United States Environmental Protection Agency Air and Radiation EPA420-R-01-054 November 2001 M6.ACE.001 

# Air Conditioning Activity Effects in MOBILE6

Printed on Recycled PaperEPA420-R-01-054 November 2001



## Air Conditioning Activity Effects in MOBILE6 M6.ACE.001

John W. Koupal Assessment and Standards Division Office of Transportation and Air Quality U.S. Environmental Protection Agency

NOTICE 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 which may form the basis for a final EPA decision, position, or regulatory action.1 “Air Conditioning Correction Factor s in MOBILE6", M6.AC.002, July 2001 M6.ACE.001 Page 2 1 ABSTRACT MOBILE6 includes revised estima tes of exhaust emissions resulting from air conditioning operation. This will require ai r conditioning behavior and the resulting emission levels to be predicted over a wide range of ambient conditions. Emission 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 appl ying the “extreme” data over the broader range of ambient conditions in which ai r conditioner operation occurs in-use. Using air conditioning activity data collected in Phoenix, a methodology has been developed wh ich relates temperature and humidity levels to air conditioner load using a combined measure known as the heat index. This methodology also incorpor ates 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 w ith air conditioning systems, and of those, the fraction of malfunctioning systems.
2 INTRODUCTION
The emission data to be used in the development of the MOBI LE6 air conditioning factors were gathered using a test procedure intended to represent extreme ambient conditions. From these data emission factors w ill be developed which re present emission levels at full air conditioning load (referred to as “full-usage” emission factor s). These emission fact ors cannot a ppropriately 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. 1 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 ai r conditioning activity is market penetration; namely , the fraction of vehicles equipped w ith air conditioning systems, and of those, the fraction of malfunctioning system s which have not underg one repair. Proposed estimates for these factors are also presented and discussed. Subsequent to publication of th e draft version of this report in January 1998, the document was put out for stakeholder review. Formal peer review comment s were also solicited from two independent sources. No comments were r eceived through the stakeholder review process, hence peer review comments represent the only external feedback received on this report. A summary of peer review comments and responses is contained in Appendix B. 3 OVERVIEW OF PROPOSED APPROACH As detailed in M6.AC.002, the “with air conditioni ng” emission levels used as the basis for MOBILE6 were generated using a test procedure meant to induce the level of A/C system load on the vehicle which would occur in the real world under extreme ambien t conditions. However,2 EPA/AAMA/AIAM meeting on MOBILE6 air conditioning issues, November 6, 1997 M6.ACE.001 Page 3 MOBILE6 will need to model air conditioning e missions at less severe conditions where the majority of vehicle operation will occur. A method for modeling air conditioning effects under intermediate ambient conditi ons is therefore required. The method proposed for use in MOBILE6 is to li nk emissions directly w ith 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 compre ssor 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 disengag ed) depending on user de mand 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 in termediate ambient conditions. With this approach driver behavior is accounted for imp licitly; however, because the compressor will only engage when the A/C system is on, compressor en gagement 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 ambien t conditions and emissions by modeling changes in compressor load (torque) as a function of change s in ambient conditions. Unfortunately, activity data which would allow such a li nk does not appear to exist. Available activ ity 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 me thodology developed for MOBILE6 is based on the relationship between emission re sponse and the percentage of time the compressor is engaged. 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 compresso r-on fraction. With this methodology, the compressor-on fraction is equal to the factor by which the fu ll-usage emission factor is scaled in MOBILE6 to derive the emission factor appr opriate 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 is termed the “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 a ll pollutants. It should be noted that air conditioning system experts from the automotive industry have identifie d several limitations with this assumption. Specifically, compressor load fl uctuates significantly when the compressor is engaged, depending on a number of factors includi ng 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 th ese factors is high ly vehicle-specific. 2 Using only compressor-on fraction is a rough estimate of actual compressor load since it assumes that3 “Study of In-Use Air Conditioner Op eration in Phoenix, Arizona”, Automotiv e Testing Laboratories, Inc. report to EPA (EPA Docket No. A-92-64 Item IV-A-1) M6.ACE.001 Page 4 fluctuations in relative compressor load av erage out over periods when the compressor is 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 within the timeframe and scope of MOBILE6. 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 partof the Supplemental Fe deral Test Procedure (SFTP) rulemaking process on 20 vehicles ove r almost 1000 trips in Phoenix, Arizona from August-October 1994 3 . Data gathered for each trip include d time and date, tota l trip time, total time the air conditioner was on, a nd 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 Nati onal Climatic Data Center (NCDC) was used to estimate dry bulb temperatur e and relative humidity at the start of each trip.
4.2 Treatment of Data The initial dataset used for this analysis containe d 987 trips. It did not include the first and last trips for each vehicle if the trip was 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 potential queuing-related cases. b. Since the location of each trip was not known, it was necessary to a ssume that the linked weather information was appropr iate 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 dele ted 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 l ong 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 al ong highway routes, and the distribution ofM6.ACE.001 Page 5 trips below and above 60 miles in the dataset. For two of the vehicles for which trip distance data was not gathered, a trip dur ation 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 all-idle trips is much lower than trips consisting of even very high percentages of idle. MOBILE6 will pr edominantly need to predict A/C emissions over trips consisting of non-idle driving, and we were concerne d that inclusion of all-idle trips would skew the non-idle results. All-idle trips were therefore dropped from the trip file for the purposes of developing the dema nd factor relationships. It is important to clarify that this does not mean the trip datase t does not contain idle events; as indicated in Figure 1, many of the remaining trips in fact have significan t percentages of idle operation.
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 impor tant 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 lo ad under the ambient conditions of the SFTP air conditioning test procedure 2 . It is considered important, th erefore, to develop a demand factor methodology which incorporates both temperature and humidity. This wa s supported by several comments received following the March 1997 MOBI LE6 workshop that advocated the inclusion of humidity in the MOBILE6 air conditioning component. To assess the effect of ambient variables (tem perature, humidity, heat index) on air conditioning demand, the Phoenix datase t was analyzed by “binning” trips according to the variable being analyzed. For example, for temperature, all trips at a given temperature were combined, and the compressor-on fraction was calculated at each te mperature as total time with the compressor engaged at that temperature divided by total trip time at that temperature. This aggregation step was taken to reduce the variability from individual vehicle and driver behavior, since the operation of air conditioning ove r the “composite” fleet and popula tion is of more concern for MOBILE6. Total trip time (including trips wh en the air conditioner was not turned on) was used as the denominator in order to character ize air conditioning usage directly from ambient conditions. This is a more direct approach than trying to model both a) air conditioning usage as a function of ambient conditions, and then b) compressor operation when the air conditioning is on, which is less supportable fro m the Phoenix data. Analyzed in this way, the Phoenix dataset indi cates a strong correlation between temperature and compressor-on fraction, as shown in Figure 2 (com pressor-on time is expre ssed as the fraction of time the compressor is engaged over the total trip time at each temperature point). The4 Gale Research Inc., The Weather Almanac , Sixth Edition (1992) 5 Meisner and Graves, “A pparent Temperature”, Weatherwise , August 1985 6 The base humidity correction factor currently in MOBI LE5 will be carried over to MOBILE6. The computation of this factor and the air conditioning demand factor w ill be based on the same humidity data. A default specific humidity value of 75 grains/pound (as in MOBILE5) is pr oposed. Users will be able to input alternate humidity levels in either specific humidity or relative humidity (s ee Section 6), with appropriate conversions made within MOBILE6. M6.ACE.001 Page 6 relationship between humidity a nd compressor-on fraction is weak, however (Figure 3), and ANOVA results indicate that when humidity is mode led with temperature it is not a significant variable. Because humidity does physically affect 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 conditions); the average relative humidity for temperatures greater than 80  F was only 28%. By contrast, historical da ta in Houston indicate that dur ing 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 wh ich could be applie d to humidity levels like those observed in Houston would require si gnificant extrapolation of the Phoenix data. Given the weakness of the rela tionship between humidity and co mpressor-on fraction within the boundries of the Phoenix humidity levels, extrapolation of thes e data is not desirable. In an attempt to more accurately assess the relative impacts of temperature and humidity on air conditioning load, a metric known as the heat index will be used in developing the demand factors. Heat index is used by the Nationa l 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 equilibri um 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 equati ons are proposed for use in MO BILE6 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 humid ity is shown in Figure 5. The approach for addressing in termediate conditions is to de velop demand factors by modeling compressor-on fraction as a function of heat i ndex based on user input of temperature and humidity. An attractive featur e 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 betw een compressor-on fraction and heat index, after “binning” trips by heat i ndex as discussed. Using heat index instead of temperature does not necessarily improve predictions of compre ssor-on fraction for the Phoenix data, because the Phoenix results are driven almost completely by temperature. Heat index does not detract from the relationship between temper ature and compressor usage, either; for low humidity conditions, the heat index is generally iden tical to the temperature, so using heat index7 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.srr b.noaa.gov/surfrad/surfpage.htm) 9 Gale Research Inc., The Weather Almanac , Sixth Edition (1992) M6.ACE.001 Page 7 or temperature would provide th e same result for low humidity conditions. 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 P hoenix data. The underlyi ng assumption of this methodology is that the temperature-driven effect s seen at high temperat ures in Phoenix would be replicated under lower temper ature but higher humidity conditions seen elsewhere in the country. Given the stated importance of humid ity on air conditioning load, this assumption is believed to be more 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 in clusion of solar load, and automotive industry experts have indicated that it is a contributing factor. Subseque nt analysis of the Phoenix data indicates that a solar load imp act can be discerned, and cons equently a method which accounts for solar load and cloud cove r (addressed in Section 4.6) is being used in MOBILE6. Since solar load or cloud cover data were not available in th e NCDC dataset linked to the Phoenix survey, an empi rical relationship between these f actors 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 Observatory 7 were used to determine appropriate trip bins. The bi n definitions were determined by analyzing solar radiation data gathered as part of the National Oceanic and Atmospheric Administration’s (NOAA) Surface Radi ation Budget Project (SURFRAD) 8 . A regression across all trips of compressor-on fraction by heat inde x was performed within each bin, with the results shown in Figure 7. Because this analysis considered all trips instead of combining them by heat index level, the regression was weighted by trip length to give more creedance to longer trips. Since cloud cover information was not available, it could not be cons idered as a variable in the analysis. Historical data from Phoenix indicates that at the time of year the survey was conducted direct radiation from th e sun (i.e. little or no cloud cove r) is present close to 90% of the time 9 , 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 dema nd equations. In suppor t of this conclusion, ANOVA performed on the trip file with compressor fraction as the depende nt variable and heatM6.ACE.001 Page 8 index and period as the indepe ndents indicated that period is significant to the 0.01 level. A second question is whether a si gnificant difference exists betw een the daytime periods. Figure 7 shows that as would be expected, the peak s un 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 furt her, 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. The period of the day was again significant to the 0.01 level for all daytime 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 were developed for MOBILE6: nighttime, morning/afternoon and peak sun. The “raw” equation for each period, as we ll as for all daytime tr ips and all trips, are shown in Table 1; these equations were developed by fitting a quadratic equation through all trips by period, weighting by trip length. The relatively low R 2 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 a nd 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 be 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 day time periods at the lowe r end, and all individual curves will be modeled with the “all combined” e quation at the high end. The heat index values at which the composite equati ons and period-specific equations diverge (at the low end) or converge (at the high end) are determined based on th e 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 fu ll day will be modele d by applying the appropriate demand equations at each hour. The night equation w ill 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 relativ ely 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 6M6.ACE.001 Page 9 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 morn ing/afternoon and nighttime demand equations are applied.
4.6 Cloud Cover As mentioned in Section 4.4, th e daytime demand equa tions were developed from the Phoenix data under the assumption that all trips were take n during periods of no cloud cover (an assumption that likely serves to slightly unders tate solar load impact). Because of this MOBILE6 will assume as a default that a s unny day is being mode led. Comments received following the October 1997 MOBILE6 workshop advocated some accounting of cloud cover, particularly for modeling seasona l emissions. MOBILE6 will incorporate an optional input for percent cloud cover on a daily basi s. 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 appropr iate heat index.
4.7 Other Factors Considered While ambient conditions are the primary factors in determining A/C syst em demand, trip-related characteristics are also likely to influence ai r 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 id le during a trip. A single An alysis of Variance was performed with compressor-on fraction as th e dependent variable and these f our variables and heat index as covariates, period as a factor, and vehicle as a random effect (as suggested in peer review comments), 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 e ffects of each. However, none of these variables showed enough significance to mer it 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 tr ip 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 pa rked in a shady location (such as a parking garage) or whetherM6.ACE.001 Page 10 the windows were left open during the soak. W ithout 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 informa tion. It is likely however, that the solar load impacts discussed in Section 4.4 are dr iven 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 impactair conditioning behavi or. Cabin temperature 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 th ese regressions: the expected downward trend as trip duration increases, and a leve ling 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 h eat indices the cooling demand remains high throughout the tri p. ANOVA results do not indicate significance, however. Since the Phoenix datase t contains a wide distribution of trip durations, these effects are assumed to be accounted for imp licitly 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 ente ring the cabin at a higher rate). Regression analysis indicates a downward trend in compre ssor-on as average speed increases (Figure 11). Again, this effect is more prevalent for the lo wer heat index levels, and drops off as the heat index increases. The ANOVA results again do not i ndicate significance, howev er. These effects are assumed to be accounted for implicitly in the demand equations since th e 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 s imilar, indicating that the effect seen on idle-only trips doesn’t carry over to idles during norma l trips. The ANOVA results agai n do not indicate significance,M6.ACE.001 Page 11 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 repaire d. This section addresses each issue.
5.1 Base Rates Base market penetrati on data by model year were gath ered from Ward’s Automotive Handbook for light-duty vehicles and light -duty trucks through the 1995 Mode l 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 car s and trucks, respectively. In the later years, the rate of increase becomes more steady. Projections be yond 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 ther e 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 ai r conditioning, it is a ppropriate to assume that not all of the systems are functional, requiri ng an estimate of the fracti on of non-functioning systems by vehicle age. Unfortunately, there appears to be little publicly availabl e data upon which to base these estimates. One available source is the annual Consumers Reports Automobile Purchase Issue, which began reporting reader survey re sults on A/C system malfunctions starting in 1994. The reported results from the 1997 survey were us ed to develop malfuncti on estimates by vehicle age based on a yearly increase in absolute malfunc tion rate of 1.5 percent (Table 4). Starting at age nine the malfunction rate w ill be held constant at 12.5 per cent. 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 al ready 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 standa rd bumper-to-bumper warranty period) would receive repair; b) after three years the majority of owners would still r eceive repair, but this percentageM6.ACE.001 Page 12 would decrease as the vehicle gr ew 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 th at 100% of R-134a systems w ould 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-wa rranty period repair ra tes 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 app lied (in other words, lowe r repair rates for Freonequipped vehicles will not be i nvoked if recharging with Freon was viable during the modeled calender year ). The resultant rate of unrep aired malfunctions combine the malfunction rates from Table 4 with the rate of nonrepair in a give n year. These estimates are shown in Figure 13. For a given model year, the estimate of vehicl es 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) w ith the unrepaired malfunc tion rates in Figure 13 for the appropriate vehicle age.
6 HANDLING OF AMBIENT INPUT DATA A significant change in MOBILE6 will be the ability to model on an hour-by-hour basis, whereas MOBILE5 is geared towards providing daily es timates. Hour-by-hour temperatures can be entered if the information is available to the user; otherwise, daily minimum and maximum temperatures can be entered as was done with MOBILE5. There are no temperature defaults in MOBILE6, so at a minimum these daily temperatures must be entered. Humidity is entered as a single daily number, and for this reason it will be entered as absolute humidity (grains per pound of air) and converted to hourly relative humidity inside the mode l based on hourly temperature. As a default, absolute is a ssumed constant at 75 grains/pound, no cloud cover would be assumed, and the sunrise/sunset/peak sun times disc ussed in Section 4.5 would be applied.
7 ACKNOWLEDGMENTS Several individuals contributed time, effort, ideas and consultation to this report. Rob French of OTAQ developed and provided guidance on the P hoenix dataset and contributed to the heat index concept. John Gilmore of OTAQ research ed the base market pe netration estimates and malfunction survey information. John German of Honda provided gene ral consultation and input. Christine Dibble of the Office of Atmo spheric Programs provi ded information on R-134a phase-in. John Augustine of NOAA provided in formation on the SURF RAD data. Dennis Kahlbaum of AIR, Inc. provided information on the derivation of the heat index measure. Harold Haskew of Haskew Associates and Dr. Mohunder Bhutti of De lphi-Harrison provided consultation on the theoretical as pects of air conditioner operation.P e r c e n t I d l e 0

- 2 5 % 2 6

- 5 0 % 5 1

- 7 5 % 7 6

- 9 9 % 1 0 0 %Average Compressor-On Fraction 1

. 0

. 9

. 8

. 7

. 6

. 5

. 4

. 3

. 2

. 1 0

. 0 Figure 2 - Compressor-On vs. Percent Idle

N o n - i d l e t r i p s ( w

e i g h t e d by n u m b e r o f t r i p s ) R s q u a r e d = 0

. 8 7 T

e m p e r a t u r e ( F )

- S ta r t o f T r i p 1 1 0 1 0 0 9 0 8 0 7 0 6 0Compressor-On Fraction 1

. 0

. 8

. 6

. 4

. 2

0

. 0 Figure 3 - Compressor-On vs. TemperatureN o n - i d l e t r i p s ( w e i g h t e d by n u m b e r o f t r i p s ) S p e c i f i c H u m i d i t y ( G r a i n s / P o u n d )

- S t a r t o f T r i p 1 2 0 1 0 0 8 0 6 0 4 0 2 0Compressor-On Fraction 1

. 0

. 8

. 6

. 4

. 2

0

. 0 Figure 4 - Compressor-On vs. Specific Humidity

N o n - i d l e t r i p s T e m p e r a t u r e ( F )

- S t a r t o f T r i p 1 1 0 1 0 0 9 0 8 0 7 0 6 0Relative Humidity - Start of Trip 1 0 0

8 0

6 0

4 0

2 0

0 Figure 5 - Humidity vs. Temperature for Phoenix DatasetR e l H u m 8 0 % 6 0 % 4 0 %

N o t e : H e a t I n d ex v a l u e s b a s e d o n s h a d y c o n d i t i o n s L i n e s r e p r e s e n t c u r v e f i t o f t a b u l a r d a t a T

e m p e r a t u r e ( F ) 1 0 5 1 0 0 9 5 9 0 8 5 8 0 7 5Heat Index (F) 1 8 0
1 6 0

1 4 0
1 2 0
1 0 0

8 0
6 0 Figure 6 - Heat Index

N o n - i d l e t r i p s ( w e i g h t e d by n u m b e r o f t r i p s ) R s q u a r e d = 0

. 84 H e a t I n d e x ( F )

- S t a r t o f T r i p 1 1 0 1 0 0 9 0 8 0 7 0 6 0Compressor-On Fraction 1

. 0

. 8

. 6

. 4

. 2

0

. 0 Figure 7 - Compressor-On vs. Heat IndexP e r i o d S u n s e t
- S u n r i s e 4 p m - S u n s e t 1 0 a m - 4 p m S u n r i s e - 1 0 a m

Q u a d r a

t i c r e g r e s s i o n o f n o n - i d l e t r i p s W e i g h t e d by t r i p d u r a

t i o n H e a t I n d e x ( F )

- S t a r t o f T r i p 1 1 0 1 0 0 9 0 8 0 7 0 6 0Compressor-On Fraction 1

. 0

. 8

. 6

. 4

. 2

0

. 0 Figure 8 - Compressor-On vs. Heat Index by Time of Day

Table 1 - Proposed “Raw” Demand Factor Equations (Demand Factor = Constant + a* (Heat Index) + b*(Heat Index) 2 )
Period Constant a b R 2 Morning/Afternoon -2.930273 0.059110 -0.000213 0.54 Peak Sun -5.307355 0.113973 -0.000521 0.17 Daytime Combined -4.101082 0.086382 -0.000367 0.43 Night -1.257412 0.006753 0.000143 0.52 All Combined -3.631541 0.072465 -0.000276 0.440.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

65 70 75 80 85 90 95 100 105 110 Heat IndexDemand Factor

Morning/Afternoon Peak Sun Night Figure 9 - Proposed Demand Factor Functions Table 2 - Proposed Demand Factor Equation Forms
Heat Index Morning/Afternoon Peak Sun Night 65 & below Constant = 0 Constant = 0 Constant = 0 66 Daytime Daytime " 74 Morning/Afternoon " Night 76 " Peak Sun " 96 All " " 101 " " All 104 " All " 110 & above Constant = 1 Constant = 1 Constant = 10 100 200 300 400 500 600 700 800 900 1000

0 2 4 6 8 10 12 14 16 18 20 22 Hour of DayDirect Solar Radiation (Watts/Meter^2)

June 26 1997 June 24 1997 Figure 10 - Solar Radiation - Sunny a nd Cloudy Day (Fort Peck, MT)Table 3 - Analysis of Variance (ANOVA) on Non-Idle Trip Dataset with Vehicle as Random Effect Tests of Between-Subjects Effects Dependent Variable: Compressor Fraction (fraction of time engaged / all driving) Source Type III Sum of Squares df Mean Square F Sig. Intercept Hypothesis .712 1 .712 14.551 .000 Error 25.255 516.244 4.892E-02 Average

Speed Hypothesis 1.607E-02 1 1.607E-02 .337 .562 Error 23.878 500 4.776E-02 Trip Duration Hypothesis 2.213E-02 1 2.213E-02 .463 .496 Error 23.878 500 4.776E-02 Soak Duration Hypothesis 4.694E-04 1 4.694E-04 .010 .921 Error 23.878 500 4.776E-02 Idle Fraction Hypothesis 4.418E-03 1 4.418E-03 .093 .761 Error 23.878 500 4.776E-02 Heat Index Hypothesis 2.687 1 2.687 56.263 .000 Error 23.878 500 4.776E-02 Period of Day Hypothesis .787 3 .262 3.472 .020 Error 5.336 70.612 7.556E-02 Vehicle Hypothesis 6.527 16 .408 5.063 .000 Error 4.677 58.048 8.056E-02 Period * Vehicle Hypothesis 3.571 36 9.918E-02 2.077 .000 Error 23.878 500 4.776E-02 a 3.197E-03 MS(VEH) + 2.718E-04 MS(PERIOD * VEH) + .997 MS(Error) b MS(Error) c .541 MS(PERIOD * VEH) + .459 MS(Error) d .638 MS(PERIOD * VEH) + .362 MS(Error)H e a t I n d e x 1 0 6

- 1 1 0 9 1

- 9 5 8 6

- 9 0 8 1

- 8 5
T

r i p D u r a t i o n ( s e c o n d s ) 2 0 0 0 1 5 0 0 1 0 0 0 5 0 0 0Compressor-On Fraction 1

. 2

1

. 0

. 8

. 6

. 4

. 2

0

. 0 Figure 11 - Compressor-On vs. Trip Duration

H e a t I n d e x 1 0 1

- 1 0 5 9 6

- 1 0 0 8 6

- 9 0

A

v

e r a g e S p e e d ( m p h ) 8 0 6 0 4 0 2 0 0Compressor-On Fraction 1

. 2

1

. 0

. 8

. 6

. 4

. 2

0

. 0 Figure 12 - Compressor-On vs. Average Speed0 10 20 30 40 50 60 70 80 90 100

72- 74 76 78 80 82 84 86 88 90 92 94 96 98 00+ Model YearPercent equipped with A/C

LDV's LDT's Figure 13 - Proposed Base MarketPenetrati on Estimates

0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0%

1 3 5 7 9 11 13 15 17 19 21 23 25+ Vehicle A ge (Y ears )Rate of Unrepaired Malfunctions

R -134a Freon (1995 or later scenario year) Figure 14 - Proposed Rate of Unrepaired MalfunctionsTable 4 - Proposed Rate of A/C Malfunction Vehicle Age (years) Consumers Reports* Proposed Estimates 1 < 2% 0.5 % 2 2 - 5 % 2.0 % 3 2 - 5 % 3.5 % 4 2 - 5 % 5.0 % 5 5 - 9.3 % 6.5 % 6 5 - 9.3 % 8.0 % 7 9.3 - 14.8 % 9.5 % 8 9.3 - 14.8 % 11.0 % 9-25 n/a 12.5 % * 1997 Automobile Purchase IssueAppendix A - 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 ach ieved with reduced clothing thickness. Severe sultrin ess indicates conditions for which re ductions 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 equati ons 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 Equation Comment TC (TF - 32)*(5/9) Temperature in Celsius ES 6.11*10^ (7.567*TC)/(239.7+TC) Sa turation Vapor Pressure E 0.01*RH*ES Relative Vapor Pressure

“Mild Sultriness”: HER 4.18+0.36*TC ERA 1/(17.4+HER) QV 180*(143-0.00112*TC-0.0168*0.1*E) EZA 0.060606/EHC HR 3.35+0.049*TC ARA 1/(11.6+HR) AZA 0.060606/CHC Q2U ((TB-TC)+(PB-PINF)* ERA/(ZS-EZA))/(RS+ERA)

QJ (Q-QV-(1-0.84)*Q2U)/0.84 K (.0387+ARA)+(0.0521+AZA) /0.124-((37-TC)+(5.65- 0.1*E)/0.124)/QJ L ((0.0387+ARA)*(0.0521+AZA)) - ((37- TC)*(0.0521+AZA)+(5.65-0.1*E)*ARA)/QJ)/R F K*K-4*L if < 0 use “Severe” RF 0.5*(-K+ SQR(F))Variable Equation Comment

DF 60*RF if < 0 use “Severe”

W1 0.2016 W2 (1-0.84)/(0.387+ERA) W3 0.084/(0.0387+RF+ARA) W4 159.0984 W5 37 W6 4.05*ERA/(0.0521+EZA) W7 4.05*(RF+ARA)/(0.0521+0.124*RF+AZA) Heat Index (-W4+W2*(W5+W6)+ W3*(W5+W7))/(W1+W2+W3)

“Severe Sultriness”: HC 12.3 HR 4.1+0.28*TC RA 1/(HC+HR) ZA 0.060606/HC QU 180-QV  ZS ((5.65-0.1*E)*RA)/(QU*(0.0387+RA)-(37-TC))-ZA if < 0 set equal to 0

R3 (ZS/600000)^2 C ABS(0.0387-R3) skips to N1 when C <= 0.0001RS 0.5*(.0387+R3) iterates to ZS

N1 159.0984 N2 37 N3 4.05*RA/(0.0521+ZA) N4 RS+RA N5 0.2016 Heat Index -N1+(N2+N3)/N4 )/(N5+1/N4)Appendix B - Peer Review Comments and Response Subsequent to publication of the draft ve rsion of this report in January 1998, the document was put out for stakeholder review. Fo rmal peer review comments were also solicited from two independent sources. No comments were received through the stakeholder review process, and thus the peer review comments repr esent the only external feedback received on this report. This section contains these peer re view comments in full and our response to these comments. The author of the first set of peer review comments requested confidentiality, and hence is not identified here. Commentor 1 ( EPA responses inserted as italicized text ) The comment text is verbatim from the commentor, minus editorial corrections to maintain the confidentiality of the commentor. General The report documents a valuable initial step towards accurate estimati on of exhaust emissions resulting from auto a/c operation. The approach is appropriate for a first step: focus scarce data collection resources on measuring emissions at extreme ambient c onditions, and make a series of assumptions to estimate emissions at other conditions. It is a valuable contribution to the published literature because it pr esents and analyzes a unique set of data from instrumented vehicles. Based on our review we offer a variety of sugge stions, which fall into three major categories: - evaluate changes in some analytical assumptions - try to extract more information from Phoenix data - plan to accomodate or acquire additional data By making these suggestions, we do not wish to detract from the value of the report and its usefulness as an initial step. It is far better than nothing, yields results that provide a credible basis for policy, and provides valu able insights into operational aspects of mobile a/c systems that have never been published before. Below we itemize suggestions by page and paragraph number in th e draft report. But first we will provide a general statement of the three types of suggestions.
1. Evaluate alternative analytical assumptions a) It is likely that the incremental emissions due to a/c operation vary with engine rpm. Presumably this effect is captured in the assumption that the effect of compressor engagement on overall engine load is linear, at a given rpm and load , thus accounting for the fact that engine efficiency is worse at idle. In other words, we assume that this compressor runtime data will be used in conjunction with measured data on emissions with compressor on and off, at each point in the driving (test) cycle. Response: An aggregate model such as MOBILE6 doesn’t explicitly addressRPM. These effects are assumed to be captured on an average basis through the use of representative driving cycles b) Heat index was assumed to be a good indicator of humidity effects, but the Phoenix data are too anecdotal to conf irm that hypothesis. On theoretical grounds, we would not expect it to be vali d. It appears from this data that the statistical significance of runtime dependence on heat index is not significantly different from its dependence on outdoor te mperature. As a measure of human comfort, it might predict a driverÕs decision to turn on the a/c unit. However it is unlikely to have a significant effect on compressor runtime fr action. Runtime will depend on the sum of the sensible and la tent heat load. While the compressor is on, the temperature of the evaporator co il is approximately 35 F on most cars. The sensible load depends on the difference between the evaporator surface and indoor air temperatures. Latent load depends on the difference between the absolute (not relative) hu midities of indoor air and that of 100% humid air at the evaporator surface temperature (about 35 ¡F ). In the absence of indoor humidity data, the total latent load might be calculated directly from an estimate of the rate at which outdoor air infiltrates into the car. Assuming the in filtration rates are constant between Phoenix and other climates, the ratio could be used to extrapolate Phoenix results to Houston and other climates. The amount of air leaking into the car woul d have to be assumed. Response: The demand factor developed for MOBILE6 attempts to account for both driver behavior and vehic le behavior, and we think heat index is the most appropriate method for extrapolation into high humidity conditions. It is beyond the scope of MOBILE6, and available data, to separate these effects out, particularly given the complexity involved in accurately modeling the operation of the compressor, which will vary from veh icle to vehicle. Thus, we believe the deterministic approach relating ambient conditions to overall compressor runtime continues to be the most appropriate approach. c) Solar load is probably irrelevant to your methodology, because most cars that automatically control compartment air temperature do so using reheat. So do drivers who prefer more m oderate temperature air bl owing on them, or who use high evaporator fan speeds to ensure th at air is distributed to throughout the vehicle. In virtually all cars the compre ssor runs whenever the evaporator is not frosted. Since many drivers control indoor air temperature by running the heater, they simply set the heater to a lower le vel on sunny days. Since emissions depend only on the total sensible plus latent lo ads, the composition of sensible loads (solar plus reheat) will not matter. The only case in which solar loads would increase emissions is when drivers control indoor temperature solely by decreasing evaporator fan speed. In th e future, introduction of automaticallycontrolled variable-displacement compressors may change this, so now is the time to start understanding solar loads.Response: Solar load is very relevant in terms of initial cabin temperature, which will effect whether driver turns the A/C on at the start of a trip. As noted, it will be less of an issue once the vehicle is cooled down initially. d) The Phoenix data set contai ns no data on air leakage into the car, since it is under the control of the driver who sets it betw een zero and 100% fresh air intake to a/c system (about 250-350 cfm max, depending on vehicle). Actua lly infiltration is not zero even at idle, since even the “recirc” setting must prevent carbon dioxide levels from reaching reaching dangerous levels when the car is full of occupants. Air leakage is a strong determinant latentloads, but contributes only a fraction of sensible loads. Response: Air infiltration isn’t something that can be realis tically accounted within the scope of the MOBILE6 analysis e) Humidity affects compressor runtime only indirectly, so the reliance on relatively dry Phoenix data may not be as shaky as it may first appear. The effect of humidity is filtered twice. The first effect on compressor runtime is the latent load caused by the humid outdoor air infiltrating into the car; this increases load and therefore compressor runtime. However at the same time the a/c system efficiency increases because the water condensing on the evaporator surface raises the refrigerant evaporating temperature. That will partially cancel the effect of adding the latent loads. Therefore a rough estimate (based on a crude estimate of infiltration loads, which vary with vehicle and average speed and fresh air intake settings) should be good enough for now. It will probably be necessary to ask auto manufacturers for an estimate of leakage rates as a function of vehicle speed and ventilation settings, unless you can extract an average from the Phoenix data (see below). Response: Humidity is relied on for the he at-index calculation, primarily to account for changes in driver behav ior rather than compressor behavior f) Fortunately your data we re taken close to the fall equinox, so day length was approximately 12 hours. If you attempt further studies of solar loads, or calculations thereof, don’t forget that day lengths in the US can exceed 15 hours near the summer solstice
2. Extract more information from Phoenix data a) The effect of solar load should be mostnoticeable on short trips dominated by the “pulldown” period. On longer trips, solar is probably i rrelevant due to use of the car heater to control air temperature. Solar load (by increasing heat stored in the passenger compartment’s thermal mass) will determine the length of time needed to cool the air to comfortable levels. Elevated indoor temp eratures translate directly into higher loads and to less- than-proportional increases in compressor torque, because of slightly higher a/c system efficiencies. This may explain theresults shown in Figure 1: compressor runtime is high for trips having high % idle, because most of these are short trip s with little cycling, in which pulldown of “soak” loads are a large fraction of the tota l. If true, the sa me behavior might be less noticeable in cloudier c limates because the initial temp erature of the vehicle’s thermal mass should be lower. Response: To address this, we analyzed whether period of the day was a significant variable for trips longer than 10 minutes; it was. However, it dropped below 95 percent significance for trip s above 15 minutes, which could be an indication of the small sample size for trip s of this length across the four periods. In general, the average trip in MOBI LE6 is estimated to be under 10 minutes. Thus, the “pulldown” eff ect would predominate the usage of A/C under average MOBILE6 conditions. b) Another hypothesis that might be te sted concerns the slight decrease in compressor runtime on trips having low % idle: most of these trips were at high speed, so the a/c operated more efficiently due to the ram air effect cooling the condenser; the effect of greater infiltra tion at high speed was probably offset by the driver’s use of reheat, since the cooling capacity of the a/c system at high rpm probably still exceeded the loads, as might be indicated by the cycling rate. Such an effect might have been hard to see in humid-climate data. Response: The decrease was relatively minor, but was not statistically significant due to small sample size. Because all trips were grouped together for the calculation of air conditioning demand facto rs, this effect would be represented in the results. c) Try to extract an estimate of the contribution of latent loads from the Phoenix data. Focusing on the longer trips, try to estimate the ratio of sensible to latent loads using the method described in 1(b) above. That is, assume that sensible loads are proportional to (Toutdoor - 75 F) and that latent lo ads are proportional to the difference between absolute humidities of indoor and outdoor air. Response: Data isn’t available on indoor air humidity to perform this calculation. 3. Accomodate and acquire additional data a) Intensive testing of mobile a/c system s is underway at the Air Conditioning & Refrigeration Center, University of Illinios at Urbana-Champaign. That will provide compressor power and torque as a function of rpm a nd indoor temperature and humidity, and outdoor ambient temperatur e. It should be published within the coming year, so it would be wise to modi fy your models in anticipation of such data. b) The next time cars are instrumented by EPA for testing, perhaps the estimate oflatent loads could be improved by meas uring indoor humidities and temperatures. Indoor humidities should be quite stable while a/c cycling rates are high; but indoor humidity could increase during long o ff-cycles. Also it might be useful to monitor fan and ventilation se ttings (fresh air vs recirculation), and the use of the heater (on/off at least). All these driver-controlled variables affect a/c compressor power, and measuring them for the purposes of making estimates is difficult. Now that a/c emissions are monitored by EPA, it is possible that future a/c systems will be controlled automatically to reduce emissions. c) Ultimately it would be he lpful to know how compresso r rpm relates to vehicle speed, on an aggregate basis. That would facilitate linkage of the University of Illinois a/c test results to various driving cycles. d) Nearly all today’s cars cycle the compressor to keep the evaporator free of frost, and use the car’s heater or the evaporator fan to modulate a/c capacity. Both are inefficient. Advanced systems using variable-displacement compressors can run the compressor continuously, operate the evaporator at (higher, above-freezing) temperatures that increase a/c system efficiency, with zero reheat. The overall reduction in sensible load due to elimination of reheat, plus the higher operating efficiency, may lead to substantial reduc tions in emissions. Automatic control of ventilation is another futu re technology that might significantly reduce both sensible and latent loads. Response: We will take these comments into account for future testing of air conditioning activity.
4. Detailed comments a) p 2 para 3: Data on compressor power as a function of rpm and evaporating and condensing temperature may be ava ilable from compressor manufacturers’ calorimeter testing. It w ill tell you nothing about lo ad (cycling rates etc) but would be better than nothing. Temperat ure differences between the refrigerant and air, indoors and outdoors, probably va ry little with clima te and do not vary much among cars. Response: Modeling an effect as a functi on of rpm would requires far more resolution than MOBILE6 provides; in addition, activity information of evaporating and condensing temperature isn’t readily available, so it could not be easily implemented in an aggregate emission model. b) p 2 para 3: If in the future you want to improve predictions of compressor runtime as a function of climate and driving cycle, you will need data on cycling rate as well as compressor runtime. Transient losses and latent loads may depend strongly on the cycling rate (e.g. whether 50% runtime results from 10 cycles of 6 min duration or 100 cycles of 0.6 min duration). Response: The Phoenix data only provided the total time the compressor was on per trip, instead of real-time data. Fu ture testing may be able to address thispoint, since real-time compressor function will be a target variable. c) p. 2 para 3: Your method appears to assume that compressor runtime is independent of rpm. It al so acknowledges that this is probably untrue. We agree that it may be important to account for this because emissions vary so greatly with rpm. d) p 4 para c: Mohinder Bhatti of Delphi Thermal Systems published an SAE paper in 1997 which assumed that idling account ed for 15% of compressor runtime. The Phoenix data appear to violate this assumption. You might want to ask him for the source of his data. Response: the Phoenix data indicates that trips with 100 percent idle have relatively low compressor-on time, but this doesn’t necessarily contradict Dr. Bhatti’s findings. The compressor fraction was significantly higher for trips with very high (but less than 100 percent) percen tages of idle. The all-idle trips in Phoenix may not be representative of idle operation in general - for example, they may reflect very short key-on events. If all idle operation could be separated from the Phoenix dataset (including the id le events on non-idle trips), it is likely the overall compressor-on time would be high. e) p 4 para 5: We doubt that humidity will acc ount for half of total loads, as it might on the test procedure. But it raises an interesting possibility for measuring indoor compartment humidity (and perhaps measur ing condensate removed) on the test, and using it to calculate in filtration rates as a functi on of ventilation setting (fresh air vs recirculation). If re heat is not used on the EPA test, the total sensible load seen by the a/c is the sum of conduction a nd infiltration and solar. By controlling the solar load and varying wind t unnel speed, EPA would have a unique opportunity to gather data on load composition. f) p. 5 para 1: Comparing noontime humidity to daily maximum temperature mixes apples and oranges. If it is 85 F and 40% humidity at noon, then the temperature peaks at 95 at 4pm, the relative humidity falls below 30% while the absolute humidity remains constant. It is the absolute humidity that determines latent load. Air at 40% relative humidity contains 40% more water at 95 than at 85 F. Response: this comparison was made simply to illustrate that humidity levels in other parts of the U.S. can be significantly higher than those observed in Phoenix. g) p 5 para 2 and 3: Fig 6 is not much different from Fig 2. Since humidity is so low in Phoenix, it is not clear that one can conclude from this data that the strong correlation between compressor runtime and heat index is better than a method based on the correlation with outdoor te mperature, adjusted for absolute humidities. Response: The purpose of using heat index as the predictor variable instead oftemperature is to develop a methodology to account for humidity as well as temperature, rather than to find the best fit for the Phoenix data. The heat index levels in Phoenix are driven almost excl usively by temperature, since very high humidity levels are required to affect the index beyond temperature. h) p 5 para 3: It may be true that using heat index is better than ignoring humidity, but we suggest that you try to track the physics better by using absolute humidities to estimate latent loads. Response: Absolute humidity was was f ound to not be a particularly good predictor of compressor fraction, as shown in Figure 3. i) p. 8, para 1: Assuming a sunny day is pr obably not a problem fo r most cases, since reheat will make up for loads blocked by clouds. j) p 8 para 1: Radiative heat loading is very complex, and some of the suggested assumptions are problematic. For exam ple even with 100% cloud cover, solar collectors work very well collecting the diffuse component of solar radiation. And nighttime radiative cooling reduces need for a/c, especially on clear nights when the surface of the car is radiating to the cold blackness of space. Response: this suggests that the treatment of 100 percent cloud cover as “nightime” may be overstating the effect of cloud cover (which is very small as it is). Because solar radiation data (o r even reliable cloud cover data) was not available for Phoenix, we don’t ha ve the ability to separate “direct” and “diffuse” radiation. k) p 8 para 3: Soak duration is likely to have a large effect on short trips having little cycling, because of the effect on pu lldown load. After indoor temperature stabilizes, effects of soak have vanished. l) Fig 1: Clarify that the first bar is deleted from the data set. m) Fig 2: It is clear that each data point represents the time-weighted average runtime of all data points in that te mperature bin. But is the least squares fit weighted too? If so, explain how. Do the averages incl ude trips taken with the a/c off for the whole time? Is there any systematic dependence of errors and scatter on long vs short trips or those having higher/lower cycling rates? Response: The demand factor curves were generated from data aggregated over every trip by heat index level. This approach accounts for the disparity in trip length implicitly, by calculating compressor fraction as the total time the compressor is engaged divided by total tr ip time at a given heat index. This approach also reduces the influence of random effects (such as vehicle), since the aggregate trips for a given heat index are comprised of several vehicles. n) Fig 6: Instead of using a parabola, wh y not select an exponential or other curve that naturally has a horizontal asymptote?o) Fig 10: All curves should approach a horiz ontal asymptote. It would help if data points were shown along with the curve f its. Try re-fitting with exponential curve to capture asymptote? Response: While the quadratic approach with truncation may not be the most elegant, the terms are significant, and another approach would be unlikely to yield results much different from the current approach. p) Fig 11: Lack of dependence on speed su ggests that ram air flow into condenser improves a/c system efficiency. Load reductions due to wind cooling the solarheated metal, and load increases due to increased infiltration are probably neutralized by reheat. q) Table 1: Explain how this relates to Fig. 7. Also show formulae or explain better how the numbers were calculated. 5. Questions a) p 2 para 1: is the 850 W/m2 solar load a pplied to a car in a wind tunnel or is the vehicle stationary? Stationary test will overestimate effect of solar, since car surface will be hotter than while driving. Response: The vehicle is stationary, but a variable speed fan provides cooling and presumably produces conditions mo re representative of the road p 2 para 3: Compressor ontime fraction should be defined caref ully to clarify whether the denominator is total trip time, or only that frac tion of the trip during which the a/c system was switched on. Response: Compressor time is the fraction of compressor time divided by total trip time, regardless of whether the A/C was on or not.

Commentor 2: John Warner, PhD Center for Statistical Consultation and Research University of Michigan Verbatim comments not available electronically. Hard copies available upon request. Comment and responses are summarized below: Comment: Given that the analysis is focused on the population of the Phoenix dataset as a whole, rather than making inferences about idividual drivers/vehicles, vehicle should be included as a random effect. Ignoring random effects may lead to unrealistically small standard errors and unrea listically low p-values. Response: The analysis for determining the si gnificance of individual variables was redone, adding vehicle as a random variable. The fundamental conclusions (as predictorvariables, only heat index and period ar e significant) are unchanged. The ANOVA results presented in Table 3 have b een updated to reflect this change. Comment: Main effects should be tested indivi dually, or alternatel y it should be made clear what approach was taken to deter mine the significance of individual effects. Response: The methodology for performing the Anal ysis of Variance on main effects has been added to the report. Comment: There are likely approaches to modeling demand factor which are better than a quadratic fit, given the leve l of truncation required with the quadratic curves to maintain results between 0 and 1. Examples include a non-linear least-square s with random effects (which a quadratic form woul d adderss); and “ideal” compone nt would be to give higher weights to trip lengths and for compre ssor fractions close to zero or one. Response: The demand factor curves were gene rated from data aggregated over every trip by heat index level. This approach accounts for the disparity in trip length implicitly, by calculating compressor fraction as the to tal time the compressor is engaged divided by total trip time at a given heat index. Th is approach also reduces the influence of random effects (such as vehicle), since the aggregate trips for a given heat index are comprised of several vehicles. While the quadratic appr oach with truncation may not be the most elegant, the terms are significant, and anothe r approach would be unlikely to yield results much different from the current approach.