- DRAFT -

                 Air Conditioning Activity Effects in MOBILE6

                           Report Number M6.ACE.001

                                  January 26, 1998

                                     John Koupal
                            Assessment & Modeling Division
                           U.S. EPA Office of Mobile Sources
1      ABSTRACT

MOBILE6 will include revised estimates of exhaust emissions resulting from air conditioning
operation.  This will require air conditioning behavior and the resultant emission levels to be
predicted over a wide range of ambient conditions. Test data used to develop these factors were
gathered under conditions meant to represent a single extreme set of conditions. This report
addresses EPA's proposed methodology for applying the "extreme" data over the broader range
of ambient conditions in which air conditioner operation occurs in-use. Using air conditioning
activity data collected in Phoenix, a methodology has been developed which relates temperature
and humidity levels to air conditioner load using a combined measure known as the heat index.
This methodology also incorporates some solar load impact and will allow adjustments for cloud
cover if desired by the user. Estimates have also been developed for the fraction of vehicles
equipped with air conditioning systems, and of those, the fraction of malfunctioning systems.

2      INTRODUCTION

The emission data to be used in the development of the MOBILE6 air conditioning factors were
gathered using a test procedure intended to represent extreme ambient conditions.  From these
data emission factors will be developed which represent emission levels at full  air conditioning
load (referred to as "full-usage" emission factors).  These emission factors cannot appropriately
be applied to all ambient conditions, since less severe conditions will result in only partial A/C
loading and hence lower emissions.  The  development of the full-usage emission factors will be
the topic of a separate report.  This report  presents EPA's proposed methodology for applying the
full-usage emission factors across the broad range of ambient conditions for which estimates of
air conditioning emissions are required. A second aspect of air conditioning activity is market
penetration; namely, the fraction of vehicles equipped with air conditioning systems, and of
those, the fraction of malfunctioning systems  which have not undergone repair. Proposed
estimates for these factors are also presented and discussed.
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3      OVERVIEW OF PROPOSED APPROACH

The full-usage emission factors are meant to simulate emissions which will occur under the
conditions specified in the new EPA air conditioning certification test procedure: 95  F, 40 %
relative humidity and full solar load (850 W/m2)1. However, MOBILE6 will need to model air
conditioning emissions at less severe conditions where the majority of vehicle operation will
occur.  Ideally, this model would be based on emission values collected over a range of
intermediate temperature, humidity and solar load levels. Unfortunately, EPA does not possess
and is not aware of any publicly available emission data which allow this sort of empirical
modeling. An alternative method for modeling intermediate ambient conditions is therefore
required.

The method proposed for use in MOBILE6 is to link emissions directly with the operation  of the
vehicle's air conditioning compressor, which is propelled by the engine using a belt in a similar
manner to the vehicle's alternator.  The compressor is the focus rather than driver behavior
because it is the direct cause of additional load on the engine and is therefore the best indicator of
how A/C system operation impacts emissions. Compressor load varies and the compressor
cycles on or off (i.e. is engaged or disengaged) depending on user demand and the vehicle's
response to ambient conditions. As a result, it is generally not inducing full load on the engine
100% of the time the A/C is turned on under intermediate ambient conditions. With this
approach driver behavior is accounted for implicitly, however; because the compressor will only
engage when the A/C system is on, compressor behavior over the course of a vehicle trip is
strongly driven by A/C demand from the user.

An ideal model of this sort would link ambient conditions and emissions by modeling changes in
compressor load (torque) as a function of changes in ambient conditions.  Unfortunately, activity
data which would allow such a link does not appear to exist.  Available activity data does not
include a direct measure  of compressor load, but only the total time the compressor was engaged
over a single vehicle trip. Therefore, the methodology proposed for MOBILE6 is to develop a
relationship between emission response and the percentage of time the compressor is engaged.
Again, publicly available data sufficient to empirically establish this relationship does not appear
to exist, requiring an estimate to be made.  Since the fraction of time the compressor is engaged
over a trip (compressor-on fraction) has a direct impact on the additional load experienced  by
engine during a trip, it is  assumed that the impact of compressor engagement on overall engine
load is linearly proportional (1:1).  The second assumption is that changes in emission response
correlate 1:1 with changes in engine load, and hence with compressor-on fraction.
With this methodology, the compressor-on fraction is equal to the factor by which the full-usage
emission factor would be scaled in MOBILE6 to derive the emission factor appropriate for the
ambient condition.  In other words, a compressor which is engaged  100% of the time would
result in the full-usage emission factor. If the compressor is engaged only 50% of the time, 50%
of the full-usage emission factor would be applied. This scaling factor will be termed the
1 40 CFR 86-160.00

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"demand factor".

The key to this approach is the assumption that the relative emission impact due to A/C
correlates 1:1 with compressor-on fraction for all pollutants.  It should be noted that air
conditioning system experts from the automotive industry have identified several limitations with
this assumption. Specifically, compressor load fluctuates significantly when the compressor is
engaged, depending on a number of factors including ambient conditions, vehicle speed, vehicle
cabin temperature, and A/C system setting (e.g. fan setting, recirculation vs. outside  air); A/C
system response to changes in all of these factors is highly vehicle-specific2. Using only
compressor-on fraction is a rough estimation of actual compressor load since it assumes that
fluctuations in relative compressor load average out over periods when the compressor in
engaged. Further, the impact of compressor load on emissions is likely not linear. However, the
complexity and data demands of a compressor-load based model are prohibitive in the MOBILE6
timeframe, and as mentioned no data exists to investigate emission impacts for intermediate
compressor loads.  Future research activity will need to address this lack of information.

4      DEVELOPMENT OF DEMAND FACTORS

4.1    Phoenix Activity Survey

The activity data used in the development of the proposed demand factors are based  on  an
instrumented vehicle survey conducted as part of the Supplemental Federal Test Procedure
(SFTP) rulemaking process on 20 vehicles over almost 1000 trips in Phoenix, Arizona from
August-October 19943. Data gathered for each trip included time and date, total trip time, total
time the air conditioner was on, and total time the compressor was engaged. The datalogger also
recorded summarized trip information including trip distance, total idle time, and time spent in
five mile-per-hour trip bins.  Hourly weather information taken from Phoenix's Sky Harbor
International Airport available through the National Climatic Data Center (NCDC) was used to
estimate dry bulb temperature and relative humidity at the start of each trip.

4.2    Treatment of Data

The initial dataset used for this analysis contained 987 trips. It did not include the first and last
trips for each vehicle if less than 0.25 miles; these cases were removed as part of an  earlier
analysis because they represented trips taken by the contractor during the datalogger installation
and deinstallation process. For the MOBILE6 analysis, this trip file was further modified to
improve the representativeness in the following manner:

a.      Trips with a duration of 30 seconds or less were removed to  eliminate stalls and other
 EPA/AAMA/AIAM meeting on MOBILE6 air conditioning issues, November 6, 1997

3 "Study of In-Use Air Conditioner Operation in Phoenix, Arizona", Automotive Testing Laboratories, Inc.  report to
EPA (EPA Docket No. A-92-64 Item IV-A-1)

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       potential queuing-related cases.

b.      Since the location of each trip was not known, it was necessary to assume that the linked
       weather information was appropriate in characterizing the conditions experienced by the
       vehicle on every trip. To reduce the chance that a trip was taken outside of the greater
       Phoenix area, all trips greater than 60 miles were deleted from the trip file. In addition, all
       trips for a given vehicle which followed a trip of more than 60 miles were also deleted, to
       reduce the chance that a vehicle made one long trip outside of the Phoenix area and
       remained outside the area for the remainder of the monitored trips. 60 miles was chosen
       as a cutpoint based on the estimated radius of the greater Phoenix area, the distance to
       higher altitude locations outside Phoenix along highway routes, and the distribution of
       trips below and above 60 miles in the dataset. For two of the vehicles for which trip
       distance data was not gathered, a trip duration cutpoint of 45 minutes was used.

c.      Preliminary analysis of the trip data indicated that trips that were comprised solely of idle
       had radically different behavior than  all other trips. As shown in Figure 1, the average
       compressor-on fraction for trips is much lower than trips consisting of even very high
       percentages of idle.  MOBILE6 will predominantly need to predict A/C emissions over
       trips consisting of non-idle driving, and inclusion of idles could skew the non-idle results.
       Idles were therefore  dropped from the trip file for the purposes of developing the non-idle
       demand factor relationships.

These modifications reduced the number of trips in the dataset to 672. All subsequent analyses
were performed on this  dataset.

4.3    Temperature, Humidity and Heat Index

Temperature and humidity are the most important drivers of A/C system demand. While
temperature is a widely recognized influence, the load placed on the air conditioning system by
humidity can account for over half of the total load under the ambient conditions of the SFTP air
conditioning test procedure2. It is considered important, therefore,  to develop a demand factor
methodology which incorporates both temperature and humidity. This was supported by several
comments received following the March  1997 MOBILE6 workshop that advocated the inclusion
of humidity in the MOBILE6 air conditioning component.

Analysis of the Phoenix dataset indicates a strong correlation between temperature and
compressor-on fraction, as shown in Figure 2 (compressor-on time is expressed as the fraction of
time the compressor is engaged over the total trip time at each temperature point). The
relationship between humidity and compressor-on fraction is weak, however (Figure 3), and
ANOVA results indicate that when humidity is modeled with temperature it is not a significant
variable. Given the importance of humidity  in overall A/C system  load, this is judged to be an
artifact of the limited humidity range in Phoenix. Figure 4 shows relative humidity at the start of
each trip as a function of temperature (the reference lines show SFTP temperature and humidity
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conditions); the average relative humidity for temperatures greater than 80 F was only 28%. By
contrast, historical data in Houston indicate that during the summer months (when the average
daily maximum temperature is over 90 F) average relative humidity at noontime is around
60%4.  The development of demand factors which could be applied to humidity levels like those
observed in Houston would require significant extrapolation of the Phoenix data. Given the
weakness of the relationship between humidity and compressor-on fraction within the boundries
of the Phoenix humidity levels, extrapolation of these data is not desirable.

In an attempt to more accurately assess the relative impacts of temperature and humidity on air
conditioning load, therefore, a metric known as the heat index is proposed for use in developing
the demand factors. Heat index is used by the National Weather Service to quantify discomfort
caused by the combined effects of temperature and relative humidity.  The basis of the index is
the human body's ability to maintain thermal equilibrium through perspiration, taking into
account numerous factors including clothing thickness, atmospheric pressure and ambient
conditions. Equations have been developed which allow heat index to be calculated using only
temperature and relative humidity; these equations are proposed for use in MOBILE6 to compute
heat index based on temperature and humidity values input by the user (Appendix 1)5>6.  Heat
index as a function of temperature and humidity is shown in Figure 5.

The proposed approach for addressing intermediate conditions, therefore, is to develop demand
factors by modeling compressor-on fraction as a function of heat index based on user input of
temperature and humidity. An attractive feature of this approach is that the air conditioning
activity component of MOBILE6 would be based directly  on driver discomfort, the most likely
factor impelling a driver's A/C behavior and thus a strong determinant in the vehicle's emission
response.  As shown in Figure 6, the Phoenix data exhibits a strong correlation between
compressor-on fraction and heat index (for this graph each heat index value is treated as a bin in
which the compressor-on factor is calculated based on all trips at that heat index level). It should
be noted, however, that using heat index instead of temperature does not necessarily provide a
better predictor of compressor-on fraction for the  Phoenix data, because the Phoenix results are
driven almost completely by temperature.   Rather, the intent of using the heat index is to
introduce a more  equitable balance in the effect of temperature and humidity on air conditioning
load not provided by the Phoenix data. The underlying assumption of this methodology is that
the temperature-driven effects seen at high temperatures in Phoenix would be replicated under
lower temperature but higher humidity conditions seen elsewhere in the country.  Given the
stated importance of humidity on air conditioning load, this assumption is believed to be more
4 Gale Research Inc., The Weather Almanac, Sixth Edition (1992)

5 Meisner and Graves, "Apparent Temperature", Weatherwise, August 1985
6 The base humidity correction factor currently in MOBILES will be carried over to MOBILE6. The computation of
this factor and the air conditioning demand factor will be based on the same humidity data. A default specific
humidity value of 75 grains/pound (as in MOBILES) is proposed. Users will be able to input alternate humidity
levels in either specific humidity or relative humidity (see Section 6), with appropriate conversions made within
MOBILE6.

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reasonable than ignoring or understating the impact of humidity altogether.

4.4    Solar Load

The proposal for air conditioning effects in MOBILE6 presented at the October 1997 workshop
did not include any accounting for solar load.  Comments received subsequent to the workshop,
however, expressed a strong desire for the inclusion of solar load,  and automotive industry
experts have indicated that it is a contributing factor.  Subsequent  analysis of the Phoenix data
indicates that a solar load impact can be discerned, and consequently a method which accounts
for solar load and cloud cover (addressed in Section 4.6) is being proposed for MOBILE6.

Since solar load or cloud cover data were not available in the NCDC dataset linked to the
Phoenix survey, an empirical relationship between these factors and compressor activity could
not be developed directly.  As an alternative, the impacts of solar load were isolated by binning
all of the trips based on time of day at the start of the trip.  Four "period" bins were created: night
(sunset-sunrise), morning (sunrise - 10 am), peak sun (10 am - 4 pm), and afternoon (4 pm -
sunset). Sunrise and sunset times for each day in the survey as reported by the U.S. Naval
Observatory7 were used to determine appropriate trip bins. The bin definitions were determined
by analyzing solar radiation data gathered as part of the National Oceanic and Atmospheric
Administration's (NOAA) Surface Radiation Budget Project (SURFRAD)8. A regression across
all trips of compressor-on fraction by heat index (weighted by trip length) was performed within
each bin, with the results shown in Figure 7. Since cloud cover information was not available, it
could not be considered as a variable in the analysis. Historical data from Phoenix indicates that
at the time of year the survey was conducted direct radiation from  the sun (i.e. little or no cloud
cover) is present close to 90% of the time9, so for the purposes of this analysis all daytime trips
were assumed to be taken during periods of no cloud cover.  The  lines show a clear difference in
compressor-on fraction between nighttime and daytime at the same heat index level, indicating
the importance of  solar load and meriting a separation of daytime and nighttime demand
equations.  In support of this conclusion, ANOVA performed on the trip file with  compressor
fraction as the dependent variable and heat index and period as the independents indicated that
period is significant to the 0.01 level.

A second question is whether a significant difference exists between the daytime periods. Figure
7 shows that as would be expected, the peak sun curve is higher than the morning and afternoon
curves above 75, while the morning and afternoon curves are similar for the mid-range heat
index levels.  To investigate this issue further,  ANOVA analyses of compressor-on versus heat
index and period were performed for all daytime trips and again for trips taken only in the
morning and afternoon periods.  Period was again significant to the 0.01 level for  all daytime
7 U.S. Naval Observatory Sunrise/Sunset Web Site (http://riemann.usno.navy.mil/aa/data/docs/rs_oneyear.html)
8 NOAA SURFRAD Web Site (http://www.srrb.noaa.gov/surfrad/surfpage.htm)
 Gale Re search Inc., The Weather Almanac, Sixth Edition (1992)
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trips, but was not significant when only the morning and afternoon trips were analyzed. From
this it was concluded that the peak sun period is the cause of the difference between the daytime
curves and merits separate treatment.

4.5   Proposed Demand Equations

Three demand factor equations are therefore being proposed for MOBILE6: nighttime,
morning/afternoon and peak sun. The "raw" equation for each period, as well as for all daytime
trips and all trips, are shown in Table 1; again, the equations reflect a weighting of the sample by
trip length. The relatively low R2 values compared to the composite result shown in Figure 5 are
attributable to the regression being performed over the entire trip sample. A quadratic curve form
is favored over more complex forms because it provides a balance between goodness of fit and
more reasonable behavior at the high and low ends of the heat index range.  Still, because a
smaller sample of trips occurred at the high and low ends (only 5% of trips occurred when the
heat index was less than  75 ) the behavior of the fitted curves at these ends tend to defy
engineering judgment.  In particular the morning/afternoon curve is higher than the peak curve
below 75 , and the night curve is higher than the daytime curves above 100.  To rectify this,
separate demand equations will by applied only in the middle of the heat index range, while the
higher and lower ends will be modeled with composite equations.  The "daytime combined" will
be used for all daytime periods at the lower end, and all individual curves will be modeled with
the "all combined"  equation at the high end. The heat index values at which the composite
equations and period-specific equations diverge (at the low end) or converge (at the high end) are
determined based on the respective points of intersection. This progression is outlined in Table
2, with the revised equation forms for each period shown in Figure 8.

Since MOBILE6 will calculate emission factors on an hourly basis, changes in solar load
throughout the course of a full day will be modeled by applying the appropriate demand
equations at each hour. The night equation will be applied from sunrise to sunset, the
morning/afternoon equation will apply from sunrise - 10 am and 4 pm - sunset, and the peak
equation will apply from 10 am - 4 pm. The peak sun cutpoints were determined based on
analysis of the NOAA data on different days throughout the summer months, which indicated
that direct solar radiation levels stay relatively high from 10 am to 4 pm but tend to  drop off
rapidly before 10 am and after 4 pm.  However, the user will be allowed to input alternate time
for which peak sun demand equations are applied if desired. Default sunrise/sunset times of 6
am and 9 pm will be used in MOBILE6 to approximate a typical summer day with daylight
savings time. The user will also have the option of inputting alternative sunrise/sunset times in
order to alter the hours for which the morning/afternoon and nighttime demand equations are
applied.

4.6   Cloud Cover

As mentioned in Section 4.4, the proposed daytime demand equations were developed from the
Phoenix data under the assumption that all trips were taken during periods of no cloud cover (an
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assumption that likely serves to slightly understate solar load impact). Because of this
MOBILE6 will assume as a default that a sunny day is being modeled. Comments received
following the October 1997 MOBILE6 workshop advocated some accounting of cloud cover,
particularly for modeling seasonal emissions.  It is proposed, therefore that MOBILE6
incorporate an optional input for percent cloud cover on a daily or hourly basis.  The method for
handling cloud cover input will be to scale back the default daytime demand equations.  Analysis
of NOAA solar radiation data indicates that direct solar radiation is reduced to zero when the
sun is obstructed by clouds (Figure 9).  Based on these data, the nighttime demand equation is
proposed to represent 100% cloud cover.  For intermediate cloud cover inputs, the model will
interpolate between the appropriate daytime demand equation and the nighttime demand
equation.  Thus, 50% cloud cover at noontime would result in a demand  factor halfway between
the demand calculated with the peak and nighttime equations at the appropriate heat index.

4.7    Other Factors Considered

While ambient conditions are the primary factors in determining A/C system demand, trip-related
characteristics are also likely to influence air conditioning behavior. Four such factors
investigated for this analysis were soak time prior to the vehicle trip, trip duration, average
vehicle speed, and percent of idle during a trip.  ANOVA was performed on compressor-on
fraction with these variables, with the results shown in Table 3. A technical basis exists for
considering each factor, and it is likely that the dataset implicitly contains the effects of each.
However, none of these variables showed enough significance to merit individual treatment; to a
large extent this is likely because the Phoenix dataset does not provide adequate resolution or
sample size to discern individual effects.  A discussion of each factor follows.

4.7.1   Soak Duration

The length of soak time prior to a daytime trip could influence A/C system demand because of
the impact on cabin temperature.  Vehicles parked in the sun for extended periods of time
experience elevated cabin temperatures compared to short soaks. However, information on
several factors which would greatly influence the impact of soak time were not available,
including whether a vehicle was parked in a shady location (such as a parking garage) or whether
the windows were left open during the soak. Without this sort of information a meaningful
assessment of soak time is difficult; not surprisingly, the ANOVA results do not show
significance for this factor.  A more thorough investigation of soak time impacts would require a
measure of cabin temperature and more detailed trip/soak information. It is likely however, that
the solar load impacts discussed in Section 4.4 are driven in part by this effect, so the impact of
differing soak times are subsumed in the solar load corrections if a representative soak
distribution is assumed.

4.7.2   Trip Duration

Trip duration could also be expected to impact air conditioning behavior. Cabin temperature


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over the course of a longer trip will be reduced by the A/C system, thereby reducing the need for
cooling and hence the amount of time the compressor is engaged relative to the start of the trip.
Figure 10 shows a series of linear regressions for compressor-on versus trip duration over four
heat index ranges.  Two trends emerge from these regressions: the expected downward trend as
trip duration increases, and a leveling of this slope as the heat index increases.  The latter trend
suggests that trip duration has a more significant impact for the intermediate heat indices where
cooling needs can be met in the early stages of a trip, but for higher heat indices the cooling
demand remains high throughout the trip. ANOVA results do not indicate significance,
however. Since the Phoenix dataset  contains a wide distribution of trip durations, these effects
are assumed to be accounted for implicitly in the demand equations.

4.7.3  Average Speed

Average speed could have an impact on A/C system load because higher rates of air flow across
the vehicle's A/C condenser will reduce the work  required to cool the ambient air (although  this
could be offset to some degree by ambient air entering the cabin at a higher rate).  Regression
analysis indicating a downward trend in compressor-on as average speed increases (Figure 11).
Again, this effect is more prevalent for the lower heat index levels, and drops off as the heat
index increases.  The ANOVA results again do not indicate significance, however.  These effects
are assumed to be accounted for implicitly in the demand equations since the equations are based
on a distribution of average speeds.

4.7.4  Idle Fraction

The fraction of idle during a trip could impact overall compressor operation because A/C
calibrations at idle appear to be unique, as discussed in Section 4.2. For many idle-only trips the
compressor is either engaged 100% of the time or 0% of the time, with most idles in the Phoenix
dataset exhibiting the latter. Not engaging the compressor at idle would presumably be used as a
strategy for driveability, because the  relative load place on engine by the A/C system at idle is
high. As shown in Figure 1, the overall average compressor-on times for all trips with idle
fractions  less than 100% appear similar, indicating that the effect seen on idle-only trips doesn't
carry over to idles during normal trips.  The ANOVA results again do not indicate significance,
but it is likely that idle fraction and average speed are higher correlated so an effect solely
attributable to idle is  difficult to separate out. Again, to the extent there are impacts they are
assumed to be accounted for in the demand equations.

5      MARKET PENETRATION ESTIMATES

The second component of activity determining how many vehicles in the fleet are equipped with
air conditioning systems (market penetration), and of those, how many are functional.  Three
steps go into the development of these estimates: determining base market penetration rates by
model year, estimating A/C system malfunction rate by vehicle age, and estimating how many
malfunctioning systems are not repaired.  This section addresses each issue.
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5.1    Base Rates

Base market penetration data by model year were gathered from Ward's Automotive Handbook
for light-duty vehicles and light-duty trucks through the 1995 Model Year.   This information
was available from 1972 for cars and 1975 for trucks.  Year-to-year rates are more variable in
the first few years of available data, so estimates for earlier model years will be estimated by
applying the 1972 and 1975 rates for cars and trucks, respectively.  In the later years, the rate of
increase becomes more steady. Projections beyond 1995 were developed by taking the average
yearly rate of increase from the last five years of available data and applying them to each
subsequent year until a predetermined cap was reached. A cap of 98% was placed on vehicles
and 95% on trucks under the  assumption that there will always be vehicles sold without air
conditioning systems, more likely on trucks than cars.  The resultant base rates are shown in
Figure 12.  The caps are in place by the 1999 model year, and will remain for subsequent years.

5.2    Malfunction Rates

Of all vehicles equipped with air conditioning, it is appropriate to assume that not all of the
systems are functional, requiring an estimate of the fraction of non-functioning systems by
vehicle age. Unfortunately, there appears to be little publicly available data upon which to  base
these estimates. One available source is the annual Consumers Reports Automobile Purchase
Issue, which began reporting reader survey results on A/C system malfunctions starting in 1994.
The reported results from the 1997 survey were used to develop malfunction estimates by vehicle
age based on a yearly increase in absolute malfunction rate of 1.5 percent (Table  4).  Starting at
age nine the malfunction rate will be held constant at 12.5 percent.  This is based on the
assumption that the increased probability of malfunction as a vehicle ages will be offset by the
increased probability a vehicle will have already undergone repair as it grows older.

The second component in developing malfunction estimates is rate of repair.  In  the absence of
concrete data, estimates were generated based on three qualitative assumptions: a) all vehicles up
to three years old (assumed to be the standard bumper-to-bumper warranty period) would receive
repair; b) after three years the majority of owners would still receive repair, but this percentage
would decrease as the vehicle grew older, and c) vehicles built prior to the 1993 model year
(estimated as a cutpoint for which Freon was replaced with R-134a on most vehicles) would
experience  a lower rate of repair due to the prohibitive cost of system recharging. From these
assumptions, it was estimated that 100% of R-134a systems would be repaired during the
warranty period, 90% in years four through eight, 80% in years nine through 13, 70% in years  14
through 18  and 60% in years  19 and up. The non-warranty period repair rates will be reduced by
a factor of 0.75 for Freon (pre-1993) systems, but only if the modeled  calender year is 1995 or
later; if not, the R-134a estimates will be applied (in other words, lower repair rates for Freon-
equipped vehicles will not be invoked if recharging with Freon was viable during the modeled
calender year ). The resultant rate of unrepaired malfunctions combine the malfunction rates
from Table 4 with the rate of nonrepair in a given year.  These estimates are shown in Figure 13.
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For a given model year, the estimate of vehicles on the road with functional air conditioning
systems (referred to as adjusted penetration rates) will combine the base market penetration
estimates for that model year (from Figure 12) with the unrepaired malfunction rates in Figure 13
for the appropriate vehicle age.

Due to the large degree of uncertainty in developing malfunction and repair rate estimates, it is
proposed that alternate base penetration, malfunction and/or repair rates can be input by the user.
This option is proposed because all three elements could vary widely depending on location. For
example, A/C systems are used more in Florida than Minnesota, and hence malfunction more
frequently but are also more likely to be  repaired when malfunction occurs.  Locality-specific
datasets could be developed through surveys conducted in I/M lanes or other means.  If the user
only inputted a subset of these rates, MOBILE6 defaults would be used for the others.  For
example, locality-specific malfunction and repair information could be input and the adjusted
penetration rates would be calculated within MOBILE using the default base penetration rates.

6      HANDLING OF  AMBIENT INPUT DATA

A significant change in MOBILE6 will be ability to model on an hour-by-hour basis, whereas
MOBILES is geared towards providing daily estimates. As proposed MOBILE6 will still
provide daily output based on a weighted hourly result, but the hour-by-hour results will also be
available if desired.  While this increases flexibility in modeling finer increments of time, it also
requires ambient conditions for every hour of the day.  If these data were input by the user, at a
maximum this would mean (for inputs affecting air conditioning emission factors) hourly
temperature, humidity and cloud cover as well as daily sunrise, sunset and peak sun times.  At a
minimum, it is proposed that MOBILE6 require only (as with MOBILES) daily maximum and
minimum temperature. From this, a temperature diurnal would be modeled to provide hourly
estimates of temperature,  specific humidity would be assumed constant at 75 grains/pound (with
relative humidity and heat index calculated on an hourly basis from this), no cloud cover would
be assumed, and the sunrise/sunset/peak sun times discussed in Section 4.5 would be applied.
For humidity and cloud cover, a logical intermediate step would be to accept single daily average
levels which would then be applied to each hour (specific  or relative humidity could be
accepted). In general, however, the handling of ambient data is still unresolved, and comments
are requested on this issue to gain a better understanding of user need and data availability.
Once MOBILE6 is finalized guidance will be provided for developing alternate inputs both for
the ambient and market penetration data.

7      ACKNOWLEDGMENTS

Several individuals contributed time, effort, ideas and consultation to this report.  Rob French of
OMS developed and provided guidance on the Phoenix dataset and contributed to the heat index
concept.  John Gilmore of OMS researched the base market penetration estimates and
malfunction survey information. John German of OMS provided general consultation and input.
Christine Dibble of the Office of Atmospheric Programs provided information on R-134a phase-
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in. John Augustine of NOAA provided information on the SUKFRAD data. Dennis Kahlbaum
of AIR, Inc. provided information on the derivation of the heat index measure. Harold Haskew
of GM and Dr. Mohunder Bhutti of Delphi-Harrison provided consultation on the theoretical
aspects of air conditioner operation.
                                                          M6.ACE.001 DRAFT Page 12

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                  Figure 1 - Compressor-On vs. Percent Idle
1.0
C q i
o -3
03 -8'
LL
6 -71
0 '6'
8 .5-
Q.
E .4-
0
a-3'
03 o i
!_ .Z
^ .1'
0.0















100%











































76-99% 51-75% 26-50% 0-25%





Percent Idle
                  Figure 2 - Compressor-On vs. Temperature
   1.0
J5  .8
o
03

LL
C  .6-
O
CL

  .2
O
   0.0
     60
70
80
90
100
110
                           Temperature (F) - Start of Trip

                         Non-idle trips (weighted by number of trips )

                                   R squared = 0.87

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               Figure 3 - Compressor-On vs. Specific Humidity
1.0
1
LL
0
b
w .4'
CD
Q-
O -2'
0.0,
2

D
D
a 
a  a a
D DD D D
D DD  D n D  D n
D Q D DDQ  D D
D D
D D D
D 
D D a
D D D
D
D D
D
D




0 40 60 80 100 120
Specific Humidity (Grains/Pound) - Start of Trip
Non-idle trips (weighted by number of trips)
           Figure 4 - Humidity vs. Temperature for Phoenix Dataset
   100
Q.

t5  80

^  60
p
  40
I
CD
^3
CD
Q:
    20
     60
                                D DQ
                                a  a
                                                    n-t-
                                                              MB|BID
                                                               D  D D D
                   70
80
90
100
110
                          Temperature (F) - Start of Trip
                                    Non-idle trips

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                             Figure 5 - Heat Index
   180-
8
ID
    60




_
	 J





L 	 J




^ -*
L 	 J


^j^
^A *"
**
	 "*
-^^**^
^ *'^^^
L 	 J

^^^
jF
^r
.**
*'*
^**
,o*-r*r

L 	 J
^
/

***^^

I 	 .
      75
80
85
90
95
100
                               Temperature (F)
                  Note: Heat Index values based on shady conditions
                       Lines represent curve fit of tabular data
                                                       Rel Hum
                                                             80%
                                                             60%
                                                             40%
105
                   Figure 6 - Compressor-On vs. Heat Index
   1.0-
O
CD
     6Q
 _

8   4
tfl  .4
o
    .2
   0.0
      60
    70
        80
            90
                100
                             Heat Index (F) - Start of Trip
                           Non-idle trips (weighted by number of trips)
                                     R squared = 0.84
                    110

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           Figure 7 - Compressor-On vs. Heat Index by Time of Day
o
03
o
    .6
8  .4
CD
fa.
    .2-
   0.0
     60
70
80
90
100
                    Heat Index (F) - Start of Trip
                    Quadratic regression of non-idle trips
                        Weighted by trip duration
                                                                  Period
                                                Sunset-Sunrise
                                                4 pm - Sunset
                                                10 am -4 pm
                                                Sunrise-10 am
110
             Table 1 - Proposed "Raw" Demand Factor Equations
        (Demand Factor = Constant + a* (Heat Index) + b*(Heat Index)2)
Period
Morning/Afternoon
Peak Sun
Daytime Combined
Night
All Combined
Constant
-2.930273
-5.307355
-4.101082
-1.257412
-3.631541
a
0.059110
0.113973
0.086382
0.006753
0.072465
b
-0.000213
-0.000521
-0.000367
0.000143
-0.000276
R2
0.54
0.17
0.43
0.52
0.44

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    Table 2 - Proposed Demand Factor Equation Forms
Heat Index
65 & below
66
74
76
96
101
104
110 & above
Morning/Afternoon
Constant = 0
Daytime
Morning/ Afternoon
ii
All
II
II
Constant = 1
Peak Sun
Constant = 0
Daytime
II
Peak Sun
II
II
All
Constant = 1
Night
Constant = 0
II
Night
II
II
All
II
Constant = 1
      Figure 8 - Proposed Demand Factor Functions
65   70    75   80    85   90    95    100   105   110
                      Heat Index
        Morning/Afternoon   	Peak Sun
Night

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Figure 9 - Solar Radiation - Sunny and Cloudy Day (Fort Peck, MT)
1000
     0
10   12    14   16   18  20   22
 Hour of Day
                     -June 26 1997	June 24 1997
Table 3 - Analysis of Variance (ANOVA) on Non-Idle Trip Dataset
Factor
Heat Index
Vehicle
Period
Trip Duration
Idle Fraction
Average Speed
Soak Duration
Significance
0.00
0.00
0.00
0.25
0.28
0.30
0.74

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 Figure 10 - Compressor-On vs. Trip Duration
Compressor-On Fraction
o ->->
b k> 4*. en bo b k>


^^\
500 1000 1500 20
Trip Duration (seconds)
Heat Index
106-110
91 -95
86-90
81 -85
00
Figure 11 - Compressor-On vs. Average Speed
1.2
1.0-
c
0
 .8-
LL
o
b
f) 4 '
E

O
.2-

0.0





-  __  	 	 __
**"----.___














Heat Index

101 -105

96-100
86-90
0 20 40 60 80
Average Speed (mph)

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   Figure 12 - Proposed Base Market Penetration Estimates
72-  74  76  78   80   82   84   86   88   90  92  94  96  98  00+
                         Model Year
                       LDV's	LDTs
        Table 4 - Proposed Rate of A/C Malfunction
Vehicle Age
(years)
1
2
3
4
5
6
7
8
9-25
Consumers
Reports *
<2%
2 - 5 %
2 - 5 %
2 - 5 %
5 - 9.3 %
5 - 9.3 %
9.3 - 14.8 %
9.3 - 14.8 %
n/a
Proposed
Estimates
0.5 %
2.0 %
3.5 %
5.0 %
6.5 %
8.0 %
9.5 %
11.0%
12.5 %
                * 1997 Automobile Purchase Issue

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         Figure 13 - Proposed Rate of Unrepaired Malfunctions
7.0%
                     7    9   11    13    15    17   19   21   23   25+
                           Vehicle Age (Years)
                  R-134a	Freon (1995 or later scenario year)

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                           Appendix 1 - Heat Index Equations

       Source: Meisner and Graves, "Apparent Temperature", Weatherwise, August 1985

This set of equations computes heat index under "mild" and "severe" sultriness.  Mild sultriness
indicates conditions under which thermal equilibrium can be achieved with reduced clothing
thickness. Severe sultriness indicates conditions for which reductions in the skin's resistance to
heat and moisture flow are required to achieve thermal equilibrium. If the required clothing
thickness is less than zero for the "mild" equations, the "severe" equations are used to calculate
heat index. This set of equations is based on an adult wearing trousers and a short-sleeved shirt,
walking in the shade at 3.1 mph, standard sea level air pressure, wind speed of 5.6 mph and a
vapor pressure of 1.6 kPa.
              Inputs: TF = Temperature (F), RH = Relative Humidity (%)
Variable
TC
ES
E

"Mild Sultriness":
HER
ERA
QV
EZA
HR
ARA
AZA
Q2U
QJ
K
L
F
RF
Equation
(TF - 32)*(5/9)
6.11*10A (7.567*TC)/(239.7+TC)
0.01*RH*ES


4.18+0.36*TC
1/(17.4+HER)
180*(143-0.00112*TC-0.0168*0.1*E)
0.060606/EHC
3.35+0.049*TC
1/(11.6+HR)
0.060606/CHC
((TB-TC)+(PB-PINF)*ERA/(ZS-EZA))/(RS+ERA)
(Q-QV-(1-0.84)*Q2U)/0.84
(.0387+ARA)+(0.0521+AZA)/0.124-((37-TC)+(5.65-
0.1*E)/0.124)/QJ
((0.0387+ARA)*(0.0521+AZA)) - ((37-
TC)*(0.0521+AZA)+(5.65-0.1*E)*ARA)/QJ)/R
K*K-4*L
0.5*(-K+ SQR(F))
Comment
Temperature in Celsius
Saturation Vapor Pressure
Relative Vapor Pressure













if <0use "Severe"


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Variable
DF
Wl
W2
W3
W4
W5
W6
W7
Heat Index

"Severe Sultriness":
HC
HR
RA
ZA
QU
-&ZS
R3
C

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