THC start temperature effect estimated from the light-duty diesel was
applied to all model year diesel vehicles in MOVES, including heavy-duty diesel vehicles. None of the
other pollutants in MOVES have temperature effects for diesel start emissions and MOVES has no
temperature adjustments for running emissions.
As described below, in developing MOVES3, we reviewed more recent studies conducted on modern
diesel and heavy-duty diesel vehicles, but additional temperature effects data for US light-duty and
heavy-duty diesel are needed to fully evaluate the values now in MOVES.
2.4.1. THC, CO, and NOx Temperature Effects for Diesel Vehicles
For the development of the original diesel temperature effects in MOVES, we were able to identify only
12 diesel vehicles tested on FTP at multiple temperatures (9 passenger cars and 3 light-duty trucks).
However, only two of those 12 vehicles were tested at temperatures within the normal FTP range (68°
to 86° F). None of these diesel trucks were equipped with aftertreatment devices.
2.4.1.1. Diesel Start Effects
The average start (Bag-1 minus Bag-3) emissions for those tests are shown in Table 2-14. We stratified
the test results into four temperature bands which yielded the following emission values (grams per
start) and average temperature value:
Table 2-14 Average Light-duty Diesel Vehicle Incremental Start Emissions (Bag 1- Bag3) by Temperature (grams
per start)
Temperature, F
Count
THC
CO
NOx
34.6
6
2.55
2.44
2.6
43.4
7
2.68
2.03
0.32
61.5
10
1.69
3
0.67
69.2
2
1.2
1.91
0.36
Figure 2-12 shows the plot of mean THC start emissions versus temperature (where the vertical lines
represent 90 percent confidence intervals and the "dashed" line represents a linear regression through
the data).
36
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4
30
40 50 60
Temperature (degrees F)
70
Figure 2-12 Mean Light-duty Diesel Cold-start THC Emissions (in grams, shown on the y-axis) with 90 percent
Confidence Intervals vs Temperature
The dashed (blue) line in Figure 2-12 represents a linear regression line:
THC = (-0.04 * Temperature) + 4.22 R2 = 0.90
Equation 2-14
Transforming this equation into an equation that predicts the (additive) change/adjustment in the cold-
start THC emissions from light-duty diesel vehicles (in the MOVES format), we obtain:
Equation 2-15
THC additive temperature adjustment = A * (Temp. - 75)
Where:
A = -0.04
Temp, is <75° F
The coefficient associated with this temperature adjustment term is statistically significant although its
coefficient of variation is relatively large (23 percent). We apply this adjustment to heavy-duty as well as
light-duty vehicles due to limited data on heavy-duty diesel starts.
37
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The modified temperature adjustments for diesel THC emissions for starts with shorter soak times
(operating modes 101-107) are described in the MOVES heavy duty exhaust report.5
On the other hand, the cold-start CO and NOx emissions did not exhibit a clear trend relative to the
ambient temperature. Plotting the mean CO and NOx cold-start emissions versus ambient temperature
(with 90 percent confidence intervals) produced the following two graphs:
5
4
3
2
1
0
30 40 50 60 70
Temperature (degrees F)
Figure 2-13 Mean Light-duty Diesel Cold-start CO Emissions (in grams) with 90 percent Confidence Intervals vs
Temperature
6
4
2
0
T
•
>
T
1
T I [I II
40 50 60 70
Temperature (degrees F)
38
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Figure 2-14 Mean Light-duty Diesel Cold-start NOx Emissions (grams) with 90 percent Confidence Intervals vs
Temperature
Statistical analyses of both the diesel cold-start CO and NOx emissions showed that the coefficients were
not significantly different from zero. Therefore, for both cold-start CO and NOx adjustments for diesel
vehicles, we set the temperature adjustment for start emissions to zero.
2.4.1.2. Diesel Running Effects
Since the diesel start temperature effects were either very small or zero, we did not evaluate the diesel
running temperature effect for THC, CO, and NOx for MOVES - we set temperature effects for diesel
running exhaust to zero, similar to the gasoline running exhaust adjustments.
2.4.2. PM Temperature Effects for Diesel Vehicles
MOVES does not include any temperature effects for particulate matter emissions from diesel vehicles.
As presented in the previous section, hydrocarbon emissions from conventional diesel engines have
much lower temperature sensitivity than catalyst-controlled light-duty gasoline emissions. Limited data
exists on the ambient temperature effects of particulate matter emissions from diesel engines.
The EPA does not have data on PM start emissions on US-certified diesel vehicles tested across different
ambient temperatures. From a literature search, we were able to find two European test programs that
measured PM diesel start emissions from European light-duty diesel engines and vehicles at cold and
warm ambient temperatures.
Mathis et al. (2005)20 evaluated particle mass and number emissions from a conventional light-duty
diesel vehicle and a light-duty diesel equipped with a diesel particulate filter (DPF) at laboratory
conditions measured at +32, -7 and -2Cf C. Although the researchers observed an increasing trend in
particle mass emissions (g/start) from the conventional diesel vehicle at colder temperatures, over the
entire drive cycle, the particle number emission rates were not significantly impacted by the cold start
contribution. The particle mass emissions from the DPF-equipped vehicle were two orders of magnitude
smaller than the conventional diesel engines, but the start contributed the majority of the particle
number emissions over the entire test cycle.
Sakunthalai et al. (201423) also reported significant increase in PM start emissions from a light-duty
diesel engine tested in a laboratory at +20 and -2Cf C. However, they only reported the PM mass
concentrations of the exhaust and not emission rates. Additionally, the engine was not equipped with an
emission control system. Other researchers have reported that PM emissions are larger at cold start
than hot start from diesel engines24,25, but have not investigated the relationship of cold starts with
ambient temperatures.
The reviewed studies suggest that temperature does influence cold start PM emissions from diesel
vehicles. However, at this time, MOVES does not include temperature adjustments to diesel start
emissions due to limited data on diesel engines and because diesel starts are a minor contributor to
particulate mass emissions to the mobile-source emission inventory. The diesel particulate matter
emission temperature effects in MOVES can be revisited in the future as additional data become
available.
39
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2.4.3. Ill) Diesel NOx Temperature Effects for Model Years 2027 and Later
MOVES4 was updated to incorporate cold temperature effects for NOx from heavy-duty diesel vehicles
of model year 2027 and later.® This update was based on a 2022 testing program on a prototype engine
designed to meet the HD2027 emission standards.26 The testing was conducted using the CARB
Southern Route Cycle with the ambient temperature between 2 °C and 9 °C. The results from the
testing showed that emissions were approximately double at low ambient temperature versus standard
laboratory temperature. In addition to engine start, the results of the tests showed that emissions were
elevated for the cold ambient test and throughout the nearly 6-hour test cycle.
Based on the temperature adjustments to the off-cycle NOx standards presented in Table 111-18 of the
HD2027 Preamble (Table 2-15)27, we calculated effective NOx running and extended idle emission rates
for each operating mode and all relevant regulatory classes (42 thru 48) during in-use operations at both
25°C and 5°C.f The details of the rate calculation process can be found in "Exhaust Emission Rates for
Heavy-Duty Onroad Vehicles in MOVES4" tech report (Sections 2.1.1.6 and 2.3.3).5
Table 2-15 Temperature Adjustments to the Off-cycle NOx Standards in the HD2027 Preamble (Table 111-18)
Service Class
Applicability
Bin
NOx standard
at 25 °C
NOx standard
at 5 °C
Applicable unit
All
All
1
10
15a
g/hr
Light HDE
Certification &
In-use
2
58
102a
mg/hp-hr
Medium and Heavy HDE
Certification
2
58
102a
mg/hp-hr
Medium and Heavy HDE
In-Use
2
73a
117a
mg/hp-hr
a The Bin 1 and Bin 2 ambient temperature adjustment and the NOx compliance allowance for in-use testing do
not scale with the FELftpnox.
Since MOVES applies the temperature adjustment after all operating mode detail has been aggregated
away, we calculated a nationally representative operating mode distribution for each regulatory class,
and derived a weighted average emission rate for each regulatory class at both 25°C and 5°C. From this,
we calculated a percent increase in NOx emissions per degree change in temperature. Since MOVES
uses the Fahrenheit scale, this was converted to a percent increase in grams of NOx per degree
Fahrenheit below 77°F.
The resulting temperature effect is applied using a 3-coefficent temperature adjustment (stored in the
TemperatureAdjustment table), which can vary by pollutant, process, fuel type, and model year range.
e We applied the NOx cold temperature effect only to MY2027 and later engines because the NOx emission rates for those
engines are projected based on the HD2027 standards, which are in part (off-cycle standards) a function of ambient
temperature. Since we have the actual data from the field that include a range of temperatures for engines that meet the
MY2026 and earlier, we assume the temperature effect on NOx is accounted for in the model.
f For MOVES emission rates, the combined impact of the duty-cycle standards (temperature independent) and the in-use off-
cycle standards are considered.
40
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The existing MOVES table does not vary by regulatory class, but since the adjustment is linear (i.e.,
requiring only one coefficient), we used the three columns to store the coefficients needed for three of
the regulatory classes: tempAdjustTermA contains the adjustment for LHD45 (regClassID 42),
tempAdjustTermB for MHD67 (regClassID 46), and tempAdjustTermC for HHD8 (regClassID 47). The
coefficient for urban buses (regClassID 48), which differs from HHD8 in terms of zero-mile emissions
level and the drive cycle, is hardcoded in the BaseRateCalculator. The multiplicative adjustment is
calculated for temperatures below 77°F as follows:
Adjustment = ((77.0 - temperature) x tempAdjustTerm) + 1
Equation 2-16
Table 2-16 shows the values of the tempAdjustTerm used in the above equation and where they are
stored in MOVES.
Table 2-16 NOx Temperature Adjustment Coefficients by Regulatory Class and Process
Process
(processID)
Regulatory Class
(regClassID)
Adjustment
Coefficient
Column Name
Running (1)
LHD45 (42)
0.005139
tempAdjustTermA
MHD67 (46)
0.003957
tempAdjustTermB
HHD8 (47)
0.006352
tempAdjustTermC
Urban Bus (48)
0.008397
N/A (hardcoded)
Extended Idle (90)
MHD67 (46)
0.01389
tempAdjustTermB
HHD8 (47)
0.01389
tempAdjustTermC
We are aware of studies suggesting that diesel NOx may be underestimated in current US emission
inventories during the wintertime28 and that there is an increase in heavy-duty diesel NOx emissions at
cold temperatures in the US.29,30,31 We will revisit the NOx temperature effects in MOVES as more data
on light-duty and heavy-duty diesels become available.
2.5.Temperature Effects on Compressed Natural Gas Vehicles
MOVES models emissions from heavy-duty vehicles running on compressed natural gas. However, at the
time the temperature corrections were developed, no data were available on temperature impacts of
compressed natural gas emissions. As discussed in the heavy-duty report,5 the start emissions for CNG
emissions for THC, CO, NOx and PM are set equal to diesel start emissions. We also applied the diesel
start temperature adjustments on THC emissions to CNG.
2.6.Temperature Effects on Start Energy Consumption
The temperature effects on energy consumption in MOVES have not been updated since MOVES2004.
No temperature correction is applied to energy consumption from running activity. As presented in
heavy-duty report,5 the energy consumption from starts is a small fraction compared to the total energy
use of both gasoline and diesel vehicles. As such, we have not prioritized updating the start energy rates
or temperature adjustments in subsequent versions of MOVES.
41
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In this section, we provide a summary of the start temperature effects on energy consumption in
MOVES. The analysis used to derive the temperature effects on start energy consumption in MOVES is
documented in the MOVES2004 energy report.32 No significant temperature effects for energy
consumption were found for warmed-up vehicles in the analysis, thus MOVES does not apply a
temperature effect on running energy consumption.
MOVES applies temperature adjustments to the start energy consumption through a multiplicative
adjustment. The form of the multiplicative adjustments used in MOVES is shown in Equation 2-17, which
is applied to all ambient temperatures. Unlike the temperature adjustments for criteria pollutants,
MOVES does not limit the energy consumption adjustments to only cold temperatures, but also adjusts
the energy consumption for hot temperatures. This ambient temperature adjustment is separate from
the air conditioning adjustment described in Section 4, below.
The multiplicative temperature adjustments are applied to all start operating modes of varying soak
lengths. MOVES does have different baseline (75°F) start energy consumption rates for different soak
times, which are documented with the baseline energy start rates in the MOVES Greenhouse Gas and
Energy report6 for light-duty vehicles and heavy-duty exhaust report.5
Multiplicative temperature adjustment
= 1.0 + tempAdjustTermA X (temperature — 75) Equation 2-17
+ tempAdjustTermB x (temperature — 75)2
Table 2-17 displays the coefficients used to adjust start energy consumption for gasoline, E85, diesel and
CNG-fueled vehicles. MOVES has no correction for electric vehicles. The temperature coefficients are
stored in the MOVES temperatureAdjustment table by pollutant, emission process, fuel type and model
year range. E85-fueled vehicles use the same energy adjustments as gasoline vehicles, because they also
use the same energy rates as comparable gasoline-fueled vehicles.6 CNG vehicles use the same
adjustments as diesel vehicles, because they use the same energy start rates as comparable diesel
vehicles.
Table 2-17. Multiplicative Temperature Coefficients for Start Emissions Used in MOVES
tempAdjustTermA
tempAdjustTermB
Fuel types
Model Years
-0.01971
0.000219
Gasoline, E85
1960-2050
-0.0086724
0.00009636
Diesel, CNG
1960-2050
Figure 2-15 displays the multiplicative temperature adjustments for starts as a function of temperature.
At 75°F, the multiplicative adjustment is one. Gasoline vehicles have a larger temperature effect than
diesel vehicles, increasing to 4.8 at -20°F, while decreasing to 0.64 at 100°F. Whereas, the adjustment
for diesel vehicles only increases to 2.7 at -20°F and decreases to 0.85 at 100°F.
42
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6
Gasoline -----Diesel
Figure 2-15. Multiplicative Temperature Adjustments for Starts from Energy Consumption as a Function of
Ambient Temperature
20 40 60 80 100 120
Temperature (F)
2.7. Temperature Adjustments for Electric and Fuel-Cell Vehicles
Electric vehicles (EV) do not have exhaust emissions like internal combustion engines, but ambient
temperature has a large impact on their energy consumption. Energy consumption can increase due to
increased resistance in the drive train and electrical components, but the largest cause for the increase
is the use of heating and air conditioning.
Heating is particularly important to consider because EVs cannot scavenge waste heat from the engine
like internal combustion engine (ICE) vehicles can. As noted in the sections above, while MOVES does
estimate a cold temperature effect on energy consumption from ICE vehicle starts, no direct or cold-
weather temperature correction is applied to energy consumption from ICE running activity. Because
MOVES does not estimate energy consumption from starts for electric vehicles, there is no start
temperature effect on EV energy consumption.
Due to resource constraints, we chose to account for these temperature effects without major changes
to the underlying MOVES code, which meant that we needed to keep the form of existing ICE
temperature and AC corrections. This sub-section describes how we used the limited available data to
develop the appropriate coefficients for the new EV adjustments. Appendix D evaluates this approach
by comparing the resulting energy consumption rates to data from independent studies and shows
reasonable agreement. As EV technologies continue to mature and as more temperature effect data
becomes available, we hope to revisit both the form and the coefficients for these adjustments.
For high temperatures, MOVES3 and earlier versions of MOVES adjust energy consumption based on
ambient temperature via an air conditioning adjustment (see Section 4). The MOVES3 air conditioning
(AC) adjustments are applied only to passenger cars, passenger trucks, and light commercial trucks. The
43
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AC adjustment algorithm is applied for these vehicle types regardless of fuel type. Therefore, the light-
duty EV source types only require a low temperature adjustment. Because heavy-duty EVs lack an AC
adjustment in MOVES, they require both a high and low temperature adjustment for energy.
Most temperature adjustments in MOVES are applied using the temperatureAdjustment table, and
changes in total energy consumption for EVs are no different. The adjustment is multiplicative, based on
Equation 2-18. This quadratic equation matches the basic form of many other MOVES temperature
adjustments, such as described in Equation 2-17, where temperature represents the ambient
temperature.
Multiplicative temperature adjustment
= 1.0 + tempAdjustTermA X (temperature — 72) Equation 2-18
+ tempAdjustTermB x (temperature — 72)2
The temperatureAdjustment table has no dimension for fuel type, regulatory class, or source type. In
order to apply total energy consumption adjustments only to EVs, the MOVES code in
baseratecalculator.go was altered for MOVES4 to adjust total energy consumption only for electric
vehicles, and only for the running process.
The primary data source for the EV temperature adjustments is an American Automobile Association
(AAA) study which tested several EV passenger cars on a chassis dynamometer at room temperature,
extreme cold (20°F), and extreme heat (95°F).33 Their testing included a 2018 BMW i3s, 2018 Chevrolet
Bolt, 2018 Nissan Leaf, 2017 Tesla Model S, and a 2017 Volkswagen e-Golf. While all vehicles are
passenger cars, they cover a variety of heating and cooling technologies, including both heat pumps
(BMW i3s and Nissan Leaf) and resistive heaters (Chevrolet Bolt, Tesla Model S, and Volkswagen e-Golf).
All five vehicles were tested at all three temperatures, with the cabin temperature always set to
maintain 72°F.
Unlike other potential data sources, the AAA study measures the influence of ambient temperature on
EV energy consumption directly through experimental design, rather than through real-world
observational data which can have several confounding factors. Therefore, we used the AAA study to
derive the exact temperature adjustment for EVs in MOVES. In Appendix D, we show that the
temperature adjustment calculated using the AAA study is broadly consistent with observational data.
Relative to room temperature, the AAA found a 39% reduction in miles per gallon equivalent (MPGe) at
20°F and a 17% reduction in MPGe at 95°F, corresponding to a 64% and 20% increase in energy
consumption, respectively. Using these changes in energy consumption, a set of linear equations can be
derived that allow us to calculate A and B for Equation 2-18. They are 0.00225 and 0.00028, respectively.
As noted above, passenger cars, passenger trucks, and light commercial trucks, are already subject to an
air conditioning adjustment in MOVES. A typical AC adjustment during a MOVES run is around 20%,
consistent with the AAA study results. To avoid double-counting, Equation 2-18 is applied only when
the air conditioning adjustment is not being used. The MOVES air conditioning activity demand function
44
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is detailed in the MOVES Population and Activity Report.34 According to this function, 67°F is the
minimum temperature at which an AC adjustment is applied. In MOVES, this value is hardcoded as the
point above which MOVES uses the AC adjustment algorithm, and below which MOVES uses the
temperature adjustment algorithm to scale light-duty EV running energy consumption.
Aside from this exception for light-duty air conditioning, we assume the coefficients derived from the
AAA report are representative of all electric vehicles, including heavy-duty. Therefore, they are used for
every electric vehicle of every class and EV technology (fuel cell and battery electric). While the
adjustments were derived using only the AAA report, we analyzed the adjustments in relation to other
published studies and test programs to ensure that the temperature adjustment in MOVES is consistent
with many sources, including testing of heavy-duty vehicles. This analysis appears in Appendix D.
2.8.Conclusions and Future Research
With improved calibration and temperature management, ambient temperatures have less impact on
emissions of newer vehicles than older ones, but MOVES estimates temperature effects for start THC,
CO, NOx and PM emissions from gasoline vehicles, THC starts for diesel and CNG vehicles, NOx running
emissions for post-2027 heavy-duty diesel vehicles, and total energy consumption for electric vehicles.
We recognize that additional data and analysis could improve the MOVES temperature effects.
Additional studies and analyses could include:
• Evaluating the benefits of applying log-linear or other mathematical models for pre-MSAT2
gasoline vehicle THC & CO temperature effects and considering whether all temperature
effects could be multiplicative rather than using additive effects for THC/CO/NOx start
emissions.
• Investigating ambient temperature effects on cold start emissions at temperatures warmer
than IS F.
• Evaluating the interaction of ambient temperature effects and fuel effects.
• Evaluating the interaction of ambient temperature effects and deterioration.
• Analyzing ambient temperature effects for modern (2007 and later) diesel vehicles from
recent studies, especially those equipped with emission control devices, including diesel
particulate filters (DPF) and selective reduction catalysts (SCR).
• Conducting studies of temperature effects in vehicles using alternative fuels such as
compressed natural gas and ethanol blends.
• Incorporating data on the impact of temperature effects on newer technology vehicles,
including Tier 3 gasoline direct injection, and dual port-fuel and direct injection, stop-start
technologies, battery electric vehicles and hybrid technologies.
• Analyzing the effect of temperature on other pollutants estimated in MOVES including
ammonia (NH3).
45
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• Evaluation of EV energy used to condition the battery at various ambient temperatures,
especially temperatures between 35 and 65°F which are most common in shoulder months.
• Evaluation of different EV heating and cooling technologies (such as resistive heating and
heat pumps) and their efficiencies at various ambient temperatures.
• Evaluation of energy used for EV heating and cooling in a wider range of vehicles, including
single-unit and combination trucks. For example, buses and cars need to maintain the
climate in close to the full volume of the vehicle, while combination trucks have a much
smaller cabin relative to their power requirements and may require a smaller multiplicative
temperature adjustment.
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3. Humidity Adjustments
Water in the air cools the peak combustion temperature and lowers engine out NOx emissions. We
adjust for this when evaluating source data for MOVES. More specifically, the NOx exhaust emissions
data used to develop emission rates for MOVES are adjusted from actual measurement conditions to a
standard humidity; this includes the emissions data from the Evaluation Sample for the Denver
Metropolitan l/M Program used to develop NOx emission rates for MY 1990 and later gasoline vehicles4
and the emissions data from the Heavy-Duty Diesel In-Use Testing Dataset used to develop NOx
emission rates for MY 2010 and later heavy-duty diesel vehicles.5 At run time, these base NOx exhaust
emission rates are adjusted from the standard humidity level to the humidity conditions specified by the
run spec as described below.
3.1.Humidity Adjustment Equation
In MOVES, the base exhaust emission rates for NOx in all modes and all processes are multiplied by a
unitless humidity factor, K. This factor is calculated separately by fuel type, with diesel using one
equation and set of coefficients while gasoline, CNG, and E-85 use another equation and set of
coefficients.
The equations and coefficients for each fuel type are determined by the Code of Federal Regulations
(CFR). The diesel adjustment is based on Part 106535 for heavy-duty in-use testing and the adjustment
for other fuel types is based on Part 8636 for light-duty vehicle emissions testing. In each case, the
equation specified is the inverse of the adjustment specified in the CFR. This is because the CFR equation
is used to adjust emissions to a standard humidity level, while MOVES is taking base rates calculated at
the standard humidity level and adjusting them based on the humidity level in the run to calculate a
real-world emission rate. For MOVES4, the equations and coefficients were updated to better
represent this inverse relationship.
Table 3-1 shows the equation coefficients, bounding humidity levels, and humidity units used for each
adjustment, as represented in the noxhumidityadjust table in the MOVES default database. If the
specific humidity input is outside the bounding humidity levels, the value of the limit is used to calculate
the adjustment. The adjustment for gasoline, CNG, and E-85 vehicles is shown in Equation 3-1 and the
adjustment for diesel vehicles is shown in Equation 3-2.
47
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Table 3-1 NOx Humidity Adjustment Parameters for all Fuel Types
fuelTypelD
CFR
Source
Adjustment
Equation Terms
Specific Humidity Bounds
Specific Humidity
Units
A
B
Lower Bound
Upper Bound
1
40 CFR
86.144-94
0.0329
3.00
17.71
grams of water / kg
of air
2
40 CFR
1065.670
9.953
0.832
0.002
0.035
moles of water /
moles of air
3
40 CFR
86.144-94
0.0329
3.00
17.71
grams of water / kg
of air
5
40 CFR
86.144-94
0.0329
3.00
17.71
grams of water / kg
of air
K = 1 — humidityTermA * (specif icHumidity — 10.71)
Equation 3-1 NOx Humidity Adjustment Equation for Gasoline, CNG, and E-85 Vehicles
1
—
(humidityTermA * xH2o) + humidityTermB
Equation 3-2 NOx Humidity Adjustment Equation for Diesel Vehicles
MOVES only uses relative humidity as the input source for humidity, either by users or in the default
database via the zonemonthhour table. Appendix A6 shows how MOVES calculates specific humidity
based on relative humidity, ambient temperature, and barometric pressure.
3.2.Future Research
Future work could investigate whether the real-world emissions impact of humidity is similar to the
corrections developed from laboratory testing used in the Code of Federal Regulations. Additional work
could evaluate the emission impact of humidity on more recent gasoline, diesel and alternative-fueled
engines and consider whether modern engine calibration and emission control technologies impact the
humidity effect.
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4. Air Conditioning Adjustments
MOVES applies air conditioning adjustments to THC, CO, NOx and energy consumption from passenger
cars, passenger trucks and commercial light trucks. The air conditioning (A/C) effects described below
and incorporated in MOVES4 were originally derived for MOVES2010.
The air conditioning adjustment factors used in MOVES are based on data collected from light-duty
vehicles in a test procedure meant to simulate air conditioning emission response under extreme "real
world" ambient conditions. These factors predict emissions which would occur during full loading of the
air conditioning system and are then scaled down in MOVES according to the ambient conditions
specified in a modeling run. The second-by-second emission data were analyzed using the MOVES
methodology of binning the data according to vehicle characteristics (MOVES source bins) and vehicle
specific power bins (MOVES operating modes). The results of the analysis showed statistically significant
and consistent air conditioning effects for three types of operation (deceleration, idle and
cruise/acceleration) and the three primary exhaust pollutants (hydrocarbon, carbon monoxide and
nitrous oxides) and energy consumption. This section shows the results of the analysis for the air
conditioning adjustments used in MOVES for THC, CO, NOx and energy consumption. The impact of A/C
on particulate matter has not been evaluated for MOVES and therefore, MOVES currently has no air
conditioning effect for PM emissions.
The MOVES A/C adjustment varies by operating mode for total energy consumption and exhaust
running THC, CO and NOx emissions and applies only to passenger cars, passenger trucks and
commercial light trucks. The HD emission rates do not require explicit A/C adjustments because they
are based on real-world driving that includes A/C usage depending on ambient conditions when the test
was conducted. For example, the model year 2010 and later HD diesel energy rates are based on
manufacturer-run Heavy-Duty In-Use Testing (HDIUT) data.5 The impact of air conditioning usage on
energy consumption for heavy-duty and fuel-cell vehicles is handled as a temperature correction as
explained in Section 2.7.
4.1. Air Conditioning Effects Data
The data for the MOVES A/C Correction Factor (ACCF) was collected in 1997 and 1998 in specially
designed test programs. In the programs, the same set of vehicles were tested at standard FTP test
conditions (baseline) and at a nominal temperature of 95°F. Use of the same set of vehicles and test
cycles was intended to eliminate most of the vehicle and test procedure variabilities and highlight the
difference between a vehicle operating at extreme ambient conditions and at a baseline condition.
The data used to develop the MOVES ACCF consisted of emission results from 54 individual cars and
light trucks tested over a variety of test schedules. Overall, the database consisted of a total of 625 test
cycles and 1,440,571 seconds of emission, speed and acceleration data. Because of the need to compute
vehicle specific power on a modal basis, only test results which consisted of second-by-second data
were used in the MOVES analysis. All second-by-second data were time-aligned and checked for errors.
The distribution of test vehicles by model year is shown in Table 4-1. Model years 1990 through 1999
were included. The data set consists of 30 cars and 24 light trucks. No test data were available on other
vehicle types (e.g., motorcycles or heavy-duty trucks). The individual test cycles on which the vehicles
49
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were run are shown with the test counts in Table 4-2. The data shows a balance between different test
cycles and cars and trucks. The individual vehicles are listed in Appendix C.
Only vehicles which were coded as having an emission test with the A/C system on were selected for
this analysis. The A/C On tests and the A/C Off (default for most EPA emission tests in general) were
matched by VIN, test schedule and EPA work assignment. The matching ensured that the same vehicles
and test schedules were contained in both the A/C On sample and the A/C Off sample.
Table 4-1 Distribution of test vehicles by Model Year
Model Year
Count
1990
5
1991
5
1992
6
1993
5
1994
7
1995
5
1996
13
1997
4
1998
3
1999
1
TOTAL
54
Table 4-2 summarizes the distribution of test-cycles analyzed. The test-cycles are defined in a MOBILE6
report.37
Table 4-2 Distribution of tests by test cycle
Schedule Name
Count
ART-AB
36
ART-CD
36
ART-EF
36
F505
21
FTP
21
FWY-AC
57
FWY-D
36
FWY-E
36
FWY-F
36
FWY-G
36
FWY-HI
36
LA4
23
LA92
35
LOCAL
36
NONFRW
36
NYCC
36
RAMP
36
ST01
36
TOTAL
625
50
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4.2. Air Conditioning Effects on Emissions and Energy
The data described above was then used to estimate factors to account for increases in emissions and
energy consumption with full loading of the air conditioning system. These factors are recorded for
running and extended idle emissions by sourcetype, pollutant and operating mode in the
fullACadjustment table of the MOVES database. Thus, the same effects are applied for all fueltypes and
model years.
4.2.1. Full A/C Adjustments for THC, CO and NOx Emissions
Average emissions for each pollutant (HC, CO and NOx) with and without A/C operation were computed
for each of the MOVES light-duty running operating modes as defined using vehicle specific power
(VSP).4 This resulted in 69 (23 VSP bins x 3 pollutants) pairs of emission averages. However, the trends
were erratic, and the results were generally not statistically significant. In addition, most of the high-
speed bins had little data. An analysis of cars versus light-duty trucks showed no statistical difference
between the two. To produce more consistent results, the individual VSP bins were consolidated to
three principal bins: Braking / Deceleration, Idle, and Cruise / Acceleration as defined in Table 4-3.
These consolidated operating mode bins are quite different in terms of engine operation and emissions
performance.
Full A/C adjustments were then generated by dividing the mean "With A/C" emission factor by the mean
"Without A/C" emission factor for each combination of consolidated operating mode and pollutant. Full
A/C adjustments are shown in Table 4-3. Measures of statistical uncertainty (coefficient of variation of
the mean) were also computed using the standard error of the mean. They are shown in Table 4-3 as
"Mean CV of CF."
A/C adjustments of less than or equal to one were found for the Braking / Deceleration mode for all
three pollutants. These were set to one for use in the MOVES model.
Table 4-3 Full air conditioning adjustments for THC, CO and NOx
Pollutant
Consolidated
Operating Mode
opModelDs
Full A/C CF
Mean CV of CF
THC
Braking / Decel
0
1.0000
0.48582
THC
Idle
1
1.0796
0.74105
THC
Cruise / Accel
11-40
1.2316
0.33376
CO
Braking / Decel
0
1.0000
0.31198
CO
Idle
1
1.1337
0.77090
CO
Cruise / Accel
11-40
2.1123
0.18849
NOx
Braking / Decel
0
1.0000
0.19366
NOx
Idle
1
6.2601
0.09108
NOx
Cruise / Accel
11-40
1.3808
0.10065
These adjustments are applied to passenger cars, passenger trucks and light commercial trucks only.
Note the higher air conditioning effect for NOx at idle. These results are consistent with those obtained
from Nam et al. (2000)38 who showed that at low load conditions, A/C greatly increased NOx emissions
due to reduced residual gas fractions in-cylinder.
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4.2.2. Full A/C Adjustments for Energy Consumption
The use of a vehicle's A/C system will often have a sizeable impact on the vehicle's energy consumption.
This was found statistically by analyzing the available second-by-second data on C02 and other gaseous
emissions and converting them to an energy basis using standard EPA vehicle fuel economy certification
equations. The vehicle emission data were binned by running operating mode and mean values were
computed. A separate analysis was done as a function of sourceBinID (combination of vehicle type, fuel
type and model year); however, the results were not statistically different across sourceBinID given the
relatively small sample sizes. As a result, the A/C adjustments for energy are a function only of running
operating mode. The resulting A/C adjustments are shown in Table 4-4.
Table 4-4 Full air conditioning adjustments for energy*
opModelD
A/C Factor
opModelD
A/C Factor
opModelD
A/C Factor
0
1.342
21
1.294
30
1.294
1
1.365
22
1.223
33
1.205
11
1.314
23
1.187
35
1.156
12
1.254
24
1.167
37
1.137
13
1.187
25
1.157
38
1.137
14
1.166
26
1.127
39
1.137
15
1.154
27
1.127
40
1.137
16
1.128
28
1.127
29
1.127
* These adjustments are applied to passenger cars, passenger trucks and light commercial trucks only.
Only very small amounts of data were available for operating modes 26 through 29 and 37 through 40.
As a result, the data from these bins was averaged together and binned into two groups. The resulting
group averages were used to fill the individual VSP bins. This averaging process has the effect of leveling
off the effect of A/C at higher power levels for an engine. This is an environmentally conservative
assumption since it is likely that the engine power devoted to an A/C compressor probably continues to
decline, sometimes to zero, as the overall power demand of the engine is increased.
Fuel economy and GHG regulations are expected to reduce energy consumption with air conditioning.
However, because, the MOVES A/C factors are multiplicative adjustments to the running energy rates, a
reduction in running energy rates also reduces energy consumption from air conditioning. In MOVES, we
project the light-duty A/C improvements of regulatory rules using the running energy rates as
documented in the MOVES Greenhouse Gas and Energy Consumption Rates Report.6
4.3. Adjustments to Air Conditioning Effects
In MOVES, the adjustments for each operating mode are weighted together by the operating mode
distribution calculated from the driving schedules used to represent the driving behavior of vehicles.
Average speed, road type and vehicle type will affect the operating mode distribution.
meanBaseRateACAdj = SUM (meanBaseRate*(fullACAdjustment-1.0)*opModeFraction)
Since not all vehicles are equipped with air conditioning and air conditioning is normally not on all the
time, the full air conditioning effect on emissions is adjusted before it is applied to the emission rate.
The adjustment account for (a) the fraction of vehicles in each model year that are equipped with air
conditioning, (b) the fraction of vehicles equipped with air conditioning of each age that have an
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operational air conditioning system and (c) the fraction of those vehicle owners who have air
conditioning available to them that will turn on the air conditioning based on the ambient temperature
and humidity (heat index39) of the air outside their vehicles. These MOVES defaults are documented in
the Population and Activity report.34 The fraction of vehicles equipped with air conditioning, the fraction
of operational air conditioning and the fraction of air conditioning use are used to adjust the amount of
"full" air conditioning that occurs in each hour of the day.
EmissionRate = (meanBaseRateACAdj *
ACPenetrati on * functi oning ACFracti on * AC OnFracti on) + meanB aseRate
The air conditioning adjustment is a multiplicative adjustment applied to the emission rate after it has
been adjusted for fuel effects.
Air conditioners are also employed for defogging at all temperatures, particularly, at lower
temperatures. This secondary use of the A/C along with associated emission effects is not addressed in
MOVES.
4.4.Conclusions and Future Research
MOVES applies air conditioning effects to emissions from passenger cars, passenger trucks and
commercial light trucks. The impact depends on pollutant, operating mode, ambient temperature and
humidity and the anticipated availability of air conditioning in the vehicle type, model year and age
being modeled.
There are a number of areas where our understanding of air conditioning impacts could be improved.
These include:
• Evaluation of the impact of air conditioning use on particulate matter emissions.
• Studies of air conditioning effects in a broader range of model years, particularly those with
the most recent emission control technologies.
• Evaluation of air conditioning effects in the highest VSP/STP bins.
• Updates to information on the fraction of vehicles equipped with air conditioning and their
malfunction rates.
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5. Inspection and Maintenance Programs
Inspection and Maintenance (l/M) programs are any state or locally mandated inspection of highway
motor vehicles intended to identify those vehicles most in need of emissions-related repair and require
repairs of those vehicles. MOVES3.1 and later models (including MOVES4) model an l/M program
reduction in emissions of HC, CO and NOx for gasoline and flexible-fueled (E-85) vehicles less than
14,000 pounds (regulatory classes 20, 30 & 41). MOVES does not model emission changes for programs
that target diesel or CNG vehicles, Class 4-or-higher heavy-duty vehicles, or particulate matter.
There is great variation in how vehicles are selected for inclusion in the programs, how and when
vehicles are tested, and what happens when vehicles fail. MOVES is designed to take these variations
into the account when estimating the emission benefits of these programs.
This section describes the MOVES calculation of l/M benefits for exhaust emissions. The calculation of
l/M benefits for evaporative emissions is described in the MOVES Evaporative Emissions report.7
5.1.Overview of Exhaust Inspection & Maintenance in MOVES
MOVES uses a number of inputs to estimate the benefits of exhaust l/M programs.
The model starts with two sets of emission rates as a function of age, model year group and regulatory
class. The "mean base rate l/M" or "l/M rates" represent emissions for an area with a "reference l/M
program." The "mean base rate", or the "non-l/M rates" represent emissions in an area without l/M.
The reference l/M program is not the same as the l/M performance standard,40 but instead is a program
used as a data source in MOVES because it provides a large sample of consistent data covering many
years. The data analysis used to determine both the l/M and non-l/M rates is detailed in the MOVES
light-duty emission rate report.4 Both sets of rates are recorded in the emissionRateByAge table.
The model scales the emission rate between (or potentially beyond) the l/M and non-l/M rate using an
"l/M Factor" by source type that accounts for differences in l/M program design, including test type and
inspection frequency, as detailed in Section 5.2. The l/M Factor assumes full coverage and compliance.
The result is also modified by the l/M coverage table. For each county and calendar year, the table lists
the source types, pollutants and model years that are covered, and the compliance factor which adjusts
l/M benefit to account for covered vehicles that are not actually subject to the program, evade testing,
or have repairs waived. In MOVES, it is assumed that any repairs attempted on vehicles receiving
waivers are not effective and do not result in any reduced emissions.
Mathematically, the IM Factor for the program design and the Compliance Factor for the program
characteristics are combined into a single factor, "IMAdjustFract" as shown in Equation 5-1. The
Compliance Factor is entered in units of percent and is converted to a fraction.
IMAdjustFract = (IMFactor * Compliance Factor * 0.01) Equation 5-1
We then estimate a net emission rate by weighing together the emission rate for the l/M reference
program and the non-l/M emission rate, using the IMAdjustFract.
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TargetRate = IMRate * IMAdjustFract
+NonIMRate * (1.0 — IMAdjustFract)
Equation 5-2
5.2. Development of MOVES I/M Factors
MOVES is designed to model the different effects of different l/M program designs, specifically different
test types and test frequencies. The relative effectiveness of the programs is input into MOVES as the
"l/M factor/' a value between zero and two, stored in the MOVES IMFactor table. It Is calculated with
Equation 5-3.
_ Ep EnonIM Equation 5-3
Eim ~ EnoniM
Where:
Ep is the adjusted emission rate for a "target" l/M program,
Eim is the reference rate,
EnoniM is the non-l/M reference rate and
R is the l/M Factor, an aggregate adjustment representing the difference in average emission
rates between the target program and the reference program.
Depending on the value of R, Ep may be greater than fnoniM, fall between fnoniM and £|M, or be less than
£im- Thus, this framework can represent target programs as more effective or less effective than the
reference program. In MOVES, R is referred to as the "IMFactor."
For our initial version of MOVES (MOVES2010), EPA developed l/M factors based on the information
incorporated in MOBILE6.2.41 These factors have been carried into later versions of MOVES.
Mechanically, this step was achieved by running the MOBILE6.2 model about 10,000 times over a
complete range of pollutant-process combinations, inspection frequencies, calendar years, vehicle
types, test types, test standards and model year group / age combinations. The mean emission results
for each combination were extracted from the output and used to compute estimated values for
IMFactor.
The IMFactor table includes the following fields8:
• Pollutant / Process
o The IMFactor table has rows for HC, CO and NOx running and start emissions, as well as
HC vapor venting.
g The IMFactor table also includes values for Test Standard "Heavy-Duty Diesel Vehicle Reflash", with
"continuous" frequency for other buses and long and short-haul combination trucks (sourcetypes 41, 61
and 62). These values were entered early in MOVES development, but are never used. We intend to
delete them in a future MOVES version.
55
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• Test Frequency
o Annual or biennial
• Test Standard
o See Table 5-1 below
• Source Type
o Passenger cars, passenger trucks, light commercial trucks, single-unit short-haul trucks
and motorhomes
• Fuel Type
o Only gasoline and gasoline/ethanol blend fuels are covered
• Model Year Group
• Age Group
• IMFactor
Table 5-1 MOVES l/M Test Standards
testStandardsID
testStandardsDesc
shortName
11
Unloaded Idle Test
Unloaded Idle
12
Two-mode, 2500 RPM/ldleTest
2500 RPM/ldle
13
Loaded / Idle Test
Loaded/Idle
21
ASM 2525 Phase-in Cutpoints
A2525 Phase
22
ASM 5015 Phase-in Cutpoints
A5015 Phase
23
ASM 2525/5015 Phase-in Cutpoints
A2525/5015 Phase
24
ASM 2525 Final Cutpoints
A2525 Final
25
ASM 5015 Final Cutpoints
A5015 Final
26
ASM 2525/5015 Final Cutpoints
A2525/5015 Final
31
IM240 Phase-in Cutpoints
IM240 Phase
33
IM240 Final Cutpoints
IM240 Final
41
Evaporative Gas Cap Check
Evp Cap
42
Evaporative System Pressure Check
Evp Pressure
43
Evaporative System OBD Check
Evp OBD
44
Evaporative Gas Cap and Pressure Check
Evp Cap, Prs
45
Evaporative Gas Cap and OBD Check
Evp Cap, OBD
46
Evaporative Pressure and OBD Check
Evp Prs, OBD
47
Evaporative Gas Cap, Pressure and OBD
Check
Evp Cap, OBD, Prs
51
Exhaust OBD Check
Exhaust OBD
61
HDDV Engine Reflash Program
HDDV Reflash
The IMFactor value was computed for all reasonable combinations of the parameters listed in the
IMFactor table. MOBILE6.2 runs were done for each parameter combination (Target design, fp) and a set
of runs were done for the reference program (Reference design, EtM)- In these runs, the reference
program has inputs matching the Phoenix, Arizona l/M program during the time in which the data used
in the MOVES2010 emission rate development were collected (CY 1995-2005). The reference design
56
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represents a biennial frequency with an exemption period for the four most recent model years. It uses
three different l/M test types (basic idle test for MY 1960-1980, transient tailpipe tests for MY 1981-
1995 (IM240, IM147) and OBD-II scans for MY 1996 and later). Each of these test types became the
reference for the respective model year groups.
The specific combinations of MOBILE6.2 runs performed are shown in Table 5-2 below. Each of these
runs represents a particular test type and test standard design. A set of these runs were done for each
calendar year 1990 through 2030, for cars, light trucks and heavy-duty gasoline vehicles and for
pollutants THC, CO and NOx.
The first four runs represent the Non-I/M reference and the three Arizona l/M references.
Table 5-2 MOBILE6.2 runs used to populate the MOVES l/M adjustment factor
RUN#
Description
Type
1
Non l/M Base
Non l/M Reference
2
IM240 Base (Biennial IM240/147)
l/M Reference
3
OBD Base (Biennial OBDTest)
l/M Reference
4
Basic Base (Loaded - Idle Test)
l/M Reference
5
Biennial - IM240 - Phase-in Cutpoints
Target l/M Design
6
Annual - IM240 - Phase-in Cutpoints
Target l/M Design
7
Biennial - IM240 - Final Cutpoints
Target l/M Design
8
Annual - IM240 - Final Cutpoints
Target l/M Design
9
Biennial - ASM 2525/5015 - Phase-in Cutpoints
Target l/M Design
10
Annual - ASM 2525/5015 - Phase-in Cutpoints
Target l/M Design
11
Biennial - ASM 2525/5015 - Final Cutpoints
Target l/M Design
12
Annual - ASM 2525/5015 - Final Cutpoints
Target l/M Design
13
Biennial - ASM 2525 - Phase-in Cutpoints
Target l/M Design
14
Annual - ASM 2525 - Phase-in Cutpoints
Target l/M Design
15
Biennial - ASM 2525 - Final Cutpoints
Target l/M Design
16
Annual - ASM 2525 - Final Cutpoints
Target l/M Design
17
Biennial - ASM 5015 - Phase-in Cutpoints
Target l/M Design
18
Annual - ASM 5015 - Phase-in Cutpoints
Target l/M Design
19
Biennial - ASM 5015 - Final Cutpoints
Target l/M Design
20
Annual - ASM 5015 - Final Cutpoints
Target l/M Design
21
Annual - OBD -
Target l/M Design
22
Annual - LOADED/IDLE
Target l/M Design
23
Biennial - IDLE
Target l/M Design
24
Annual - IDLE
Target l/M Design
25
Biennial -2500/IDLE
Target l/M Design
26
Annual - 2500/IDLE
Target l/M Design
The MOBILE6.2 database output option was chosen for all runs. This step produced large sets of results
detailed by age, roadway type, and emission type. This output format necessitated additional processing
into composite running and start factors.
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The IMFactor (R) was then calculated using the mean emission results from the target program, the l/M
reference program and the non-l/M reference using Equation 5-3.
5.2.1. Inspection & Maintenance in M0RILE6
Because the IMFactors used in MOVES were generated with MOBILE6.2, it is useful to briefly review
MOBIL6 modeling of l/M. Readers interested in a more thorough treatment of the topic are encouraged
to review the relevant MOBILE6 documentation.42'43,44
The MOBILE6.2 model used a methodology that categorized vehicles according to emitter status (High
emitters and Normal emitters) and applied a linear growth model to project the fraction of the fleet that
progresses from the Normal emitter to the High emitter status as a function of age. Average emission
rates of High and Normal emitters were weighted using the High emitter fraction to produce an overall
average emission rate as a function of age, model year group and vehicle type. The emissions generated
represented the emissions of the fleet in the absence of l/M (the No l/M emission rate).
A similar approach was used to generate l/M emission rates. In this case, the initial starting point for the
function (where age=0) was the same as the No l/M case. However, the effects of l/M programs and
associated repairs were represented by reductions in the fraction of high emitters, which consequently
affected the average emission level of the fleet. We also modelled the re-introduction of high emitters
in the fleet due to deterioration of vehicle emission control systems after repairs. The underlying l/M
and non-l/M deterioration rates were assumed to be the same.
MOBILE6 modeled the non-l/M and l/M emission cases diverging from each other over time, with the
l/M rates being lower. The percentage difference between these two rates is often referred to as the
overall l/M reduction or l/M benefit.
The relative effectiveness of various l/M programs was modeled using "high emitter identification rates"
that varied by test type. Since we lacked new data for MOBILE6, the effectiveness of biennial programs
as compared to annual programs and the effectiveness of ASM tests relative to IM240 were calculated
by running MOBILE5. To determine the high emitter identification rates for the IM240 test, MOBILE6
relied on a database of 910 results from 1981-and-later cars and trucks from EPA emission factor testing
in Ann Arbor, Indiana and Arizona in which vehicles were randomly recruited and tested on both a
running LA4 test (derived from the FTP test) and the IM240 test. There was little data for OBD and the
high emitter identification rate for OBD testing was set at 85 percent.42
5.3. I/M Compliance Factors
While the IMFactor (R, Equation 5-3)) represents the theoretical effectiveness of a specific l/M program
design relative to the reference design, MOVES uses a "compliance factor" to account for l/M program
compliance rates, waiver rates, failure rates, and adjustments needed to account for the fraction of
vehicles within a source type that are covered by the l/M program (these last adjustments are referred
to as the "regulatory class coverage adjustment").
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When modeling for State Implementation Plans or conformity determinations, EPA guidance
recommends that modelers review program descriptive parameters and enter compliance factors which
reflect current and expected future program operation.45
MOVES values of the l/M compliance factor (CF) are specific to individual programs. The compliance
factor is entered as a decimal number from 0 to 100 and represents the percentage of vehicles within a
source type that actually receive the benefits of the program. The compliance factor is calculated as
shown in Equation 5-4.
CF = (CR * (1 -WR* FR) * RCCA) Equation 5-4
Where:
CF= Compliance factor
CR = Compliance rate
WR = Waiver rate
FR= Failure rate
RCCA = Regulatory class coverage adjustment
The MOVES Technical Guidance provides instructions for modelers on using l/M program data to
calculate each of these values and compute an appropriate compliance factor for use in
MOVES.45MOVES Technical Guidance provides instructions for modelers on using l/M program data to
calculate each of these values and compute an appropriate compliance factor for use in MOVES.45
The default compliance rates in MOVES represent a mixture of state-submitted values and values
carried over from MOBILE6. For the latter, the MOBILE6 compliance rate, waiver rate and effectiveness
rate were used to determine the default MOVES l/M Compliance Factor. Equation 5-5 shows the
relationship.
CF = M6ComplianceRate * M6EffectivenessRate Equation 5-5
* (1 — M6WaiverRate)
5.4.Default I/M Program Descriptions (IMC"overage)
Information about which pollutant-processes are covered by l/M programs in various counties and
calendar years is listed in the MOVES database table IMCoverage. This coverage information may vary
by pollutant, process, county, year, sourcetype and fuel type. The table also lists the l/M compliance
factors described above.
The IMCoverage table includes the use of l/M program identifiers called IMProgramlDs. A particular
county will likely have several IMProgramlDs that reflect different test types, test standards or
inspection frequencies applied to different sourcetypes, model year groups or pollutant-process
combinations. For example, a county in calendar year 2007 may have an IMProgramlD=l that annually
59
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inspects pre-1981 model year cars using an Idle test and an IMProgramlD=2 that biennially inspects
1996 and later model year light-trucks using an OBD-II test.
The IMCoverage table also shows other important l/M parameters for each IMProgramlD. These include
the relevant model year range (beginning and ending model year), the frequency of inspection (annual
or biennial), test type (Idle, IM240, ASM, OBD-II) and the test standard.
The structure of the IMCoverage table in the MOVES database is:
• Pollutant / Process
• State / County
• Calendar Year
• Source Use Type
• Fuel Type (only gasoline and ethanol fuels)
• IMProgramlD
• Beginning Model Year of Coverage
• Ending Model Year of Coverage
• Inspection Frequency (annual or biennial)
• l/M Test Standards (see Table 5-1)
• UselMyn
• Compliance Factor
The UselMyn toggle is a user feature that allows the user to completely disable the modeling of l/M for
one or more of the parameter combinations.
When modeling for regulatory purposes, it is expected that a state will enter their own set of program
descriptive parameters and compliance factors which reflect current and expected future program
operation. However, MOVES contains a set of l/M program descriptions for all calendar years intended
to reflect our best assessment of the programs in each state.
The data used to construct the default inputs for l/M programs before calendar year 2011 were taken
from MOBILE6.2 input files used in the NMIM model to compute the National Emission Inventory of
2011. The MOBILE6 data fields listed in Table 5-3 were extracted and processed into the various fields in
the MOVES IMCoverage table for each state and county.
As seen in Table 5-3, MOBILE6.2 and MOVES do not have exactly compatible parameter definitions. The
MOBILE6.2 l/M Cutpoints data were used only to determine level of stringency of a state's IM240
program (if any). The MOBILE6.2 Test Type inputs provided a description of the specific l/M tests
performed by the state and test standards for the ASM and Basic l/M tests. The MOBILE6.2 inputs of
Grace Period and Model Year Range were used to determine the MOVES Beginning and Ending model
year data values for each l/M program. The MOBILE6.2 vehicle type input was mapped to the MOVES
sourcetype.
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Table 5-3 l/M Coverage table data sources
MOBILE6 Data
MOVES l/M Coverage Parameter
Compliance Rate
Used in the MOVES Compliance Rate Calculation
l/M Cutpoints
Used to determine MOVES l/M Test Standards
Effectiveness Rate
Used in the MOVES Compliance Rate Calculation
Grace Period
Used in MOVES to Determine Beginning Model Year of Coverage
Model Year Range
Used in MOVES to Determine Ending Model Year of Coverage
Test Type
Used to determine MOVES l/M Test Type
Vehicle Type
Used to determine MOVES Sourcetype
Waiver Rate
Used in the MOVES Compliance Rate Calculation
For calendar year 2011 through 2013, the IMCoverage table default parameters were derived using the
IMCoverage tables from the county databases (CDBs) provided to EPA for the 2011 National Emission
Inventory (NEI) project46 (Versionl). These tables were available for review by states and updated as
needed. The l/M program descriptions were extracted from the CDBs and compiled in the default
IMCoverage table for calendar year 2011. The l/M descriptions for 2012 and 2013 calendar years were
derived from the 2011 l/M descriptions, assuming no changes in the basic l/M program design;
however, the model year coverage values were updated to properly account for the existing grace
periods in the future calendar years.
The calendar year 2014 and later values were initially derived from the 2014 NEI (Version l)47 CDBs
following review by the states, with the 2015 and later calendar year values computed assuming no
changes in the basic 2014 l/M program design but updating the model year coverage values to properly
account for the existing grace periods in the future calendar years. All of the l/M program descriptions
were checked using a script to look for cases where a model year coverage either conflicted with other
rows in the l/M description or where gaps without coverage were left between model years. This check
also looked for cases where the coverage beginning model year occurred later than the ending model
year coverage. Each problem identified was compared to the l/M program descriptions found in the
2013 EPA l/M Program Data, Cost and Design Information report48 to resolve conflicts. The county
coverage values in some states were also updated for some calendar years. In addition to the updates
in the l/M program descriptions, the table was updated to make sure each l/M program covered E85-
fueled vehicles in the same way as gasoline in all calendar years. Any program elements claiming
benefits for inspections to reduce liquid fuel leaks (pollutant process ID 113) were dropped from the
default l/M program descriptions. MOVES does not offer any benefits from inspection programs to
detect liquid fuel leaks.
For MOVES3, the table was further updated based on state supplied data through the OBD
Clearinghouse website49 and 2017 National Emissions Inventory (NEI).50 The updates include adding l/M
programs for Ascension Parish, Iberville Parish, and Livingston Parish in Louisiana; for Hamilton County,
Tennessee, and for Cache County, Utah. We also updated the program stop years for terminated l/M
programs. Terminated programs include programs in Anchorage Borough, Alaska; Grundy County,
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Illinois; Clark County and Floyd County, Indiana; Shelby County, Tennessee; and seven counties in
Minnesota, 26 counties in North Carolina, and six counties in Ohio.
We also deleted the l/M program for Harrison County, Indiana (for all the CY years), since it was
confirmed that Harrison County, IN has never been in nonattainment for any National Ambient Air
Quality Standards (NAAQS) and does not have a l/M program. We also updated the beginning model
year for North Carolina l/M counties to reflect changes to their program for 2020 and later.51 In
addition, to reflect the termination of l/M program in Washington state, l/M programs have been
removed from IMCoverage table for all counties in Washington state after CY2019.
California currently has three different l/M programs: an enhanced program, basic program, and
ownership change program. These may vary by zip code within a county; however, MOVES lacks this
specificity. We mapped California counties with l/M program types by checking all the zip codes in each
county. We use the basic program to represent a county if it has mixed programs. This methodology is
consistent with previous work. We updated l/M program details for ten counties in California based on
our research.
In MOVES3.0.4 and later versions, we updated compliance factors using data from the 2020 National
Emissions Inventory for existing IM programs that match the description in the default database, for
year 2020 and after. We also used the 2020 NEI information to update Cache County, UT for calendar
year 2020 and beyond. Finally, we removed l/M program information for Montgomery County, OH for
2020 and beyond, and removed programs for all counties in Tennessee starting with calendar year 2023.
In MOVES4 we further updated information for Montgomery Co, OH for historical years, to reflect that
the county had an active l/M program only between 1990 and 2008.
Table 5-4 shows the states with l/M program descriptions in the MOVES4 l/M coverage table and shows
the number of counties covered by the programs by calendar year. For example, Idaho has two counties
that have l/M programs; one county's program covers from CY 1990 to CY 2060, while the other
county's program covers from CY 2011 to CY 2060.
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Table 5-4 States With l/M Programs as Listed in MOVES
Calendar Years
State
StatelD
Minimum
Maximum
Counties
Alaska
2
1990
2009
1
1990
2012
1
Arizona
4
1990
2060
2
California
6
1990
2060
14
1999
2060
26
Colorado
8
1990
2060
7
8
2015
2060
2
Connecticut
9
1999
2060
8
Delaware
10
1990
2060
3
District of
Columbia
11
1990
2060
1
Georgia
13
1999
2060
13
Idaho
16
1990
2060
1
2011
2060
1
Illinois
17
1990
2060
10
1990
2005
1
Indiana
18
1990
2007
2
1990
2060
2
Kentucky
21
1990
2005
4
Louisiana
22
2000
2060
5
Maine
23
1990
2060
1
Maryland
24
1990
2060
14
Massachusetts
25
1990
2060
14
Minnesota
27
1990
1999
7
Missouri
29
1990
2060
5
Nevada
32
1990
2060
2
New Hampshire
33
2002
2060
3
2011
2060
7
New Jersey
34
1990
2060
21
New Mexico
35
1990
2060
1
New York
36
1990
2060
9
2001
2060
53
North Carolina
37
2003
2060
9
2006
2060
13
2003
2005
1
2003
2018
2
2006
2018
24
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Calendar Years
State
StatelD
Minimum
Maximum
Counties
Ohio
39
1990
2008
7
1990
2060
7
Oregon
41
1990
2060
4
2001
2060
2
Pennsylvania
42
1990
2060
11
2001
2060
14
Rhode Island
44
2000
2060
5
Tennessee
47
1990
2016
1
1990
2022
6
Texas
48
1990
2060
4
2000
2060
6
2011
2060
7
Utah
49
1990
2060
4
2020
2060
1
Vermont
50
1990
2060
14
Virginia
51
1990
2060
10
Washington
53
1990
2019
5
Wisconsin
55
1999
2060
7
5.5.Future Research
For thoughts on potential improvements to the MOVES l/M and non-l/M rates, see the MOVES light-
duty report where the calculation of MOVES current rates is explained in detail.4
Values for IMFactor are generally based on analysis for MOBILE6 or earlier and should be updated to
reflect current vehicle technology and testing practices and to better correspond to the current l/M
reference program. An IMFactor update is particularly needed for OBD which is commonplace now but
was in its infancy when the current MOVES values were developed.
While county modelers can and should always review the MOVES default IMCoverage table to assure
values are up-to-date for a given county, the defaults should be compared to state and local l/M
program records to assure that the default values reflect the best information about historical, current
and future l/M coverage and compliance data.
In addition, the MOVES algorithm could be improved to allow l/M Coverage by regulatory class to better
match program design and the underlying MOVES emission rates. This would eliminate the need for the
regulatory class coverage adjustment in computation of the Compliance Factor.
Furthermore, there are vehicle inspection programs not currently modelled in MOVES, including
programs to reduce tampering and deterioration of heavy-duty diesel trucks, programs based on remote
sensing, and programs intended to reduce emissions of particulate matter. Expanding the scope of
MOVES to estimate the benefits of such additional programs would be useful for those considering such
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programs. However, such expansion would require a significant and long-lasting investment in research
and analysis, as illustrated by the difficulty in collecting and updating data to support MOVES current
l/M algorithms.
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6. Electric Vehicle Charging and Battery Efficiency
€_7 €_7 »/ J
MOVES base energy consumption rates include the power needed at the wheels for each operating
mode plus energy losses through the drivetrain,6 but this does not account for an electric vehicle's total
demand on the electric power grid. By calculating total energy demand of vehicles on the grid, MOVES
can better facilitate the modeling of emissions from power plants and associated air quality changes.h
This section details how MOVES3 was updated for MOVES4 to account for charging and battery
efficiency when estimating energy consumption for electric vehicles (EVs).
For MOVES purposes, charging efficiency captures the energy lost in the wall charger - essentially the
difference between energy drawn from the wall outlet and energy added to the battery. Battery
efficiency, meanwhile, captures the relative energy lost in the battery itself - the difference between
energy produced at the output terminal and energy added to the battery. Each of these can range from
0 to 1, with higher values being more efficient.
While these efficiencies are related, they depend on different physical components that are engineered
independently, so their baseline efficiency and deterioration are likely to be different. MOVES models
them individually to account for these differences, but in practice, they are difficult to measure
separately. Most studies and lab data report them together in a measure we call "wall-to-output"
efficiency.
6.1. MOVES Design and Implementation
For MOVES4, we created a new table, called evEfficiency, which contains the charging and battery
efficiency for electric vehicles. Similar to emissionRateAdjustment,5 the values in this table are applied
once the base rates have been calculated, at the same time as other adjustments and corrections like
those for ambient temperature (see Section 2.7).
MOVES4 models fuel cell vehicles as vehicles of the "electric" fuelType (fuelTypelD=9), but with a
separate engine technology type (engTechID = 40). However, a limitation of this approach is that when
charging and battery efficiencies are applied during MOVES runtime, the different EV technologies have
already been aggregated together to produce an average EV base energy consumption rate. Therefore,
the evEfficiency values implicitly apply to all electric vehicles, including fuel cells. This is not desired,
because fuel cell vehicles get their power from the fuel cell rather than the grid. Therefore, the fuel cell
base energy consumption rates in emissionRate were scaled down by the appropriate values in
evEfficiency. This ensures that the final energy consumption for fuel cell vehicles represents their actual
operation, after all adjustments are incorporated.
The evEfficiency table contains separate columns for battery and charging efficiency, with dimensions
for pollutant and emission process, source type, regulatory class, model year range, and age range. This
design provides maximum flexibility to improve the modeling of chargers and batteries in future
versions of MOVES, including by specific vehicle types (regulatory class) and vocations (source type).
h Similarly, estimating energy consumption of internal combustion engines is useful for estimating the emissions associated
with the production and distribution of gasoline, diesel, and other combustion fuels.
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This flexibility can be used in future MOVES versions to model the impact different driving behaviors,
charging behaviors, and drivetrain configurations have on overall EV efficiency.
The adjustments are applied using Equation 6-1.
baSeRate Fnnatinn fi 1
finalRate = — — — Equation 6-1
(batteryEff iciency * chargingEf f iciency)
Consistent with MOVES design for electric vehicles, the only pollutant and process in the table is total
energy consumption while the vehicle is running. In MOVES4, all electric vehicles will use the same
efficiencies and deterioration trends, regardless of source type, regulatory class, or model year due to a
lack of specific data pertaining to these fields. MOVES design allows more granular efficiency values by
source type, regulatory class, and model year, provided sufficient data becomes available.
6.2. Data Analysis and Literature Review
6.2.1. Charging Efficiency
Data on EV charging efficiency is limited, and the technology is evolving rapidly. Our primary data source
for charging efficiency is from the Altoona Bus Research and Test Center in the Penn State College of
Engineering.52 They tested battery electric buses from a variety of manufacturers and reported the
energy consumption of the bus on various drive cycles as well as the power drawn from the charger for
each test. From these, an overall wall-to-output efficiency can be calculated, which represents the
combination of charging efficiency and battery efficiency.
The wall-to-output efficiencies vary from approximately 75% to 91% as shown in Figure 6-1. However,
most buses, including the newer model years with better technologies, range from 85% to 91%. Most
data reported by Altoona as well as other sources contains wall-to-output efficiency and is not
separated by battery and charger efficiency. Therefore, we had to combine the Altoona data with a
literature review and engineering judgement to separate the battery and charging efficiency values in
MOVES. We assign new EVs a battery efficiency of 95% and a charging efficiency of 94%, which results in
a wall-to-output efficiency of 89.3%.
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100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Figure 6-1. Average of wall-to-output efficiencies of electric buses tested by the Altoona Bus Research and Test
Center, grouped by manufacturer. Only buses with test reports that included both battery energy levels and
total charging energy consumption were included.
Our use of a 94% charging efficiency is informed by a number of factors in combination with engineering
judgement based on conversations within the MOVES team and with external experts, including those at
the Altoona Bus Research and Test Center. The chosen value of 94% is broadly consistent with a variety
of sources for heavy and light-duty vehicles. Tan, et al. (2014)53 show values ranging from 97% to 98.5%
and Kreiger and Arnold (2012)54 show values ranging from 85% to 95%. Both studies are modeling
studies, so we feel they are good confirmation of our efficiency values, but we chose to use observed
real-world data to calculate our charging efficiency adjustment. Apostolaki-losifidou, et al. (20 17)55 show
values ranging from 85% to 98% based in part on observed data. This study contains detailed data, but
only for a single charging system and two vehicles, which we feel is adequate to help confirm our
adjustment in MOVES but not to calculate the adjustment.
The literature cited above doesn't report that charging efficiency changes with age, and discussions with
experts in the field, including the Altoona Bus Research and Test Center, indicate no physical reason to
expect a deterioration with age. Therefore, we assume there is no age trend for EV charging efficiency.
6.2.2. Battery Efficiency
Battery efficiency, however, does deteriorate with age. Loss of EV range as battery ages is well
documented, but most studies focus on a loss of capacity. In theory, a loss of capacity can explain
reduced range without a drop in efficiency. We could not find any real-world data on the change in
battery efficiency with age. However, Yang et al (2018)56 modeled battery aging in typical driving
conditions in each U.S. state to cover a wide range of operational conditions. While their battery model
68
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is based on batteries used in most passenger car EVs, the fundamental battery cell technology and
specifications are also commonly used in heavy-duty BEVs.
Yang et al. show that internal resistance increases as batteries age, which means that the reduction in
EV range with age can be attributed, at least in part, to a change in energy consumption. Energy
consumption is related to resistance, as shown in Equation 6-2 where R is internal resistance and e is
energy consumption:
. 1 Equation 6-2
= S * —
1 + A R
Yang et al show that the average increase in energy consumption related to increased internal
resistance is 17.29% over 10 years. Starting with no increase in resistance for new vehicles, we linearly
interpolate between ages 1 and 10, binned according to MOVES age groups in the evEfficiency table.
Assigning new EVs a battery efficiency of 95% based on engineering judgement and our literature
review,5455 57 we can calculate the average efficiency for each age group using Equation 6-3.
0.95 c c 3
batteryEf ficiencyageGroup = _ Equation 6-3
ageGroup U.7D
Because electric vehicles are a relatively new technology, there is considerable uncertainty about how
batteries age beyond 10 years. While some will continue to deteriorate, other vehicles may get
efficiency improvements or battery replacements under warranty. Electric vehicles have an ability to
manage battery degredation through software improvements as well, which may also limit battery
aging. Therefore, we assume overall battery efficiency doesn't deteriorate beyond the first 10 years.
This approach is similar to how we model criteria pollutant emission rate deterioration for ICE vehicles.
6.2.3. Conclusion
The resulting charging efficiency and battery efficiency values used in MOVES are in Table 6-1. We use
the same charging and battery efficiency assumptions for all electric vehicles, regardless of vehicle class
and model year.
Table 6-1 Battery and Charging Efficiency by Age1
Age Group
Battery Efficiency
Charging Efficiency
0-3 years
0.95
0.94
' As noted in Section 6.1, the current MOVES code requires application of these values to both BEVs and FCEVs. Thus, the FCEV
base energy consumption rates were adjusted ("back-calculated") to generate the correct net energy consumption.
69
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4-5 years
0.903153
0.94
6-7 years
0.874407
0.94
8-9 years
0.847435
0.94
10-14 years
0.828273
0.94
15-20 years
0.828273
0.94
20+ years
0.828273
0.94
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7. Averaging, Banking and Trading with ro, \
Some EPA regulations allow manufacturers to meet emission targets through what are known as
"averaging, banking and trading" (ABT) provisions. These provisions allow higher emissions from some
vehicles in return for lower emissions from others.
In general, MOVES does not need to account for these details because MOVES is designed to estimate
emissions of "fleet-average" vehicles rather than individual vehicles or vehicle families. The impact is
relevant only when the low-emission vehicles are modeled separately from the high-emission vehicles,
as is true for electric vehicles. However, in previous versions of MOVES, we've called this impact
negligible because the need for modeling electric vehicles was more hypothetical and the fraction of
electric vehicles was so low in the past.
Now, as EV sales have grown and there is more need to model separate emission rates for electric
vehicles and vehicles with internal combustion engines (ICE), we have updated MOVES to account for
the impact of ABT programs. MOVES4 explicitly accounts for expected increases in the emissions and
energy consumption from conventional LD vehicles when national electric vehicle sales increase. There
are no such adjustments for heavy-duty vehicles.
The inputs needed for these calculations are stored in the MOVES default database in a new
EVPoplCEAdjustLD table which lists the pollutants, emission processes, and model year ranges affected
by the ABT algorithm. For criteria pollutants, the table is used only to indicate which pollutants, emission
processes and model year ranges are covered. For energy, this table also contains the following fields:
• "adjustmentWeight", which is used as an EV multiplier (which increases the apparent total
number of vehicles when calculating the impact of the ABT provisions). Currently, it is only
used when calculating energy consumption and C02for certain model years, as described in
Section 7.2.
• "adjustment", which can be used to scale the final emission rates. Currently, it is only used to
adjust energy consumption and C02 calculations, as described in Section 7.2.
7.1 .ABT Impacts for Criteria Pollutants
Under the LD Tier 3 rule,58,59 ABT provisions are relevant for the NOx+NMOG (non-methane organic
gases) exhaust emission standard, but do not apply to PM or CO. The rule allows averaging electric
vehicles exhaust emissions with other light-duty vehicles with a one-to-one weighting.' Manufacturers
may average across cars and light trucks. Thus, the sale of battery electric vehicles (BEV) in the U.S.
light-duty fleet effectively increases the Tier 3 NOx+NMOG limit for internal combustion LD vehicles.
We assume manufacturers will take full advantage of these higher effective standards for ICE vehicles
because this allows them to reduce costs by applying measures such as installing simpler after-
treatment technologies on hybrid vehicles or reducing precious metal loading in catalytic converters.
j Note, the rule also allows fleet averaging with electric vehicles for Regulatory Class 41 vehicles, but this is not modeled in
MOVES4.
71
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Alternately, they may sell more vehicles in higher Tier 3 emission bins or sell credits to another
manufacturer.
Because the Tier 3 standard gives electric vehicles the same weighting as other vehicles when calculating
the fleet average, the multiplicative adjustment to the effective Tier 3 NOx+NMOG limit may be
calculated using Equation 7-1. This ratio between the actual standard and the effective standard is
calculated at run time in MOVES and applied to the fleet-average base emission rates described in the
light duty technical report.4
Effective_ICE_standard 1
ABT_Adjustment = - - = - Equation 7-1
Tiers _standard (1 — evSalesFractionLD)
Where:
evSalesFractionLD is the national fraction of battery electric vehicles for a given model year
across all light-duty cars and trucks as provided in the MOVES sampleVehiclePopulation table.
Note, this calculation does not use the user-provided EV fraction in the AVFT table. That is
because compliance with fleet-wide averaging is based on national sales rather than the local
fraction of EVs.
Effective_ICE_standard is the average target that internal combustion vehicles must meet once
electric vehicle credits are considered.
Tier3_standard is the original Tier 3 standard for the model year.
For example, if the evSalesFractionLD is 9.5 percent, the effective standard for the remaining
conventional vehicles increases by approximately 10% (an ABT_adjustment of 1.105).
In MOVES, the evSalesFractionLD is calculated at run time as the fraction of electric vehicles among all
regClass 20 and 30 vehicles for each model year. Individual counties will, of course, have different
electric vehicle sales fractions, but emission compliance is determined at the national level, thus we use
MOVES national default values when calculating the ABT adjustment for internal combustion engine
emissions even when MOVES is run at county or project scale. Specifically, the calculation uses the
model year vehicle population from the default sourceTypeYear table, the ageFraction at age zero from
the default sourceTypeAgeDistribution table, and the regClass 20 and 30 fractions from the
sampleVehiclePopulation table.
The ABT adjustment is then applied to regClass 20 and 30 running and start exhaust emission rates for
NOx and THC. The same ratio is applied to all operating modes and ages and to all fuelTypes except
electricity. While the Tier 3 standard and the ABT provisions are for NMOG, we follow the general
MOVES practice of modeling relative changes in THC as proportional to changes in the NMOG standard.4
Note that the Tier 3 NOx+NMOG standard begins to phase-in in 2017, and light-duty cars and light-duty
trucks have different standards until 2025. We considered explicitly accounting for the difference in
72
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standards during these years with different ratios for cars and light-duty trucks, but given the low
fraction of BEVs in the fleet through 2021, we chose to calculate a single ratio for each model year from
2017 forward.
Equation 7-2, below, is a rearrangement of Equation 7-1, with the addition of an adjustment term and
an adjustment weighting factor. However, since the Tier 3 rule has no special weighting for electric
vehicles, the hydrocarbon and NOx rows in the EVPoplCEAdjustLD table have an adjustmentWeight value
of 1. Pollutants, processes and model years not listed in the table are not adjusted. And, as noted
below, the EVPoplCEAdjust "adjustment" field is not relevant for criteria pollutants and thus also has a
value of 1.
7.2. ABT Impacts for Energy Consumption and C02
Energy consumption and C02 emissions are modelled in MOVES4 based on current standards, including
the Revised 2023 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions Standards.60
"Electric vehicle multipliers" are used in the standards as one way to measure and promote the
advanced technology benefits. The fleet average standards also allow credit trading between light-duty
cars and trucks.
Similar to how we model the impact for criteria pollutants, we assume the manufactures will take the
full advantage of these EV-related benefits with regard to energy and C02 emissions. Specifically, we
used the equation below to calculate an adjusted meanBaseRate for ICE vehicles.
Fleet average meanBaseRate * Adjustment
ICE meanBaseRate =
EV fraction * EV multiplier Equation 7-2
(1 — EV fraction) + (EV fraction * EV multiplier)
Where:
Fleet average meanBaseRate is the running energy rate stored in emissionrate table in the
MOVES default database.6
Adjustment is used to convert energy consumption from values reported from 2-cycle testing to
a value more representative of real-world driving. This value is independent of EV fractions and is
not intended for use with the criteria pollutants discussed in Section 7.1. See the MOVES Energy
and GHG report6 for more information about this adjustment factor.
EV fraction is the national EV sales fraction, as listed in the sampleVehiclePopulation table of the
MOVES default database. These vary by model year and are different for light duty cars and
trucks. The MOVES default values are based on projections from the analysis supporting the
Revised 2023 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions Standards.60
More detail about EV fractions is available in the MOVES Population and Activity report.34 Note,
this calculation does not use the user-provided EV fraction in the AVFT table. That is because
compliance with fleet-wide averaging is based on national sales rather than the local fraction of
EVs.
73
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EV multiplier is stored as adjustmentWeight in EVPoplCEAdjustLD table in the MOVES default
database as shown in Table 7-1. The values vary with model years as determined from EPA
regulations, including the LD GHG Phase 2 rule,61 SAFE,62 and the revised 2023 and later
standards.60
Table 7-1 Electric Vehicle Energy Adjustment Weights
Model Years
Adjustment
Weight (a.k.a. "EV
multiplier")
2017-2019
2.0
2020
1.75
2021
1.5
2022
1.0
2023-2024
1.3
2025+
1.0
With the EV energy rates in the emissionRate table and the ICE emission rates calculated with Equation
7-2, MOVES can calculate energy consumption and C02 emission rates that meet the most recent EPA
fleet-average standards, and can also provide appropriate ICE and EV energy consumption emission
rates for scenarios with user-supplied EV population fractions.
To illustrate, imagine a MY2024 fleet with 10 percent light-duty EVs and an adjustment weight of 1.3.
To compensate for the flexibility allowed in current regulations, the average energy consumption rate
for the ICE vehicles would be increased as shown in Equation 7-3 below.
Fleet average meanBaseRate
ICE meanBaseRate =
0.10 * 1.3
(1 - .10) + (0.10 * 1.3)
Fleet average meanBaseRate
= 1 °-13 =
(0.90 + 0.13) Equation 7-3
Fleet average meanBaseRate
~ 0.874
= Fleet average meanBaseRate * 1.144
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To further illustrate, adjustments for both energy and for criteria pollutants are shown for a range of EV
fractions and model years in Table 7-2, below.
Table 7-2. Illustrative ABT Adjustments for Energy Consumption and Criteria Pollutants
Model Year
Example
National
LDV EV
Fraction
Example
National
LDT EV
Fraction
LD average
EV
Fraction*
EV
Adjustment
Weight**
ICE Energy
mean base
rate
Multiplier
ICE HC and
NOx "ABT
Adjustment"
2020
0.04
0
0.02
1.75
1.04
1.02
2024
0.11
0.08
0.095
1.3
1.14
1.10
2028
0.22
0.18
0.2
1
1.25
1.25
2032
0.23
0.19
0.21
1
1.27
1.27
2036
0.24
0.19
0.215
1
1.27
1.27
2050
0.26
0.21
0.235
1
1.31
1.31
* MOVES actually calculates a weighted average based on national LD sales by sourcetype, but this
illustration uses a straight average.
** EV Adjustment Weights are from Table 7-1.
75
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mces
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116. Assessment and Standards Division. Office of Transportation and Air Quality. US Environmental
Protection Agency. Ann Arbor, Ml. 2020. https://www.epa.gov/moves/moves-onroad-technical-reports
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emissions-characterization-shing-mun-tunnel-hong-kong-and-fort
31 Grange, S. K., N. J. Farren, A. R. Vaughan, R. A. Rose and D. C. Carslaw (2019). Strong Temperature
Dependence for Light-Duty Diesel Vehicle NOx Emissions. Environ Sci Technol, 53 (11), 6587-6596. DOI:
10.1021/acs.est.9b01024.
32 USEPA (2005). Energy and Emissions Inputs. EPA-420-P-05-003. Office of Transportation and Air
Quality. US Environmental Protection Agency. Ann Arbor, Ml. March, 2005.
https://www.epa.gov/moves/moves-onroad-technical-reports
33 American Automobile Association, Inc. (2019). AAA Electric Vehicle Range Testing. AAA proprietary
research into the effect of ambient temperature and HVAC use on driving range and MPGe. American
Automobile Association, Inc. https://www.aaa.com/AAA/common/AAR/files/AAA-Electric-Vehicle-
Range-Testing-Report.pdf
34 USEPA (2023). Population and Activity ofOnroad Vehicles in MOVES4. EPA-420-R-23-005 Office of
Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, Ml. August 2023
https://www.epa.gov/moves/moves-onroad-technical-reports
78
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35 40 Code of Federal Regulations 1065.670 (a)(Page 230). Available online:
https://www.govinfo.gov/content/pkg/CFR-2017-title40-vol37/pdf/CFR-2017-title40-vol37-seclQ65-
670.pdf
36 40 Code of Federal Regulations 86.144-94(c)(7)(iv-viii)(Page 706). Available online:
https://www.govinfo.gov/content/pkg/CFR-2015-title40-voll9/pdf/CFR-2015-title4Q-voll9-sec86-144-
94.pdf
37 USEPA (2001). Air Conditioning Correction Factors in MOBILE6. EPA-420-R-01-055. Assessment and
Standards Division. Office of Transportation and Air Quality. US Environmental Protection Agency. Ann
Arbor, Ml. November 2011.
38 Nam, E. K., Understanding and Modeling NOx Emissions from Air Conditioned Automobiles, SAE
Technical Paper Series 2000-01-0858, 2000.
39 National Oceanic and Atmospheric Administration (2014). The Heat Index Equation. Weather
Prediction Center. National Weather Service
http://www.wpc.ncep.noaa.gov/html/heatindex equation.shtml
40 USEPA(2022) Performance Standard Modeling for New and Existing Vehicle Inspection and
Maintenance (l/M) Programs Using the MOVES Mobile Source Emissions Model, USEPA Office of
Transportation and Air Quality, Transportation and Climate Division. EPA Report Number EPA-420-B-22-
034 October 2022. Available at: https://www.epa.gov/state-and-local-transportation/vehicle-emissions-
inspection-and-maintenance-im-policy-and-technical
41 USEPA (2002) User's Guide to MOBILE6.1 and MOBILE6.2 Mobile Source Emission Factor Model,
USEPA Office of Transportation and Air Quality, Assessment and Standards Division. EPA Report Number
EPA-420-R-02-028 October 2002.
42 USEPA (2002) MOBILE6 Inspection / Maintenance Benefit Methodology for 1981 through 1995 Model
Year Light Vehicles, USEPA Office of Transportation and Air Quality, Assessment and Standards Division.
EPA Report Number EPA420-R-02-014 (M6.IM.001) March 2002. Available at:
https://nepis.epa.gov/Exe/ZvPDF.cgi/P10022PN.PDF?Dockev=P10022PN.PDF
43 USEPA (2001) Determination of NOx and HC Basic Emission Rates, OBD and l/M Effects for Tier 1 and
later LDVs and LDTs Final Report M6.EXH.007, USEPA Office of Transportation and Air Quality,
Assessment and Standards Division. EPA Report Number EPA-420-R-01-056 (M6.EXH.007) November,
2001.
44 USEPA (2001) Determination of CO Basic Emission Rates, OBD and l/M Effects for Tier 1 and later LDVs
and LDTs M6.EXH.009, USEPA Office of Transportation and Air Quality, Assessment and Standards
Division. EPA Report Number EPA-420-R-01-032 (M6.EXH.009) April, 2001.
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45 USEPA (2023). MOVES4 Technical Guidance: Using MOVES to Prepare Emission Inventories for State
Implementation Plans and Transportation Conformity, USEPA Office of Transportation and Air Quality,
Assessment and Standards Division. EPA Report Number EPA-420-B-23-011. August 2023.
46 USEPA (2011). 2011 National Emission Inventory - https://www.epa.gov/air-emissions-
inventories/national-emissions-inventorv-nei
47 USEPA (2016). 2014 National Emission Inventory, https://www.epa.gov/air-emissions-
inventories/national-emissions-inventorv-nei
48 USEPA (2013), l/M Program Data, Cost and Design Information, Final Report, Prepared by ERG for EPA,
Project No.: 0303.00.009.001, August 2, 2013.
49 OBD Clearinghouse (2019) "l/M Jurisdiction Report,"
https://www.obdclearinghouse.com/Jurisdiction/iurisdictionPDFs?
50 USEPA (2020). 2017 National Emission Inventory, https://www.epa.gov/air-emissions-
inventories/national-emissions-inventory-nei
51 USEPA (2019), Air Plan Approval; NC: Revision to l/M Program & Update to Charlotte Maintenance
Plan for the 2008 8-Hour Ozone NAAQS, Federal Register/Vol. 84, No. 176/Wednesday, September 11,
2019/Rules and Regulations.
52 LTI Bus Research and Testing Center (2012-2020). Bus Testing Report.
https://www.altoonabustest.psu.edu/bus-list.aspx
53 Tan, K., Yong, J., and Ramachandaramurthy, V. (2014). Bidirectional battery charger for electric
vehicle. 2014 IEEE Innovative Smart Grid Technologies. DOI: 10.1109/ISGT-Asia.2014.6873826.
54 Elena M. Kreiger and Craig B. Arnold. (2012). Effects of undercharge and internal loss on the rate
dependence of battery storage efficiency. Journal of Power Sources 210 (2012) 286-291. DOI:
10.1016/j.jpowsour.2012.03.029
55 Apostolaki-losifidou, E., Codani, P., and Kempton, W. (2017). Measurement of power loss during
electric vehicle charging and discharging. Energy 127 (2017) 730-742.
https://doi.Org/10.1016/i.energy.2017.03.015
56 Yang, F., Xie, Y., Deng, Y., and Yuan, C. (2018). Predictive modeling of battery degredation and
greenhouse gas emissons from U.S. state-level electric vehicle operation. Nature Communications. DOI:
10.1038/s41467-018-04826-0.
57 Kostopoulos, E., Spyropoulos, G., Kaldellis, J. (2020). Real-world study for the optimal charging of
electric vehicles. Energy Reports, Vol. 6, 418-426. DOI: https://doi.Org/10.1016/i.egyr.2019.12.008.
58 40 CFR§ 86.1811-17
80
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59 40 CFR§ 86.1860-17
60 USEPA(2021), Revised 2023 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions
Standards, Federal Register, Vol. 86, No.151.
61 USEPA(2012), 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate
Average Fuel Economy Standards, Federal Register, Vol . 77, No. 199.
62 USEPA(2020), The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model Years 2021-2026
Passenger Cars and Light Trucks, Federal Register, Vol.85, No.84.
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Appendix A Derivation of Temperature, Humidity and
Meteorology Calculations
The MOVES default database includes default ambient temperature and humidity values for every
county, month, and hour. If modelers do not enter local data, MOVES will use these values to calculate
the temperature and humidity adjustments described in the main body of this report. These values
were derived from 10-year average temperature and relative humidity values from calendar years 2001
through 2011 by month and by hour (standard time) for each county in the United States for all
calendar years.
Due to the limited number of hourly observation stations (about 200 sites), interpolation of the
available data was required. This interpolation will not always produce accurate results, particularly in
areas where climate can vary significantly over distance, such as in mountainous terrain and near
coastlines or deserts. Moreover, it is important that the diurnal range of the average hourly
temperatures match those of the average monthly minimum and maximum values. This aspect arises
due to the averaging process and to the fact that daily maximum and minimum temperatures do not
always occur at the same hourly observation time.
To correct the diurnal range problem, EPA has developed a method to adjust the average hourly
temperatures so that the corresponding hourly-based maximum and minimum temperatures match
those of the true monthly maximum and minimum values. To correct the spatial problem, all of the
daily and monthly maximum and minimum temperature observations made by the National Weather
Service (NWS) and its Cooperative Observation branch (over 6000 sites), and the Federal Aviation
Administration (FAA) are used.
Note, temperature and humidity data are one of the many inputs that are averaged for simplified
national and state level onroad MOVES runs. The algorithms for this averaging ("aggregation") are
described in the MOVES code documentation at https://github.com/USEPA/EPA MOVES Model.
Data Sets and Quality Control
The National Climatic Data Center (NCDC) is the national and international depository for weather
observations. As part of its many duties, the NCDC publishes and maintains many climatic data sets.
"Quality Controlled Local Climatological Data" (QCLCD) files were obtained for all locations across the
United States, Puerto Rico and the Virgin Islands from the NCDC for this analysis.
There can be significant problems with this information. Primary among these problems is that many
stations with daily data do not have corresponding monthly averages, and vice-versa. Further, some
stations may have the same identification numbers while others may have missing or incorrect latitude
and longitude coordinates. During the processing of the 2009 data, nearly 10 percent of the 1654
stations were found to have identification and/or location problems.
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Missing monthly temperatures can be calculated from the daily maximum and minimum observations
for these stations for the years of interest. To resolve the mislabeled station IDs and location data, it
was necessary to contact NCDC to obtain updated tables with corrected IDs before processing the data.
In addition to the hourly temperature and dew point data, the identification number and geographic
location (latitude and longitude) for all available weather stations across the United States, Puerto Rico,
and the Virgin Islands were obtained from the NCDC files. Using Geographical Information System (GIS)
software, the locations of the hourly weather observation stations were validated. To resolve duplicate
IDs and latitude/longitude issues, careful analysis of the station history files and conversations with
state climatologists and National Weather Service offices were made. Our contractor, Air Improvement
Resource Inc. (AIR), hand-edited the IDs and latitude/longitude data and supplied updates to our data
and to the NCDC.
For temperature disputes, such as maximum temperature less than minimum temperature (caused by
mistyped data), hourly and/or daily data from nearby sites were consulted and the data corrected
accordingly.
For each station, an inventory was made as to the number of hours with joint temperature and dew
point data. In order to be included in the analysis, each station had to have at least 50 percent data
recovery for each hour of each month.
The daily absolute maximum and minimum temperature data for all available stations were processed
into monthly averages. These stations covered all classifications, including First-Order (National
Weather Service), Second-Order (both Automated Surface Observing System (ASOS) and Automated
Weather Observing System (AWOS)) and cooperative (local). Following NCDC guidelines, a month's
averages were considered valid when no more than 5 days had missing data during that month. For
each station, the hourly temperature and dew point data was scanned for missing values. For missing
data periods lasting only 1 hour, the missing values was replaced with an interpolated value from the
two adjacent valid readings
After these filters were applied, the average monthly maximum and minimum temperature data were
adjusted to the common midnight-to-midnight observational period. This adjustment is necessary since
many of the cooperative stations take their observations either early in the morning or late in the
afternoon rather than at midnight. These observation times induce a bias into the monthly temperature
averages. Correction values were obtained from the NCDC and applied to the monthly averages.
County Temperature Assignment
An octal search with inverse distance weighting was used to assign the monthly maximum and
minimum temperatures to each U.S. County. Population centroids (latitude and longitude) for each
county were obtained from the 2010 United States Census. Population, rather than geographic,
centroids were used to provide a reasonable estimate of where the county's vehicle miles traveled and
nonroad activity would be concentrated. From each county's centroid, the distance and direction to
each weather station was calculated. The shortest distance was computed using the standard great
circle navigation method and the constant course direction was computed using the standard rhumb
line method. A rhumb line is a line on a sphere that cuts all meridians at the same angle; for example,
83
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the path taken by a ship or plane that maintains a constant compass direction. Based on the computed
directions, the stations were assigned to an octant, as follows:
• Octant 1: 0°
-------
AMin is the average monthly minimum temperature,
AMax is the average monthly maximum temperature,
PMin is the minimum temperature based on the averaged 24 hourly temperatures in the profile,
PMax is the maximum temperature based on the averaged 24 hourly temperatures in the
profile.
After this adjustment is applied, the maximum and minimum of the adjusted hourly temperatures will
exactly match the average monthly maximum and minimum temperatures.
A4. Rclath midity Recalculation
Relative humidity depends on both temperature and dew point. Unfortunately, unlike daily maximum
and minimum temperatures, supplemental dew point data is not available. Consequently, an
investigation and literature search were made to determine a suitable estimation method. Surprisingly,
few were found. The scheme outlined below was suggested by the NCDC and was used in this analysis:
At any given time, the difference between the temperature and dew point is known as the dew point
depression (DPD). Since the dew point can never exceed the temperature, the minimum DPD is zero
(100 percent relative humidity) while the maximum can be several tens of degrees, depending on how
dry the air is. From the original data, the DPD was computed at each hour.
After the hourly temperatures were adjusted to be consistent with the county minimum and maximum
temperatures as described above, the DPDs were subtracted from the hourly temperatures to estimate
the corresponding dew point. The corresponding relative humidity was then computed from these two
values. In keeping with standard meteorological practices, the relative humidity is always computed
with respect to water, even if the temperature is below freezing. Comparative tests showed that the
new calculated relative humidity results were very close to the original values, which is the desired
outcome.
A5. Calculation < ir Averages
The monthly average hourly temperatures for each county from each calendar year from 2001 through
2011 were averaged to determine the default 10-year average temperatures stored in the MOVES
ZoneMonthHour table for each county. The relative humidity values were converted to specific
humidity (humidity ratio) for each hour before averaging and then converted back to relative humidity.
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Calculation of Specific Humidity
While the MOVES default humidity is stored as relative humidity, the humidity adjustment uses specific
humidity. The adjustment for diesel fuel type uses specific humidity expressed as a molar fraction, while
the adjustment for other fuel types uses specific humidity expressed as grams of water per kilogram of
air.
MOVES uses the following equations to calculate specific humidity based on pressure, relative humidity,
and ambient temperature.
Inputs:
Tf is the temperature in degrees Fahrenheit, TK is the temperature in degrees Kelvin
PB is the barometric pressure, in inches of mercury
Hrei is the relative humidity
First, MOVES calculates the vapor pressure of water at the saturation temperature in kPa.
Ph20
10.79574-(l-27^16)-5.02800-toglo(27^16)+1.50475-10^4/ 1-10
= 10
( [4.769S5-(l^2"-16)l \
+0,42873-10 i 10" V TK /I-l -0.
1.2138602
Next, MOVES calculates the molar fraction of water in the air. This is the molar fraction used to
calculate the NOx adjustment for diesel vehicles.
XH20
(Hrel \
1100 J Fh2°
PB * 3.38639
Finally, MOVES calculates specific humidity in grams of water per kilogram of air using the following two
equations (1 inHg = 3.38639 kPa).
/Hrel \
PV(kPa) = (—J ¦ (PH20)
specificHumidity =
Hrel \
621.1 * PV
(PB * 3.38639) - PV
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\ Calculation of I U\it Index
MOVES air conditioning demand is calculated as a function of the heat index as described in the MOVES
Population and Activity report.34 In MOVES, the heat index is a function of temperature and relative
humidity. For temperatures below 78° Fahrenheit, the heat index is equal to the temperature. For
temperatures above 78, the following equation (which is a simplification of the National Weather
Service heat index equationk) is used,
Heat Index = min ( ( -42.379 + 2.04901523T + 10.14333127H
- 0.22475541 TH - 0.00683783T2 - 0.05481717H2
+ 0.00122874T2H + 0.00085282 TH2
- 0.00000199T2H2), 120)
Where:
T= temperature
H = relative humidity
T>=78° F
k National Weather Service, Weather Prediction Center, The Heat Index Equation, May 2014.
https://www.wpc.ncep.noaa.eov/html/heatindex eauation.shtml
87
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Appendix B OTAQ Light-duty gasoline 2012 Cold Temperature
Program
EPA's Office of Transportation and Air Quality (OTAQ) contracted the testing of nine Tier 2 vehicles
(2006 and 2010 model year car and light-duty trucks). Eight of the nine vehicles were Mobile Source Air
Toxics (MSAT-2) rule compliant. Vehicles were tested on the FTP and US06 under controlled conditions
75, 20, and O^F. Note: we excluded the two GDI vehicles (Cadillac STS and the VW Passat) from the
estimation of the THC and CO cold starts'5 as mentioned in Section 0.
Information on the tested vehicles is summarized in Table B-l.
Table B-l Vehicles Tested in 2012 Cold Temperature Study
Vehicle Name
Model Year
Injection
Emissions Std
MSAT?
Odometer
Displ (L)
Cyl.
Buick Lucerne*
2010
PFI
Tier 2/Bin 4
MSAT-2
22000
3.9
V-6
Honda Accord*
2010
PFI
Tier 2/Bin 5
MSAT-2
24000
2.4
1-4
Hyundai Sante Fe
2010
PFI
Tier 2/Bin 5
MSAT-2
18000
2.4
1-4
Jeep Patriot*
2010
PFI
Tier 2/Bin 5
MSAT-2
22000
2
1-4
Kia Forte EX*
2010
PFI
Tier 2/Bin 5
MSAT-2
25000
2
1-4
Mazda 6*
2010
PFI
Tier 2/Bin 5
MSAT-2
24000
2.5
1-4
Mitsubishi Gallant*
2010
PFI
Tier 2/Bin 5
MSAT-2
38000
2.4
1-4
Cadillac STS
2010
GDI
Tier 2/Bin 5
MSAT-2
21000
3.6
V-6
VW Passat
2006
GDI
Tier 2/Bin 5
pre-MSAT
103000
2
1-4
*Tested at 0 F
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Appendix sis Vehicle Sample
The data for the MOVES A/C Correction Factor (ACCF) was collected in 1997 and 1998 in specially
designed test programs. In the programs, the same set of vehicles were tested at standard FTP test
conditions (baseline) and at a nominal temperature of 95 F.
Table C-l lists the vehicles in the test program.
Table C-l Vehicle Sample for the Air Conditioning Analysis
Model Year
Make
Model
Vehicle Class
Weight
1990
DODGE
DYNA
CAR
3625
1990
NISSAN
MAXIO
CAR
3375
1991
CHEVROLET
CAVA 0
CAR
2750
1991
FORD
ESCO GT
CAR
2625
1992
CHEVROLET
CAVA
CAR
3000
1992
CHEVROLET
LUMI
CAR
3375
1992
MAZDA
PROT
CAR
2750
1992
SATURN
SL
CAR
2625
1992
TOYOTA
CORO
CAR
2500
1993
CHEVROLET
CORS
CAR
3000
1993
EAGLE
SUMMO
CAR
2500
1993
HONDA
ACCOO
CAR
3250
1993
TOYOTA
CAMRO
CAR
3250
1994
CHRYSLER
LHS
CAR
3750
1994
FORD
ESCO
CAR
2875
1994
HYUNDAI
ELAN
CAR
3000
1994
SATURN
SL
CAR
2750
1995
BUICK
CENT
CAR
3995
1995
BUICK
REGA LIMI
CAR
3658
1995
FORD
ESCO
CAR
2849
1995
SATURN
SL
CAR
2610
1995
SATURN
SL
CAR
2581
1996
CHEVROLET
LUMI 0
CAR
3625
1996
HONDA
ACCO
CAR
3500
1996
HONDA
CIVI
CAR
2750
1996
PONTIAC
GRAN PRIX
CAR
3625
1996
TOYOTA
CAMR
CAR
3625
1997
FORD
TAUR
CAR
3650
1998
MERCURY
GRAN MARQ
CAR
4250
1998
TOYOTA
CAMRLE
CAR
3628
1990
JEEP
CHER
LDT1
3750
1990
PLYMOUTH
VOYA
LDT1
3375
1991
CHEVROLET
ASTRO
LDT1
4250
1991
PLYMOUTH
VOYA
LDT1
3750
1992
CHEVROLET
LUMI
LDT1
3875
1993
CHEVROLET
S10
LDT1
2875
1994
CHEVROLET
ASTR
LDT1
4750
89
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Modi
1994
1996
1996
1990
1991
1994
1996
1996
1996
1996
1996
1996
1997
1997
1997
1998
1999
Make
Model
Vehicle Class
PONTIAC
TRAN
LDT1
FORD
EXPL
LDT1
FORD
RANG
LDT1
CHEVROLET
SURB
LDT2
FORD
E150 0
LDT2
FORD
F150
LDT2
FORD
F150
LDT2
DODGE
DAKO PICK
TRUCK
DODGE
D250 RAM
TRUCK
DODGE
GRANCARA
TRUCK
DODGE
CARA
TRUCK
FORD
F150 PICK
TRUCK
DODGE
GRANCARA
TRUCK
DODGE
DAKOT
TRUCK
PONTIAC
TRANSSPOR
TRUCK
DODGE
CARA GRAN
TRUCK
FORD
WIND
TRUCK
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Appendix D Consistency of MOVES EV Temperature Adjustment
with Other Sources
As explained in Section 2.7, MOVES applies a temperature adjustment to energy consumption from
electric vehicles. While the adjustments were derived using only values from the AAA report33, we
analyzed the adjustments in relation to other published studies and test programs to ensure that the
temperature adjustment in MOVES is consistent with many sources.
rth American Transit Bus Study
Henning, Thomas, and Smyth published a paper which included observational data from both battery
electric and fuel cell urban buses'. The data was collected by eight transportation agencies in North
America, ranging from California to Minnesota, meaning they were able to collect data at a wide range
of ambient temperatures. The data was collected at the daily level, comparing daily energy
consumption, daily mileage, and daily temperature.
This means their observed temperature effects are approximate and not experimentally derived.
Attributing average change in energy consumption versus ambient temperature is difficult because of a
number of confounding factors, the most important of which is the uncertainty introduced by daily
averaging. Over the course of a day, temperature can change by as much as 20-30 degrees Fahrenheit
and this is not reflected in the data. However, the data is still precise enough to provide a general
comparison to the existing MOVES temperature adjustment and confirm that the adjustment is not
fundamentally different for HD EVs compared to the passenger cars measured in the AAA study.
Their data shows a similar temperature impact for both fuel cell and battery electric EVs, with fuel cells
possibly having a smaller impact. Despite the uncertainties in the data, it is possible to calculate a more
precise temperature effect, but we believe the difference is small enough that creating additional
complexity in MOVES to apply different temperature adjustments for each engine technology is
unwarranted.
Henning, Thomas, and Smyth note a drop in MPGe with decreasing temperature. For battery electric
buses, the average MPGe drops from 18.8 at 65°F to 14.4 at 32°F. This corresponds to a 27% increase in
energy consumption, while the MOVES temperature adjustment estimates a 29% increase. Henning,
Thomas, and Smyth report an average increase of about 6% at higher temperatures (80-95°F), which is
smaller than the MOVES' high temperature adjustment and the AAA findings of a 20% increase, but
directionally consistent with the AAA finding of less impact at warm than at cold temperatures (20 -
32°F).
Table D-l shows Henning, Thomas, and Smyth's observed temperature impacts on fuel economy of both
fuel cell and battery electric buses.
1 Henning, Mark; Thomas, Andrew R.; and Smyth, Alison, "An Analysis of the Association between Changes in Ambient
Temperature, Fuel Economy, and Vehicle Range for Battery Electric and Fuel Cell Electric Buses" (2019). Urban Publications. 0 1
2 3 1630. https://eneaeedscholarship.csuohio.edu/urban facpub/1630.
91
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Table D-l. Fuel Economy Reductions for EV Buses observed by Henning, Thomas, and Smyth
Ambient Temperature Range
Fuel Cell Reduction
Battery Electric Reduction
22-32 F
28.6%
32.1%
50-60 F
0%
0%
70-80 F
6.6%
6.4%
D2. Japanese Passenger Car Study
In 2018, Liu et al collected GPS and real-world energy consumption data from 68 passenger car EVs
being driven across Japan, at a wide range of ambient temperatures"1. They used the data to fit an EV
energy consumption model based on several factors, of which two key variables are the ambient
temperature and accessory load usage, which are related.
Because the energy consumption model is calibrated based on real-world data and temperature is a key
component of the model, it can be used to attribute an increase in energy consumption to a change in
temperature. First, they show that a quadratic equation similar to Equation 2-16 is a good fit for their
data. Second, they show that their quadratic fit is close to the MOVES adjustments, although it is a bit
steeper (a doubling of energy consumption, relative to about 65°F, at about 23°F instead of MOVES'
estimated 8°F).
We did not use this paper as a direct source for a MOVES temperature adjustment for three reasons.
First, the EVs in the study were owned and operated in Japan, and therefore may not be representative
of the American fleet or American driver behavior. Second, the attribution of a change in energy
consumption to temperature is done via a calibrated model, and not direct measurement. The AAA
study source is a more direct observation of the effect of temperature on EV efficiency via controlled
experimental design, which is a better input to MOVES. Third, the paper does not provide enough data
to calculate a temperature effect at the level of precision required by MOVES. Nonetheless, we should
expect the temperature effect modeled by Liu et al. to be broadly consistent with other sources such as
MOVES, and it is.
D3. Canadian Passenger Car Study
Environment and Climate Change Canada (ECCC) performed on-road, real-world testing of a 2018
Chevrolet Bolt in January and July of 2019, collecting energy consumption data at a frequency of 2 Hz
from the battery terminal". Their instrumentation was able to collect energy consumption of various
components as well, and they show that HVAC is the dominant factor increasing energy consumption at
m Liu, K., et al. (2018). Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle
energy consumption. Applied Energy, 227, 324-331. DOI: https://doi.Org/10.1016/i.apenergy.2017.08.074.
n Emissions Research and Measurement Section (Environment and Climate Change Canada) and ecoTechnology for Vehicles
Program (Transport Canada), Government of Canada.
92
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extreme temperatures. The vehicle was driven on similar routes each day of testing, and therefore the
results of several trips are directly comparable.
There were only eight days' worth of testing, and the temperatures tended to be either extreme cold
(below 35°F) or at room temperature and above. Therefore, it is not appropriate to fit a quadratic curve
to the data and calculate an exact temperature effect. However, Figure D-l shows that, given the
expected variance that exists between individual tests, the ECCC data generally agrees with the MOVES
adjustments, including the AC adjustment and light-duty EV cold temperature adjustment.
Energy consumption rates for all full-day tests
compared to MOVES EV ambient temperature adjustments
1
E
300
0
03
a:
C 200
o
"•4—1'
Q_
E
D
(J)
C
o
° 100
>*
O)
!_
CD
c
LU
o-
20 40
Temperature (F)
60
80
MOVES Adjustment EV Component: | | HVAC/Accessory | | Axle/Traction ~ DCDC
Motor
Figure D-l Comparison of ECCC test data and MOVES EV energy consumption with temperature adjustment.
D4. Conclusion
Overall, the MOVES EV temperature adjustment algorithm is generally consistent with the limited
available real-world data on changes in total energy consumption with temperature for electric vehicles
of all classes and technologies.
93
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Appendix ' Vrhu Irs in the 2021 ORD Cold-temperature
Program
The vehicles measured in this program, designated as ORD (2021) in Section 0, are described in
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Appendix F Model-Fitting Information for Analysis of Fuel-
Injection Technology
The following tables include additional model-fitting information for the for the model presented in
Table 2-12, on page 31.
Table F-l. Dimensions for the best-fit Model
Covariance parameters
14
Columns in X
6
Columns in Z per Subject
2
Subjects
12
Maximum observations per subject
27
The 'subjects' are the 12 vehicles. The total number of observations was 112.
The table below presents the 14 covariances associated with the 'random' component of the best-fit
model. These include variances for the random intercepts and slopes for the vehicle subjects, as well as
individual residual error variances for each vehicle.
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Table F-2. Covariance Parameters for the best-fit model
Parameter
Subject
Group
Estimate
Intercept (o2bo)
vehicle
0.1028
Temperature (7) (o2bi)
vehicle
0.000079
Residual (o2^
(ORD) Accord
0.03175
Residual (o2^
(ORD) Fusion
0.006631
Residual (o2^
(ORD)_Jetta
0.01786
Residual (a2£)
(OTAQ) Accord
0.1275
Residual (a2£)
(OTAQ) Forte
0.007208
Residual (o2^
(OTAQ) Gallant
0.02544
Residual (a2^)
(OTAQ) Lucerne
0.1377
Residual (a2^)
(OTAQ) Mazda6
0.4537
Residual (a2£)
(OTAQ) Passat
0.01273
Residual (a2£)
(OTAQ) Patriot
0.02139
Residual (a2^)
(OTAQ) STS
0.01867
Residual (a2^)
(OTAQ) Santa Fe
0.004494
The following table includes 'random' intercepts and slopes for the 12 vehicles included in the analysis.
See Equation 2-13 and discussion on page 30.
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Table F-3. Solution for the Random Effects for the Best-fit Model
Vehicle
Effect
Estimate
Std. Err. Pred.
d.f.
(value
Pr > |t|
(ORD) Accord
Intercept (b0)
-0.1100
0.1604
9.17
-0.69
0.5099
Slope (foi)
-0.01970
0.004186
10.7
-4.71
0.0007
(ORD) Fusion
Intercept (b0)
0.1084
0.1507
7.42
0.72
0.4939
Slope (foi)
0.005961
0.004069
9.65
1.47
0.1747
(ORD) Jetta
Intercept (b0)
-0.1434
0.1554
8.27
-0.92
0.3823
Slope (foi)
0.001323
0.004143
10.3
0.32
0.7558
(OTAQ) Accord
Intercept (b0)
-0.01107
0.2345
11.3
-0.05
0.9632
Slope (foi)
-0.000003.44
0.004952
16.1
-0.00
0.9995
(OTAQ) Forte
Intercept (b0)
-0.1180
0.1577
11.6
-0.75
0.4693
Slope (foi)
0.001859
0.003860
13.3
0.48
0.6380
(OTAQ)_Gallant
Intercept (b0)
-0.2306
0.1810
10.3
-1.27
0.2306
Slope (foi)
0.005038
0.004207
13.5
1.20
0.2517
(OTAQ)_Lucerne
Intercept (b0)
-0.3904
0.2127
16.2
-1.83
0.0849
Slope (foi)
0.006119
0.004741
18.7
1.29
0.2126
(OTAQ) Mazda6
Intercept (b0)
-0.07965
0.2717
9.79
-0.29
0.7755
Slope (foi)
-0.00304
0.006207
14.3
-0.49
0.6321
(OTAQ) Passat
Intercept (b0)
0.3832
0.1628
9.02
2.35
0.0429
Slope (foi)
0.002394
0.004266
11.1
0.56
0.5859
(OTAQ) Patriot
Intercept (b0)
0.3810
0.1766
10.5
2.16
0.0550
Slope (foi)
0.000341
0.004139
13.1
0.08
0.9356
(OTAQ) STS
Intercept (b0)
-0.2382
0.1681
9.7
-1.42
0.1878
Slope (foi)
0.01003
0.004297
11.5
2.33
0.0387
(OTAQ) Santa Fe
Intercept (b0)
0.4486
0.1533
11.7
2.93
0.0130
Slope (foi)
-0.01032
0.003798
13
-2.72
0.0176
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