Final Technical Support Document 

Fuel Economy Labeling of Motor Vehicle 

Revisions to Improve Calculation of Fuel 

Economy Estimates 

Office of Transportation and Air Quality 

EPA420-R-06-017 

December 2006 
Final Technical Support Document


Fuel Economy Labeling of Motor Vehicles: 


Revisions to Improve Calculation of Fuel 


Economy Estimates


Assessment and Standards Division


and


Certification and Innovative Strategies Division


Office of Transportation and Air Quality 


U.S. Environmental Protection Agency 

EPA420-R-06-017


December 2006

Table of Contents 

LIST OF TABLES....................................................................................................................................................... III

LIST OF FIGURES....................................................................................................................................................... V

LIST OF ACRONYMS................................................................................................................................................. VI

CHAPTER I: EXECUTIVE SUMMARY.............................................................................................................1

CHAPTER II: CURRENT AND PROPOSED LABEL VALUES COMPARED TO ONROAD 

ESTIMATES ...................................................................................................................................7

A. ONROAD FUEL ECONOMY ESTIMATES DURING TYPICAL OPERATION..............................................................7

1. ORNL “YourMPG” Program.....................................................................................................................7

2. DOE FreedomCar Program.......................................................................................................................9

3. Strategic Visions New Vehicle Survey ......................................................................................................12

4. Kansas City Instrumented Vehicle Study..................................................................................................13

B. FUEL ECONOMY ESTIMATES BY INDEPENDENT ORGANIZATIONS...................................................................15

1. Consumer Reports Estimates of Onroad Fuel Economy ..........................................................................15

2. AAA Estimates of Onroad Fuel Economy.................................................................................................20

3. Edmunds ...................................................................................................................................................22

C. FLEET-WIDE ESTIMATES OF ONROAD FUEL ECONOMY ..................................................................................25

D. OVERALL COMPARISON OF HYBRID FUEL ECONOMY ....................................................................................28

CHAPTER II REFERENCES ........................................................................................................................................32

CHAPTER III: DOCUMENTATION OF FINAL APPROACH FOR ESTIMATING ON-ROAD FUEL 

ECONOMY ...................................................................................................................................33

A. VEHICLE SPECIFIC 5-CYCLE METHOD FOR ESTIMATING ON-ROAD FUEL ECONOMY FROM DYNAMOMETER

TESTS.............................................................................................................................................................34

1. Start Fuel Use...........................................................................................................................................36

a. Start Fuel .............................................................................................................................................................. 36

b. Trip Length........................................................................................................................................................... 44

c. Formula for Start Fuel Use ................................................................................................................................... 50

2. Running Fuel Use at 75°F Without Air Conditioning ..............................................................................51

a. On-Road Driving Patterns .................................................................................................................................... 51

b. Representative Mix of Dynamometer Driving Cycles.......................................................................................... 61

3. Effect of Air Conditioning on Fuel Economy............................................................................................69

4. Effect of Cold Ambient Temperatures on Running Fuel Use....................................................................78

5. Adjustment Factor for Non-Dynamometer Effects ...................................................................................81

6. 5-Cycle Fuel Economy Formulae.............................................................................................................92

a. 5-Cycle Fuel Economy Formulae......................................................................................................................... 92

b. Alternative 5-cycle Highway Fuel Economy Formula ......................................................................................... 97

B. DERIVATION OF THE MPG-BASED APPROACH.............................................................................................100

C. VARIABILITY IN ONROAD FUEL ECONOMY ..................................................................................................106

D. IMPACT OF THE 5-CYCLE AND MPG-BASED FORMULAE ON FUEL ECONOMY LABELS ................................110

E. SENSITIVITIES AND UNCERTAINTIES IN THE 5-CYCLE FUEL ECONOMY FORMULAE .....................................113

1. Start Fuel Use.........................................................................................................................................114

2. Running Fuel Use At 75°F......................................................................................................................118

a. Alternative Definition of US06 City and Highway Bags.................................................................................... 118

b. Elimination of Three Highest Speed Freeway Cycles in Draft MOVES2004 .................................................... 122

c. Alternative Fuel Rates and Number of VSP Bins............................................................................................... 123

d. Kansas City VSP Distributions and Fuel Rates .................................................................................................. 125

e. California Chase Car Studies.............................................................................................................................. 127

f. Alternative Splits of City/Highway Driving....................................................................................................... 128

g. Complete Cycles................................................................................................................................................. 132

3. Air Conditioning Effects .........................................................................................................................134

4. Cold Temperature Running Fuel Use.....................................................................................................135

CHAPTER III REFERENCES .....................................................................................................................................138

i 
CHAPTER IV: ECONOMIC IMPACTS ............................................................................................................142

A. TESTING AND FACILITIES BURDEN...............................................................................................................142

1. Test Volume ............................................................................................................................................142

a. Testing Burden for MY 2008 through 2010 ....................................................................................................... 142

b. Testing Burden for MY 2011 and After ............................................................................................................. 143

2. Facilities Burden ....................................................................................................................................146

3. Startup Burden........................................................................................................................................147

4. Summary.................................................................................................................................................149

B. IMPACT ON CONFIRMATORY TESTING OF VEHICLES ....................................................................................151

C. CHANGES TO LABEL FORMAT AND CONTENT ..............................................................................................152

D. CERTIFICATION FEES....................................................................................................................................152

APPENDIX A: EPA KANSAS CITY TEST PROGRAM ..................................................................................153

A. QUALITY ASSURANCE..................................................................................................................................154

B. ONROAD FUEL ECONOMY ............................................................................................................................155

C. RECENT DRIVING ACTIVITY IN KANSAS CITY AND CALIFORNIA..................................................................156

D. EVALUATION OF 5-CYCLE APPROACH TO FUEL ECONOMY ESTIMATION .....................................................161

ii 
List of Tables 

TABLE I-1 AGGREGATE COSTS ...........................................................................................................................6

TABLE II.A-1. YOURMPG VERSUS CURRENT EPA LABEL FUEL ECONOMY ............................................................8

TABLE II.A-2. FREEDOMCAR HYBRID FLEET CUMULATIVE VERSUS EPA COMPOSITE LABEL FUEL ECONOMY ...10

TABLE II.B-1. CONSUMER REPORTS AND CURRENT EPA AND MPG-BASED FUEL ECONOMY: 303 VEHICLES ......16

TABLE II.B-2. CR AND CURRENT EPA, 5-CYCLE AND MPG-BASED FUEL ECONOMY: 70 VEHICLES.....................16

TABLE II.B-3. COMPARISON OF CONSUMER REPORTS AND EPA FUEL ECONOMY VALUES FOR HYBRIDS .............18

TABLE II.B-4. AAA AND CURRENT EPA, 5-CYCLE AND MPG-BASED FUEL ECONOMY ESTIMATES .....................20

TABLE II.B-5. 2003-2006 EDMUNDS LONG-TERM TEST VEHICLES ........................................................................23

TABLE II.B-6. EDMUNDS LONG-TERM TEST VEHICLES COMPARED TO EPA COMBINED MPG ESTIMATES............24

TABLE II.C-1. FHWA-BASED ESTIMATE OF ONROAD FUEL ECONOMY .................................................................26

TABLE II.C-2. BREAKDOWN OF VMT AND FUEL USE BY 4-WHEEL, 2-AXLE TRUCKS ...........................................27

TABLE II.D-1. ONROAD HYBRID FUEL ECONOMY VERSUS EPA LABEL ESTIMATES (MPG)....................................30

TABLE II.D-2. ONROAD HYBRID FUEL ECONOMY ESTIMATES VERSUS EPA LABEL ESTIMATES (MPG) .................31

TABLE III.A-1. KEY FEATURES OF THE FIVE CURRENT EMISSION AND FUEL ECONOMY TESTS...............................34

TABLE III.A-2. DISTRIBUTION OF STARTS BY HOUR OF THE DAY (IN PERCENT) ......................................................40

TABLE III.A-3. BREAKDOWN OF ANNUAL VMT BY MONTH....................................................................................41

TABLE III.A-4. DISTRIBUTION OF STARTS BY SOAK TIME: THREE HOURS DURING WEEKDAYS..............................42

TABLE III.A-5. ESTIMATION OF DAILY AVERAGE OVERNIGHT SOAK EQUIVALENT.................................................43

TABLE III.A-6. TRIP AND START RELATED INFORMATION IN DRAFT MOVES2004 ................................................44

TABLE III.A-7. ESTIMATES OF IN-USE AVERAGE TRIP LENGTH...............................................................................45

TABLE III.A-8. INVENTORY DRIVING CYCLES IN DRAFT MOVES2004...................................................................49

TABLE III.A-9. VSP-SPEED BINS IN DRAFT 2004MOVES.......................................................................................54

TABLE III.A-10. EXPANDED SET OF 26 VSP-SPEED BINS ..........................................................................................55

TABLE III.A-11. VSP FREQUENCY DISTRIBUTIONS FOR ONROAD DRIVING CYCLES IN MOVES ...............................56

TABLE III.A-12. VSP FREQUENCY DISTRIBUTIONS FOR ONROAD DRIVING CYCLES IN MOVES ...............................57

TABLE III.A-13. DISTRIBUTION OF ONROAD DRIVING PATTERNS: DRAFT MOVES2004 ..........................................58

TABLE III.A-14. VSP DISTRIBUTIONS FOR U.S. DRIVING (% OF TIME) ......................................................................59

TABLE III.A-15. DRIVING CHARACTERISTICS OF THE CURRENT DYNAMOMETER TESTS ...........................................62

TABLE III.A-16. SPLIT OF US06 CYCLE INTO CITY AND HIGHWAY PORTIONS...........................................................63

TABLE III.A-17. VSP DISTRIBUTIONS FOR DYNAMOMETER CYCLES (% OF TIME).....................................................64

TABLE III.A-18. 26-BIN VSP FUEL RATES (GRAM PER SECOND) ...............................................................................66

TABLE III.A-19. BEST-FIT COMBINATIONS OF DYNAMOMETER CYCLES ...................................................................67

TABLE III.A-20. COEFFICIENTS FOR A/C COMPRESSOR USAGE EQUATIONS..............................................................72

TABLE III.A-21. INCREASED FUEL USE DUE TO AIR CONDITIONING AS A FUNCTION OF VEHICLE SPEED .................75

TABLE III.A-22. WARMED UP FUEL USE VERSUS TEMPERATURE: HONDA DATA .....................................................79

TABLE III.A-23. NHTSA ONROAD TIRE PRESSURE SURVEY .....................................................................................83

TABLE III.A-24. EFFECT OF WIND ANGLE ON VEHICLE DRAG COEFFICIENT .............................................................86

TABLE III.A-25. FREQUENCY OF WIND SPEEDS IN THE U.S. ......................................................................................87

TABLE III.A-26. EFFECT OF ROAD ROUGHNESS ON ONROAD FUEL ECONOMY: 1977 ................................................88

TABLE III.A-27. MAPPING OF ROADWAY SURFACES .................................................................................................89

TABLE III.A-28. EFFECT OF NON-DYNAMOMETER FACTORS ON ONROAD FUEL ECONOMY ......................................90

TABLE III.B-1. RATIO OF FTP BAG TO CYCLE FUEL CONSUMPTION .....................................................................100

TABLE III.B-2. EFFECT OF HEATER/DEFROSTER USE ON COLD FTP FUEL USE.....................................................102

TABLE III.D-1. CURRENT AND 5-CYCLE LABEL FUEL ECONOMIES BY MODEL TYPE.............................................111

TABLE III.D-2. CURRENT AND 5-CYCLE LABEL FUEL ECONOMY BY PROPULSION SYSTEM ...................................111

TABLE III.D-3. EFFECT OF MPG-BASED FORMULAE ON CITY AND HIGHWAY FUEL ECONOMY ............................112

TABLE II.D-4. EFFECT OF NEW METHODS ON FUEL ECONOMY ESTIMATES FOR MAJOR MANUFACTURERS.........113

TABLE III.D-5. EFFECT OF VARIOUS FACTORS ON 5-CYCLE FUEL ECONOMY ........................................................113

TABLE III.E-1. SENSITIVITY OF HYBRID START FUEL USE TO AMBIENT TEMPERATURE .......................................115

TABLE III.E-2. SPLIT OF US06 CYCLE INTO CITY AND HIGHWAY PORTIONS.........................................................119

TABLE III.E-3. VSP DISTRIBUTIONS FOR US06 CITY AND HIGHWAY BAGS (% OF TIME)......................................120

TABLE III.E-4. BAG/CYCLE COMBINATIONS FOR CITY AND HIGHWAY DRIVING: ALTERNATIVE US06 SPLITS.....121

TABLE III.E-5. AVERAGE 5-CYCLE FUEL ECONOMY: ALTERNATIVE US06 SPLITS ...............................................121

iii 
TABLE III.E-6. BAG/CYCLE COMBINATIONS FOR HIGHWAY DRIVING: HIGH SPEED FREEWAY CYCLES ...............123

TABLE III.E-12. VSP DISTRIBUTIONS FOR U.S. DRIVING WITH ALTERNATIVE DEFINITION OF CITY DRIVING (% OF 

TABLE IV.A-2. ESTIMATED INCREASE IN NUMBER OF TESTS FOR MODEL YEARS 2008-2010: PROPOSED AND FINAL

TABLE III.E-7. BAG/CYCLE COMBINATIONS FOR CITY AND HIGHWAY DRIVING: ALTERNATIVE FUEL RATES .....124

TABLE III.E-8. KANSAS CITY VSP DISTRIBUTIONS AND FUEL RATES...................................................................125

TABLE III.E-9. BAG/CYCLE COMBINATIONS FOR CITY AND HIGHWAY DRIVING: KANSAS CITY ..........................126

TABLE III.E-10. CALIFORNIA URBAN AND RURAL VSP DISTRIBUTIONS .................................................................127

TABLE III.E-11. BAG/CYCLE COMBINATIONS FOR CITY AND HIGHWAY DRIVING: CALIFORNIA.............................128

TIME) .............................................................................................................................................130

TABLE III.E-13. CYCLE COMBINATIONS FOR CITY AND HIGHWAY DRIVING: REVISED CITY/HIGHWAY SPLIT .......131

TABLE III.E-14. 5-CYCLE FUEL ECONOMY VALUES: EFFECT OF THE DEFINITION OF CITY DRIVING (MPG) ............132

TABLE III.E-15. BAG/CYCLE COMBINATIONS FOR COMPLETE CYCLE ALTERNATIVES ...........................................133

TABLE III.E-16. EFFECT OF USING WHOLE CYCLES ON 5-CYCLE FUEL ECONOMY VALUES (MPG).........................133

TABLE IV.A-1. ESTIMATED COST PER TEST: PROPOSED AND FINAL ......................................................................145

 ..................................................................................................................................................145

TABLE IV.A-3. ESTIMATED INCREASE IN NUMBER OF TESTS FOR MODEL YEARS 2011 AND LATER: PROPOSED...145

TABLE IV.A-4. ESTIMATED INCREASE IN NUMBER OF TESTS FOR MODEL YEARS 2011 AND LATER: FINAL..........146

TABLE IV.A-5. ESTIMATED FACILITY COSTS: PROPOSED ......................................................................................147

TABLE IV.A-6. ESTIMATED FACILITY COSTS: FINAL .............................................................................................147

TABLE IV.A-7. ESTIMATED STARTUP COSTS: PROPOSED.......................................................................................149

TABLE IV.A-8. ESTIMATED STARTUP COSTS: FINAL..............................................................................................149

TABLE IV.A-9. ESTIMATED TOTAL COSTS: PROPOSED ..........................................................................................150

TABLE IV.A-10. ESTIMATED TOTAL COSTS: FINAL .................................................................................................150

TABLE IV.A-11. ESTIMATED TOTAL HOURS: PROPOSED..........................................................................................150

TABLE IV.A-12. ESTIMATED TOTAL HOURS: FINAL ................................................................................................151

TABLE A-1. PROCESSING OF RAW DATA OBTAINED IN KANSAS CITY ..............................................................155

TABLE A-2. CYCLE COMBINATIONS FOR THE MITSUBISHI MONTERO SPORT....................................................164

TABLE A-3. ONROAD AND PREDICTED FUEL ECONOMY: KANSAS CITY TEST PROGRAM..................................166

TABLE A-4. ON-ROAD AND MODELED FUEL ECONOMIES USING VEHICLE-SPECIFIC CYCLE WEIGHTS (MPG)..168

TABLE A-5. COMPARISON OF CYCLE FUEL ECONOMY ......................................................................................169

iv 
List of Figures 

FIGURE II-1. DISTRIBUTION OF ONROAD FUEL ECONOMY ESTIMATES – STRATEGIC VISION, 2006 .....................12

FIGURE II-2. COMPARISON ONROAD TO CURRENT LABEL ECONOMY: KANSAS CITY ..........................................14

FIGURE III-1. SPEED-ACCELERATION FREQUENCY DISTRIBUTION: KANSAS CITY VS. TEST CYCLES ...................52

FIGURE III-2. SPEED-ACCELERATION FREQUENCY DISTRIBUTION: URBAN CALIFORNIA VS. TEST CYCLES .........53

FIGURE III-3. KANSAS CITY AND CALIFORNIA VSP FREQUENCY DISTRIBUTIONS VS. MOVES............................60

FIGURE III-4. VSP FREQUENCY DISTRIBUTIONS IN KANSAS CITY: HYBRIDS VS. NON-HYBRIDS ..........................61

FIGURE III-5. HEAT INDEX VS. TEMPERATURE AND HUMIDITY .............................................................................71

FIGURE III-6. AIR CONDITIONING USE IN PHOENIX ...............................................................................................73

FIGURE III-7. COMPRESSOR ENGAGEMENT AS A FUNCTION OF AMBIENT TEMPERATURE .....................................73

FIGURE III-8. 5-CYCLE CITY VERSUS FTP FUEL CONSUMPTION.........................................................................104

FIGURE III-9. 5-CYCLE HIGHWAY VERSUS HFET FUEL CONSUMPTION..............................................................104

FIGURE III-10. MPG-BASED CITY FUEL ECONOMY ..............................................................................................105

FIGURE III-11. MPG-BASED HIGHWAY FUEL ECONOMY ......................................................................................105

FIGURE III-12. ONROAD FE VERSUS PRE-1984 EPA CITY LABEL FOR CITY DRIVEN CARS..................................106

FIGURE III-13. VARIABILITY IN ONROAD FE.........................................................................................................110

FIGURE A-1. COMPARISON ONROAD TO CURRENT LABEL ECONOMY: KANSAS CITY ........................................156

FIGURE A-2. SPEED-ACCELERATION FREQUENCY DISTRIBUTION: KANSAS CITY VS. TEST CYCLES .................157

FIGURE A-3. SPEED-ACCELERATION FREQUENCY DISTRIBUTION: URBAN CALIFORNIA VS. TEST CYCLES .......158

FIGURE A-4. KANSAS CITY AND CALIFORNIA VSP FREQUENCY DISTRIBUTIONS VS. MOVES..........................159

FIGURE A-5. VSP FREQUENCY DISTRIBUTIONS IN KANSAS CITY: HYBRIDS VS. NON-HYBRIDS ........................160

v 
List of Acronyms 

AAA American Automobile Association 

ADFE analytically derived fuel economy 

AVTA Advanced Vehicle Testing Activity 

CAFE Corporate Average Fuel Economy 

CO Carbon Monoxide 

CO2 Carbon Dioxide 

CR Consumer Reports 

CRC Coordinating Research Council 

DOE Department of Energy 

DOT Department of Transportation 

EIA Energy Information Administration 

EPA Environmental Protection Agency 

FHWA Federal Highway Administration 

FTP Federal Test Procedure 

GPS geographical positioning system 

HC Hydrocarbon 

HFET Highway Fuel Economy Test 

INL Idaho National Laboratory 

LDT light-duty truck 

LDV light-duty vehicle 

LOS level of service (volume of traffic) 

MOVES Motor Vehicle Emission Inventory System 

mpg miles per gallon 

MY model year 

NHTS National Household Travel Survey 

NHTSA National Highway Traffic Safety Administration 

NOx Oxides of Nitrogen 

NREL National Renewable Energy Laboratory 

OAP Office of Atmospheric Programs 

ORNL Oak Ridge National Laboratory 

PEMS Portable Emissions Measurement System 

PERE Physical Emission Rate Estimator 

RVP Reid Vapor Pressure 

SAFD speed-acceleration frequency distribution 

SFTP Supplemental Federal Test Procedure 

SUV sport utility vehicle 

TRLHP tractive road load horsepower 

TSD Technical Support Document 

UDDS Urban Dynamometer Driving Schedule 

VMT vehicle miles traveled 

VSP vehicle specific power 

vi 
Chapter I: Executive Summary 

The EPA fuel economy estimates have appeared on the window stickers of all new cars 

and light trucks since the late 1970’s and are well-recognized by consumers. The fuel economy 

estimates essentially serve two purposes: to provide consumers with a basis on which to 

compare the fuel economy of different vehicles, and to provide consumers with a reasonable 

estimate of the range of fuel economy they can expect to achieve. While the estimates 

historically have been a valuable tool for comparison shopping purposes, attention has been 

focused recently on how closely the EPA estimates approximate consumers’ real-world fuel 

economy experience. 

We are making changes to EPA’s fuel economy test methods to bring the estimates closer 

to the fuel economy consumers are achieving in the real-world. We believe these estimates will 

provide car buyers with useful information when comparing the fuel economy of different 

vehicles. It is important to emphasize that fuel economy varies from driver to driver for a wide 

variety of reasons, such as different driving styles, climates, traffic patterns, use of accessories, 

loads, weather, and vehicle maintenance. Even different drivers of the same vehicle will 

experience different fuel economy as these and other factors vary. Therefore, it is impossible to 

design a “perfect” fuel economy test that will provide accurate real-world fuel economy 

estimates for every consumer. With any estimate, there will always be consumers that get better 

or worse actual fuel economy. The EPA estimates are meant to be a general guideline for 

consumers, particularly to compare the relative fuel economy of one vehicle to another. 

Nevertheless, we do believe that today’s new fuel economy test methods will do a better job of 

giving consumers a more accurate estimate of the fuel economy they can achieve in the realworld. 

It is essential that our fuel economy estimates continue to be derived from controlled, 

repeatable, laboratory tests. However, the inputs to our estimates are based on data from actual 

real-world driving behavior and conditions. Because the test is controlled and repeatable, an 

EPA fuel economy test result can be used for comparison of different vehicle models and types. 

EPA and manufacturers test over 1,250 vehicle models annually and every test is run under 

identical conditions and under a precise driver’s trace, which assures that the result will be the 

same for an individual vehicle model no matter when and where the laboratory test is performed. 

Variations in temperature, road grade, driving patterns, and other variables do not impact the 

result of the test. While such external conditions impact fuel economy on a trip-to-trip basis, 

they do not change the laboratory test result. Therefore, a repeatable test provides a level playing 

field for all vehicles, which is essential for comparing the fuel economy of one vehicle to 

another. Finally, EPA must preserve the ability to confirm the values achieved by the 

manufacturers’ testing, and this can only be achieved with a highly repeatable test or set of tests. 

No other fuel economy test program provides the level of repeatability as the EPA program. 

However, the EPA fuel economy test methods need to reflect real world conditions as 

well as being a repeatable test. While some consumer groups have issued their own fuel 

economy numbers based on on-road driving, this approach introduces a wide number of 

variables – different drivers, driving patterns, weather conditions, temperatures, etc. – that make 

repeatability impossible. Our new fuel economy test methods are more representative of real1 
world conditions than the current fuel economy tests – yet we retain our practice of relying on 

controlled, repeatable, laboratory tests. 

The methods used today for calculating the city and highway mpg estimates were 

established in the 1970’s, and were adjusted in the mid-1980’s. Since these adjustments were 

made, America’s driving behavior has changed. In the past 20 years, speed limits have increased 

and vehicles have been designed for higher power – as a result, Americans are driving faster and 

more aggressively than ever before. Vehicle technology has changed markedly, and many more 

vehicles are equipped with energy-consuming accessories like air conditioning. These and other 

factors are not accounted for in the current test procedures used to determine the city and 

highway mpg estimates. Our analyses indicate that if these factors were better accounted for, the 

city and highway fuel economy label estimates would be generally lower and closer to the 

average real-world experience of consumers. 

A fundamental issue with today’s fuel economy estimates is that the underlying test 

procedures do not fully represent real-world driving conditions. Some of the key limitations are 

that the highway test has a top speed of only 60 miles per hour, both the city and highway tests 

are run at mild climatic conditions (75°F), both tests have mild acceleration rates, and neither test 

is run with the use of accessories, such as air conditioning. However, since the time of the last 

fuel economy labeling revisions in the mid-1980’s, EPA has established several additional test 

procedures, used for emissions compliance purposes, which capture a much broader range of 

real-world driving conditions. Specifically, these emissions test cycles capture the effects of 

higher speeds, more aggressive driving (i.e., higher acceleration rates), the use of air 

conditioning at higher ambient temperatures, and colder temperature operation. Our analysis 

indicates that these factors can have a significant impact on fuel economy, and that the impacts 

can vary widely across different vehicles. 

We are now requiring that three additional emission tests, already used by manufacturers, 

will be utilized to derive more accurate fuel economy estimates. These three test procedures 

encompass a much broader range of real-world driving, as they incorporate the effects of higher 

speeds, more rapid accelerations, air conditioning use, and cold temperatures. Our new approach 

will utilize these additional emission tests, together with the current two fuel economy tests, so 

that our fuel economy test methods reflect a much broader range of driving conditions. 

Our final rule revises the test methods by which the city and highway fuel economy 

estimates are calculated. We are replacing the current method of adjusting the city (FTP) test 

result downward by 10% and the highway (HFET) test result downward by 22%. Instead, we are 

finalizing a new approach that incorporates additional test methods that address factors that 

impact fuel economy, but are missing from today’s tests – specifically, higher speeds, more 

aggressive driving (higher acceleration rates), the use of air conditioning, and the effect of cold 

temperature. The new test methods will bring into the fuel economy estimates the test results 

from the five emissions tests in place today: FTP, HFET, US06, SC03, and Cold FTP.a Thus, 

a

 The US06 test is designed to represent high speed highway driving and aggressive (i.e., rapid accelerations and 

decelerations) urban driving. The SC03 test is designed to represent the impact of air conditioner operation at high 

temperatures. The Cold FTP, which is conducted at 20°F, is designed to reflect the impact of cold temperatures. 

2 
we refer to this as the “5-cycle” method. Under this new method, rather than basing the city mpg 

estimate solely on the adjusted FTP test result, and the highway mpg estimate solely on the 

adjusted HFET test result, each estimate will be based on a “composite” calculation of all five 

tests, weighting each appropriately to arrive at new city and highway mpg estimates. The new 

city and highway estimates will each be calculated according to separate city and highway “5­

cycle” formulae that are based on fuel economy results over these five tests. The conditions 

represented by each test will be “weighted” according to how much they occur over average realworld city or highway driving. For example, we have derived weightings to represent driving 

cycle effects, trip length, air conditioner compressor-on usage, and operation over various 

temperatures. The derivation of this methodology and the relevant weighting factors and 

formulae are the principal subject of this Final Technical Support Document. 

We also are finalizing an additional downward adjustment to fuel economy estimates 

within the 5-cycle method. We put in place a downward adjustment to account for effects that 

cannot be replicated on the dynamometer. There are many factors that affect fuel economy that 

are not accounted for in any of our existing test cycles. These include road grade, wind, tire 

pressure, heavier loads, hills, snow/ice, effects of ethanol in gasoline, and others. We are 

finalizing a 9.5% downward adjustment to account for these effects. The detailed technical basis 

for this adjustment factor is contained in section III.A.5 of this Final Technical Support 

Document. 

Because the 5-cycle method is inherently vehicle-specific, the difference between today’s 

values and the new fuel economy estimates could vary widely from vehicle to vehicle. Our new 

approach will result in city fuel economy estimates that are between 8 to 15 percent lower than 

today’s labels for the majority of conventional vehicles. The city mpg estimates for the 

manufacturers of most vehicles will drop by about 12 percent on average relative to today’s 

estimates. For vehicles that achieve generally better fuel economy, such as gasoline-electric 

hybrid vehicles, the new city estimates will be about 20 to 30 percent lower than today’s labels. 

The new highway fuel economy estimates will be 5 to 15 percent lower for the majority of 

vehicles, including most hybrids. The highway mpg estimates for the manufacturers of most 

vehicles will drop on average by about 8 percent, with estimates for most hybrid vehicles 

dropping by 10 to 20 percent relative to today’s estimates. 

In Chapter II of this Final Technical Support Document, we compare current EPA label 

fuel economy values, as well as the proposed 5-cycle and mpg-based values, to several 

independent estimates of onroad fuel economy. The independent estimates fall into several 

general categories, depending on the type of data involved. One type of estimate involves the 

measurement or estimate of onroad fuel economy of vehicles in typical operation. Two 

examples in this category are the U.S. Department of Energy (DOE) FreedomCar program and 

the DOE Your MPG program. A second type of estimate involves onroad measurement of fuel 

economy according to some established protocol by an independent organization. Examples in 

this category are fuel economy estimates developed by Consumer Report, Edmunds, and AAA. 

A third type of estimate involves broad estimates of national fuel consumption and national 

VMT and the development of fleet-wide fuel economy estimates. Examples in this category are 

fleet-wide fuel economy estimates developed by Federal Highway Administration (FHWA) and 

the Energy Information Administration (EIA). 

3 
These estimates and studies often suggest that there is a shortfall between the EPA 

estimates and real-world fuel economy. For example, Consumer Reports derives city, highway, 

and overall fuel economy estimates, and their methods clearly demonstrate the large degree of 

variation across vehicles. While their city fuel economy estimates fall on average below the 

EPA label values, their highway estimates are, on average, higher than the EPA label values. 

Consumer Reports’ overall fuel economy estimates range from 27% below to 20% above the 

EPA overall rating. The Automobile Association of America (AAA) likewise publishes the fuel 

economy results they achieve in their annual auto guide for new cars and trucks. In their 2004 

auto guide, about half of their estimates were below the EPA combined city/highway value, and 

about one half were above the EPA city/highway combined value. Their estimates ranged from 

40% lower than EPA’s to 22% higher, again reflecting a great deal of vehicle-to-vehicle 

variation. 

Each of these studies differs in its test methods, driving cycles, sampling of vehicles, and 

methods of measuring fuel economy. There are strengths and weaknesses of each study, which 

we discuss further in this Technical Support Document. Collectively, these studies and data 

indicate there are many cases where real-world fuel economy falls below the EPA estimates. 

The studies also indicate that real-world fuel economy varies significantly depending on the 

conditions under which it is evaluated. Nevertheless, taken as a whole, these studies reflect a 

wide range of real-world driving conditions, and show that fuel economy can be much lower 

than EPA’s estimates if more real-world conditions are considered. Where possible, we also 

compare the results of these studies with the new label values that would result from the 5-cycle 

and mpg-based methods, and we found that in virtually every case the 5-cycle method resulted in 

fuel economy values that were significantly closer to these other estimates than the existing 

labels. 

In Chapter III of this Final Technical Support Document we describe the development of 

the vehicle specific 5-cycle and mpg-based methods. We also evaluate the range and variability 

of onroad fuel economy experienced by drivers of the same vehicle and we develop an 

adjustment factor that accounts for fuel economy impacts not reproducible on the dynamometer 

or in the testing laboratory. We describe the final vehicle specific 5-cycle formulae and the final 

mpg-based formulae, followed by a discussion of how the current city and highway fuel 

economy values would change under the two methods. Finally, we evaluate the sensitivities and 

uncertainties in the vehicle specific 5-cycle formulae. 

We describe how these different elements of our fuel economy model are developed and 

assembled from the test data. We develop methodologies for estimating the following: 

• Fuel use related to engine start-up, or start fuel use; 

• Fuel use once the engine is warmed up at 75°F (with no air conditioner operation); 

• Fuel use due to air conditioner use; 

• Fuel use once the engine is warmed up at colder temperatures; and 

• Factors that affect onroad fuel economy but which are not addressed by any of the five 

dynamometer cycles. 

4 
We describe the derivation of the mpg-based approach – a simplified method that will be 

an interim option in the first three years of the program and an available option under certain 

circumstances in subsequent years. The 5-cycle fuel economy formulae assume that fuel 

economy estimates are available for specific vehicles for all five dynamometer cycles and their 

respective bags of emission measurements. As discussed in the preamble to the final rule, these 

estimates may be based on fuel economy measurements, or on estimates based on test results 

from a similar vehicle. A simplified approach to implementing the 5-cycle formulae is to apply 

these formulae to test results on recent model vehicles and develop correlations between the 5­

cycle city and highway fuel economy estimates for these vehicles and their fuel economy over 

the FTP and HFET, respectively. This simplified approach is referred to as the mpg-based 

approach, since the resultant label adjustment will vary depending on the measured fuel economy 

(i.e., mpg) of a vehicle over the FTP and HFET tests, and will not require any additional tests. 

Following the detailed discussion of the 5-cycle and mpg-based approaches, we present 

the actual formulae and an assessment of the impact our new approaches will have on fuel 

economy label values. The impact of today’s final rule on city and highway fuel economy label 

values was assessed using the same database of 615 late model year vehicles used to develop the 

mpg-based adjustments. Use of the 5-cycle formulae will reduce both current city and highway 

fuel economy label values. For conventional vehicles, city and highway fuel economy values 

would be reduced an average of 11% and 8%, respectively. For higher than average fuel 

economy vehicles, the reduction in city fuel economy will be slightly higher, while for lower 

than average fuel economy vehicles, the reduction in city fuel economy will be slightly lower. 

The change in highway fuel economy is essentially independent of current highway fuel 

economy. 

The impact on hybrid vehicles will be significantly greater for city fuel economy, 

averaging a 22% reduction. However, the reduction in highway fuel economy will be similar, 

but toward the higher end of the range as for conventional gasoline-fueled vehicles. The impacts 

of the 5-cycle formulae on the single diesel vehicle in the database are very similar to those for 

conventional gasoline fueled vehicles. 

In Chapter IV we detail our estimates of the cost impacts of our new regulation. For 

model years 2008 through 2010, manufacturers may use the mpg-based calculation for the fivecycle fuel economy values or they may conduct voluntary 5-cycle testing. For model years 2011 

and after, if the five-cycle city and highway fuel economy values for an emission data vehicle 

group are not more than 4% and 5% below the mpg-based regression line, respectively, then all 

the vehicle configurations represented by the emission data vehicle (e.g., all vehicles within the 

vehicle test group) could continue to use the mpg-based approach. Vehicles within a test group 

falling more than 5% below the tolerance band for highway fuel economy values would be 

required to conduct US06 tests; those falling more than 4% below the city fuel economy 

tolerance band would be required to conduct SC03, US06, and Cold FTP tests. In addition, we 

expect that some of these vehicles falling below the tolerance levels may be eligible to estimate 

fuel economy for a given test through the application of analytically derived fuel economy 

(ADFE) values. Some data is currently available for vehicles that have conducted all five tests; 

based on this data, EPA has estimated the number of vehicles for which additional testing would 

be required because they fall below the 4 and 5% tolerance bands. 

5 
We prepared a range of burden estimates for this analysis, estimating minimum and 

maximum cost scenarios. These low and high estimates are intended to be our estimate of the 

outer boundaries of the likely testing and information costs. Aggregate annual costs are 

estimated to be between $1.4 and $1.7 million. A complete discussion of how these costs were 

estimated is in Chapter IV of this Technical Support Document. 

Table I-1 Aggregate Costs 

MY 2008 through MY 2010 MY 2011 and After 

Cost Element Minimum Maximum Minimum Maximum 

Test Volume $0 $0 $343,000 $424,000 

Facilities $0 $0 $375,000 $560,000 

Startup $659,000 $748,000 $659,000 $748,000 

TOTAL $659,000 $748,000 $1,377,000 $1,731,000 

6 
Chapter II: Current and Proposed Label Values Compared to 

Onroad Estimates 

A. Onroad Fuel Economy Estimates During Typical Operation 

In the 1984 label adjustment rule, EPA was able to compare fleetwide estimates of a 

variety of city and highway fuel economy label options to a number of independent estimates of 

onroad fleet fuel economy. In the late 1970's and early 1980's, EPA and several auto 

manufacturers had collected onroad fuel economy estimates from tens of thousands of drivers 

which could be compared to the EPA city and highway fuel economy labels. EPA primarily 

used the driver-based fuel economy estimates to develop the current 10% and 22% adjustments 

to fuel economy over the FTP and HFET, respectively. 

It is not possible to repeat this type of comparison today, as the auto manufacturers no 

longer conduct the extensive monitoring of fuel economy that was performed in the late 1970’s 

and early 1980’s. At the same time, we have discovered three new sources of similar 

information. One, Oak Ridge National Laboratory (ORNL) has recently begun a program where 

drivers can submit their own fuel economy measurements via the Internet. This program is 

referred to as “YourMPG.” Two, DOE has also been operating an extensive hybrid 

demonstration project for a few years as part of their Freedom Car project. This program 

carefully monitors both VMT and fuel consumption, so accurate fuel economy estimates are 

available for a number of hybrid vehicles. Three, a private survey firm, Strategic Visions, 

performs two surveys of new vehicle purchasers a year to assess consumer satisfaction. The 

survey includes questions regarding the fuel economy being achieved to date. We have 

purchased the Strategic Visions survey results for model years 2004-06. The results of our 

analysis to date are discussed below. 

In addition to these three programs, EPA conducted it own testing of vehicle fuel 

economy in Kansas City, in conjunction with cooperative efforts between EPA and the 

Coordinating Research Council (CRC). The state of California, in conjunction with automobile 

manufacturers and others have been obtaining vehicle operational data via chase car studies. All 

of these studies are discussed below. 

1. ORNL “YourMPG” Program 

The ORNL YourMPG data are similar in nature to the much larger databases analyzed 

for the 1985 label adjustment rule. Drivers measure their own fuel economy and provide a 

perceived split of their driving into city and highway categories. The strength of this type of data 

is the fact that the vehicle is being operated by the owner or regular driver in typical use. The 

weaknesses are the unknown representativeness of the sample, the unknown nature of the 

technique used by the owner/driver to measure fuel economy and the unknown time period over 

which fuel economy is generally assessed (e.g., a couple of tanks full or the past year). In the 

particular case of the ORNL database, its current size is still small (8180 estimates of fuel 

economy for 4092 vehicles) compared to those available in 1985, though it is growing daily. 

7 
We compared the fuel economy estimates submitted to the ORNL website with each 

vehicle’s fuel economy label. We combined the city and highway labels using each driver’s 

estimate of the percentage which was city and highway. If a driver did not provide an estimate 

of the breakdown of their driving pattern, we assumed that their driving was 55% city and 45% 

highway with respect to the current label values and 43% city and 57% highway with respect to 

the mpg-based label values. We conducted separate comparisons for conventional gasoline 

vehicles, conventional vehicles with relatively high fuel economy, hybrids and diesels. The 

results are shown below. 

Table II.A-1. YourMPG Versus Current EPA Label Fuel Economy 

Vehicle 

Type 

No. of 

Estimates 

YourMPG Current 

Label 

Difference MPG-Based 

Label 

Difference 

Conventional 

Gasoline 

7330 23.8 24.1 -1.4% 21.7 9.1% 

High MPG 

Conventional 

Gasoline* 

680 35.1 35.8 -1.7% 31.6 11.2% 

Hybrid 

Gasoline 

520 43.2 47.1 -8.2% 40.5 6.3% 

Diesel 221 41.8 40.1 4.3% 35.3 18.3% 

* Combined EPA Label fuel economy value of 32 mpg or greater, representing about the top 10% fuel 

economy conventional vehicles. 

As can be seen, diesels appear to perform the best with respect to their label fuel economy, 

outperforming the label by 4.3%. Conventional gasoline vehicles come very close to meeting 

their label, falling short by only 1.4%. Conventional vehicles with relatively high combined fuel 

economy (here assumed to be 32 mpg or more, representing the top 10% of conventional 

vehicles in terms of fuel economy) performed only slightly worse, falling short by 1.7%. 

Hybrids fall short by a much larger margin, 8.2%. Thus, the greater shortfall seen with hybrids 

appears to be more related to hybrid technology than to simply high levels of fuel economy. 

With respect to the mpg-based label values, diesels still perform the best of the four types 

of vehicles, now exceeding their label values by 18%.b Those conventional vehicles with 

relatively high fuel economy fall next, followed by the typical conventional vehicle and hybrids. 

Thus, the YourMPG estimates indicate that hybrid performance differs from that of conventional 

vehicles, including those with high fuel economy. 

b

 There is a larger apparent difference between the mpg-based label values and the current label values in Table 

II.A.2 than the 6% average impact of the mpg-based approach on current label values cited elsewhere in this Final 

Technical Support Document . This occurs because when working with the YourMPG estimates, we are using the 

driver’s estimate of city/highway driving breakdown in all cases. When speaking of combined label values, 

however, we use a 55/45 breakdown for the current label values and a 43/57 breakdown for the mpg-based values. 

The lower city driving weight in the mpg-based formula increases the combined value relative to that for the current 

label values and reduces the difference between the two approaches. 

8 
The YourMPG database also provides us with an estimate of drivers’ perception of the 

type of driving which they perform. On average, they estimated that 43.2% of their driving was 

city driving and 56.8% was highway. These two figures are essentially identical to the weights 

developed from the Draft MOVES2004 depiction of onroad driving and embedded in the 5-cycle 

combined fuel economy formula. This is encouraging. However, this change from the 

traditional 55/45 city/highway split of onroad driving also causes some unusual relationships 

between onroad and label fuel economy as depicted in Table II.A-1 above. 

Focusing on conventional vehicles, which comprise the great majority of the database, 

Table II.A-1 shows that people’s onroad fuel economy is only 1.4% lower than the current label 

values indicates and 9.1% higher than the mpg-based label values would have been. If a 55/45 

split would have been used with the current label values, onroad fuel economy would have been 

1-2% higher than the label values for these vehicles. This differs dramatically from the onroad 

fuel economy shortfall indicated by the FHWA estimates of onroad fuel economy discussed in 

Section II.C below. In other words, the YourMPG database indicates that onroad fuel economy 

is exceeding the standard 55/45 label fuel economy, while FHWA indicates the opposite. 

Examining the YourMPG database further, we found that the average 55/45 label value in the 

database was 25.4 mpg. Per MOBILE6.2, the average 55/45 label value of the onroad vehicle 

fleet is 21.2 mpg. Thus, people submitting their onroad fuel economy estimates to the YourMPG 

database drive more fuel efficient vehicles than the average vehicle. This could indicate that 

those participating in the program have a greater interest in reducing fuel consumption than the 

average driver, but this cannot be known for certain. However, if true, this would explain the 

difference seen between the YourMPG database and the FHWA fleetwide estimates. 

2. DOE FreedomCar Program 

The Department of Energy has overseen the real world operation of a number of electric 

hybrid vehicles for a period of years. The Advanced Vehicle Testing Activity (AVTA), 

conducted jointly by the Idaho National Laboratory (INL) and the National Renewable Energy 

Laboratory (NREL), has been benchmarking hybrid electric vehicle performance as part of the 

FreedomCAR & Vehicle Technologies Program. The strength of the FreedomCAR program 

testing of hybrid vehicles lies in the fact that the vehicles are operated on the road over long term 

periods similar to what consumer-purchased vehicles experience, albeit often in commercial 

applications. Over a million miles of operation have been assessed and careful fuel consumption 

and mileage records are kept. The weaknesses are that some of the vehicles are in commercial 

use (e.g., company pool vehicles) for accelerated mileage accumulation and that the vehicles are 

operated exclusively in the Southwest, mainly in Phoenix, Arizona and surrounding areas. 

Nevertheless, the vehicles are operated just as any other vehicle would be in that application and 

the vehicles are subject to all of the environmental and roadway factors which affect the fuel 

economy of typical vehicles, such as winds, rough roads, hills, traffic congestion, etc. Because 

of the limited geographic area of the program, the vehicles are more likely to experience hot 

temperatures and air conditioning use than cold temperatures. 

The vehicles’ operators report mileage and fuel usage to FreedomCAR which posts the 

monthly and cumulative fuel economy of each electric hybrid fleet on a monthly schedule. 

9 
Therefore, seasonal changes in fuel economy can be observed. The results of the fleets are 

shown in Table II.A-2. 

Table II.A-2. FreedomCAR Hybrid Fleet Cumulative Versus EPA Composite Label Fuel 

Economy 

Fuel Economy (mpg) Difference (%) 

Vehicle Accumulated Fleet Onroad EPA Composite Label * 

Mileage Size Current 5-Cycle MPGBased 

Current 5-Cycle MPGBased 

2001 Honda Insight 417,000 6 45.2 61.0 50.4 51.5 35% 12% 14% 

2002 Toyota Prius 458,000 6 41.0 48.6 ---- ---- 19% 

2003 Honda Civic 378,000 4 37.6 46.3 37.9 39.7 23% 1% 6% 

2004 Toyota Prius 186,000 2 44.9 54.6 44.1 45.5 22% -2% 1% 

2004 Chevrolet 

Silverado 2wd 

48,000 1 17.7 18.8 ---- ---- 6% 

2004 Chevrolet 

Silverado 4wd 

53,000 1 17.9 16.9 15.1 15.2 -6% -16% -15% 

2005 Ford Escape 

2wd 

70,000 1 28.6 33.6 ---- ---- 17% 

2005 Ford Escape 

4wd 

78,000 1 27.0 29.9 23.6 26.1 11% -13% -3% 

2005 Honda Accord 158,000 2 27.8 32.3 25.9 28.3 16% -7% 2% 

2005 Lexus RX400h 67,000 2 24.8 28.1 24.0 24.4 13% -3% -2% 

2006 Toyota 

Highlander 

69,000 2 24.7 28.1 24.0 24.4 14% -3% -1% 

Average 180,000 2.5 30.7 37.0 31.6 32.9 16% -3% 1% 

* Current combined is a 55/45 weighting of city/highway fuel economy. Combined 5-cycle and mpg-based fuel 

economy is a 43/57 weighting of city/highway fuel economy. All label values from EPA certification database. 

Current combined label fuel economy values shown will not match official label values due to differences in vehicle 

configurations. The FreedomCAR fleet information as reported thru August 2006. 

As can be seen, EPA’s current label formulae over-estimate the onroad fuel economy achieved 

by all but one of the hybrid vehicle fleets. It should be noted that the values for current 

combined fuel economy are those from EPA’s certification database and are not the official label 

values. The official label values are even higher due to differences between the worse case 

vehicles tested over the Supplemental FTP cycles and the average vehicle sold. The largest 

shortfall was 35% for the Honda Insights. The Chevrolet Silverado was the only model which 

exceeded the current label value of the test vehicle in our certification database. This is likely 

related to the fact that its hybrid design includes limited fuel economy targeted features. Except 

for the Chevrolet Silverado, the onroad fuel economy for each fleet never exceeded either the 

city or highway fuel economy label. This indicates that regardless of whether the vehicles were 

driven predominantly in city or highway driving modes, other real world factors reduced onroad 

fuel economy beyond that captured in the FTP and HFET and the current 10% and 22% 

adjustment factors. 

10 
Table II.A-2 also presents combined fuel economy values using the final 5-cycle and 

mpg-based formulae for those vehicles for which we have 5-cycle fuel economy data. The final 

combined 5-cycle label values exceed onroad fuel economy for two out of eight models, while 

the final mpg-based values do so for four out of eight models. The average of the differences is 

very small in both cases. On average, the combined 5-cycle value is 3% lower than those 

measured onroad. However, as mentioned above, the specific vehicles in our 5-cycle database 

tend to be worse case. For example, the current official label values exceed those shown in 

Table II.A-2 by 3%. If we increased the combined 5-cycle values commensurately, they would 

match the onroad values on average. Thus, while both of the final approaches do a much more 

reasonable job at predicting the onroad fuel economy achieved in the DOE FreedomCar program 

than the current label formulae, the final 5-cycle formulae appear to be particularly accurate 

when compared to the FreedomCar experience. 

The close match between the final 5-cycle formulae and the FreedomCar experience is 

somewhat fortuitous, as the climate where the vehicles were primarily driven is not typical of 

most of the U.S. The FreedomCar program focuses on the southwest U.S. There, air 

conditioning use is much higher than average, while cold temperature operation is much lower 

than for the U.S. on average. While both factors reduce fuel economy, they do so to different 

extents. Colder temperatures have a much larger impact on national average, 5-cycle fuel 

economy than air conditioning. In projecting 5-cycle fuel economy values for individual 

vehicles, we have had to estimate the impact of heater-defroster operation on fuel economy 

during the cold FTP. For conventional vehicles, the effect is likely very small (i.e., less than 

2%). However, for a couple of hybrids tested, the effect was much larger. The impact of heaterdefroster operation is likely to vary significantly across individual hybrids, but without data, we 

cannot anticipate this variability. Overall, basing this impact on the two hybrids tested reduced 

the combined 5-cycle fuel economy of the hybrids in our certification database by 3%. Clearly 

this change is not relevant in areas like Phoenix. This simply indicates the limitations involved 

in very direct comparisons of vehicle test programs and 5-cycle fuel economy estimates. 

Overall, the fact that both the final mpg-based equations and 5-cycle formulae yield fuel 

economy label values quite close to the FreedomCar findings is very encouraging and the best 

that one could hope for without fine-tuning the 5-cycle formulae to exactly match the driving 

activity and conditions of the FreedomCar vehicles. 

When analyzing monthly reported fuel economy, large seasonal fluctuations in fuel 

economy were observed on most of the hybrid fleets. The seasonal fluctuations are especially 

noticeable on the fleets that had been in service for over one year. The fuel economy during the 

hot and often humid summer weather months when heavy air conditioning usage could be 

expected was as much as 15 mpg lower than observed fuel economy during mild Phoenix area 

winter months. Fuel economy over the SC03 air conditioning test for the three hybrids with the 

highest rated fuel economy shown in Table II.A-2 (Prius, Insight and Civic) tends to be 15-20 

mpg lower than that over the FTP. No cold weather operation similar to northern states or the 

Cold FTP (20°F) was reported which would likely have resulted in further shortfalls. 

11 
3. Strategic Visions New Vehicle Survey 

Strategic Visions surveys roughly 100,000 purchasers of new cars and light trucks each 

year. The survey recipients are selected randomly from among the purchasers of each vehicle 

model and therefore represent all regions of the country. Some models are more heavily 

surveyed than others, particularly models which have just been introduced or significantly 

redesigned. Therefore, the results should be assessed on a model by model basis and not 

averaged across models before averaged within each model. 

The survey asks recipients to write down the fuel economy which they are currently 

achieving. About half of the recipients respond to the request for fuel economy information. 

Thus, about 50,000 estimates of onroad fuel economy are received each year. The strengths of 

this survey are the large number of estimates and the fact that the survey recipients are randomly 

selected. The weaknesses are the unknown source of each consumer’s fuel economy estimate 

and the survey response rate for this question (i.e., only 50%). Still the fact that a wide range of 

models are surveyed with each model having a number of independent estimates allows very 

direct comparison to the current, 5-cycle and mpg-based label values on a model by model basis. 

EPA purchased the Strategic Visions survey results for the 2004-06 model years. In 

preparing the data for analysis, we noticed a peculiarity. The frequency of consumers’ estimated 

fuel economy for fuel economy values being a multiple of five were much higher than those with 

other values. Figure II-1 shows the distribution of estimated city fuel economy for 2006 model 

year vehicles. 

Figure II-1. Distribution of Onroad Fuel Economy Estimates – Strategic Vision, 2006 

0% 

2% 

4% 

6% 

8% 

10% 

12% 

0 10 20 30 40 50 

City FE (MPG) 

Arrows indicate 

multiples of 5 

As can be seen from Figure II-1, each of the frequencies of fuel economy values ending 

in 5 or 0 is much higher than those of nearby values. By comparing the difference between the 

12 
frequencies of fuel economy values of a multiple of five with those just above and below that 

value, we estimate that there is an excess response of 13% for fuel economy values of a multiple 

of five compared to other values. This implies that 13% of the respondents were only estimating 

their onroad fuel economy to within ±5 mpg. We found the same effect with highway fuel 

economy estimates. Thus, it is very likely that the same respondents rounded both their city and 

highway fuel economy estimates to a factor of five. 

It is unknown whether this tendency biases the estimated onroad fuel economy upward or 

downward. However, the presence of such rough estimates significantly reduces the value of 

this database to distinguish between two sets of fuel economy estimates. This is particularly true 

for comparisons between the mpg-based and 5-cycle formulae, which often only differ by 1-2 

mpg. We are working with Strategic Visions to better identify the method used by respondents 

to estimate their onroad fuel economy so that we can focus on those who actually kept mileage 

and fuel usage records. This should also avoid those respondents who estimate, versus measure, 

their actual fuel economy. We plan to analyze this improved data over the next couple of years. 

4. Kansas City Instrumented Vehicle Study 

During 2004–2005, EPA in association with the Coordinating Research Council, DOT 

and DOE, recruited and tested over 600 privately owned passenger vehicles in the Kansas City 

area. The vehicles included an assortment of compact cars, mid-size cars, pick-ups and SUVs 

from a variety of manufacturers. The program was split into 3 rounds (1, 1.5 and 2), each 

consisting of 120-300 vehicles. In all three rounds, vehicles were recruited randomly from lists 

of vehicle registrations in the Kansas City area. Care was taken to ensure that the sample were 

random with respect to the geographic location of the owner and socio-economic status. In 

rounds 1 and 2, the desired sample of vehicles was stratified into four groups of model years, 

with emphasis on older vehicles1,2. The primary purpose of Rounds 1 and 2 was the 

quantification of particulate emissions, particularly those from high emitters. In Round 1.5, only 

2001 and later model year vehicles were sampled. (Details about the design and performance of 

Round 1.5 are described the study’s final report.3) The primary purpose of Round 1.5 was the 

measurement of onroad fuel economy from vehicles for which we could estimate 5-cycle fuel 

economy. This meant that we had to have fuel economy estimates over all five cycles for these 

vehicles (i.e., that the vehicle had to be certified to the Supplemental FTP standards). These 

standards began phasing in with the 2001 model year. 

Only a few of the vehicles tested in Rounds 1 and 2 were instrumented with a Portable 

Emissions Measurement System (PEMS) and tested in the hands of their owners. As these 

vehicles ranged in model year from 1968 to 2005, very few of the vehicles tested in Rounds 1 

and 2 had been certified to the Supplemental FTP standards. However, all of the vehicles tested 

in Round 1.5 were instrumented with PEMS and had their fuel economy measured while being 

driven in normal use by their owners. The round 1.5 vehicle fleet consisted of approximately 

120 vehicles, including over 30 hybrid electric vehicles. The PEMS measures driving activity, as 

well as second-by-second mass emissions of CO2, CO, HC, and NOX for roughly 24 hours while 

the owners of the vehicles are utilizing their vehicles on the road under normal, real-world 

conditions. 

13 
Total fuel consumption for each vehicle was determined from the carbon balance of the 

CO2, HC, and CO emissions. (Details of the test program and the methods used to process the 

data obtained are described in Appendix A of this Final Technical Support Document.) Each 

vehicle’s fuel was sampled and tested for density and weight percent carbon. The total distance 

of driving was determined by summing vehicle speed and multiplying by total time of operation. 

This total distance traveled was then divided by total fuel consumption to determine onroad fuel 

economy. 

EPA city and highway label fuel economy values were obtained from EPA mileage 

guides. The test vehicles were matched to those tested in Kansas City to the closest degree 

possible. Figure II-2 compares the measured fuel economy to the 55/45 composite label fuel 

economy from Round 1.5 (newer vehicles). We segregated the vehicles into two groups: 

conventional gasoline-fueled vehicles and hybrids. A linear regression with no constant of the 

conventional vehicles showed nearly one-to-one correlation, with a slope of 1.006. The 

correlation was also quite good (r-squared value of 0.77). The largest difference was only 6 

mpg, or about 30%. Thus, the onroad fuel economy data indicate no offset from the current EPA 

label values on average. 

Figure II-2. Comparison Onroad to Current Label Economy: Kansas City 

The correlation of hybrid data shows much more scatter. This is partially explained by 

the fact that only three hybrid models were tested, a number of Toyota Prius and Honda Civic 

vehicles and one Honda Insight. The range of fuel economy label values for these three vehicles 

is very small, 48-56 mpg, plus one vehicle at 64 mpg. With the high degree of variability in 

measured onroad fuel economy, it is not surprising that the correlation coefficient was small. 

On average, hybrid fuel economy was 11% less than the composite EPA label values. 

The average onroad fuel economy of the Toyota Prius vehicles was closer to their composite 

label values than those for the two Honda models. On average, the onroad fuel economy of the 

14 
hybrids tested varied more than the conventional vehicles. This could be due to hybrids’ greater 

sensitivity to operating conditions which can either take full advantage of the hybrid technology 

or essentially nullify it. The fact that many vehicles started out testing with a hot start likely 

biased onroad fuel economy upwards to some degree. Thus, the actual shortfalls found would 

have been greater to some degree if testing had begun with a cold start. 

We also performed a regression of onroad fuel consumption per mile versus the inverse 

of the current fuel economy value for hybrid vehicles, as Honda suggested in their comments 

(see Section 5.5 of the Response to Comments document). First, we found that the intercept was 

not statistically significant (p-value of 0.684). Thus, we performed a new regression with an 

intercept of zero. We found an r-squared value of 0.18, which is not much different than that for 

the regression of fuel economy. The slope of the regression was 1.135, indicating that the 

hybrids consumed 13.5% more fuel than predicted by the inverse of their label values. More 

importantly, this slope had a p-value of 10-31

, indicating that it was extremely unlikely to be zero. 

The 95% confidence interval for the slope ranged from 1.09 to 1.18. Thus, on average, the data 

collected in Kansas City indicate that the hybrid vehicles tested did not perform as well as the 

conventional vehicles compared to their current fuel economy label values. 

B. Fuel Economy Estimates by Independent Organizations 

Several consumer organizations perform their own fuel economy assessments. Of these, 

the American Automobile Association (AAA) and Consumer Reports (CR) have tested the 

greatest number of vehicles. The relative strengths of this testing include the fact that the 

vehicles are tested on actual roads, usually in traffic and under real environmental conditions. 

The primary weaknesses of this testing include: 

1) The fact that the drivers or driving patterns involved are not typically published, so 

they may or may not be representative of average U.S. drivers or driving, 

2) Vehicles are tested throughout the year, so some vehicles are tested in hot weather and 

other in cold weather and some under moderate conditions, and 

3) In some cases, the actual test procedures used to measure the volume of fuel consumed 

during the test are not described, leaving some doubt as to their accuracy. Still, because of the 

public interest in these estimates, we believed that they should be considered here. We will 

begin with an analysis of the Consumer Reports estimates, followed by those of Edmund’s and 

AAA. 

1. Consumer Reports Estimates of Onroad Fuel Economy 

Consumer Reports published their fuel economy estimates for 303 2000-2006 model year 

vehicles. They publish both EPA’s current city, highway and combined fuel economy estimates, 

as well as their own city, highway and combined fuel economy estimates. Therefore, we can 

compare EPA’s current label values to those of CR for all 303 vehicles. As the mpg-based 

formulae only require knowledge of fuel economy over the FTP and HFET, we can apply these 

formulae to the EPA city and highway fuel economy values presented by CR (after removing the 

current label adjustments of 10% and 22%) and calculate mpg-based fuel economy values for all 

303 vehicles. We were also able to match 70 of these vehicles with those in our 5-cycle fuel 

15 
economy database.c

 Thus, for these 70 vehicles, we were able to calculate 5-cycle fuel economy 

values. 

We made two sets of comparisons. One set included all 303 vehicles. The other set 

included only 70 vehicles. The results of the first comparison are shown in Table II.B-1. 

Table II.B-1. Consumer Reports and Current EPA and MPG-Based Fuel Economy: 

303 Vehicles 

Consumer Reports Current EPA Label MPG-Based 

MPG MPG Difference * MPG Difference 

City 14.2 20.4 -30% 18.0 -21% 

Highway 29.3 26.9 9% 24.7 19% 

Combined 20.7 22.9 -9% 21.2 -3% 

* Consumer Reports fuel economy compared to EPA label value. 

As can be seen, the CR city fuel economy values are well below both the current label or mpgbased label values (21% to 30%). The reverse is true for highway fuel economy. The CR 

estimate of combined fuel economy is 9% lower on average than the 55/45 composite of the 

current EPA city and highway label values. However, the CR estimate of combined fuel 

economy is only 3% lower on average than the 43/57 composite of the mpg-based city and 

highway fuel economy values. Thus, there is a much better match up between the composite 

mpg-based fuel economy and the CR combined fuel economy than with current label values. 

Table II.B-2 presents the same comparisons, except that it includes the 5-cycle estimates 

and only includes the 70 matched vehicles. 

Table II.B-2. CR and Current EPA, 5-Cycle and MPG-Based Fuel Economy: 70 

Vehicles 

 Consumer 

Reports 

Current EPA Label 5-Cycle MPG-Based 

MPG MPG Difference* MPG Difference MPG Difference 

City 14.3 20.4 -30% 18.0 -21% 17.8 -20% 

Highway 29.3 26.4 11% 24.3 21% 24.1 22% 

Combined 20.6 22.7 -9% 21.0 -2% 20.9 -2% 

* Consumer Reports fuel economy compared to EPA label value. 

As can be seen, the comparisons between the CR, current EPA and mpg-based fuel economies 

are very similar to those in Table II.B-1. On average across 70 vehicles, the CR combined fuel 

economy estimates differ from the current, mpg-based and 5-cycle combined fuel economy 

 In the Draft Technical Support Document , we identified 151 vehicles which were both tested by Consumer 

Reports and in our certification database. However, many of these matching vehicles were not from the same model 

year. 

16 

c
values 9%, 2%, and 2%. The standard deviations of the percentage differs for individual models 

provides an indication of the consistency in the offset, or in the ability of the various label 

approaches to predict relative fuel economy differences between models. Across 70 vehicles, the 

standard deviation of the percentage differences between the CR combined fuel economy 

estimates and the current, mpg-based and 5-cycle combined fuel economy values are 6.5%, 

6.6%, and 6.3%. These standard deviations are very similar. The 5-cycle label values provide 

slightly better estimates of relative vehicle fuel economy than the other two label approaches. As 

mentioned above, the CR test procedures do not include cold starts or air conditioning operation. 

As these are two important features of the 5-cycle formulae, much of the potential improvement 

associated with the 5-cycle approach is not reflected in CR’s fuel economy estimates. 

Of particular interest here are the fuel economy values for hybrid vehicles. For hybrids, 

the 5-cycle and mpg-based formulae often give different results. The 303 vehicles tested by 

Consumer Reports include six hybrid vehicles. We have 5-cycle fuel economy estimates for four 

of these vehicles, all except the 2001 Prius and 2000 Insight . A comparison of the various fuel 

economy estimates for the five hybrids values are shown in the Table II.B-3. To make the 

comparison between the three label approaches as equitable as possible, we show based the 

current label values on the FTP and HFET fuel economy values in the 5-cycle fuel economy 

database and not those shown in the CR report. 

17 
Table II.B-3. Comparison of Consumer Reports and EPA Fuel Economy Values for 

Hybrids 

Consumer 

Reports Current EPA Label 5-Cycle Fuel Economy 

MPG-Based Fuel 

Economy 

MPG MPG Difference MPG Difference MPG Difference 

City Fuel Economy 

Escape 22 32 -31% 27 -20% 23 -4% 

Accord 18 30 -39% 26 -29% 22 -18% 

Civic 26 47 -44% 38 -32% 34 -24% 

2005 Prius 35 57 -38% 45 -23% 45 -22% 

Average 25 41 -38% 34 -26% 31 -17% 

Highway Fuel Economy 

Escape 29 28 5% 25 14% 24 20% 

Accord 37 36 2% 33 12% 30 25% 

Civic 45 46 -2% 41 9% 39 15% 

2005 Prius 50 50 0% 45 12% 45 10% 

Average 40 40 1% 36 12% 35 17% 

Combined Fuel Economy 

Escape 26 30 -13% 26 -1% 24 10% 

Accord 25 32 -23% 29 -15% 26 -3% 

Civic 36 46 -22% 40 -10% 37 -2% 

2005 Prius 44 54 -18% 45 -2% 45 -2% 

Average 33 41 -19% 33 -7% 0 1% 

A lot of information is presented in Table II.B-3. We will focus first on the results for all 

five hybrids averaged together, indicated in bold in the table, starting with the city, then the 

highway, then the combined fuel economy estimates. 

Starting with the city values, the CR city fuel economy estimates average 38% less than 

the current EPA city label values. This is greater than for the average vehicle, where the 

difference was 30%. The differences are smaller for the mpg-based and 5-cycle city values (26% 

and 17%, respectively). While the 26% difference for the mpg-based approach is greater than 

that for the average vehicle (21%), the 17% difference for the 5-cycle approach is less than that 

for the average vehicle (20%). This indicates that the 5-cycle formula for city driving is likely 

reflecting factors which are included in CR’s city test protocol and which are not included in the 

FTP, nor a constant 10% adjustment factor. In contrast, the current and mpg-based label 

approaches do not. While the mpg-based formula for city driving produces fuel economy 

estimates more closely resembling those of CR than the current label values, the mpg-based city 

18 
formula does not pick up factors which are apparently unique to hybrids which are included in 

CR’s test procedure. 

With respect to highway fuel economy, as described earlier for conventional vehicles, the 

CR highway fuel economy estimates tend to be much higher than all the label approaches (as 

opposed to the CR city fuel economy estimates, which tend to be much lower than all the label 

approaches). However, except for this fundamental shift, the relative performance of the three 

label approaches with respect to CR highway fuel economy estimates is the same for city and 

highway fuel economy. The current and mpg-based approaches predict greater relative fuel 

economy benefits for hybrids that the CR highway testing is not finding. In contrast, the benefits 

of hybrid technology on highway fuel economy as indicated by the 5-cycle formulae are 

reflected in the CR testing. 

Again, the story is similar for the combined label values. The current and mpg-based 

label approaches average 27% higher than CR’s combined values for the five hybrids. This is 

more than twice the difference for the average vehicle, where the difference was only 11%. 

Again, this difference in combined values indicates that the current EPA label formulae are 

granting some relative benefits to hybrid vehicles which are not reflected in CR’s combined test 

protocol. The 5-cycle combined values average 5% higher than the CR combined values, which 

is only 2% more than the 3% difference found for 70 vehicles. This indicates that the 5-cycle 

formula on a combined driving basis is only granting a very slight relative benefit to hybrid 

vehicles compared to CR’s combined test protocol. In contrast, the mpg-based equations appear 

to be granting hybrids a greater relative benefit compared to the CR test protocols. The mpgbased combined fuel economy averages 8% higher than the CR values for the five hybrids, while 

only 2% for the 70 and 303 vehicle fleets. 

Fourth, moving to the comparison of combined fuel economy for individual hybrid 

vehicles, the differences between the three “EPA” estimates and the CR estimates tend to be 

consistent in percentage terms, with the exception of the Escape and to some extent, the Accord. 

The differences for these two hybrids are not surprising. The other hybrids have very high fuel 

economy values compared to conventional vehicles. Thus, they essentially “set” the mpg-based 

equations for their range of fuel economy. The fuel economies of the Escape and the Accord fall 

within the range of conventional vehicle fuel economy. Here, the mpg-based equations are “set” 

by the more numerous conventional vehicle data. 

Overall, fuel economy estimates based on the 5-cycle and mpg-based formulae both 

match the CR test results more closely than the current label values for conventional vehicles, 

which dominate the 70 and 303 vehicle samples. In addition, hybrid fuel economy estimates 

based on the 5-cycle formulae more closely match those of CR compared to either the current 

label or mpg-based formulae. The CR estimates do not necessarily match those of the average 

driver. Their driving cycles, in particular, are only generally described. Still, they represent an 

ostensibly consistent set of estimates. The CR test procedures find a lower benefit to hybrid 

technology than the FTP and HFET indicate. The 5-cycle formulae perform similarly, though to 

a slightly less extent (i.e., 2%). 

19 
Of additional interest is whether the performance of hybrids is due to their relatively high 

fuel economy or to their hybrid technology. To shed some light on this question, we compared 

the mpg-based label values to the Consumer Reports fuel economy values for all conventional 

vehicles, the top ten percentile (in terms of combined current label values) of all conventional 

vehicles and hybrids. As described above in Table II.B-1, the mpg-based combined label values 

averaged 2% higher than the combined fuel economy measured by Consumer Reports. The 

mpg-based combined label values for the top 10 percentile of conventional vehicles matched the 

combined fuel economy measured by Consumer Reports, those performing better than 

conventional vehicles as a whole, at least in terms of Consumer Reports fuel economy 

measurements. However, as shown in Table II.B-3 above, the mpg-based combined label values 

for hybrids averaged 8% higher than the combined fuel economy measured by Consumer 

Reports. Thus, hybrids performed worse in the Consumer Reports testing than the average 

conventional vehicles and worse to a slighter greater degree than those conventional vehicles 

with the highest fuel economy. Thus, based on the Consumer Reports testing, the differential 

performance of hybrids appears to be more related to their hybrid technology than to their 

relatively high fuel economy. The same relationships hold for the 5-cycle fuel economy values. 

2. AAA Estimates of Onroad Fuel Economy 

The American Automobile Association (AAA) also develops its own fuel economy 

estimates. In their 2004 report, AAA presented their test results and the EPA label values for 

163 models. AAA only presents a single fuel economy estimate, which we understand to be a 

composite of city and highway operation. 

Overall, AAA found a higher overall fuel economy than the current combined EPA label 

value for 85 models, and lower fuel economy for 73 models. On average, the AAA fuel 

economy estimates were 1.5% lower than the current combined EPA label values. We 

calculated an mpg-based 43/57 combined fuel economy using the mpg-based equations. (FTP 

and HFET fuel economy values were back calculated from the current EPA fuel economy label 

values.) On average, the AAA fuel economy estimates were 5.3% higher than the mpg-based 

combined fuel economy values. Table II.B-4 shows these comparisons. 

Table II.B-4. AAA and Current EPA, 5-Cycle and MPG-Based Fuel Economy Estimates 

AAA Current 

EPA 

Label 

5­

Cycle 

MPGBased 

MPG MPG Difference* MPG Difference MPG Difference 

163 vehicles 21.7 22.1 -1.5% N/A N/A 20.6 5.3% 

61 vehicles 23.2 23.4 -0.4% 21.7 6.7% 21.9 6.1% 

We were able to match 61 out of the 163 AAA-tested vehicles with similar vehicles in 

our 5-cycle certification database. This is lower than the 98 models which we matched in the 

analysis described in the Draft Technical Support Document . The lower figure is due to the use 

of a more stringent criterion that the vehicles match in terms of model year. (We assumed that 

all of the vehicles tested by AAA were 2004 model year vehicles.) As was the case with the 

Consumer Reports data, the FTP and HFET fuel economies for the certification vehicles in our 

20 
5-cycle fuel economy database were generally lower than those tested by AAA. Thus, we 

adjusted the 5-cycle city and highway fuel economy values using the ratio of the FTP and HFET 

fuel economy values, respectively, from our certification database and the AAA database. On 

average, the AAA estimates were 6.1% higher than the combined, 5-cycle fuel economy values 

for these 61 vehicles. On average, the AAA estimates were 6.7% higher than the combined 

mpg-based fuel economy values for these 61 vehicles, just slightly greater than with the 5-cycle 

formulae. While not shown in the table, the standard deviation of the percentage differences is 

9% for all three EPA label approaches. Thus, none of the three EPA label approaches stands out 

with respect to their ability to predict relative onroad fuel economy as measured by AAA. 

The AAA fuel economy values include two hybrids, a Prius and an Insight. Based on the 

official EPA label values, the AAA estimates were 6.6% lower than the current EPA composite 

fuel economy values for these two vehicles, 5% lower than for the average vehicle. Thus, the 

current label adjustments are indicating greater fuel economy improvement due to hybrid 

technology than AAA is finding during their testing. 

In contrast, the AAA estimates average 10.6% and 12.0% higher than the mpg-based and 

5-cycle combined fuel economy values for these two vehicles, respectively. These differences 

are about 6% and 4% greater difference than for the average vehicle, respectively. Thus, the 5­

cycle and mpg-based formulae are indicating less fuel economy improvement due to hybrid 

technology than AAA is finding during their testing. Thus, the AAA testing is finding hybridrelated benefits somewhere in between those indicated by the current label formulae and those 

indicated by the mpg-based and 5-cycle formulae. This is in contrast to the Consumer Reports 

estimates, where their testing indicated hybrid benefits more consistent with those indicated by 

the 5-cycle formulae. 

The consistency between the mpg-based equations and 5-cycle formulae with respect to 

the AAA hybrid testing is due to the fact that AAA only tested the two hybrids with the highest 

fuel economy label values. In this range of fuel economy, both the 5-cycle formulae and the 

mpg-based equations predict very similar values for hybrids. Consumer Reports, on the other 

hand, tested four hybrids, two of which have much lower fuel economy values. In this range, the 

mpg-based equations are dominated by conventional vehicles and the 5-cycle values for hybrids 

tend to fall below the mpg-based lines. 

The main reason for the difference, however, is that AAA found much higher fuel 

economy values for the two hybrids which both organizations tested than Consumer Reports. 

Consumer Reports found that a 2000 Insight and 2004 Prius achieved combined fuel economy 

values of 51 mpg and 41 mpg, respectively. AAA found that a 2004 Insight and 2004 Prius 

achieved combined fuel economy values of 58 mpg and 52 mpg, respectively. In terms of fuel 

consumption, AAA found 17% less fuel use for these two hybrids than that found by Consumer 

Reports. We developed an analogous estimate for conventional vehicles by comparing the 

difference in fuel economy found by each organization for all the vehicles tested relative to the 

current and mpg-based label formulae. For conventional vehicles, in terms of fuel consumption, 

AAA found 8% less fuel use for these two hybrids than that found by Consumer Reports (. 

Thus, the different test procedures used by the two organizations are finding much different 

benefits of hybrid technology relative to conventional vehicles. This reinforces the observation 

21 
that the capability of hybrid technology is sensitive to how and where the vehicles are operated 

(e.g., colder temperatures, trip length, driving pattern, etc.). The comparison of the fuel economy 

values predicted by the various label approaches to the onroad fuel economy estimated by 

Consumer Reports, AAA and other organizations is investigated further in Section II.D. below. 

As part of their comments on the rule, AAA provided onroad and dynamometer fuel 

economy estimates for 42 additional vehicles. The average fuel economy recorded by the 

owners was 25.6 mpg, while the average of the current combined EPA label values was 29.6 

mpg, Thus, this additional data indicates a 15% shortfall in onroad fuel economy relative to the 

current EPA label values. In addition, AAA tested 17 of these vehicles over the FTP, HFET, a 

hot start US06 and a US06 test with a cold start. On average, current EPA label values based the 

AAA FTP and HFET testing was very consistent with those implied by the vehicle labels. The 

average of the vehicles’ combined city/highway label values (26.9 mpg) and those based on the 

AAA testing (26.8 mpg) differed by only 0.1 mpg. The average onroad fuel economy of these 

vehicles as estimated by the owners was 23.5 mpg. This indicated an onroad fuel economy 

shortfall of 12%, or slightly smaller than that for the complete set of 41 vehicles. The average 

cold start and hot start US06 fuel economy values for these vehicles were 23.1 and 25.0 mpg, 

respectively, bracketing the owners’ experience. In contrast, the owner’s fuel economy was 

lower than either the current city or highway label values for these 17 vehicles. AAA 

commented that the US06 test appeared to be a better predictor of onroad fuel economy than 

either the current city or highway label values and encouraged EPA to move forward with its 

proposed 5-cycle formulae, which included fuel economy measured over the US06 test. 

3. Edmunds 

The on-line car journal Edmunds.com measures fuel economy on new cars they evaluate 

for reviews. Edmunds reviews and road tests cars in a variety of ways, but the most relevant data 

come from their “long-term tests.” For these tests they purchase or lease vehicles directly from 

dealers and keep the vehicle for 1-2 years, generally accumulating as much as 30,000 miles of 

experience with the vehicles from several different reviewers. Edmunds reports the best, worst, 

and average fuel economy achieved during their long-term use of the vehicle. We reviewed 

Edmunds data from 40 model year 2003-2006 long-term test vehicles and compared their 

average to the EPA “combined” city/highway fuel economy value. On average, the Edmunds 

reviewers achieved fuel economy about 14% lower than the current EPA combined label value. 

Hybrid vehicles performed even more poorly; the four included in the recent Edmunds long-term 

tests on average fell 24% below the current EPA combined label value. The data from Edmunds 

and current EPA City, Highway, and Combined label values are shown in Table II.B-5 below. 

As can be seen in this table, the average fuel economy achieved by Edmunds reviewers is 

frequently lower than the current EPA City estimate – in fact this occurs in more than half of the 

vehicles. And only in a minority of cases (8) does the best achieved by Edmunds exceed the 

EPA highway estimate, supporting the belief of many that the current highway fuel economy 

label value is a near best-case estimate. 

22 
Table II.B-5. 2003-2006 Edmunds Long-term Test Vehicles 

Edmunds.com (mpg) Current EPA Label (mpg) 

MY MFR Model Best Worst Avg. City Hwy Combined 

2006 Honda Ridgeline 16.1 14.3 14.9 16.0 21.0 17.9 

2006 Lexus RX 400h 27.9 22.6 25.4 31.0 27.0 29.1 

2006 Mitsubishi Eclipse GT 19.2 14.6 16.5 18.0 27.0 21.2 

2006 VW Jetta 21.0 15.0 17.7 22.0 30.0 25.0 

2005 Audi A4 2.0T 27.7 16.2 22.3 22.0 30.0 25.0 

2005 BMW X3 21.6 16.9 17.2 16.0 23.0 18.5 

2005 Chevrolet Cobalt 27.3 20.2 24.9 24.0 32.0 27.0 

2005 Dodge Magnum 24.4 11.8 17.3 17.0 25.0 19.9 

2005 Ford Escape Hybrid 29.9 19.0 23.0 36.0 31.0 33.6 

2005 Ford GT 17.5 14.8 16.0 13.0 21.0 15.7 

2005 Ford Mustang GT 17.8 13.0 16.5 17.0 25.0 19.9 

2005 Honda Accord Hybrid 35.0 14.9 23.4 29.0 37.0 32.1 

2005 Honda Odyssey 22.7 15.3 18.5 20.0 28.0 23.0 

2005 Kia Spectra 28.9 17.5 23.6 25.0 34.0 28.4 

2005 Landrover LR3 27.6 12.6 16.3 14.0 18.0 15.6 

2005 Nissan Frontier 4x4 Nismo 18.2 12.7 14.6 15.0 20.0 16.9 

2005 Scion tC 31.6 14.7 21.6 22.0 29.0 24.7 

2005 Subaru Legacy GT 25.2 15.4 20.3 19.0 25.0 21.3 

2005 Toyota Solara 21.4 14.3 18.3 20.0 29.0 23.2 

2005 Volvo S40 25.3 19.6 22.4 20.0 27.0 22.6 

2004 Acura TL 25.2 18.1 22.8 20.0 28.0 23.0 

2004 Chevrolet Malibu 30.8 16.8 22.6 23.0 32.0 26.3 

2004 Chrysler Pacifica 20.7 9.9 15.8 17.0 22.0 18.9 

2004 Ford F-150 17.7 9.9 13.3 14.0 18.0 15.6 

2004 GMC Canyon 21.1 13.7 17.3 18.0 23.0 20.0 

2004 Mazda RX-8 22.5 12.0 17.5 18.0 24.0 20.3 

2004 Mitsubishi Endeavor 25.0 9.0 16.9 17.0 21.0 18.6 

2004 Nissan Quest 22.9 11.7 17.8 19.0 26.0 21.6 

2004 Nissan Titan 15.7 10.7 13.4 14.0 18.0 15.6 

2004 Toyota Prius 45.2 31.4 41.0 60.0 51.0 55.6 

2004 Toyota Sienna 19.1 12.5 15.7 19.0 27.0 21.9 

2004 Volvo XC90 19.6 15.1 17.7 15.0 20.0 16.9 

2003 Honda Accord 30.0 14.5 24.1 24.0 33.0 27.4 

2003 Honda Pilot EX 25.6 12.6 18.3 17.0 22.0 18.9 

2003 Infiniti G35 Coupe 23.2 13.1 15.4 20.0 27.0 22.6 

2003 Lexus SC 430 18.3 15.4 15.9 18.0 23.0 20.0 

2003 Mazda Mazda6 26.4 14.6 21.5 20.0 27.0 22.6 

2003 Mitsubishi Outlander 26.8 13.8 19.5 20.0 25.0 22.0 

2003 Nissan 350Z 25.7 13.1 19.0 20.0 26.0 22.3 

2003 Subaru Forester 27.7 15.0 21.6 21.0 26.0 23.0 

Table II.B-6 illustrates the differences between the Edmunds average values and the 

current EPA label, the MPG-specific values, and, for the hybrids, the 5-cycle values. 

23 
Table II.B-6. Edmunds Long-term Test Vehicles Compared to EPA Combined MPG 

Estimates 

Edmunds 

EPA Combined Label 

(mpg) 

Difference: Edmunds 

Vs. EPA (%) 

MY MFR Model Avg. Current 

MPGBased 5-

Cycle Current 

MPGBased 5-

Cycle 

2006 

2006 

2006 

2006 

2005 

2005 

2005 

2005 

2005 

2005 

2005 

2005 

2005 

2005 

2005 

2005 

2005 

2005 

2005 

2005 

2004 

2004 

2004 

2004 

2004 

2004 

2004 

2004 

2004 

2004 

2004 

2004 

2003 

2003 

2003 

2003 

2003 

2003 

2003 

2003 

Honda 

Lexus 

Mitsubishi 

VW 

Audi 

BMW 

Chevrolet 

Dodge 

Ford 

Ford 

Ford 

Honda 

Honda 

Kia 

Landrover 

Nissan 

Scion 

Subaru 

Toyota 

Volvo 

Acura 

Chevrolet 

Chrysler 

Ford 

GMC 

Mazda 

Mitsubishi 

Nissan 

Nissan 

Toyota 

Toyota 

Volvo 

Honda 

Honda 

Infiniti 

Lexus 

Mazda 

Mitsubishi 

Nissan 

Subaru 

Ridgeline 

RX 400h 

Eclipse GT 

Jetta 

A4 2.0T 

X3 

Cobalt 

Magnum 

Escape Hybrid 

GT 

Mustang GT 

Accord Hybrid 

Odyssey 

Spectra 

LR3 

Frontier 4x4 Nismo 

tC 

Legacy GT 

Solara 

S40 

TL 

Malibu 

Pacifica 

F-150 

Canyon 

RX-8 

Endeavor 

Quest 

Titan 

Prius 

Sienna 

XC90 

Accord 

Pilot EX 

G35 Coupe 

SC 430 

Mazda6 

Outlander 

350Z 

Forester 

15 

25 

17 

18 

22 

17 

25 

17 

23 

16 

17 

23 

19 

24 

16 

15 

22 

20 

18 

22 

23 

23 

16 

13 

17 

18 

17 

18 

13 

41 

16 

18 

24 

18 

15 

16 

22 

20 

19 

22 

18 16 

29 25 26 

21 20 20 

25 23 24 

25 23 

19 17 

27 25 25 

20 18 

34 29 27 

16 15 15 

20 18 19 

32 29 26 

23 21 20 

28 26 26 

16 14 

17 16 15 

25 22 24 

21 19 19 

23 21 

23 21 22 

23 21 23 

26 24 24 

19 17 

16 14 13 

20 18 

20 19 19 

19 17 17 

22 20 19 

16 14 

56 46 47 

22 20 20 

17 16 16 

27 25 26 

19 18 18 

23 21 22 

20 19 19 

23 21 22 

22 20 

22 21 22 

23 21 

-17% -9% 

-13% 1% -1% 

-22% -16% -19% 

-29% -22% -28% 

-11% -2% 

-7% 0% 

-8% 1% -2% 

-13% -6% 

-31% -20% -15% 

2% 8% 4% 

-17% -10% -12% 

-27% -19% -10% 

-19% -12% -9% 

-17% -9% -8% 

5% 14% 

-14% -6% -6% 

-12% -4% -8% 

-5% 4% 7% 

-21% -15% 

-1% 8% 1% 

-1% 8% 0% 

-14% -6% -6% 

-17% -9% 

-15% -7% 1% 

-13% -5% 

-14% -6% -7% 

-9% 0% -3% 

-18% -10% -9% 

-14% -6% 

-26% -12% -12% 

-28% -22% -22% 

5% 14% 7% 

-12% -4% -7% 

-3% 3% 4% 

-32% -27% -29% 

-21% -15% -18% 

-5% 3% -2% 

-11% -5% 

-15% -8% -13% 

-6% 1% 

Average 19 23 21 -14% -6% -8% 

24 
As can be seen from Table II.B-6, both the mpg-based and 5-cycle formulae still produce 

fuel economy label values higher than those developed by Edmunds. However, the differences 

are smaller than with the current label formulae. The difference for the mpg-based equations is 

8%, while that for the 5-cycle formulae is 7% (the Edmunds estimates being the lower of the two 

in both cases). While not shown, the standard deviation of the percentage difference is 12% for 

the mpg-based equations and 9% for the 5-cycle formulae. Thus, the relative differences in 

vehicles’ fuel economy are slightly better indicated by the 5-cycle formulae. 

The vehicles which Edmunds tested include four hybrids. The Edmunds fuel economy 

measurements are on average 24%, 13%, and 9% lower than the current, mpg-based and 5-cycle 

fuel economy label values, respectively. These percentage differences are 5-6% higher than 

those for the 40 vehicles tested on average for the current and mpg-based labels, but only 2% 

higher than that for the 5-cycle approach. Thus, the current and mpg-based approaches are 

indicating hybrid-related benefits which the Edmunds testing is not confirming. However, the 

relative benefit of hybrid technology as indicated by Edmunds testing and the 5-cycle approach 

are very similar. In this sense, the Edmunds testing is more consistent with that of Consumer 

Reports, versus AAA. 

In contrast, the current, mpg-based and 5-cycle fuel economy label values for the 10% 

conventional vehicles with the highest fuel economy average 18%, 8%, and 8%, respectively. 

These latter differences are very similar to those for the average conventional vehicle. The same 

findings hold for the 20% of conventional vehicles with the highest fuel economy. Thus, the 

Edmunds testing is finding something about hybrids which is not occurring with either typical or 

high fuel economy conventional vehicles. 

C. Fleet-wide Estimates of Onroad Fuel Economy 

We begin with a comparison of the 5-cycle fuel economy values with the fleetwide fuel 

economy estimates developed by FHWA. Because we do not have fuel economy data for all 

vehicles over all 5 dynamometer cycles, and therefore cannot develop a 5-cycle fuel economy 

estimate for the current onroad fleet directly, this comparison requires a two step process. 

The first step in this process compares fleetwide fuel economy estimates based on EPA’s 

current fuel economy labels to the FHWA estimate of onroad fuel economy. The second step in 

this process is to compare combined city-highway fuel economy using the 5-cycle formulae to 

that using the current EPA city and highway label procedures. This comparison is performed for 

vehicles for which we have 5-cycle fuel economy data. We will assume that this relationship 

also applies to those vehicles for which we do not have 5-cycle data. 

In the NPRM, we added a third step which evaluated changes in FTP and HFET test 

procedures which accompanied the implementation of the US06 and SC03 testing requirements. 

We estimated that these changes had a positive impact on the fuel economy values measured 

during these tests. However, as discussed in the Response to Comments document, we now 

believe that these changes had a neutral impact on fuel economy. Thus, this third step is no 

longer needed. 

25 
Overall, the difference between 5-cycle fuel economy and FHWA onroad fuel economy 

is the combination of the percentage differences from the two comparisons: 

1) Current EPA label fuel economy to FHWA onroad fuel economy, and 

2) 5-cycle fuel economy to current EPA label fuel economy (without 10% road load 

adjustment). 

FHWA publishes fleet-wide estimates of onroad fuel economy for cars and light trucks in 

their annual Highway Statistics publication.4 We will focus on the combined estimates for cars 

and light trucks here, since various states use different criteria to distinguish between the two 

vehicle classes. At the same time, the criteria used to distinguish between cars plus light trucks 

and other vehicles are very consistent. 

Table II.C-1 presents the FHWA estimates of vehicle miles traveled (VMT), fuel 

consumption and onroad fuel economy for passenger cars and 4-tire, 2-wheel trucks. 

Table II.C-1. FHWA-Based Estimate of Onroad Fuel Economy 

Year 2003 2004 

VMT (million miles) 1,672,079 1,704,982 

Passenger cars Fuel Use (thousand gallons) 75,455 76,007 

MPG (mpg) 22.2 22.4 

VMT (million miles) 984,094 1,014,342 

2 axle 4 tire Trucks Fuel Use (thousand gallons) 60,758 62,626 

MPG (mpg) 16.2 16.2 

VMT (million miles) 908,712 936,643 

Light trucks Fuel Use (thousand gallons) 55,271 56,970 

MPG (mpg) 16.4 16.4 

Passenger cars and 

light trucks 

VMT (million miles) 2,580,791 2,641,625 

Fuel Use (thousand gallons) 130,726 132,976 

MPG (mpg) 19.7 19.9 

The FHWA category of 4-tire, 2-wheel trucks includes some vehicles which EPA classifies as 

heavy-duty vehicles. We have adjusted the FHWA estimates upward to provide a more direct 

comparison. This adjustment is based on a study performed by Oak Ridge National Laboratory 

(ORNL).5 ORNL estimated both VMT and fuel use for several categories of light trucks. Class 

1 and 2a trucks fall into EPA’s definition of light-duty trucks, while Class 2b trucks do not. 

Together, the three classes of trucks are approximately equivalent to FHWA’s 4-wheel, 2-axle 

truck class. The results of this study are shown in Table II.C-2. 

26 
Table II.C-2. Breakdown of VMT and Fuel Use by 4-Wheel, 2-Axle 

 VMT (billions) Fuel use (billion gallons) 

Class 1 672.7 37.4 

Class 2a 251.9 18 

Class 2b 76.7 5.5 

Total 1,001 60.9 

Class 1+2a 92.3% 91.0% 

As can be seen, ORNL estimated that 92.3% of the VMT and 91.0% of the fuel use by 4-wheel, 

2-axle trucks was by vehicles falling into EPA’s light-duty truck category (and which are labeled 

for fuel economy). Therefore, we adjusted FHWA’s VMT and fuel use estimates for 4-wheel, 2­

axle trucks by these percentages to convert them to values applicable to EPA’s light-duty truck 

class. These adjusted values and the resulting onroad fuel economy are shown in Table II.C-1. 

We then added the VMT and fuel use by passenger cars and EPA light trucks together and 

calculated an overall fuel economy for the two vehicle classes. These values are also shown at 

the bottom of Table II.C-1. The result is that the FHWA-based estimate of fleet-wide onroad 

fuel economy for cars and EPA light trucks is 16.4 mpg for 2003 and 2004. This is nearly 20% 

lower than the onroad fuel economy for light trucks presented in the NPRM analysis. The 

difference is due to the use of more recent figures from FHWA which considers fuel economy 

data indicating lower fuel economy from light trucks than previously estimated. 

We then used the EPA MOBILE6.2 in-use emission model to calculate fleet-wide 

average EPA combined fuel economy label values for these two years. MOBILE6.2 estimates 

fuel economy using a sales-weighted average of the combined EPA city/highway label values for 

each model year of cars and light trucks. MOBILE6.2 then estimates an average fuel economy 

for the onroad fleet by weighting the fuel economy values for each model year by the fraction of 

vehicles on the road from each model and their typical annual mileage (which decreases with 

age). Thus, MOBILE6.2 is an ideal tool for estimating the EPA label fuel economy using the 

current label formulae for the onroad vehicle fleet in any particular calendar year. 

For 2003, MOBILE6.2 estimates average passenger car and light truck fuel economy of 

24.0 mpg and 17.3 mpg, respectively. For 2004, MOBILE6.2 estimates average passenger car 

and light truck fuel economy of 24.0 mpg and 17.4 mpg, respectively. We weighted the fuel 

economy values for cars and light trucks together using their respective VMT from Table II.C-1. 

The result were overall average label fuel economy values of 21.1 mpg for 2003 and 21.2 mpg 

for 2004. Thus, for 2003 and 2004, the FHWA-based onroad fuel economy was 6.5% and 6.1% 

lower than the current combined EPA label value, respectively. Thus, the result of the first step 

in this process is an indication that the current labeling formulae could be over-estimating onroad 

fuel economy by 6-7%. 

Moving to the second step, in Tables III.E-1 and III.E-2 shown in Section III.E below, we 

present city and highway fuel economy label values using both current and 5-cycle formulae for 

615 2003-2006 model year vehicles. The FHWA estimates apply to all driving, both city and 

highway. Therefore, we are primarily interested in combined city-highway fuel economy values. 

Also, we are using FHWA estimates for the 2003 and 2004 calendar years, as these are the most 

recent available. The number of hybrid vehicles on the road was very low during this timeframe, 

27 
much lower than the 2% level present in our certification database. Therefore, we will only use 

the 5-cycle fuel economy estimates for the 601 non-hybrid vehicles in our database. There is no 

need to perform this comparison separately for the mpg-based formulae, since the average fuel 

economy from the 5-cycle and mpg-based formulae are identical for non-hybrid vehicles. 

The combined fuel economy using the current label formulae is a 55/45 harmonic 

weighting of the current city and highway fuel economy labels. The average combined fuel 

economy using the current EPA label values for these 601 vehicles is 20.9 mpg. For the final 5­

cycle formulae, combined fuel economy is a 43/57 harmonic weighting of the 5-cycle city and 

highway fuel economies. This city/highway split for the 5-cycle fuel economies is based on: 

1) the assumption that driving generally less than 45 mph is city driving and that above 

45 mph is highway driving, and 

2) the description of onroad driving patterns contained in MOVES. 

The mathematical formula for converting the 5-cycle city and highway fuel economy values into 

an estimate of average onroad fuel economy is as follows: 

1 Averageonroad fuel economy = ⎛ 0.43 ⎞ ⎛ 0.57 ⎞

⎜

⎜

5 − cycleCity FE ⎟

⎟ + ⎜⎜

5 − cycle Highway FE ⎟

⎟

⎝ ⎠ ⎝ ⎠

The average combined 5-cycle fuel economy using this formula for the 601 conventional 

vehicles is 19.6 mpg, which is 6.2% lower than that based on the current label values. This is the 

result of the second step in the process. 

Overall, then, the current label values over-estimate onroad fuel economy per FHWA 

(with some adjustments by EPA) by 6-6.5%, while the 5-cycle formulae decrease current label 

values (of the 2002-2003 fleet) by 6.2%. Thus, the final 5-cycle formulae should move the 

combined fuel economy label values to within a few tenths of a percent of a comparable estimate 

of fleetwide fuel economy using FHWA techniques. This should not be surprising, since the 

value of the factor in the 5-cycle formulae representing factors not represented in dynamometer 

tests was set to match onroad fuel economy as estimated by FHWA (see Section III.A.5 below). 

D. Overall Comparison of Hybrid Fuel Economy 

When comparing onroad fuel economy to EPA estimates, it is often appropriate to focus 

on the EPA combined fuel economy, as it is not possible to determine whether a particular 

vehicle’s driving was city-like or highway-like. Overall, the 5-cycle formulae predict a 

combined fuel economy about 6% less than the current combined fuel economy label for our 615 

vehicle certification fuel economy database. 

These relationships hold for the complete 615 vehicle database, as well as the 601 

conventional vehicles which dominate the database. However, the effect of the 5-cycle formulae 

on the combined fuel economy of hybrids is much more significant. Overall, the 5-cycle 

formulae predict a combined fuel economy about 18% less than the current combined fuel 

economy label for the 14 hybrids in our certification fuel economy database. Thus, the 5-cycle 

28 
formulae reduce hybrid fuel economy roughly 12% more than non-hybrids compared to today’s 

labels. Two thirds of this difference occurs in city fuel economy. It is primarily due to a greater 

impact of running fuel use at colder temperatures and inclusion of US06 city driving in the 5­

cycle formulae. This difference appears to be related more to hybrid technology itself, as 

opposed to just high fuel economy values. For example, the average impact of the 5-cycle 

formulae on the combined fuel economy of the top 10% of conventional vehicles in terms of 

current combined fuel economy is 8%. This is only 2% greater than that for all conventional 

vehicles and well below that for hybrids. 

Some care must be taken in relating to this difference between the impact of the 5-cycle 

formulae on conventional and hybrid vehicles. First and foremost, the impact of the 5-cycle 

formulae on label fuel economy involves a number of projections which may not be accurate for 

individual vehicles. For example, we currently lack measured fuel economy values over the 

US06 city and highway bags. Thus, these must be estimated. We are using one relationship for 

conventional vehicles and another relationship for hybrids. The former is based on the testing of 

over 100 vehicles, which shows fairly consistent results. The latter is based on the testing of two 

vehicles. The relative performance of other hybrid vehicles over the two bags of the US06 test 

could differ significantly from our current estimate. Similarly, we have included the effect of 

turning on the heater or defroster during the cold FTP, as this will be required in the future. 

However, the data currently available on fuel economy over the cold FTP do not include this 

factor. Our projected impact for conventional vehicles is relatively small. Thus, variability 

between vehicles is likely even smaller. However, the testing of two hybrids showed large 

impacts on Bag 1 fuel economy at 20°F. Other hybrids may show different impacts. 

Second, manufacturers may improve their hybrid designs in the future to reduce the 

impact of colder temperatures, air conditioning operation, etc. While we believe that 

manufacturers have always been concerned about fuel efficiency under realistic conditions, the 

fact that fuel economy labels have been based solely on operation at 75°F has inevitably focused 

their attention especially on this type of operation. Hybrid technology is still relatively new and 

improvements are constantly being made. With the 5-cycle formulae, we expect that these 

improvements will affect vehicle operation over a much wider set of in-use conditions than 

might have been the case with the current label formulae. 

Nonetheless, the question still arises: is this greater reduction in combined fuel economy 

of today’s hybrids appropriate? In this section, we compile all the onroad fuel economy 

measurements and consumer organization estimates in one place and compare them to combined 

fuel economy estimated using the current, 5-cycle and mpg-based formulae. 

Table II.D-1 compares three sets of onroad fuel economy measurements to both current 

and final EPA combined fuel economy label values. Combined fuel economy using the current 

label formulae weights city fuel economy 55% and highway fuel economy 45%. Combined fuel 

economy using the 5-cycle and mpg-based label formulae weighs city fuel economy by 43% and 

highway fuel economy by 57%. It should be noted that the values for current combined fuel 

economy are those from EPA’s certification database in order to be directly comparable to the 5­

cycle and mpg-based values. However, they are not the current official label values. The 

29 
official label values average about 3% higher due to differences between the worse case vehicles 

tested over the Supplemental FTP cycles and the average vehicle sold. 

Table II.D-1. Onroad Hybrid Fuel Economy Versus EPA Label Estimates (mpg) 

EPA Combined Fuel Economy Onroad Fuel Economy Measurements 

 Current MPG-Based 5-Cycle DOE 

FreedomCar 

YourMPG EPA Kansas 

City 

2001 Honda Insight 61 51 50 45 66 47 

2003 Honda Civic 46 40 37-38 38 46 40 

2004 Toyota Prius 54 45 45 45 48 50 

2004 Chevrolet Silverado 4wd 16 15 15 18 15 --- 

2005 Ford Escape 4wd 30 26 24 27 30 --- 

2005 Honda Accord 32 29 26 28 31 --- 

2005 Lexus RX400h 28 24 24 25 25 --- 

It is difficult to draw any definite conclusions from the above data due to its scatter. The 

onroad fuel economy estimates from the YourMPG database tend to be significantly higher than 

those from the DOE FreedomCar project. The Kansas City measurements tend to fall in 

between, except for the Prius. Label fuel economy values based on all three approaches can 

come close to one of the three onroad fuel economy estimates, even for the same vehicle. 

For example, the current EPA label formulae appear to significantly over-estimate fuel 

economy as measured in the FreedomCar program, except for the Chevrolet Silverado. This 

vehicle has the least hybrid capability with respect to improved fuel economy of the vehicles 

listed in Table II-D.1. The mpg-based and 5-cycle label formulae tend to perform equally well 

with respect to the DOE FreedomCar program 

In contrast, the current label formulae provide reasonable estimates of onroad fuel 

economy for five of the seven hybrids based on the YourMPG database. The mpg-based and 5­

cycle label formulae do so for four of the seven hybrids based on the YourMPG database. As 

would be expected, the sets of five and four hybrids tend not to overlap. 

Finally, the current label formulae over-estimate onroad fuel economy as measured in the 

Kansas City test program. The mpg-based and 5-cycle label formulae tend to provide reasonable 

estimates of onroad fuel economy as measured in Kansas City on average, over-estimating fuel 

economy for one of the three matched hybrids and under-estimating fuel economy for one 

hybrid. 

Table II.D-2 compares onroad fuel economy estimates by consumer organizations to EPA 

estimates. 

30 
Table II.D-2. Onroad Hybrid Fuel Economy Estimates Versus EPA Label Estimates 

(mpg) 

EPA Combined Fuel Economy Onroad Fuel Economy Estimates 

 Current MPG-Based 5-Cycle Consumer Reports Edmunds AAA 

2001 Honda Insight 61 51 50 51 --- 58 

2003 Honda Civic 46 40 37-38 36 --- --- 

2004 Toyota Prius 54 45 45 44 41 52 

2005 Ford Escape 4wd 30 26 24 26 23 --- 

2005 Honda Accord 32 29 26 25 23.4 --- 

2005 Lexus RX400h 28 24 24 --- 25.4 --- 

Again, there is significant scatter in the data. Of the three consumer organizations, 

Edmunds predicts the lowest fuel economy. The Edmunds fuel economy values are lower than 

all three sets of EPA fuel economy estimates. Since the 5-cycle formulae produce the lowest 

estimates on label fuel economy, the 5-cycle formulae come closest to matching the Edmunds’ 

fuel economy values. 

The 5-cycle fuel economy values match those of Consumer Reports very closely, 

differing by at most 2 mpg for any individual vehicle. The mpg-based estimates are higher for 

three of the five matching hybrids. The current label values exceed those found by Consumer 

Reports significantly in all cases. 

The two AAA estimates are higher than the 5-cycle and mpg-based estimates, but lower 

than (but closer to) the current EPA estimates. Overall, the 5-cycle formulae tend to match the 

findings of the three consumer organizations more closely than the other two label approaches. 

However, there is significant scatter in the data. Over time, we will continue to assess how our 

5-cycle estimates compare with those of other studies. In particular, it would be useful to have 

both vehicle activity and fuel economy data, coupled with environmental conditions in order to 

more precisely verify the ability of the five cycles to estimate onroad fuel economy under the full 

range of ambient conditions. 

31 
Chapter II References 

1. Eastern Research Group. Kansas City PM Characterization Study: Round 1 Testing Report. 

ERG No. 0133.18.001.001, March 7, 2005. 

2. Eastern Research Group. Kansas City Field Testing: Round 2 Testing Final Progress Report. 

ERG No. 0133.18.005.003, April 15, 2005. 

3. Eastern Research Group. Late Model Vehicle Emissions and Fuel Economy Characterization 

Study: Addendum to the Kansas City Exhaust Characterization Study-Draft Report. ERG No. 

0133.18.004.001, September 26, 2005. 

4. U.S. Department of Transportation, Federal Highway Administration. Highway Statistics 

2004. See Table VM-1. Website: http://www.fhwa.dot.gov/policy/ohim/hs04/htm/vm1.htm. 

5. Davis, S. C. and S. W. Diegel. Transportation Energy Data Book: Edition 24. Oak Ridge 

National Laboratory, prepared for the U.S. Department of Energy, Report No. ORNL-6973, 

December 2004, Table 4.3. Website: http://cta.ornl.gov/data/download24.shtml. 

32 
Chapter III: Documentation of Final Approach for Estimating 

On-Road Fuel Economy 

The current fuel economy label values utilize measured fuel economy over city (FTP) 

and highway (HFET) driving cycles and adjust these values downward by 10 and 22%, 

respectively, to account for a variety of factors not addressed in EPA’s vehicle test procedures. 

These factors include differences between the way vehicles are driven on the road and over the 

test cycles, air conditioning use, widely varying ambient temperature and humidity, widely 

varying trip lengths, wind, precipitation, rough road conditions, hills, etc. 

The purpose of the new formulae for city and highway fuel economies is to better 

account for three of these factors: 1) on-road driving patterns (i.e., vehicle speeds and 

accelerations), 2) air conditioning, and 3) colder temperatures. Vehicles are often driven more 

aggressively and at higher speeds than is represented in the FTP and HFET tests, which have 

maximum speeds of 55-60 mph and maximum acceleration rates of 3.2-3.3 mph per second. The 

incorporation of measured fuel economy over the US06 test cycle into the fuel economy label 

values makes the label values more realistic, as this cycle includes speeds up to 80 mph and 

acceleration rates of 8.4 mph per second. 

Drivers often use air conditioning in warm, humid conditions, while the air conditioner is 

turned off during the FTP and HFET tests. The incorporation of measured fuel economy over 

the SC03 test cycle into the fuel economy label values reflects the added fuel needed to operate 

the air conditioning system. The SC03 test is performed at 95 degrees Fahrenheit (o

F) with 

simulated solar heating. 

Vehicles are also often driven at temperatures below 75oF, at which the FTP and HFET 

tests are performed. The incorporation of measured fuel economy over the cold temperature FTP 

test into the fuel economy label values reflects the additional fuel needed to start up a cold 

engine at colder temperatures. 

We developed two methods for incorporating these three factors into our onroad fuel 

economy predictions. The first method, termed vehicle specific 5-cycle, combines the fuel 

economy over all five dynamometer cycles in a vehicle specific manner (i.e., as much 

information as possible reflects the fuel economy performance of the specific vehicle being 

examined). The second method, termed mpg-based, utilizes fuel economy estimates based on 

the vehicle specific 5-cycle method for a large number of vehicles and develops generic 

adjustment factors as a function of the vehicle’s FTP and HFET fuel economy values. 

In Section III.A, we describe the development of the vehicle specific 5-cycle method. In 

Section III.B, we describe the mpg-based method. In Section III.C, we evaluate the range and 

variability of onroad fuel economy experienced by drivers of the same vehicle. In section III.D, 

we describe how the current city and highway fuel economy values would change under the two 

final methods. Finally, in section III.E, we evaluate the sensitivities and uncertainties in the 

vehicle specific 5-cycle formulae. 

33 
A. Vehicle Specific 5-Cycle Method for Estimating On-Road Fuel 

Economy from Dynamometer Tests 

The city and highway fuel economy label values are intended to give the vehicle 

purchaser an idea of the fuel economy that they should expect to achieve while driving the 

vehicle during normal use. As such, the label values are intended to take into account the effect 

of seasonal and geographical variations on automotive fuel economy, as well as the different 

driving habits of individual drivers. Due to variations in climate and the way various drivers 

drive their vehicle, no one set of fuel economy values can accurately predict any individual 

drivers’ actual fuel economy while driving. However, the set of fuel economy values should be 

able to predict on-road fuel economy for the average driver under average environmental 

conditions. The goal of the analysis presented below is to use the available fuel economy 

information developed during emission and fuel economy testing to predict on-road fuel 

economy under these “average” conditions to the greatest extent possible. This onroad average 

fuel economy can then be adjusted to represent more worse-case conditions, such as 25th or 10th 

percentile estimates (i.e., fuel economy levels achieved by 75% or 90% of all drivers, 

respectively), which is discussed in Section III.C below. 

As described in the preamble to this final rule, we chose to base the fuel economy label 

values on vehicle emission and fuel economy tests which are already being performed. This 

minimizes the costs associated with the final rule, as described below in Chapter IV. The five 

current emission and fuel economy tests and their key aspects are described below in Table 

III.A-1. 

Table III.A-1. Key Features of the Five Current Emission and Fuel Economy Tests 

Test Driving Ambient 

Temperature 

Engine Condition 

at Start 

Accessories 

FTP Low speed 75°F Cold and hot None 

HFET Mid-speed 75°F Hot None 

US06 Aggressive; low 

and high speed 

75°F Hot None 

SC03 Low speed 95°F Hot A/C on 

Cold FTP Low speed 20°F Cold and hot None 

We have highlighted in bold the distinctive features of the five current vehicle tests. The 

FTP, HFET and US06 are all performed at an ambient temperature of 75°F. Each test consists of 

a distinctive driving pattern. In addition, the FTP test consists of three distinct measurements, 

called bags because the emissions produced during each portion of the test are literally collected 

in separate plastic “bags”. Bags 1 and 3 consist of the exact same driving pattern, while Bag 2 

consists of a different pattern. Thus, fuel economy measurements at 75°F are available for four 

distinct driving cycles. 

Bag 1 begins with an engine start after the vehicle has been sitting with the engine off for 

at least 12 hours, representing an overnight soak (soak refers to the time during which a vehicle 

sits with its engine off). This is referred to as a “cold” start. In this case, the cold start occurs at 

34 
75°F, which may not seem not very “cold”. However, the engine is at the same temperature as 

the ambient air, so it is referred to as being “cold”. After sitting this long, the engine has 

essentially fully cooled off and sitting longer has no effect on either emissions or fuel economy. 

Bag 3 begins with an engine start after the vehicle has been sitting with the engine off for only 

10 minutes, representing a short stop to refuel, a short shopping trip, etc. This is referred to as a 

“hot” start, since the engine is still essentially at its fully warmed up temperature. In-use, vehicle 

soaks between starts occur anywhere between a few seconds to a few days. The hot and cold 

starts are intended to bracket this distribution of in-use soak times and ensure that vehicle 

manufacturers design their emission control systems to work efficiently under a wide range of 

operation. Practically speaking, very little fuel is necessary to warm up the vehicle after it has 

been sitting with the engine off for only 10 minutes. Thus, the difference between fuel use over 

Bags 1 and 3 is that associated with a cold start. As indicated in Table III.A-1, this estimate is 

available at both 75 and 20°F. 

The SC03 test is the only test performed with the air conditioning system operational. 

Therefore, its results are used to augment the fuel economy from the five driving pattern tests for 

the fuel needed to operate air conditioning. The cold FTP is the only test performed at a 

temperature below 75°F. Therefore, its results are used to represent the additional fuel needed to 

warm up an engine after a cold start, as well as any fuel needed to operate a warmed up engine, 

at colder temperatures. 

Conceptually, our approach to modeling fuel economy can be depicted as follows: 

Onroad fuel economy = 

 Warmed up fuel economy (from Bags 2 and 3 of the FTP, HFET and US06 cycles), 

 decreased by the need to warm up a cold engine (from Bags 1 and 3 of the FTP and cold FTP), 

 decreased by use of the air conditioner (from SC03), and 

 decreased due to operation at colder temperatures (from the cold FTP). 

Actually, since cold starts, air conditioning and colder temperatures simply add fuel use over and 

above that needed for warmed up driving, it is more straightforward to model these effects in 

terms of fuel consumption (e.g., gallons of fuel used per mile) than in terms of fuel economy. In 

terms of fuel consumption, the above equation looks like this: 

Onroad fuel economy = 1/ Onroad fuel consumption 

Onroad fuel consumption = 

Warmed up fuel consumption (from Bags 2 and 3 of the FTP, HFET and US06 cycles), 

plus the extra fuel associated with 

1) warming up a cold engine (from Bags 1 and 3 of the FTP and the cold FTP), 

2) use of the air conditioner (from SC03), and 

3) operation at colder temperatures (from the cold FTP). 

The remainder of this section is broken up into 6 pieces. The first, section III.A.1, 

develops a methodology for estimating fuel use related to engine start-up, or start fuel use. The 

second, Section III.A.2, develops a methodology for estimating fuel use once the engine is 

35 
warmed up at 75°F, assuming that the air conditioner is not turned on. The third, Section III.A.3, 

develops a methodology for estimating fuel use due to air conditioner use. The fourth, Section 

III.A.4, develops a methodology for estimating fuel use once the engine is warmed up at colder 

temperatures. This fourth section also presents a combination of the results from the previous 

two sections into overall formulae for running fuel use during city, highway and composite 

driving. The fifth, Section III.A.5, evaluates the impact of factors that affect onroad fuel 

economy but which are not addressed by any of the five dynamometer cycles. The sixth, Section 

III.A.6, presents for final 5-cycle city and highway fuel economy formulae. 

1. Start Fuel Use 

We estimate the fuel needed to start and warm up the engine separately from fuel used to 

operate the engine after start-up, or running fuel use primarily to be able to estimate fuel 

economy for trips of various lengths. The longer the trip, the less significant is start fuel use. 

This is consistent with the approach taken in EPA emission models, such as MOBILE6.2 and 

MOVES. We estimate the volume of fuel needed to start and warm up an engine first. We then 

estimate average trip lengths for both city and highway driving. Finally, we combine the two 

estimates to develop a formula for start fuel use for typical trip lengths during city and highway 

driving. 

a. Start Fuel 

For a specific vehicle, the fuel needed to warm up the engine depends primarily on two 

factors: 

1) The ambient temperature at which the vehicle has been sitting, and 

2) The length of time that the vehicle has been sitting since it was last used 

(commonly referred to as soak time). 

Emissions during engine start up have been studied for some time.

1

 Most recently, 

estimates of start fuel use as a function of ambient temperature were made for use in EPA’s new 

emission inventory model, MOVES (MOtor Vehicle Emission inventory System).

d For 

MOVES, EPA analyzed start fuel use from 580 gasoline fueled vehicles.2 In this analysis, the 

start fuel use measured was the difference between fuel use during Bag 1 of the FTP and Bag 3 

of the FTP. The only difference between these two bags is the time prior to the test that the 

engine has been turned off, or “soaking.” Prior to Bag 1, the engine has been off for 12 hours or 

more. This start is commonly referred to as a cold start. Prior to Bag 3, the engine has been off 

for only 10 minutes. This start is commonly referred to as a hot start. Since start fuel use during 

a hot start is much lower than that during a cold start, the difference between start fuel use for a 

cold start and a hot start is usually assumed to be that of the cold start. 

The resulting relationship between cold start fuel use at other ambient temperatures 

relative to that at 75°F (a typical temperature for the standard FTP test) is as follows: 

d

 A draft of MOVES2004 was released for public comment on Dec. 31, 2004. 

36 
Equation 1 

Cold Start FuelUse Relativetothat at 75 F =

1− (0.01971× (AmbientTemperature − 75))+ (0.000219 × (AmbientTemperature − 75)

2 )

At 75°F, the nominal temperature of the FTP test, this formula yields a value of 1.0. At 20°F, for 

example, the temperature of the cold temperature FTP, it yields a value of 2.75. This means that 

the volume of fuel needed to start and warm up an engine and drivetrain is 2.75 times as great 

after a 12 hour soak at 20°F as it is at 75°F. These relationships assume that the vehicle had been 

sitting with the engine turned off for the same amount of time at each temperature. 

It should be noted that none of the 580 vehicles tested incorporated hybrid technology. 

Thus, the application of Equation 1 to hybrids involves greater uncertainty than for other 

gasoline vehicles. This issue is addressed in detail in Section III.E below. 

EPA also analyzed start fuel use for diesel vehicles. Relevant data were only available 

for 66 vehicles, or roughly one-tenth the number of gasoline vehicles. Based on these data, EPA 

found that start fuel use for diesels was roughly 44% that of gasoline vehicles. Thus, the 

relationship between cold start fuel use at other ambient temperatures relative to that at 75°F for 

diesels is as follows: 

Equation 2 

DieselCold Start FuelUse Relativetothat at 75 F =

1− (0.00867 × (AmbientTemperature − 75)) + (0.000096 × (AmbientTemperature − 75)

2 )

For a typical diesel vehicle, start fuel use after a cold start at 20°F is only 1.77 times that at 75°F. 

Moving to the issue of soak time prior to engine start up, the Draft MOVES2004 model 

does not yet include estimates for the effect of soak time on start fuel use. Therefore, we 

obtained a relationship between start fuel use and ambient temperature which was developed by 

the California Air Resources Board for use in their emission inventory model, EMFAC2000.3 

These relationships were based on the testing of 238 vehicles. EPA utilizes the results of this 

study in our current emission model, MOBILE6.2, to estimate the effect of soak time on 

regulated emissions (VOC, CO, NOX) during start-up. The equation for fuel use versus soak 

time (in minutes) relative to the fuel use after a 12 hour soak is as follows: 

For soaks of 90 minutes or less: 

Equation 3 

StartFuelUse = 0.00433672 × SoakTime − 0.000002393× SoakTime 2 

x 

37 
For soaks greater than 90 minutes: 

Equation 4 

StartFuelUse = 0.25889542 + 0.0014848× SoakTime − 0.0000006364 × SoakTime 2 

x 

As is done in EMFAC2000 and MOBILE6.2, we assumed that these relationships are 

independent of ambient temperature. 

In order obtain the combined effect of ambient temperature and soak time, we combined 

the above equations multiplicatively, as follows: 

For gasoline vehicles: 

For soaks of 90 minutes or less: 

Equation 5 

2 StartFuelUsex = [0.00433672 × SoakTime − 0.000002393× SoakTime ]×

2 [1− 0.01971× (AmbientTemperature − 75)+ 0.000219 × (AmbientTemperature − 75) ]

For soaks greater than 90 minutes: 

Equation 6 

2 StartFuelUsex = [0.25889542 + 0.0014848× SoakTime − 0.0000006364 × SoakTime ]×

2 [1− 0.01971× (AmbientTemperature − 75)+ 0.000219 × (AmbientTemperature − 75) ]

For diesel vehicles:

For soaks of 90 minutes or less: 

Equation 7 

2 StartFuelUsex = [0.00433672 × SoakTime − 0.000002393× SoakTime ]×

2 [1− 0.00867 × (AmbientTemperature − 75) + 0.000096 × (AmbientTemperature − 75) ]

For soaks greater than 90 minutes: 

Equation 8 

2 StartFuelUsex = [0.25889542 + 0.0014848× SoakTime − 0.0000006364 × SoakTime ]×

2 [1− 0.00867 × (AmbientTemperature − 75) + 0.000096 × (AmbientTemperature − 75) ]

All of the above equations estimate start fuel use in terms of the fraction of start fuel use 

following an overnight soak at 75°F, which are the conditions of the “cold start” contained in the 

FTP. We will use these equations to estimate the relative start fuel use for the range of starting 

conditions occurring throughout the nation. We will then sum up these start fuel volumes and 

estimate the average start fuel use for an engine start in the U.S. Then, we will estimate the 

38 
combination of start fuel volumes measured in the FTP tests performed at 20 and 75°F which is 

equal to the national average start fuel use. 

The “hot” and “cold” starts contained in the standard and cold temperature FTP tests 

occur after 10 minute and 12 hour or longer hour soaks, respectively. With the hot start, the 

engine was fully warmed up prior to the test and turned off for 10 minutes prior to being turned 

on again at the start of the hot start portion of the FTP. With the cold start, the vehicle has been 

sitting with the engine off for at least 12 hours in a room at the temperature specified. 

Equations 3 and 4 relating the effect of soak time on start fuel use indicate that the start 

fuel use after a 10 minute soak is only 4% of that after a 12 hour soak. Equation 1 relating the 

effect of temperature on start fuel use from gasoline vehicles indicates that start fuel use at 20°F 

is 2.75 times that at 75°F. Combining these effects, the start fuel use after a 10 minutes soak at 

20°F is about 11% (0.04 * 2.75) of the start fuel use following a 12 hour soak at 75°F. Thus, the 

start fuel use after the hot starts of both standard and cold temperature FTP tests are relatively 

small compared to that of a cold start at 75°F. The US06, SC03 and HFET tests begin with a hot 

start at 75°F, as is the case with Bag 3 of the FTP. Hereafter, we ignore any start fuel use 

included in these three tests due to their hot start. (Bag 2 of the FTP does not begin with an 

engine start. The sampling equipment simply switches the emissions from Bag 1 to Bag 2 during 

a vehicle idle at second 505 of the test.) 

In order to estimate start fuel use throughout the U.S. under average ambient conditions, 

we need estimates of the soak times for typical vehicle operation, as well as the ambient 

temperature at start up. The amount of time a vehicle has sat prior to start up varies dramatically 

depending on the time of day at which it is started. For example, for vehicles started up at 6 am, 

nearly all have sat overnight. However, for vehicles started at noon, most have been driven in 

the past 4-5 hours. Ambient temperatures vary significantly during the day, so it is more 

accurate to evaluate start fuel use by hour of the day rather than simply at the daily average 

temperature. Ambient temperatures also vary dramatically across the U.S., as does the 

distribution of vehicle miles traveled (VMT). Therefore, we combined estimates of vehicle starts 

and prior soak times by hour of the day with estimates of ambient temperature and VMT by 

county in order to reflect the effects of both soak time and ambient temperature on start fuel use. 

We obtained estimates of each of these input parameters from EPA’s MOBLE6.2 and 

MOVES2004 emission models. The Draft MOVES2004 model includes estimates of ambient 

temperature by hour of the day for each month of the year for each county in the U.S.4 These 

estimates were obtained from the National Weather Service and represent 30-year averages. The 

Draft MOVES2004 model also includes estimates of vehicle miles traveled (VMT) by vehicle 

type for every county in the U.S. during 2002.5

 We assumed that the distribution of engine starts 

across counties was the same as that for VMT (i.e., that trip length was the same across the U.S.). 

We used these estimates to determine the percentage of total U.S. VMT by cars (LDVs) and light 

trucks (LDTs) occurring in each county (excluding Puerto Rico and the Virgin Islands). 

MOBILE6.2 includes estimates of the frequency distributions of vehicle soak times prior 

to vehicle start-up by time of day, as well as the frequency distribution of vehicle starts by hour 

of the day.6

 Table III.A-2 presents the distribution of starts by the hour of the day. MOBILE6.2 

39 
includes separate estimates for weekdays and weekends. Since we are primarily concerned with 

fuel use on an annual average basis, we combined the two sets of estimates by weighting the 

percentage of vehicle starts occurring after specific lengths of soak at each hour of the day for 

weekdays by five-sevenths and those for weekends by two-sevenths and added the two sets of 

percentages. This is indicated below. 

Table III.A-2. Distribution of Starts by Hour of the Day (in percent) 

Hour of the 

Day 

Weekday Weekend Average Day 

6 am 2.04 0.91 1.72 

7 am 5.54 1.93 4.51 

8 am 6.02 3.10 5.19 

9 am 4.73 6.45 5.22 

10 am 5.16 6.91 5.66 

11 am 6.72 7.97 7.08 

Noon 8.07 10.16 8.67 

1 pm 7.30 7.26 7.29 

2 pm 8.04 8.89 8.28 

3 pm 8.98 7.36 8.52 

4 pm 8.41 8.02 8.30 

5 pm 7.73 7.11 7.55 

6 pm 6.02 6.15 6.05 

7 pm - 5 am 15.24 17.78 15.97 

The relative amounts of VMT by month of the year were taken from the Draft MOVE2004 

model and are shown in Table III.A.-3 below.7

 MOVES includes estimates for both non leap 

years and leap years. We averaged the two estimates in a 3:1 ratio to develop estimates for a 

typical year. 

40 
Table III.A-3. Breakdown of Annual VMT by Month 

Month Non Leap 

Year 

Leap Year Average Year 

January 0.0731 0.0729 0.0731 

February 0.0697 0.0720 0.0703 

March 0.0817 0.0815 0.0817 

April 0.0823 0.0821 0.0823 

May 0.0875 0.0873 0.0875 

June 0.0883 0.0881 0.0883 

July 0.0923 0.0921 0.0923 

August 0.0934 0.0932 0.0934 

September 0.0847 0.0845 0.0847 

October 0.0865 0.0863 0.0865 

November 0.0802 0.0800 0.0802 

December 0.0802 0.0800 0.0802 

We obtained our estimate of total VMT by LDVs and LDTs by county from that used in 

the Draft 2002 Mobile National Emission Inventory.5

 We assumed that trip length is 

independent of the season of the year. 

We first estimated the effect of soak time on start fuel use by hour of the day. The first 

step in this procedure was to estimate the percentage of vehicle starts occurring after specific 

lengths of soak at each hour of the day. As is the case for vehicle starts by time of day, 

MOBILE6.2 includes estimates of soak times for weekdays and weekends. Again, we weighted 

the percentage of vehicle starts occurring after specific lengths of soak at each hour of the day 

for weekdays by five-sevenths and those for weekends by two-sevenths and added the two sets 

of percentages. MOBILE6.2 tracks 69 distinct intervals of soak time for 14 “hours” of the day 

(starts between 9 pm and 5:59 am are combined into a single “hour”). Thus, the specific 

estimates are too extensive for presentation here. However, to provide an indication of how soak 

times are distributed throughout the day, Table III.A-4 presents the distribution of starts by soak 

time for weekdays for several aggregated soak time intervals. 

41 
Table III.A-4. Distribution of Starts by Soak Time: Three Hours 

During Weekdays 

Soak Time (minutes) 7:00 AM Noon 5:00 PM 

0-20 25.7% 48.3% 43.4% 

21-40 2.2% 12.0% 12.2% 

41-60 1.7% 8.6% 6.4% 

61-120 0.3% 7.4% 10.2% 

121-240 0.3% 8.8% 7.4% 

241-720 27.1% 8.4% 18.9% 

720+ 42.7% 6.5% 1.4% 

The second step in this procedure is to weight the relative start fuel use for each soak 

time interval by the percentage of starts occurring after that range of soak times. The result is a 

percentage of an overnight soak equivalent for each hour of the day. These estimates are shown 

in the second column of Table III.A-5 for each hour of the day. For example, between 6 and 7 

a.m., each start uses a volume of fuel equivalent to 68.3% of that associated with a start 

following an overnight soak. As can be seen, these estimates ranged from a low of 0.25 of an 

overnight soak at 2 pm to a high of 0.68 of an overnight soak at 6 am. This follows common 

sense, as most vehicles being started at 6 am in the morning have sat overnight, while many 

vehicles being started in the middle of the afternoon have been used in the past few minutes or 

hours. We assume that these estimates are independent of temperature, because the variation in 

temperature during any particular hour of the day is relatively small. 

42 
Table III.A-5. Estimation of Daily Average Overnight Soak Equivalent 

Hour of the Day Overnight Soak, Start 

Fuel Use Equivalent 

Distribution of Starts by 

Hour 

Product of Columns 2 

and 3 

6 a.m. 68.3% 1.7% 1.2% 

7a.m. 68.5% 4.5% 3.1% 

8 a.m 51.5% 5.2% 2.7% 

9 a.m. 41.1% 5.2% 2.1% 

10 a.m 34.2% 5.7% 1.9% 

11 a.m. 29.1% 7.1% 2.1% 

12 Noon 25.5% 8.7% 2.2% 

1 p.m. 25.8% 7.3% 1.9% 

2 p.m. 25.1% 8.3% 2.1% 

3 p.m. 26.9% 8.5% 2.3% 

4 p.m. 25.4% 8.3% 2.1% 

5 p.m. 27.8% 7.5% 2.1% 

6 p.m. 26.9% 6.1% 1.6% 

7 p.m - 5 a.m. 35.5% 16.0% 5.7% 

Sum 33.0% 

The distribution of starts by hour of the day is also shown in Table III.A-5. By the weighting the 

overnight soak, start fuel use equivalents by the percentage of starts for each hour of the day, we 

can calculate the average overnight soak, start fuel use equivalent for each start throughout the 

day. As shown by the sum of the last column in Table III.A-5, the average start occurring 

throughout the day uses a volume of fuel equal to 33% of that following an overnight soak. In 

comparison, the cold start in the FTP has a weighting of 43%. 

While this analysis produces a reasonable estimate of the relative number of hot and cold 

start equivalents during real world driving, it ignores the effect of ambient temperature, which 

varies throughout the day, as well as between seasons. In order to estimate start fuel use across 

the nation throughout each day and throughout the year, we estimated the start fuel use for each 

hour of the day by month for each county in the U.S. and then weighted each estimate by the 

relative number of starts occurring in each hour of the day and by the relative amount VMT in 

each month and county. Finally we summed the weighted start fuel use estimates across all 

hours of the days, months and counties to determine the average start fuel use in terms of a cold 

start at 75o

F. 

The national average start fuel use resulting from this process was 0.4665 of an overnight 

soak at 75o

F for gasoline vehicles and 0.4137 for diesels. We can simulate these average start 

fuel use estimates with a variety of combinations of hot and cold starts at 20°F and 75°F. In 

order to select a single combination, we used the estimate described in Table III.A-5 above that 

the daily average start fuel use at a constant temperature is 33.0% of that of a cold start (i.e., that 

following an overnight soak at that temperature). Assuming that start fuel use after a hot start 

43 
(10 minute soak) is negligible and using equations (5) and (6) for relative start fuel use as a 

function of ambient temperature and soak time, we determined that a 24% weighting of a cold 

start at 20°F and a 76% weighting of a cold start at 75°F, both multiplied by 33%, has the same 

start fuel usage as 46.65% of a cold start at 75°F. Thus, for gasoline vehicles, we can simulate 

the entire distribution of vehicle starts throughout the nation annually by summing the excess 

start fuel from the cold start for the FTP at 20°F times 0.0904 (24% * 33.0%) and that for the 

cold start for the FTP at 75°F times 0.2394 (76% * 33.0%). 

For diesels, the appropriate weight for the cold start at 20°F is the same as that for 

gasoline vehicles, 24%. The weight is the same, because the shape of the curves of cold start 

fuel use for both temperature and soak time for diesels is the same as that for gasoline vehicles. 

Thus, for diesels, start fuel use under national average conditions is the excess start fuel from the 

cold start for the FTP at 20°F times 0.0904 plus the cold start for the FTP at 75°F times 0.2394. 

As mentioned above, cold start fuel use is the difference in total fuel use in Bags 1 and 3 of the 

FTP, either at 75°F or 20°F. As also mentioned above, we will evaluate the appropriateness of 

applying these weights to hybrid vehicles in Section III.E below. 

b. Trip Length 

The previous section estimated start fuel use in terms of total fuel use per start. In this 

section, we address the frequency of starts per mile of typical on-road driving. The inverse of 

starts per miles is trip length. The FTP implicitly includes one engine start for every 7.5 miles of 

driving (i.e., an average trip length of 7.5 miles). This was the average trip length estimated for 

Los Angeles in 1969. We have updated this estimate using several sources of information. 

First, the Draft MOVES2004 model contains an estimate of average trip length in-use.8 

These estimates are shown in Table III.A-6. 

Table III.A-6. Trip and Start Related Information in Draft MOVES2004 

Starts per Day Miles per 

Day 

Average 

Trip 

Length 

VMT: 2003 

Vehicle Class W (billion miles) eekday Weekend Average Day 

Passenger Cars 7.28 5.41 6.75 29.48 4.37 1,661 

Light Trucks < 

6000 pounds 

8.06 5.68 7.38 35.29 4.78 670 

Light Trucks > 

6000 pounds 

8.06 5.68 7.38 34.08 4.62 251 

The estimates of starts and miles per day are based on the results of instrumenting 168 

vehicles in Baltimore and Spokane in 1992. The estimates of starts per day for an average day 

were again determined by weighting the starts per day for weekdays by five-sevenths and for 

weekends by two-sevenths and summing. The estimates of miles per day were also taken from 

the Draft MOVES2004 model.9

 Average trip lengths (i.e., miles per start) were determined by 

dividing miles per day for each vehicle class by the number of starts per day for the average day. 

44 
The number of miles per start across all three vehicle classes was determined by dividing 

total VMT by the total number of starts. From FHWA Highway Statistics 2003, total VMT for 

LDVs in 2003 was 1,661 billion miles.10 Total VMT for 2-axle, 4 wheel trucks in 2003 was 998 

billion miles. This latter vehicle class includes trucks over 8500 pounds gross vehicle weight 

rating (GVWR), which are not included within EPA’s definition of LDTs. Based on an analysis 

by Oak Ridge National Laboratory, 92.34% of the VMT by 2-axle, 4-wheel trucks was by 

vehicles below 8500 pounds GVWR, or LDTs.11

 Thus, LDT VMT in 2003 is estimated as 923 

billion miles. Based on this same study, 72.8% of LDT VMT is by LDT1s and LDT2s and 

27.2% by LDT3s and LDT4s. Using this breakdown, we estimate LDT1 and LDT2 VMT in 

2003 as 670 billion miles and LDT3 and LDT4 VMT as 251 billion miles. Total VMT is then 

2,585 billion miles. The total number of starts was estimated by dividing the total VMT for each 

class by the number of miles per start for each class and then summing. The result was one start 

for every 4.49 miles, or 0.223 starts per mile. 

As mentioned above, the estimates of starts and miles per day came from vehicles 

operating in the Baltimore and Spokane areas. Therefore, most of this operation was likely 

urban (not to be confused with “city” when defining driving for fuel economy labeling 

purposes). These studies were performed along with several others in the early 1990's when 

EPA was developing the Supplemental FTP rule, which developed and implemented the US06 

and SC03 test cycles.12

 In addition to Baltimore and Spokane, vehicle operational information 

was obtained in Atlanta and Los Angeles. The data obtained in Baltimore and Spokane received 

primary focus, as the vehicles were recruited from centralized inspection and maintenance 

stations and the study involved instrumented vehicles. The data from Atlanta involved 

instrumented vehicles, but the vehicles were recruited via phone contact. While the initial 

vehicle selection was random, the rate of people declining to participate was higher than at the 

centralized inspection and maintenance stations. Thus, there is slightly more concern that the 

final vehicle sample in Atlanta may not be as representative of onroad driving as those in 

Baltimore and Spokane. The findings from these studies are shown in Table III.A-7. 

Table III.A-7. Estimates of In-Use Average Trip Length 

Location Average Trip Length (miles) 

Instrumented Vehicle Studies Chase Car Studies 

Baltimore - Exeter 4.0 Not Available 

Baltimore - Rossville 5.9 Not Available 

Baltimore - Combined 4.9 7.5 

Spokane 3.6 5.8 

Atlanta 6.0 Not Available 

Los Angeles Not Available 7.8 

As can be seen from Table III.A-7, there is some variability in average trip length 

depending on where people reside. People living in the central city section of Baltimore and in 

the less populated city of Spokane take much shorter trips than those living in the outskirts of 

Baltimore and the more sprawling city of Atlanta. 

45 
Table III.A-7 also shows estimates of average trip lengths from chase car studies 

conducted at the same time as the instrumented vehicle studies. While chase car studies are 

ideally suited to study travel on selected roadways, their weakness is that they are not designed 

to follow a vehicle from trip start to trip end. Thus, they utilize other information in order to 

piece together trip start and end distances and thus, trip length. As can be seen from Table III.A7, estimates of average trip length from chase car studies tend to exceed those of instrumented 

vehicle studies. The latter clearly yield a more accurate estimate, due to the instrument’s ability 

to accurately and precisely determine if the engine is running or not.e However, consideration of 

the chase car studies is useful in that it brings in an additional city, Los Angeles. Based on the 

relative trip lengths from the chase car studies, it appears that trips in Los Angeles are just 

slightly longer than in Baltimore and probably shorter than those in Atlanta. Thus, or 

urban/suburban dwellers, it appears that the average trip length is somewhere between 4 and 6 

miles and could be close to 5 miles plus or minus a half mile or so. This estimate includes all 

trips by these people, those involving both city and highway driving. 

No instrumented vehicle studies have been performed in rural areas. Chase car studies 

have recently been performed in several rural California areas. However, chase car studies do 

not obtain trip based information, as they do not follow the same vehicle from start to stop. Still, 

it is likely that trips by rural dwellers are longer than those living in cities. 

A second estimate of average trip length in the U.S. is available from the National 

Household Travel Survey (NHTS).13

 The NHTS was performed in 2001 by the Department of 

Transportation and statistically surveyed approximately 26,000 households in the U.S. This 

survey represents the sixth in a series of surveys dating back to 1969. (The name of the survey 

has changed a few times and the precise survey methods have varied to some degree.) NHTS 

found that the average trip taken using a personal vehicle in the U.S. was 9.8 miles long. While 

the average trip length was relatively constant from 1969 to 1990, it has increased roughly one 

mile since 1990.14

 Thus, the estimates shown in Table III.A-7 above could under-estimate trip 

length today to some degree. 

As noted in the NHTS, the 9.8 mile estimate excludes very long trips, such as those taken 

on vacations, as well as commercial trips, such as those by taxi cabs, police officers, municipal 

workers, etc.. These excluded trips could be both shorter and longer than those covered by the 

survey. However, the number of commercial vehicle operators far exceeds those on vacation at 

any given time. Thus, the exclusions likely bias the survey results upwards (i.e., result in an 

over-estimation of national average trip length for all light-duty vehicles and trucks). 

As the case with the instrumented vehicle studies, the average trip length of 9.8 miles 

includes all driving, both city and highway oriented. NHTS does not attempt to split driving into 

city and highway categories. However, unlike the instrumented vehicle studies, which studied 

the driving patterns of urban/suburban dwellers, NHTS is intended to cover the entire U.S. 

e

 This is often not the case for recent model hybrid vehicles, whose engines shut off frequently during city 

driving. However, as the engine is usually turned off for only seconds or minutes, the added fuel due to the 

perceived engine off is negligible both on the road and per our modeling. This issue did not affect the studies being 

cited here, as there were no hybrids on the road in the early 1990's. 

46 
population, both urban and rural. Per the Federal Highway Administration, 60-63% of car and 

light truck VMT is urban and suburban and 37-40% is rural. Assuming that urban dwellers drive 

primarily in urban areas and rural dwellers do likewise, a mile 5 average trip length for 60-63% 

of all VMT translates into an 8 mile trip length for all driving, even if rural dwellers never shut 

off their engines. In other words, there appears to be an inconsistency between the two sets of 

estimates, with the instrumented vehicle studies yielding a shorter trip length than that which can 

be consistent with the estimate from NHTS. An increase in average trip length of roughly one 

mile since the time of the instrumented vehicle studies could explain part of this difference. 

However, some difference still remains. Obviously, the average trip length for rural driving is 

finite, not infinite. 

Based on the NHTS survey questionnaire, we believe that the survey could miss brief 

stops between the primary trip origination and destination points (e.g., those at gas stations or 

convenience stores), as well as extremely short trips (e.g., moving a vehicle out of a driveway to 

allow another vehicle to exit, moving from one parking place at a shopping center to another, or 

from one shopping center to another just across the street). Using trip information from 

instrumented vehicles in Baltimore and Spokane (described in more detail below), about 28% of 

all trips fall into one of these two categories. In the NPRM, we assumed that the NHTS survey 

missed 28% of the trips, reducing the average trip length 7.66 miles (9.8 miles divided by 1.28). 

Since the time of the NPRM, EPA obtained more recent data on the operation of vehicles 

in Atlanta from Georgia Tech.15

 Georgia Tech has been instrumenting vehicles in the Atlanta 

area for some time and gathering operational data. As with the previous instrumented vehicle 

studies, this type of study captures all vehicle trips no matter how short. The major difference 

between this more recent work and the previous studies is the amount of data being collected. 

To date, Georgia Tech has collected data on over 620,000 vehicle trips. In contrast, the previous 

instrumented vehicle studies involved about a week’s worth of operation for less than a hundred 

vehicles per city. Thus, the recent data represents almost two orders of magnitude more data 

than was evaluated in all of the instrumented vehicle studies described in Table III.A-7 above, 

albeit from one urban area and one of the most sprawling at that. 

The average trip length found for these 620,000 trips around Atlanta was 7.25 miles.16 

The average number of trips per day was 4.62. This means that the vehicles surveyed in Atlanta 

drove 33 miles per day, which is very close to the national average of 34 miles per day (per 

MOVES/MOBILE6.2). This is 1.2 miles, or 20% greater than the average trip length of 6.0 

miles found in the early 1990’s (see Table III.A-7). The series of NHTS have also found a 

general increase in trip length nationally of about 1.0 mile between 1990 and 2001.17 Thus, it is 

likely that the average trip length in the other cities shown in Table III.A-7 have also increased, 

as well, since the early 1990’s when the data was collected. If the average trip length for urban 

driving was slightly more than 5 miles in the early 1990’s, then it is likely more than 6 miles 

today. 

A comparison of the trip lengths resulting from the instrumented vehicle studies and the 

chase car studies shown in Table III.A-7 indicates that the latter clearly over-estimate trip length. 

This is likely because chase car studies do not focus on entire vehicle trips, but on vehicle 

operation along a specified highway segment. However, there is considerable consistency 

47 
between the relative trip lengths in Baltimore and Spokane based on the instrumented vehicle 

data and the chase car data. Extrapolating this to Los Angeles, average trip length in Los 

Angeles appears to be slightly longer than that in Baltimore and considerable longer than that in 

Spokane. This is not unexpected, given the relative sizes of these cities and their geographical 

constraints. Including the Atlanta and implied Los Angeles estimates with those from Baltimore 

and Spokane would produce an average trip length for urban driving of considerably more than 

4.49 miles. An overall estimate closer to 5.0 miles would appear to be reasonable for the early 

1990’s timeframe, given Spokane’s relatively unique combination of size and geographical 

constraints. 

Given the growth in average trip length indicated by the two Atlanta studies and the 

series of NHTS, we estimate that average urban trip length today is roughly 6.2 miles. Assuming 

an average trip length for urban driving of 6.2 miles, an urban VMT fraction of 0.61, and an 

overall national average trip length of 9.8 miles from NHTS, the average trip length of highway 

driving is 135 miles. While not impossible, this is still likely too high. Thus, it still appears 

likely that the NHTS is missing trips of very short length or combining trips with very short 

engine off times, though not to the degree assumed in the NPRM analysis. Assuming that the 

NHTS misses 11% of all trips for these reasons reduces the average trip length for all driving to 

8.7 miles and that for rural driving to about 24 miles. This 24 mile figure for an average rural 

trip is much more reasonable than 135 miles. Therefore, we will update our estimate the national 

average trip length to be 8.7 miles, from the estimate of 7.7 miles in the NPRM. 

This estimate includes all driving, both city and highway oriented. Start fuel use must be 

split between city and highway driving. Neither MOBILE6.2 nor Draft MOVES2004 includes a 

direct estimate of the split between city and highway driving as defined for fuel economy 

labeling purposes. However, such a split can be derived from the driving patterns contained in 

Draft MOVES2004. This derivation is described next. 

Table III.A-8 presents the 14 driving cycles which are used in Draft MOVES2004 to 

estimate on-road emissions, along with each cycle’s average speed.18

 We ran the Draft 

MOVES2004 model for the entire nation using national default inputs and determined the 

percentage of driving time which LDVs and LDTs spend in each type of driving (i.e., driving 

cycle). These percentages are shown in the third column of Table III.A-8. 

48 
Table III.A-8. Inventory Driving Cycles in Draft MOVES2004 

Cycle Avg. Speed 

(mph) 

Time Spent During 

On-Road Driving 

Assumed City 

Percentage 

Low Speed 2.5 5.2% 100

 New York City 7.1 5.8% 100

 LOS EF Non-Freeway 11.6 16.0% 100

 LOS CD Non-Freeway 19.2 8.7% 100

 LOS AB Non-Freeway 24.8 6.2% 100

 LOS G Freeway 13.1 0.1% 100

 LOS F Freeway 18.6 0.5% 100

 LOS E Freeway 30.5 24.7% 100

 LOS D Freeway 52.9 17.4% 0

 LOS AC Freeway 59.7 7.3% 0

 High Speed Freeway 1 63.2 1.9% 0

 High Speed Freeway 2 68.2 2.5% 0

 High Speed Freeway 3 76 2.0% 0

 Freeway Ramp 34.6 1.7% 44.9 

We assigned driving spent in each driving cycle to either city or highway driving, based 

on its average speed. If the cycle’s average speed was less than 45 mph, we assumed that it 

represented city driving. If the cycle’s average speed was greater than 45 mph, we assumed that 

it represented highway driving. The only exception was driving on freeway ramps. Since 

freeway driving occurs in both city and highway modes, we assumed that driving on ramps also 

occurs in both modes. No information exists concerning the use of ramps to access freeways 

with respect the average speed of the freeway driving at the time. Therefore, we assigned ramp 

driving to city and highway driving simply in proportion to the percentage of time spent in city 

freeway driving and highway freeway driving. Therefore, the city percentage of ramp driving 

was the driving percentages for the LOS E, F, and G freeway cycles (25.3%) divided by the 

driving percentages for all eight freeway cycles (56.3%), or 44.9%. 

In order to calculate the percentage of VMT occurring during city and highway driving, 

we multiplied the average speed of each cycle by its driving time percentage. We then summed 

this product of speed and percentage time across all fourteen cycles (31.77 mph). We then 

multiplied each cycle’s product of speed and percentage time by its city percentage and summed 

again (13.51 mph). The percentage of VMT occurring as city driving is the ratio of these two 

numbers (13.51/31.77), or 42.6%. We repeated this procedure using the percentage of highway 

driving (equal to 100% minus the percentage of city driving). The sum of the product of speed, 

percentage time and highway percentage was 18.26 mph. Thus, the percentage of highway 

driving is the ratio of 18.26 to 31.77, or 57.4% of VMT. 

This city/highway VMT split of 43/57 is quite different from the current 55/45 split. 

Some analysts have suggested that the 55/45 split should be updated to reflect an even higher 

city fraction, such as 60/40 to 63/37. The current 55/45 split and the suggested updates are based 

49 
on FHWA estimates of the urban and rural fractions of national VMT. This assumes that city 

driving is equivalent to urban driving and highway driving is equivalent to rural driving. There 

is some merit to this approach for the current city and highway label fuel economy estimates, as 

the original Los Angeles Road Route No. 4 (the basis for the LA-4 driving cycle included in the 

FTP) includes some freeway driving which reaches 55 mph. Most of the driving used to develop 

the HFET occurred on uncongested rural highways. 

Generally, we can expect trips dominated by low speed driving to usually involve more 

starts per mile than high speed trips. The current highway driving cycle, the HFET, contains no 

cold starts (i.e., infinite trip length). Of course, the 22% adjustment factor applied to the HFET 

fuel economy to calculate the highway fuel economy label value could account for some cold 

starts, but no specific trip length is specified. Thus, for simplicity purposes, we assigned an 

average trip length of 60 miles to highway driving. This is approximate. However, once trip 

length is over 30-40 miles, start fuel use has a very small effect on fuel economy. Still, a finite 

trip length for highway driving helps the public to relate to the way highway fuel economy is 

estimated. Back calculating using the city and highway VMT split of 43/57 and an overall trip 

length of 8.7 miles, the average trip length for city driving is 4.1 miles.f

 This estimate is 17% 

longer than the 3.5 mile city trip length estimated in the NPRM. 

By itself, this increase in trip length increases 5-cycle city fuel economy for the 615 

vehicles in our certification database by 1%. Highway fuel economy remains unchanged, since 

the average trip length remains at 60 miles. However, this higher 5-cycle city fuel economy will 

indirectly cause the non-dynamometer adjustment factor to be increased by roughly 0.5% (see 

Section III.A.5). This factor applies to both city and highway fuel economy. Thus, the net effect 

of increasing the average trip length of city driving is about a 0.5% increase in 5-cycle city fuel 

economy and a 0.5% decrease in highway fuel economy. 

As it will be useful later in this section, using the information shown in Table III.A-8, we 

calculated the average speed of city and highway driving. The average speed of city driving is 

the sum of the product of speed, percentage time and city driving percentage (13.51 mph), 

divided by the total percentage of city driving (68.0%), or 19.9 mph. The average speed of 

highway driving is the sum of the product of speed, percentage time and highway driving 

percentage (18.26 mph), divided by the total percentage of city driving (32.0%), or 57.1 mph. 

The average speed of all driving represented in Draft MOVES2004 is 31.8 mph. 

c. Formula for Start Fuel Use 

The total fuel usage in either Bag 1 or Bag 3 can be determined by dividing the number 

of miles of driving during these portions of the test (3.59 miles for either bag) by the fuel 

economy measured during that bag. Thus, the equation for start fuel use at either 20°F or 75°F is 

as follows: 

f

 Given 100 miles of total VMT, 42.6 miles consists of city driving and 57.4 miles of highway driving. At 

3.5 miles per city trip, this means 12.14 city trips. At 60 miles per highway trip, this means 0.96 highway trips. 

The total number of trips is therefore 13.1, for an overall trip length of 7.66 miles (100/13.1). 

50 
⎛ 1 1 ⎞ StartFuel x = 3.6 × − ⎜

⎜

⎝ Bag1 FEx Bag 3 FEx 

⎟

⎟

⎠

where x is either 20°F or 75°F. 

The equation for start fuel use in terms of gallons per mile is: 

For City Fuel Economy: 

StartFC (gallons per mile) = 0.33×

⎛

⎜

(0.76 × StartFuel ) + (0.24 × StartFuel )⎞

⎟ 75 20 

⎝ 4.1 ⎠

For Highway Fuel Economy: 

StartFC (gallons per mile) = 0.33×

⎛

⎜

(0.76 × StartFuel75 ) + (0.24 × StartFuel20 )⎞

⎟ ⎝ 60 ⎠

2. Running Fuel Use at 75°F Without Air Conditioning 

Running fuel use depends primarily on how the vehicle is driven, particularly the 

distribution of speed and power. In this section, we develop a description of onroad driving from 

the Draft MOVES2004 model and then represent this onroad driving using the available 

dynamometer tests. 

a. On-Road Driving Patterns 

On-road driving patterns have been studied for the purpose of emissions since at least the 

late 1960's. The driving cycle contained in the FTP, the LA-4, was developed from following 

typical vehicle operation over a particular road route in Los Angeles in the late 1960's. The 

HFET was based on instrumented vehicles operated over a rural road route outside of Ann 

Arbor, Michigan in the late 1970's. The US06 and SC03 cycles, among others, were developed 

in the early 1990's to augment the LA-4 and HFET cycles. These cycles were based on 

instrumented vehicles monitored during normal operation in Baltimore, Spokane and Atlanta, 

which were already discussed in the section on start fuel use above. 

Based on the driving data obtained in Baltimore, Spokane and Atlanta, EPA developed 

three cycles which together did a reasonable job of representing the complete breadth of urban 

driving: SC03, REP05, and REM01. The SC03 cycle represented driving immediately following 

engine start-up. REP05 represented high speed and aggressive driving. REM01 represented all 

other driving. Ignoring changes in driving habits since the early 1990's, on-road fuel economy 

(at least in urban areas) could be reasonably represented by the fuel economies measured over 

these three cycles. However, tests over the REP05 and REM01 cycles are not regularly 

performed during certification. Performing these tests would entail additional testing costs. The 

SC03 test is only performed with the air conditioning on, so it too would need to be re-performed 

with the air conditioning turned off. We are primarily interested here in approaches to estimating 

51 
on-road fuel economy using the current dynamometer tests. Thus, using REP05, SC03 and 

REM01 to project onroad fuel economy is not a practical option. 

The US06 cycle was developed as a concentrated version of the REP05 cycle. Thus, it 

can conceivably be used in lieu of REP05. The REM01 cycle is similar to the LA-4 cycle which 

comprises the driving in the FTP. SC03 is only performed with the air conditioning turned on. 

Thus, it cannot be used to estimate fuel use without air conditioning. SC03 includes higher rates 

of acceleration than the FTP. However, the US06 cycle includes some low speed driving with 

higher acceleration rates. Thus, with correct weighting, the US06 test and the warmed up portion 

of the FTP test should be able to represent much or most of the driving contained in the SC03, 

REP05 and REM01 cycles. 

Our recent testing of driving in Kansas City indicates the need to include the US06 

driving pattern in our estimation of fuel economy. This test program and our processing of the 

data collected are described in detail in Appendix A. We grouped the vehicle operation 

monitored in Kansas City into combinations of vehicle speed and acceleration and compared it to 

similar combinations represented in the FTP, HFET and US06 cycles. The breadth or envelope 

of operation over the FTP and HFET cycles is shown as the innermost area (colored in dark blue 

in the following figure). The envelope of vehicle operation monitored in Kansas City which 

exceeds that of the FTP and HFET is shown in purple. Finally, the envelope of the US06 cycle 

is shown where it exceeds that of the vehicle operation monitored in Kansas City. 

Figure III-1. Speed-Acceleration Frequency Distribution: Kansas City Vs. Test Cycles ACCEL SPEED BIN 

BIN (mph) 

(mph/s) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 

14 

13 KEY 

12 Driving frequency covered by FTP/HFET style driving 

11 Driving frequency covered by Kansas City Real-World Driving 

10 Driving frequency covered by US06 style driving 

9 

8 

7 US06 

6 

5 KC 

4 

3 

2 

1 

0 FTP/HFET KC 

-1 

-2 

-3 

-4 KC 

-5 

-6 US06 KC activity cut off < 0.1% 

-7 

-8 18% of KC driving outside FTP/HFET driving envelope 

-9 

-10 0.6% of KC driving is outside US06 driving envelope 

-11 90% of US06 driving is within the 0.1% KC boundary 

Overall, 18% of the onroad driving activity (time based) in Kansas City fell outside of the 

FTP/HFET envelope. This corresponds to 33% in VMT terms. As can be seen, most of this 

operation which exceeds the FTP/HFET envelope has either a higher rate of acceleration or 

52 
higher vehicle speed. However, only 0.6% of the Kansas City operation fell outside the US06 

(0.4% of the VMT). 

The SAFD envelopes for hybrid and conventional vehicles did not differ significantly 

from each other. However, the percentages of driving in the various bins did vary, as will 

become more evident below when we evaluate the VSP frequency distributions for the two types 

of vehicles. 

Recent chase car studies of driving in California show even more operation outside of the 

FTP/HFET envelope. (The details of these test programs are also discussed in Appendix A.) 

The combinations of speed and acceleration for the monitored California urban driving are 

shown in the following figure. 

Figure III-2. Speed-Acceleration Frequency Distribution: Urban California Vs. Test 

Cycles 

ACCEL SPEED BIN 

BIN (mph) 

(mph/s) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 

14 

13 KEY 

12 Driving frequency covered by FTP/HFET style driving 

11 Driving frequency covered by real-world California urban style driving 

10 Driving frequency covered by US06 

9 Driving frequency covered by real-world CA but NOT US06 

8 

7 US06 

6 

5 CA Urban 

4 

3 

2 

1 

0 FTP/H FTP/HFET CA Urban 

-1 

-2 

-3 

-4 

-5 CA Urban 

-6 

-7 CA activity cut off < 0.1% 

-8 

-9 20% of CA URBAN driving is outside FTP/HFET driving envelope 

-10 1.2% of CA URBAN driving is outside US06 driving envelope 

-11 41% of CA RURAL driving is outside FTP/HFET driving envelope 

-12 3% of CA RURAL driving is outside US06 driving envelope 

As can be seen, the breadth of California urban driving which exceeds the FTP/HFET 

envelope is much more expansive than that found in Kansas City. Onroad driving includes much 

higher speeds and both higher and lower rates of acceleration. Overall, 20% of California urban 

driving lies outside the FTP/HFET envelope (34% of the VMT). Just over 1% fell outside the 

US06 envelope (1.3% of the VMT). Rural driving was found to be more aggressive than urban 

driving. While not shown in the above figure, 3% of the rural driving was outside the US06 

envelope. 

Both the MOBILE6.2 and Draft MOVES2004 models contain estimates of on-road 

driving. MOBILE6.2 describes driving in a more traditional fashion by assigning VMT to 

53 
various driving cycles, like those listed in Table III.A-8 above. This is very acceptable for 

estimating emission inventories. However, it is not straightforward to convert driving over such 

cycles to driving over the five available dynamometer cycles or bags. Average speed and power 

are available for each cycle. However, with only two degrees of freedom, one cannot determine 

weightings for five dynamometer cycles. A more detailed distribution of speed and power is 

necessary. This brings us to the approach taken in Draft MOVES2004, which is more amenable 

to the task faced here. 

Draft MOVES2004 takes a much different approach to describing on-road driving than 

previous EPA emission inventory models, for example, MOBILE6.2. While starting with whole 

driving cycles, like those listed in Table III.A-8 above, it goes further by breaking down vehicle 

operation on a second by second basis into 17 categories or bins. One bin (Bin 0) contains 

significant decelerations. Another bin (Bin 1) contains idling operation. The other 15 bins 

contain brief or modest decelerations, cruising operation and accelerations. The 15 bins are 

broken down into three sets of bins by vehicle speed: Bins 11-16 contain operation at 1-25 mph, 

Bins 21-26 contain operation at 25-50 mph, Bins 33-36 contain operation at 51 mph or faster. 

These three sets of bins are further sub-divided according to the power required of the engine 

divided by vehicle mass. This ratio is termed vehicle specific power, or VSP, and has the units 

of kilowatt per megagram (kW/Mg). The VSP bins are described in Table III.A-9. 

Table III.A-9. VSP-Speed Bins in Draft 2004MOVES 

Bin Label Vehicle Speed (mph) Vehicle Specific 

Power (kW/Mg) 

MOVES 

0 Deceleration --- 

1 Idle --- 

11 

1-25 

<0 

12 0-3 

13 3-6 

14 6-9 

15 9-12 

16 >12 

21 <0 

22 0-3 

23 25-50 3-6 

24 6-9 

25 9-12 

26 >12 

33 <6 

35 >50 6-12 

36 >12 

54 
The three bins with open ended power levels (bins 16, 26, and 36) have lower limits of 12 

kW/Mg. Power during onroad driving can exceed 50 kW/Mg. Nearly 35% of the US06 cycle 

falls into bin 36. Therefore, it is useful to split these bins up further into smaller power 

increments. This is being considered for the next version of the MOVES model. 

Here, we expanded the set of 17 VSP bins by splitting bins 16, 26, and 36 into four bins, 

for a net increase of 9 bins. The three speed ranges stay the same. However, instead of bin x6 

(i.e., 16, 26, and 36) including all power levels above 12 kW/Mg, bin x6, x7, and x8 will all 

include a range in power of 3 kW/Mg, while bin x9 will be open ended. The expanded set of 

VSP bins is shown along with that from Draft MOVES2004 in Table III.A-10. 

Table III.A-10. Expanded Set of 26 VSP-Speed Bins 

Bin Label Vehicle Speed 

(mph) 

Vehicle Specific 

Power (kW/Mg) 

MOVES Expanded Set 

0 0 Deceleration --- 

1 1 Idle --- 

11 11 

1-25 

<0 

12 12 0-3 

13 13 3-6 

14 14 6-9 

15 15 9-12 

16 16 12-15 

17 15-18 

18 18-21 

19 >21 

21 21 

25-50 

<0 

22 22 0-3 

23 23 3-6 

24 24 6-9 

25 25 9-12 

26 26 12-15 

27 15-18 

28 18-21 

29 >21 

33 33 

>50 

<6 

35 35 6-12 

36 

36 12-15 

37 15-18 

38 18-21 

39 >21 

55 
Now that the VSP concept has been introduced, it will be easier to understand how Draft 

MOVES2004 describes on-road driving, which is accomplished in three steps. First, a VSP 

frequency distribution is developed for each of the fourteen inventory cycles listed in Table 

III.A-8 above. Most of these inventory cycles were developed from the on-road driving data 

obtained in Baltimore, Spokane and Atlanta in the early 1980's. The three highest speed freeway 

cycles have been added more recently to represent driving at speeds higher than those typical of 

this earlier timeframe, since the highest speed limit during the time of these studies was 55 mph. 

Each VSP distribution shows the percentage of time spent driving in each of the 26 VSP bins. 

Different vehicles will produce slightly different VSP distributions. However, because VSP is 

defined as the ratio of required power to vehicle weight, the differences in VSP across various 

vehicles are relatively small for a given driving pattern. Draft MOVES2004 includes VSP 

distributions for typical LDVs and LDTs. We have combined these two sets of VSP 

distributions using a mix of 50% cars and 50% light trucks, which represents the split of onroad 

VMT in calendar year 2004 from MOBILE6.2. These LDV/LDT weighted VSP distributions 

are shown in Tables III.A-11 and III.A-12. (LOS stands for level of service, or volume of traffic. 

LOS A involves the least volume of traffic. LOS G is the most congested.) 

Table III.A-11. VSP Frequency Distributions for Onroad driving Cycles in MOVES 

VSP 

Bin 

Low 

Speed 

 New York 

City

 LOS EF 

Non-Freeway

 LOS CD 

Non-Freeway

 LOS AB 

Non-Freeway

 LOS G 

Freeway 

LOS F 

Freeway 

0 2.2% 11.0% 14.3% 14.5% 13.0% 8.5% 11.3% 

1 49.7% 41.7% 33.9% 22.4% 15.1% 4.4% 3.6% 

11 15.3% 13.1% 7.5% 7.3% 3.8% 29.2% 16.5% 

12 32.6% 17.7% 13.9% 7.5% 6.4% 34.0% 23.5% 

13 0.3% 7.9% 6.0% 4.3% 4.2% 11.4% 11.5% 

14 0.0% 2.5% 3.3% 4.4% 2.8% 3.8% 4.1% 

15 0.0% 2.2% 2.5% 2.4% 2.9% 1.3% 1.2% 

16 0.0% 0.8% 1.1% 0.8% 0.9% 0.0% 1.8% 

17 0.0% 0.3% 0.0% 0.2% 0.3% 0.0% 0.5% 

18 0.0% 0.5% 0.0% 0.3% 0.1% 0.0% 0.0% 

19 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

21 0.0% 0.2% 4.1% 7.7% 8.7% 0.5% 5.3% 

22 0.0% 0.4% 2.1% 7.5% 7.1% 0.8% 5.4% 

23 0.0% 1.2% 3.9% 5.5% 10.3% 1.8% 4.1% 

24 0.0% 0.2% 2.7% 6.0% 8.3% 2.1% 3.2% 

25 0.0% 0.2% 2.5% 4.1% 3.8% 2.1% 3.7% 

26 0.0% 0.2% 0.9% 3.1% 2.4% 0.3% 2.3% 

27 0.0% 0.0% 1.2% 1.3% 0.9% 0.0% 1.2% 

28 0.0% 0.0% 0.0% 0.5% 0.8% 0.0% 0.5% 

29 0.0% 0.0% 0.2% 0.2% 0.3% 0.0% 0.3% 

33 0.0% 0.0% 0.0% 0.0% 2.8% 0.0% 0.0% 

35 0.0% 0.0% 0.0% 0.0% 3.2% 0.0% 0.0% 

36 0.0% 0.0% 0.0% 0.0% 1.6% 0.0% 0.0% 

37 0.0% 0.0% 0.0% 0.0% 0.4% 0.0% 0.0% 

38 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

39 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

56 
Table III.A-12. VSP Frequency Distributions for Onroad driving Cycles in MOVES 

VSP Bin 

LOS E 

Freeway 

LOS D 

Freeway 

LOS AC 

Freeway 

High Speed 

Freeway 1 

High Speed 

Freeway 2 

High Speed 

Freeway 3 

Freeway 

Ramp 

0 11.2% 5.2% 1.4% 2.1% 3.3% 0.9% 10.5% 

1 1.8% 0.0% 0.0% 0.0% 0.0% 0.0% 6.8% 

11 7.8% 0.0% 0.0% 0.0% 0.0% 0.0% 4.5% 

12 10.5% 0.0% 0.0% 0.0% 0.0% 0.0% 4.1% 

13 7.5% 0.0% 0.0% 0.0% 0.0% 0.0% 4.5% 

14 4.6% 0.0% 0.0% 0.0% 0.0% 0.0% 1.9% 

15 2.1% 0.0% 0.0% 0.0% 0.0% 0.0% 1.1% 

16 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.4% 

17 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.8% 

18 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

19 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

21 7.6% 4.7% 0.2% 0.0% 0.0% 0.0% 8.3% 

22 11.0% 4.9% 0.5% 0.0% 0.0% 0.0% 4.4% 

23 5.3% 5.4% 0.6% 0.0% 0.0% 0.0% 4.1% 

24 5.4% 4.8% 0.5% 0.0% 0.0% 0.0% 4.2% 

25 3.8% 4.3% 0.5% 0.0% 0.0% 0.0% 6.4% 

26 3.4% 3.9% 0.5% 0.0% 0.0% 0.0% 5.3% 

27 0.5% 1.3% 0.5% 0.0% 0.0% 0.0% 4.1% 

28 0.7% 0.5% 0.1% 0.0% 0.0% 0.0% 3.3% 

29 0.4% 1.0% 0.8% 0.0% 0.0% 0.0% 5.5% 

33 4.2% 17.4% 23.2% 18.5% 9.6% 7.4% 9.2% 

35 5.8% 18.1% 30.2% 29.9% 22.2% 14.9% 3.2% 

36 2.1% 9.1% 10.7% 13.9% 15.0% 7.3% 2.6% 

37 2.5% 7.2% 12.8% 12.0% 18.0% 11.9% 0.8% 

38 1.2% 5.8% 9.1% 10.5% 12.2% 18.6% 2.1% 

39 0.4% 6.4% 8.4% 12.9% 19.6% 39.0% 2.0% 

Second, a distribution of average speeds by hour of the day and road segment for the area 

of interest are developed. In urban areas, these estimates come from travel demand models for 

five specific urban areas (Ada County, ID, Charlotte, NC, Chicago, IL, Houston, TX, and New 

York, NY).19

 Travel in other urban areas is assigned to one of the five modeled areas. In rural 

areas, these estimates come from chase car data recently obtained in California. Based on 

average speed and roadway type, travel is assigned to two of the fourteen inventory cycles (listed 

in Table III.A-8 above). The two cycles selected are those which have average speeds which 

most closely match the average speed of that roadway type during that hour of the day. Cycles 

are chosen which also represent driving on the same roadway type, if they are available.g The 

weighting of the two cycles are determined so that the average speed in-use is matched exactly. 

Then, the VSP distributions of these two cycles are combined using these same weightings. 

Third, these combinations of VSP distributions for the various cycles are aggregated over 

the geographical and temporal intervals of interest, weighting each by the amount of driving 

g

 Sometimes, higher speed driving on non-freeways in rural areas exceeds the average speed of the 

available non-freeway driving cycles. In this case, freeway cycles are used. 

57 
occurring over each roadway segment and time interval. Here, we are concerned with national, 

annual average driving. Thus, Draft MOVES2004 was run for the nation as a whole for an entire 

year and the distribution of driving over the various inventory cycles was output. 

Table III.A-13 shows the resulting distribution of on-road driving into the 14 inventory 

cycles. Three distributions are shown. The first distribution represents all U.S. driving, which 

was the direct output from Draft MOVES2004. The second represents city driving. In this case, 

the all U.S. driving percentages were multiplied by the city percentages shown in Table III.A-8 

above and normalized to sum to 100%. The third shows the distribution for highway driving, 

using 100% minus the city percentages shown in Table III.A-8 above. Also shown is the average 

speed for each cycle. Weighting the average speed of each cycle by its percentage of driving 

time also yields the average speed of that type of driving (e.g., city). These speeds are shown in 

the last row of the table. 

Table III.A-13. Distribution of Onroad Driving Patterns: Draft MOVES2004 

Inventory 

Cycle 

Average Speed 

(mph) 

Distribution of Driving Time 

All Driving City Driving Highway Driving 

Low Speed 1 2.5 5.2% 7.7% 0.0%

 New York City 7.1 5.8% 8.6% 0.0%

 LOS EF Non-Freeway 11.6 16.0% 23.5% 0.0%

 LOS CD Non-Freeway 19.2 8.7% 12.8% 0.0%

 LOS AB Non-Freeway 24.8 6.2% 9.1% 0.0%

 LOS G Freeway 13.1 0.1% 0.2% 0.0%

 LOS F Freeway 18.6 0.5% 0.7% 0.0%

 LOS E Freeway 30.5 24.7% 36.3% 0.0%

 LOS D Freeway 52.9 17.4% 0.0% 54.5%

 LOS AC Freeway 59.7 7.3% 0.0% 22.8%

 High Speed Freeway 1 63.2 1.9% 0.0% 5.8%

 High Speed Freeway 2 68.2 2.5% 0.0% 7.7%

 High Speed Freeway 3 76.0 2.0% 0.0% 6.4%

 Freeway Ramp 34.6 1.7% 1.1% 2.9% 

Average Speed (mph) --- 19.9 57.1 31.8 

The average speed of city driving, 19.9 mph, is just slightly higher than that of the FTP, 

19.6 mph. The average speed of highway driving is well above that of both the HFET and US06 

cycles, but slightly below the highway portion of the US06 cycle. As described in section III.A.1 

above, according to this methodology, the percentage of national VMT that is like city driving is 

42.6% and that which is like highway driving is 57.4%. As also mentioned in section III.A.1 

above, we evaluate two alternatives which assign portions of the driving over the LOS D 

Freeway to city driving in section III.E.2 below. These two options increase the city percentage 

of national VMT to 50% and 55%, respectively. 

58 
We then weighted the VSP distributions of each inventory cycle (from Table III.A-11 and 

III.A-12) by the percentage of driving represented by each cycle (from Table III.A-13). This 

produced VSP distributions for all U.S. driving, city driving and highway driving. These three 

VSP distributions are shown in Table III.A-14. 

Table III.A-14. VSP Distributions for U.S. Driving (% of time) 

VSP Bin City Highway All U.S. 

0 11.8% 3.9% 9.2% 

1 20.4% 0.2% 13.9% 

11 8.3% 0.1% 5.7% 

12 13.0% 0.1% 8.8% 

13 6.0% 0.1% 4.1% 

14 3.5% 0.1% 2.4% 

15 2.2% 0.0% 1.5% 

16 0.6% 0.0% 0.4% 

17 0.1% 0.0% 0.1% 

18 0.1% 0.0% 0.1% 

19 0.0% 0.0% 0.0% 

21 5.5% 2.8% 4.6% 

22 6.2% 2.9% 5.2% 

23 4.7% 3.2% 4.2% 

24 4.2% 2.9% 3.8% 

25 3.0% 2.6% 2.9% 

26 2.2% 2.4% 2.2% 

27 0.8% 0.9% 0.9% 

28 0.4% 0.5% 0.4% 

29 0.3% 0.9% 0.5% 

33 1.9% 16.7% 6.6% 

35 2.3% 21.0% 8.3% 

36 1.1% 10.1% 3.9% 

37 0.9% 9.8% 3.7% 

38 0.5% 8.0% 2.9% 

39 0.2% 10.7% 3.6% 

These VSP distributions represent national average city and highway driving in the U.S. 

The next step is to determine what combination of the five dynamometer cycles best matches 

each VSP distribution. 

The following figure compares the VSP distribution of vehicle operation monitored in 

Kansas City to that in Draft MOVES2004. 

59 
Figure III-3. Kansas City and California VSP Frequency Distributions vs. MOVES 

0 

0.02 

0.04 

0.06 

0.08 

0.1 

0.12 

0.14 

0.16 

Freq 

KC Freq Measured 

MOVES 

California Urban 

0 1 11 12 13 14 15 16 17 18 19 21 22 23 24 25 26 27 28 29 33 35 36 37 38 39 

VSP Bin 

The match in trends is reasonable, though Draft MOVES2004 projects roughly 4% more activity 

in the high speed, high power Bins 36-39 and 4% less activity in the lower power Bin 35. 

This figure also shows the VSP distribution of the chase car data study conducted in Los 

Angeles in 2000 in this figure. The portion of driving found the Bins 36-39 in Los Angeles 

matches that in MOVES2004, though there is about 1% less operation in Bins 26-29. One must 

be cautious when comparing activity data from instrumented vehicles and chase cars. Chase cars 

do not follow drivers throughout their entire trip, so operation on local neighbor roads is often 

missed. Also, the VSP distribution from Draft MOVES2004 includes both urban and rural 

operation, while those of Kansas City and Los Angeles are primarily urban. This could 

introduce some differences, as well. 

The following figure shows the VSP frequency distributions for three groups of Kansas 

City vehicles: all vehicles, hybrids and non-hybrids. We removed idle (bin 0) prior to calculating 

the VSP distributions for two reasons. One, idle percentages were extremely high and if 

depicted in the figure, would have made the rest of the bars difficult to read. Second, given that 

little fuel is consumed during idle, including the percentage of time at idle distorts the 

comparison of those VSP bins where fuel consumption is more significant. 

60 
Figure III-4. VSP Frequency Distributions in Kansas City: Hybrids vs. Non-Hybrids 

0 

0.02 

0.04 

0.06 

0.08 

0.1 

0.12 

0.14 

0.16 

0.18 

0.2 

0 1 11 12 13 14 15 16 17 18 19 21 22 23 24 25 26 27 28 29 33 35 36 37 38 39 

VSP Bin 

Freq 

Total Activity 

Non-Hybrid 

Hybrid 

As can be seen from a close look at the operation in the higher power bins (x6-x9), the 

driving of hybrids tends to be less aggressive than that of conventional vehicles. The percentage 

of time spent driving in bins 26-29 and 36-39 is 5-6% lower for hybrids than conventional 

vehicles. This is of interest, as this study is likely to be the first to examine the relative operation 

of conventional and hybrid vehicles. If there was something about hybrid vehicles which always 

led them to be operated less aggressively, this might need to be considered in developing their 

fuel economy label value. 

The possible explanations for this difference are discussed in Appendix A. Most of the 

possible explanations imply that the difference could diminish or disappear in the future. This is 

the first study of the onroad operation of hybrids in the hands of typical owners. Only about 45 

hours of operation were studied. Thus, there is significant uncertainty in the difference found in 

the operation of conventional and hybrid vehicles. As discussed in Section III.A, we are in the 

process of obtaining a large volume of operational data obtained in Atlanta. Some hybrid 

vehicles may be included in this database. We plan to compare the operation of conventional 

and hybrid vehicles in that study as soon as we receive the data. Still, the potential impact of the 

difference in driving behavior for the two types of vehicles is examined in Section III.E.4. 

b. Representative Mix of Dynamometer Driving Cycles 

The five current certification dynamometer tests include four distinct driving cycles, or 

patterns of driving. In addition, the FTP includes two distinct driving patterns, as emissions over 

the FTP is usually collected in two separate “bags”. Two basic characteristics of these driving 

patterns are depicted in Table III.A-15: average speed and a basic measure of the average power 

required by the engine. 

61 
Table III.A-15. Driving Characteristics of the Current Dynamometer Tests 

Cycle Average Speed 

(mph) 

Average Power* 

(mph2

 per second) 

FTP (Bags 2 and 3) 19.6 40.9 

FTP: Bag 3 25.6 53.6 

FTP: Bag 2 16.1 33.8 

HFET 48.2 34.9 

US06 48.0 104.3 

US06: City Bag 21.7 152.9 

US06: Highway Bag 61.2 78.2 

SC03 (run with air conditioning on) 21.5 49.2 

Cold Temperature FTP (same driving cycle as FTP) 19.6 40.9 

* Power defined as velocity times the change in velocity per second during cruise or accelerations. Power is set 

equal to zero during decelerations and not considered in the determination of average power. 

The FTP and the cold temperature FTP both involve the same driving cycle, just at 

different ambient temperatures. Thus, their average speeds and power are identical, both for the 

total cycle and for each bag of emissions measured. The FTP and SC03 involve distinct, but 

similar driving cycles. Both are low speed cycles having similar average speeds and power 

levels. As the SC03 test is only run with the air conditioning on and all the other tests are run 

with air conditioning off, it is not possible to isolate the effect of the driving cycle differences 

between the FTP and SC03 tests directly. Thus, this leaves five distinct driving patterns which 

can be used to represent typical U.S. driving: Bag 2 of the FTP, Bag 3 of the FTP, HFET, City 

Bag of US06 and Highway Bag of US06. 

As shown in Table III.A-15, both Bags 2 and 3 of the FTP are low speed cycles, but their 

average power requirements differ by a factor of 1.7. As will be seen below, it may be useful to 

consider each bag separately in simulating typical city and highway driving. 

The current US06 test currently consists of 600 seconds of driving and the emissions are 

collected in one bag (i.e., one single collection of pollutants emitted during the test). Thus, the 

fuel economy is measured over the entire cycle. The US06 driving cycle consists of 5 hills, or 5 

driving segments which begin and end with the vehicle at idle.h

 The first hill of the cycle peaks 

at 44.2 mph, while the last three hills peak at 28.5, 28.0 and 51.6 mph. All of these hills are also 

relatively short in duration. These hills are indicative of city like driving. The second and third 

hills peak at 70.6 and 80.3 mph, which are more indicative of highway driving. The second hill 

is relatively short (roughly 90 seconds), while the third hill comprises most of the US06 test 

(roughly 360 seconds). 

h

 A “hill” within a driving cycle is a segment of driving which starts and finishes with the vehicle at rest 

(zero speed). The term hill comes from the view of a driving trace where vehicle speed is plotted versus time. As 

the vehicle accelerates, its speed increases, causing this trace to climb up a hill. Then as the vehicle decelerates, it 

proceeds down the hill. 

62 
As discussed in the preamble to the proposed rule, we are proposing that two separate 

emission measurements be made during the US06 cycle to better estimate city and highway fuel 

economy. To best distinguish between city and highway like driving, we would group hills 1, 4, 

and 5 into a city bag and hills 2 and 3 into a highway bag. However, the total length of time of 

hill 1 is quite short, only 45 seconds. In some cases, emissions can be collected into a bag at the 

beginning of a test (i.e., hill 1), emissions then collected into a second bag (i.e., hills 2 and 3), 

and then emissions collected into the first bag again (i.e., hills 4 and 5). In this case, there should 

be sufficient emission sample in each of the two bags to measure accurately with today’s 

equipment. However, in some cases, a manufacturer might have to collect the sample in three 

bags, with the first bag only containing the emissions from the first hill. We are concerned that 

this is not sufficient time to generate enough emissions to measure accurately. Including hill 2 in 

the first bag triples the driving time and ensures the ability to accurately measure emissions and 

fuel use. Thus, we are proposing to place the second hill into the city portion of the cycle, 

essentially separating the measurement of emissions during the third hill from the other four 

hills. Bag 1 would consist of hills 1 and 2, Bag 2 would consist of hill 3 and Bag 3 would consist 

of hills 4, and 5. While this incorporates some highway like driving into the “city” portion of the 

segregated US06 test, as will be shown below, this is still a much improved segregation of city 

and highway like driving than the US06 cycle as a whole. 

For example, even with the second hill included in the city portion, the average speed of 

the city portion of US06 is only 27.7 mph. The average speed of the highway portion of US06 is 

61.2 mph. The average speed of the entire cycle is 48.0 mph. Thus, separating the cycle in this 

way creates two dramatically different driving cycles, each of which falls much more clearly into 

our definitions of city and highway driving than the US06 cycle as a whole. To avoid any 

confusion with the bags of the FTP, we will refer to the city and highway portions of the US06 

cycle as US06 city and US06 highway. Overall, seconds 0-131 and 496-600 of the cycle would 

comprise the city bag and seconds 132-495 would comprise the highway bag. The description of 

the hills within US06 and their designation is summarized in the table below. 

Table III.A-16. Split of US06 Cycle into City and Highway Portions 

Hill Portion of Driving Cycle (cumulative seconds) Maximum Speed (mph) Designation 

1 0-43 44.2 City 

2 44-131 70.7 City 

3 132-495 80.3 Highway 

4 496-563 29.8 City 

5 564-600 51.6 City 

We evaluate the impact of a more ideal separation of US06 into city and highway driving in 

section III.E below. There, the US06 highway bag contains hills 2 and 3 and the US06 city bag 

contains hills 1, 4 and 5. 

With the split of US06 into two bags, we have available fuel economy estimates for five 

distinct driving patterns: 

1) Bags 1 and 3 of the FTP; 

2) Bag 2 of the FTP; 

63 
3) HFET; 

4) City portion of US06; and 

5) Highway portion of US06. 

We propose to combine the results of these five tests to represent typical city and highway 

driving patterns. 

The VSP distributions for the four complete dynamometer cycles plus individual bags of 

the FTP and US06 cycles are shown in Table III.A-17. As was the case for the Draft 

MOVES2004 inventory cycles, these VSP distributions represent a 50-50 mix of cars and light 

trucks. 

Table III.A-17. VSP Distributions for Dynamometer Cycles (% of time) 

BinID LA4 HFET US06 

US06 

City 

US06 

Hwy SC03 Bag 3 Bag 2 

0 12.0% 3.5% 16.8% 32.6% 7.1% 10.5% 13.3% 11.2% 

1 18.6% 0.7% 7.5% 14.3% 2.5% 19.7% 18.8% 18.5% 

11 6.4% 0.0% 0.7% 1.3% 0.3% 3.7% 2.8% 8.5% 

12 10.6% 0.1% 0.8% 1.7% 0.3% 9.2% 4.4% 14.3% 

13 8.0% 0.3% 0.3% 0.9% 0.0% 5.0% 3.0% 10.9% 

14 5.0% 0.1% 0.5% 1.3% 0.0% 2.4% 2.4% 6.5% 

15 2.3% 0.5% 0.2% 0.4% 0.0% 2.0% 2.9% 1.9% 

16 0.7% 0.1% 1.0% 2.2% 0.3% 2.1% 1.7% 0.2% 

17 0.1% 0.0% 0.8% 2.2% 0.0% 0.0% 0.4% 0.0% 

18 0.0% 0.0% 0.5% 1.3% 0.0% 0.0% 0.0% 0.0% 

19 0.0% 0.0% 1.7% 3.5% 0.5% 0.0% 0.0% 0.0% 

21 4.4% 4.1% 1.4% 3.3% 0.3% 7.7% 4.6% 4.3% 

22 10.9% 3.6% 0.7% 1.5% 0.1% 9.6% 7.3% 13.0% 

23 9.5% 12.4% 0.8% 1.7% 0.1% 10.3% 12.4% 7.9% 

24 2.9% 17.3% 0.1% 0.2% 0.0% 6.6% 4.5% 2.0% 

25 1.5% 7.5% 0.4% 1.1% 0.0% 3.9% 2.7% 0.8% 

26 0.7% 2.5% 0.2% 0.4% 0.0% 4.0% 2.0% 0.0% 

27 0.2% 0.4% 0.7% 1.5% 0.1% 0.0% 0.6% 0.0% 

28 0.3% 0.3% 1.2% 2.4% 0.4% 0.0% 0.9% 0.0% 

29 0.2% 0.0% 4.5% 9.1% 1.6% 0.0% 0.6% 0.0% 

33 1.6% 6.1% 7.8% 4.6% 9.9% 1.8% 4.3% 0.0% 

35 2.6% 32.3% 14.1% 3.7% 20.7% 0.6% 7.1% 0.0% 

36 1.1% 6.0% 8.6% 0.4% 13.8% 0.5% 2.9% 0.0% 

37 0.1% 2.0% 10.3% 0.9% 16.2% 0.4% 0.3% 0.0% 

38 0.2% 0.1% 6.6% 0.9% 10.2% 0.0% 0.5% 0.0% 

39 0.0% 0.0% 12.0% 6.5% 15.5% 0.2% 0.0% 0.0% 

We then performed a set of linear regressions of the VSP distributions of the various 

dynamometer cycles against the city and highway VSP distributions. To maximize the ability of 

the dynamometer cycles to predict on-road fuel economy, we weighted the squared error in each 

VSP bin by its average rate of fuel consumption in each bin. We could not use the Draft 

MOVES2004 fuel rates directly, since they only exist for 17 VSP bins. Given this, we 

64 
developed several sets of 26-bin VSP fuel rates, as each approach had its relative strengths and 

weaknesses. 

One set of fuel rates was based on the results of an EPA study performed on 15 cars in 

2001.20

 This study instrumented 15 passenger cars with portable emission measurement devices 

and measured vehicle activity and fuel economy on a second by second basis. The strength of 

this study is that it consisted of onroad testing, though the selection of vehicles and drivers were 

not necessarily representative of those in the U.S. The weakness is that only cars were tested; no 

light trucks. 

Another set of fuel rates was taken from a recent testing program conducted in Kansas 

City. This program is described in detail in Appendix A. We used the average fuel rates for the 

63 non-hybrid vehicles tested in the program, 30 cars and 33 light trucks. 

A final set of fuel rates was developed by extrapolating the Draft MOVES2004 fuel rates 

(average of a 50/50 mix of cars and light trucks). The fuel rates for the x5 bins and those of 

lower power were takes directly from those in Draft MOVES2004. The x6, x7, x8 and x9 bins 

were determined by multiplying the x5 bin fuel rates from Draft MOVES2004 by the ratios of 

the fuel rates in the higher power bins to the fuel rates of the analogous x5 bins from the EPA 15 

car study. 

These four alternative sets of fuel rates are shown in Table III.A-18. 

65 
Table III.A-18. 26-Bin VSP Fuel Rates (gram per second) 

VSP Bin EPA 15 Car Kansas City 

Extrapolated MOVES 

EPA 15 Car Kansas City 

0 0.295 0.412 0.391 0.391 

1 0.286 0.384 0.326 0.326 

11 0.382 0.454 0.509 0.509 

12 0.610 0.641 0.660 0.660 

13 0.911 1.122 0.978 0.978 

14 1.223 1.406 1.260 1.260 

15 1.516 1.715 1.536 1.536 

16 1.897 2.006 1.797 1.921 

17 2.161 2.172 1.945 2.189 

18 2.591 2.358 2.112 2.624 

19 3.447 2.296 2.056 3.491 

21 0.457 0.593 0.659 0.659 

22 0.637 0.833 0.766 0.766 

23 0.857 0.986 0.971 0.971 

24 1.040 1.180 1.264 1.264 

25 1.379 1.352 1.629 1.629 

26 1.709 1.631 1.965 2.019 

27 2.053 1.917 2.310 2.426 

28 2.346 2.272 2.737 2.772 

29 3.686 2.424 2.921 4.356 

33 1.004 1.069 1.001 1.001 

35 1.430 1.486 1.573 1.573 

36 1.813 1.753 1.856 1.994 

37 1.985 1.937 2.051 2.183 

38 2.163 1.948 2.061 2.379 

39 3.315 2.309 2.444 3.646 

We selected the fuel rates from the Kansas City test program over the other two sets of 

fuel rates. The Kansas City fuel rates are based on a mix of cars and light trucks which were 

selected randomly from vehicle registrations of 2001 and later model year vehicles. The vehicles 

were also driven by their owners in normal operation. The 15 car test program only included 

cars, no light trucks. Also, the operators of the vehicles were EPA employees or contractors who 

were aware of the purpose of the test program. The fuel rates from Draft MOVES2004 include 

data from a large number of cars and light trucks. However, the extrapolation of the fuel rates 

for the 12 highest power bins is based on data from the 15 cars. Thus, the fuel rates from the 

Kansas City program are the most balanced and representative of actual onroad operation of the 

three sets of fuel rates. 

We performed the regressions using the regression function in Excel. The intercept was 

set to zero. This function does not provide for weighting the residuals. Thus, we incorporated 

this weighting by multiplying the VSP frequencies in both Tables III.A-12 and III.A-15 by the 

square root of the fuel rate for each VSP bin from the middle column of Table III.A-18. We also 

66 
performed the regressions in a stepwise fashion. For both city and highway driving, we started 

with all five dynamometer driving cycles in the regression. Any driving cycle with a negative 

coefficient was dropped from the regression. If two or more cycles had negative coefficients, the 

cycle (of this group of cycles with negative coefficients) with the lowest p-value (most 

statistically significant) was dropped first. The regression was then rerun. Once all cycles with 

negative coefficients were deleted, we continued to drop the cycle with the highest p-value until 

the adjusted r-squared value began to decrease. We then selected the set of cycles and their 

coefficients which produced the greatest adjusted r-squared value. The final set of coefficients 

generally did not sum to 1.0. Therefore, we normalized them to sum to 1.0. The results of the 

final regressions for city and highway driving are shown in the first two columns of numbers in 

Table III.A-19 below. Cycle coefficients were rounded to the nearest percentage. 

Table III.A-19. Best-Fit Combinations of Dynamometer Cycles 

Cycles Time Basis Mileage Basis 

Coefficients City Highway All City Highway All 

Bag 3 FTP 32% 0% 30% 41% 0% 24% 

Bag 2 FTP 60% 0% 38% 48% 0% 19% 

HFET 0% 25% 6% 0% 21% 9% 

US06 City 8% 0% 0% 11% 0% 0% 

US06 Hwy 0% 75% 26% 0% 79% 48% 

Adjusted R-Squared 0.6927 0.8649 0.6034 N/A N/A N/A 

A number of conclusions can be drawn from Table III.A-19. First, as might be expected, 

the two higher speed cycles are absent from the MOVES description of city driving; vice versa 

for highway driving. Second, the relative weighting for Bags 2 and 3 of the FTP in the 

description of city driving are similar, but not identical to those inherent in the FTP. Third, FTP 

driving is indicative of the great majority of the MOVES representation of city driving, nearly 

90%. Fourth, the modeling indicates a strong preference to split the low and high speed driving 

of US06 into city and highway driving, respectively. In contrast, HFET driving is only 

indicative of 21% of the MOVES representation of highway driving. Even more interesting is 

the fact that the percentage of average U.S. driving as a whole which is represented by the HFET 

is only 9%. Based on the results shown in Table III.A-19, the HFET is the least representative 

and presumably contributes the least amount of predictive information regarding onroad fuel 

economy, of the three complete cycles. Of the individual bags, the US06 city bag is the least 

representative of driving overall. However, separating this low speed aggressive driving out of 

US06 appears to help focus the contribution of high speed, aggressive driving over the highway 

and overall, given the large contributions assigned to the US06 highway bag. 

Because VSP is defined on a second by second basis, the VSP distributions used in the 

above regression analyses are on a time basis, not a mileage basis. For example, 60% of the time 

spent city driving is like that of Bag 2 of the FTP. However, because this Bag’s driving is the 

slowest of all the cycles, only 48% of the mileage spent city driving is like Bag 2. Since fuel 

economy is most commonly reported on a mileage basis, and not on a time basis, it is useful to 

convert the cycle combinations shown in Table III.A-19 above to a mileage basis. This is done 

67 
by multiplying the percentages shown in Table III.A-19 by the average speed of the 

dynamometer cycle or bag presented in Table III.A-15 above. 

The changes in the cycle weights for city and highway are relatively small, because the 

average speed of the cycles which predominate the city and highway weights all have similar 

(e.g., either low or high) speeds. Most of the percentages on a time basis change significantly 

when converted to a mileage basis for all U.S. driving, as every cycle other than US06 city has 

either much lower or much higher average speed than the overall U.S. average. 

In their comments, Honda expressed concern over the high percentage of highway 

driving represented by the US06 highway bag given their impression of the extreme nature of the 

US06 highway driving cycle. As discussed in detail in the Response to Comments document, we 

used second by second fuel economy values of 80 vehicles tested over the FTP and US06 tests to 

estimate fuel economy over the HFET, US06 highway and onroad highway driving per the Draft 

MOVES2004 model. We found that the 5-cycle 79%/21% weighting of the US06 and HFET 

fuel consumption values predicted onroad highway fuel consumption much more accurately on 

an individual vehicle basis than the HFET fuel consumption alone (analogous to the current 

highway label formula). Thus, Honda’s concern does not appear to be a real problem with the 5­

cycle formulae. 

Practically, the fuel economies of the various cycles or bags are combined harmonically, 

as is done today with the combination of city and highway fuel economies to estimate the 55/45 

composite fuel economy. Mathematically, the formulae for running fuel use without 

consideration of air conditioning or cold temperature are: 

City Driving: 

RunningFC = ⎜

⎜

⎛ 0.48 

⎟

⎟

⎞

+ ⎜

⎜

⎛ 0.41 

⎟

⎟

⎞

+ ⎜

⎜

⎛ 0.11 

⎟

⎟

⎞

⎝ Bag275 FE ⎠ ⎝ Bag375 FE ⎠ ⎝US06City FE ⎠

Highway Driving: 

⎛ 0.21 ⎞ ⎛ 0.79 ⎞ RunningFC = ⎜

⎜ ⎟⎟ + ⎜⎜ ⎟⎟

⎝ HFET FE ⎠ ⎝US06 Highway FE ⎠

All U.S. Driving: 

RunningFC = ⎜

⎜

⎛ 0.24 

⎟

⎟

⎞

+ ⎜

⎜

⎛ 0.19 

⎟

⎟

⎞

+ ⎜

⎜

⎛ 0.09 

⎟

⎟

⎞

+ ⎜

⎜

⎛ 0.48 

⎟

⎟

⎞

⎝ Bag375 FE ⎠ ⎝ Bag275 FE ⎠ ⎝ HFET FE ⎠ ⎝US06 Highway FE ⎠

For the NPRM, we developed a number of alternatives to these combinations. These 

alternatives were developed using: 1) different estimates of fuel consumption as a function of 

VSP, 2) whole cycles instead of bags, 3) a different split of the US06 test into city and highway 

portions, etc. We received very little comment on these alternatives. No new vehicle activity 

68 
data has become available since the NPRM. Thus, we are retaining the cycle combinations 

shown in Table III.A-19 in the final 5-cycle formulae. 

Hybrid vehicles are required to be tested over a full 4-bag FTP. Some manufacturers 

prefer to perform this test as a two bag test, with Bags 1 and 2 being combined into a single bag 

and Bags 3 and 4 being similarly combined. We developed cycle weighting factors for this case 

using the same methodology as described in Section III.A.2 of the Draft Technical Support 

Document to the NPRM. The result was a weighting factor of 0.90 for Bag 2 of a 2-Bag FTP 

and 0.10 for the US06 City Bag. These factors are only slightly different from a simple 

combination of the weighting factors for Bags 2 and 3 of the FTP (i.e., 89%) and US06 city 

shown in Table III.A-19 above. 

Practically, the fuel economies of the various cycles or bags are combined harmonically, 

as is done today with the combination of city and highway fuel economies to estimate the 55/45 

composite fuel economy. Mathematically, the formulae for running fuel use without 

consideration of air conditioning or cold temperature are: 

City Driving: 

⎛ 0.48 ⎞ ⎛ 0.41 ⎞ ⎛ 0.11 ⎞ RunningFC = ⎜

⎜ ⎟⎟ + ⎜⎜ ⎟⎟ + ⎜

⎜ ⎟⎟

⎝ Bag275 FE ⎠ ⎝ Bag375 FE ⎠ ⎝US06City FE ⎠

Highway Driving: 

⎛ 0.21 ⎞ ⎛ 0.79 ⎞ RunningFC =

⎝

⎜

⎜

HFET FE ⎠

⎟

⎟ +

⎝

⎜

⎜

US06 Highway FE ⎠

⎟

⎟

For hybrids tested over a four bag FTP, the fuel consumption measured over Bag 4 can 

be substituted for Bag 2 in the city driving equation. The equation for hybrids tested over a two 

bag FTP is as follows: 

City Driving (Two-Bag FTP): 

RunningFC = ⎜

⎜

⎛ 0.90 

⎟

⎟

⎞

+ ⎜

⎜

⎛ 0.10 

⎟

⎟

⎞

⎝ Bag275 FE ⎠ ⎝US06City FE ⎠

3. Effect of Air Conditioning on Fuel Economy 

The performance of emission controls while the air conditioning system is operating is 

assessed via the SC03 test. The SC03 test begins with a hot start (i.e., the engine has been turned 

off for 10 minutes after having been fully warmed up prior to engine shutdown). The test cell is 

at 95°F and 40% relative humidity, with a solar load of 850 Watts per square meter on the 

vehicle. The vehicle is also pre-heated at this solar load for 10 minutes prior to the test, so the air 

69 
conditioning compressor is generally engaged throughout the entire test. The driving pattern of 

the SC03 test is designed to represent driving performed immediately after vehicle start-up, so it 

is a relatively low speed cycle.21,22 The driving pattern contained in the SC03 test is similar to 

that of the FTP, but not identical. 

We estimate the impact of air conditioning operation on fuel economy based on the 

difference in fuel use over the SC03 and Bags 2 and 3 of the FTP. The most significant 

difference between these two tests from the perspective of fuel economy is the operation of the 

air conditioning system. However, differences in the driving pattern between the two tests 

should also be considered. Also, the air conditioning system is not always on in-use, so this 

needs to be accounted for. In addition, when the air conditioning system is on in-use, the 

temperature and relative humidity are not always 95°F and 40%, respectively. Temperature, in 

particular, can affect the load applied by the compressor on the engine. Thus, the effect of 

different ambient conditions should be considered, as well. Finally, the air conditioning 

compressor can also be engaged when the defroster is turned on. Each of these factors will be 

assessed below, starting with the difference in driving pattern. 

Using the same methodology for modeling fuel use described above, we determined the 

combination of Bags 2 and 3 of the FTP and the US06 city cycle which matches the fuel use over 

the SC03 cycle with the air conditioning turned off. The adjusted r-squared value was higher 

without the US06 city bag than with it included. Therefore, we excluded this cycle from the 

final cycle combination. Overall, a combination of 39% of Bag 2 and 61% of Bag 3 on a 

mileage basis best represents the speed and power distribution of SC03. Thus, we propose to 

estimate the incremental fuel use due to the operation of the air conditioner at 95°F and 40% 

relative humidity at an average speed of 21.5 mph as the difference in fuel consumption 

measured over the SC03 versus this combination of fuel consumption over Bags 2 and 3 of the 

standard FTP. The following equation depicts this mathematically: 

Excess fuel use dueto air conditioning at 95 F =

⎡ ⎤ ⎢

⎢

⎢

⎢

⎢⎣

(

1 1 − Fuel economy over the SC03test) ⎛ ⎞

+

⎛

⎜

⎝

⎜

⎞

⎟

⎠

⎟

0.39 0.61

⎜

⎜

⎝ ⎟

⎟

⎠ (FueleconomyoverBag 2) (Fuel economy over Bag 3)

The next factor to address is that of compressor operation. The length of the SC03 test is 

10 minutes. Since the vehicle has been sitting at 95°F for some time and has been under a solar 

load of 850 Watts per square meter for 10 minutes, the air conditioning compressor is usually 

engaged throughout the test. We assume here that the air conditioning compressor is engaged 

during 100% of the SC03 test. However, this estimate could be too high. The effect of a lower 

estimate will be evaluated in section III.E below. 

This is not the case in-use. The air conditioning compressor generally cycles on and off 

depending on the ambient temperature, humidity, solar load and the length of time that the 

vehicle has been operating. The greater the temperature and humidity, the more often drivers 

⎥

⎥

⎥

⎥

⎥⎦

70 
turn the air conditioning on. The greater the temperature and humidity, the more frequent the 

compressor needs to operate in order to keep the cabin at a comfortable temperature; the shorter 

the trip, the more relevant any solar loading of the vehicle. Thus, it is appropriate to adjust the 

excess fuel use determined from the SC03 test to account for the times when the air conditioning 

compressor is not engaged. 

The Draft MOVES2004 model contains an algorithm which estimates the percentage of 

time which the compressor is engaged as a function of heat index. Heat index is a complex 

combination of ambient temperature and humidity which was developed to predict degrees of 

personal comfort.23

 Heat index is used by the National Weather Service to quantify discomfort 

caused by the combined effects of temperature and relative humidity. Figure III-5 is reproduced 

from the MOBILE6 report and shows how heat index varies with both temperature and humidity. 

Figure III-5. Heat Index vs. Temperature and Humidity 

Rel Hum

 80%

 60%

 40% 

Note: Heat Index values based on shady conditions 

Lines represent curve fit of tabular data 

Temperature (F) 

75 80 85 90 95 100 105 

H

eat I

ndex (

F) 180 

160 

140 

120 

100 

80 

60 

The Draft MOVES2004 algorithm of compressor on fraction versus heat index was 

developed from the direct measurement of air conditioning operation of over 1000 trips by 20 

vehicles in Phoenix, Arizona during the summer and fall of 1992.24

 The algorithm considers 

both the frequency that the system is turned on by the driver and the frequency that the 

compressor is engaged once the system is turned on. The algorithm is of the form: 

A / C compressor on fraction = A + B × HeatIndex + C × HeatIndex 2 

71 
The coefficients vary depending on time of day, basically causing the predicted compressor use 

to increase when the sun rises higher in the sky for a given level of heat index. The coefficients 

are shown in Table III.A-20 below. 

Table III.A-20. Coefficients for A/C Compressor Usage Equations 

Heat 

Index 

7-10 am, 5-9 pm 11 am - 4 pm 10 pm - 6 am 

A B C A B C A B C 

<65 0 0 0 0 0 0 0 0 0 

65-74 -4.101 0.0864 -0.000367 -4.101 0.0864 -0.00037 0.000 0.0000 0.000000 

74-76 -2.930 0.0591 -0.000213 -4.101 0.0864 -0.00037 -1.257 0.0068 0.000143 

76-96 -2.930 0.0591 -0.000213 -5.307 0.1140 -0.00052 -1.257 0.0068 0.000143 

96-101 -3.632 0.0725 -0.000276 -5.307 0.1140 -0.00052 -1.257 0.0068 0.000143 

101-104 -3.632 0.0725 -0.000276 -5.307 0.1140 -0.00052 -3.632 0.0725 -0.000276 

104-110 -3.632 0.0725 -0.000276 -3.632 0.0725 -0.00028 -3.632 0.0725 -0.000276 

>110 A/C compressor is engaged 100% of the time 

Since emissions and fuel economy are affected by the operation of the air conditioning 

compressor and not simply whether the switch is turned on or off, the MOBILE6 analysis did not 

develop analogous correlations for drivers turning their air conditioning systems on as a function 

of heat index. However, whether the switch was turned on or off was recorded during the test 

program. In order to provide a point of comparison with other studies, which have focused on 

the frequency that the switch is turned on, we estimated this parameter, as well. Using the data 

collected during the 1992 study in Phoenix,25

 we performed a regression of the average 

percentage of time during each trip that the A/C system was turned on against the ambient 

temperature at the time of the trip. For 5 degree F intervals, we calculated the average 

percentage of time that the air conditioning system was turned on and that the air conditioning 

compressor was engaged. These data are plotted in Figure III-6. 

72 
Figure III-6. Air Conditioning Use in Phoenix 

A/C Use in Phoenix 

0% 

20% 

40% 

60% 

80% 

100% 

60 80 100 120 

Temperature (F) 

AC On 

Compress 

or On 

We then calculated the ratio of the percentage of time that the compressor was engaged to the 

percentage of time that the air conditioning system was turned during each temperature interval. 

This ratio is essentially the percentage of time that the compressor was engaged while the system 

was turned on. These ratios are plotted in Figure III-7. 

Figure III-7. Compressor Engagement as a Function of Ambient Temperature 

Compressor Use When A/C is On 

y = 1.0535Ln(x) - 3.9981 

50% 

60% 

70% 

80% 

90% 

100% 

70 80 90 100 110 

Temperature (F) 

73 
We then performed a simple regression of these percentages as a function of temperature. The 

result was: 

Compressor engagement (fraction) = 1.0535 ln (ambient temperature (F)) - 3.9981 

We then divided the MOBILE6 estimate of compressor on fraction by this estimate in order to 

convert the fraction of time that the compressor is on to the fraction of time that the air 

conditioning system is on. 

We combined the estimates of air conditioning system on and compressor on fractions 

with 30-year average meteorological conditions for each hour of the day for every month of the 

year for each county in the U.S. to estimate the percentage of driving time during which the 

system and compressor were engaged under those conditions. We then weighted these 

percentages by the relative driving time occurring during each hour of the day and month in each 

county to obtain an estimate of the overall percentage of the time which air conditioning 

compressors are engaged in the U.S. From this, we estimate that, on average across the nation 

and throughout the year, the air conditioning system is turned on 23.9% of the time and the 

compressor is engaged 15.2% of the time. 

We then adjusted this latter percentage to account for reduced compressor loads at 

temperatures less than 95°F and higher loads above 95°F.26

 Ed Nam, at the University of 

Michigan, developed a model of air conditioning load on the engine as a function of 

temperature.27

 From Figure 4 of this paper, we derived the following equation of compressor 

torque in foot-pounds versus temperature in degrees F. 

Compressor torque = 1.70 + 0.084 * Ambient Temperature (°F) 

At the temperature of the SC03 test, 95°F, compressor torque is 9.68 foot-pounds. Therefore, the 

estimated torque at a specific ambient temperature was divided by 9.68 and multiplied by the 

estimated compressor on fraction from Draft MOVES2004 in order to derive a compressor on 

fraction which was consistent with the conditions of the SC03 test. This adjusted formula for 

compressor on fraction was again applied to each county in the U.S., accounting for diurnal and 

seasonal temperature and driving differences. Adjusting for load, the compressor is on in-use 

13.3% of the time, versus 15.2% without adjustment for load. Thus, the average load in-use is 

87.5% of the load experienced during the SC03 test at 95°F. 

Finally, the impact of air conditioning on fuel economy varies with vehicle driving 

pattern. Most air conditioning compressors are belt-driven by the engine. The efficiency of both 

the engine and compressor will vary with engine speed and load. This variation is difficult to 

model, as the speed and load of engines in various vehicles will vary dramatically based on the 

vehicle’s drivetrain design, even over the same driving cycle. Lacking specific information on 

each vehicle’s air conditioning system design and how engine speed and load affect it efficiency, 

we assume that the efficiency of the engine and air conditioning compressor implied in the SC03 

test applies to other types of driving, as well. However, a more basic effect related to driving 

pattern is that the faster a vehicle is moving, the shorter the amount of time that the vehicle needs 

to be cooled while it travels a specific distance. Other factors being equal, this reduces the 

74 
amount of energy needed to cool the vehicle per mile of travel. Therefore, for a specific set of 

ambient conditions, we assume that the impact of air conditioning on fuel use is constant with 

driving time (i.e., fuel use in terms of gallons per hour is constant). This means that the excess 

fuel use due to operating the air conditioner varies inversely proportional to vehicle speed. In 

other words, at low vehicle speeds, like that of the SC03 test, excess fuel use is relatively high on 

a per mile basis. At high vehicle speeds, like that of highway driving, the excess fuel use due to 

operating the air condition is relatively low on a per mile basis. 

We confirmed this assumption by testing five vehicles over a variety of test cycles at 

EPA’s Ann Arbor laboratory with both the air conditioning turned on and off. These tests were 

not run in an environmental chamber which simulates numerous aspects of higher temperature 

conditions, like humidity and solar load. Vehicles were tested as they normally required for an 

FTP at 75°F, except that the ambient temperature of the test cell was either higher or lower. 

Since the primary purpose of the test program was to determine the relative load of the 

compressor on the engine and the relative effect on fuel economy, this simplified test procedure 

was sufficient. A full report of this test program is contained in the docket.28 The data are 

summarized in Table III.A-21. 

Table III.A-21. Increased Fuel Use Due to Air Conditioning as a Function of Vehicle 

Speed 

60°F 75°F 95°F 

Absolute increase in fuel consumption (gallons per 100 miles) 

FTP Bag 3 0.46 0.81 1.01 

FTP Bag 2 0.70 1.11 1.41 

HFET 0.27 0.41 0.50 

SC03 ---- 0.87 ---- 

Absolute increase in fuel consumption adjusted to 21.5 mph (gallons per 100 miles) 

FTP Bag 3 0.55 0.97 1.20 

FTP Bag 2 0.52 0.83 1.06 

HFET 0.60 0.91 1.13 

SC03 ---- 0.87 ---- 

The upper half of Table III.A-21 shows the increased fuel use as directly measured over 

the four cycles. The lower half multiplies these fuel increases by the ratio of the average speed 

of SC03 to the average speed of the cycle tested. As can be seen in the upper half of the table, 

the increase in fuel use varies by roughly a factor of three across the 3-4 cycles at each ambient 

temperature. The cycle with the highest vehicle speed, HFET, showed the smallest increase at 

all three temperatures. Bag 2 of the FTP, with the lowest average speed, showed the largest 

increase at all three temperatures. The fuel increases shown in the lower half of Table III.A-21 

show much lower variability. Fuel increases now vary by less than 20% from lowest to highest 

at any given temperature. These data convincingly confirm the assumption that the increased 

fuel needed to operate the air conditioning system is roughly constant with time, as opposed to 

mileage. 

75 
Third, the air conditioning compressor also can be engaged in defroster mode to 

dehumidify the air and keep the windshield from fogging up. Due to the fact that the defroster 

tends to be operated at lower ambient temperatures than the air conditioner, the load on the 

engine is generally lower. We do not have a direct estimate of the frequency that the defroster is 

turned on, nor the frequency that the compressor is engaged during defroster mode. No study 

analogous to that performed in Phoenix has yet been performed. However, a recent study by the 

National Renewable Energy Laboratory (NREL) and the Office of Atmospheric Programs (OAP) 

within EPA estimated the percentage of time that people turned on the defroster, as well as the 

air conditioning system, while driving.29,30 

This study uses a personal comfort model to predict when a driver would be likely to turn 

on the air conditioner. The greater the combination of ambient temperature and humidity, the 

more uncomfortably hot a person would feel. The more the model predicted that a person would 

feel uncomfortably hot, the greater the estimated likelihood that the driver would turn on the air 

conditioner. A simpler model was used for defroster use. If the ambient temperature is between 

35 and 55°F and the relative humidity is greater than 80%, then the driver is assumed to turn on 

the defroster. 

The NREL-OAP studies presented estimates of air conditioning use and air conditioning 

use plus defroster use by state, but not for the nation as a whole. We combined these statespecific estimates with the VMT estimates described above (aggregated by state) in order to 

estimate national averages. In the NPRM, we estimated that the national average air 

conditioning use was 22.9% and combined air conditioning plus defroster use was 33.5%. 

However, comments provided by NREL indicate that their more recent estimates of air 

conditioning and air conditioning plus defroster use are 28.1% and 32.6%, respectively. Since 

the conditions for air conditioning and defroster use do not overlap, national average defroster 

use, according to this model is 4.5% (versus the 10.6% figure estimated in the NPRM). Thus, 

the NREL-OAP model estimates slightly higher average air conditioning use nationwide (28.1%) 

than MOBILE6.2 (23.9%). These two estimates are still remarkably close given the differences 

in methodology. It is also not surprising that the MOBILE6.2 estimate would be the lower of the 

two, given that the NREL-OAP model does not account for people choosing alternatives to air 

conditioning use under hot-humid conditions, such as putting a convertible top down, driving 

with the windows open, or inoperative air conditioning systems. 

The NREL-OAP study also performed vehicle modeling in order to estimate the impact 

of air conditioning and defroster load on vehicle fuel economy. They assumed an average 

temperature of 81°F for air conditioning use and 61°F for defroster use. (This defroster 

temperature exceeds the range of defroster use and appears to consider higher under the hood 

temperatures. However, the air conditioning temperature appears reasonable without such an 

adjustment.) The also used the FTP as their driving cycle. Assuming a mix of 65% cars and 

35% trucks, the load of the air conditioner increased fuel consumption 19.8%, while that for the 

defroster was 4.1%. Thus, the impact of the defroster on fuel consumption is 20.7% of the 

impact of air conditioning. This includes the impact of a lower ambient temperature and the 

periodic cycling of the compressor on and off. NREL-OAP’s modeling of the air conditioner is 

comparable to our estimate of 13.3% air conditioning use adjusted for compressor load. Thus, 

the 13.3% estimate can then be scaled based on the results of the NREL-OAP study. 

76 
Two scaling factors are necessary. One, defroster use is 13.8% of that air conditioning, 

based on the NREL-OAP results in both cases for consistency (4.5%/32.6%). (Use of our own 

estimate for air conditioning (23.9%) would increase this percentage by 36%, but we believe that 

it would be more appropriate to use estimates from the same source in this case.) Two, when 

turned on, defroster use has 20.7% of the fuel economy impact as air conditioning. Combining 

these two factors (20.7% times 13.8%) produces an overall scaling factor of 2.9%. Applying this 

to our estimate of 13.3% for the compressor on percentage in terms of the ambient conditions of 

the SC03 test produces an analogous estimate of 0.4% for defroster use. Combined air 

conditioning and defroster use would be 13.7% (13.3% + 0.4%), or 3% higher than that of air 

conditioning alone. This is a very small impact. We decided not to include the impact of 

defroster use in the 5-cycle formulae at this time for two reasons. One, no vehicle studies have 

yet been performed to confirm the projection that drivers actually turn on the defroster as 

assumed by NREL-OAP. Two, the ambient conditions existing during defroster use differ 

dramatically from those of the SC03 test. 

Based on these three assumptions, the impact of air conditioning on running fuel use is 

estimated as 13.3% of the difference between fuel use per mile over the SC03 and a combination 

of Bag 2 and Bag 3 tests times 21.5 mph and divided by the average speed of either city or 

highway driving. Based on the descriptions of city and highway driving from Draft 

MOVES2004, the average speeds are 19.9 mph and 57.2 mph, respectively. Thus, the excess 

fuel use due to air conditioning operation is as follows. 

For city driving: 

Excess fuel use due to air conditioning 

21.5

19.9

For highway driving: 

0.133

=

⎡ ⎤

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎦

⎞

⎟

⎟

⎟

⎟

⎞ ⎛

⎠

⎞

⎟

⎠

Fuel economy 

over Bag 3 

0.61

⎛

⎜

⎝

⎜

⎜

⎜

⎜

⎟

⎟

⎟

⎟

⎠

⎞

⎟

⎠ ⎝

1

+

0.39

Fuel economy 

over Bag 2 

⎛

⎜

⎝

⎛

⎜

⎜

⎜

⎜

⎞

⎟

⎠

⎝

−

test 

Fuel economy 

over the SC03 

⎛

⎜

⎝

1

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎣

× ×

77 
0.133 

=

⎡ ⎤

Excess fuel use due to air conditioning 

21.5

57.2

We received several other comments on this methodology. However, as discussed in the 

Response to Comments document, these comments did not lead us to revise the above equations. 

Thus, the 5-cycle formulae will continue to use the above relationships to estimate the impact of 

air conditioning use on city and highway fuel economy. 

4. Effect of Cold Ambient Temperatures on Running Fuel Use 

Finally, we added the impact of colder ambient temperatures on running fuel use. As was 

done for start fuel use, we base our estimate of the impact of colder ambient temperatures on 

running fuel use by comparing the fuel use over the standard and cold temperature FTP tests. At 

75°F, engine, drivetrain and tires are generally assumed to be fully warmed up by the end of Bag 

1. Thus, Bag 2 of the FTP at 75°F includes only fully warmed up operation. As was discussed 

above, the start fuel use associated with a 10 minute soak at 75°F is very small and generally 

considered to be negligible. Thus, Bag 3 at 75°F can also be considered to consist of essentially 

fully warmed up vehicle operation. 

As discussed in detail in the Draft Technical Support Document to the NPRM, it is not 

clear that vehicles are fully warmed up during Bags 2 and 3 at 20°F. However, as described 

above, the average city driving trip is only 4.1 miles, well below that of the FTP (7.5 miles). 

Thus, Bags 2 and 3 of the cold FTP provide a reasonable estimate of warmed up driving during 

city-like driving (i.e., the vehicle is warmed up to the extent that it typically reaches during short 

trips). The effect of cold temperature on fuel use during city driving can be estimated from the 

difference in fuel use over Bags 2 and 3 of the FTP at 20°F and that at 75°F. 

However, we could not make the same conclusion for either longer highway-like driving 

trips, nor conclude that the effect of colder temperatures would be the same at higher vehicle 

speeds. Based on a number of studies which investigated the impact of colder temperatures on 

fuel economy, we estimated that running fuel use at 20°F at higher vehicle speeds would be 

roughly 4% higher than that at 75°F. 

When we determined the appropriate weighting factors for running fuel use at 20°F and 

75°F in the NPRM, we assumed that running fuel use increased linearly with temperature below 

75°F. We received one comment which challenged this assumption. As discussed in Section 

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎦

⎞

⎟

⎟

⎟

⎟

⎟

⎛ ⎞ ⎛ ⎜

⎜

⎜

⎜

⎜ ⎠

⎞

⎟

⎟

⎠

Fuel economy 

3

0.61

over Bag ⎜

⎜

⎝

⎜

⎜

⎜

⎜

⎜

⎟

⎟

⎟

⎟

⎟

⎠

⎟

⎟

⎠

⎞ ⎛

⎝

+

1

Fuel economy 

2

0.39

over Bag 

⎛

⎜

⎜

⎝⎝

− 1

⎛ Fuel economy ⎞

⎟

⎟

over ⎠ the SC03test ⎜

⎜

⎝

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎣

× ×

78 
5.2.4 of the Response to Comment document, we evaluated the running fuel use over Bags 2 and 

3 of the FTP of several conventional Honda vehicles at 20, 50 and 75°F to determine if running 

fuel use did in fact change linearly with a decrease in temperature from 75°F. These data are 

summarized in Table III.A-22. 

Table III.A-22. Warmed Up Fuel Use Versus Temperature: Honda Data 

Bag 2 mpg Bag 3 mpg Bag 2+3 Fuel Consumption (gal/mi) 

75°F 

Civic 38.1 41.9 0.0251 

Element 23.2 25.3 0.0414 

Accord (L4) 25.6 30.0 0.0363 

Accord (V6) 21.7 26.1 0.0424 

MDX 17.2 20.2 0.0540 

TSX 23.0 27.4 0.0401 

Odyssey 20.9 24.5 0.0445 

RSX PRB 24.0 28.5 0.0386 

Average 0.0403

 50°F 

Civic 35.3 39.1 0.0270 

Element 22.2 24.6 0.0429 

Accord (L4) 23.4 28.4 0.0391 

Accord (V6) 20.8 26.1 0.0434 

MDX 16.8 20.3 0.0546 

TSX 22.2 26.6 0.0415 

Odyssey 19.1 22.6 0.0485 

RSX PRB 22.7 28 0.0401 

Average 0.0421

 20°F 

Civic 29.2 33.9 0.0320 

Element 19.2 21.5 0.0494 

Accord (L4) 20.6 25.2 0.0443 

Accord (V6) 19.2 23.7 0.0473 

MDX 15.2 17.9 0.0610 

TSX 20.1 24.6 0.0454 

Odyssey 17.5 21.4 0.0522 

RSX PRB 20.7 25.3 0.0441 

Average 0.0470 

These data indicate that running fuel use at 50°F averaged 0.0421 gallon per mile, or 0.0018 

gallon per mile higher than that at 75°F. Running fuel use at 20°F averaged 0.0470 gallon per 

mile, or 0.0067 gallon per mile higher than that at 75°F. Thus, fuel use increased faster below 

50°F than from 75 to 50°F. On average the increase in running fuel use at 50°F was 27% that at 

79 
20°F. The same relationship was true when four hybrids (two Honda vehicles and two Toyota 

vehicles) were added to the database. 

We used this relationship to evaluate the impact of ambient temperature on running fuel 

use throughout the U.S. using the same methodology that was used in conjunction with the 

assumption of linearity performed for the NPRM. The result was a weighting factor for running 

fuel use at 20°F of 0.18, versus the weighting factor of 0.30 developed for the NPRM. Thus, we 

have reduced the weighting factor for running fuel use at 20°F in the 5-cycle formulae for both 

city and highway fuel economy to 0.18. This is shown in the equations below. 

For city driving: 

Excess fuel use due to colder temperatures =

⎡⎛ 0.5 0.5 ⎞ ⎛ 0.41 0.48 0.11 ⎞⎤

0.18 × ⎢⎜

⎜ + ⎟⎟ − ⎜⎜ + + ⎟⎟⎥ ⎣⎝ Bag 2 20 FE Bag 3 20 FE ⎠ ⎝ Bag375 FE Bag275 FE US06City FE ⎠⎦

For highway driving: 

Excess fuel use due to colder temperatures =

0.18 × 0.04 × running fuel use without air conditioning at 75 F =

⎡ 0.21 0.79 ⎤ 0.18 × 0.04 × ⎢ + ⎥ ⎣HFET FE US06 Highway FE ⎦

Combining the estimates of running fuel use at 75°F with the air conditioning turned off 

with the estimate of excess fuel use of running the air conditioning system and the estimate of 

fuel use due to colder ambient temperatures produces the following formulae for running fuel 

use: 

80 
For city driving: 

Running FuelUse =

⎡ 0.48 0.41 0.11 ⎤ ⎡ 0.5 0.5 ⎤ 0.82 × ⎢ + + ⎥ + 0.18× ⎢ + ⎥ ⎣Bag275 FE Bag375 FE US06City FE ⎦ ⎣Bag220 FE Bag320 FE ⎦

⎡ 1 ⎛ 0.61 0.39 ⎞⎤

+ 0.133×1.083× ⎢

⎣SC03 FE − ⎜

⎜

⎝ Bag375 FE 

+

Bag275 FE ⎟

⎟

⎠

⎥

⎦

For highway driving: 

Running FuelUse =

(1.007)×

⎡ 0.79 

+

0.21 ⎤ 0.133×0.377 ×

⎡ 1 − ⎛ 0.61 

+

0.39 ⎞⎤

⎢

US06 Highway FE HFET FE ⎥ + ⎢SC03 FE ⎜

⎜

Bag3 FE Bag2 FE ⎟

⎟⎥ ⎣ ⎦ ⎣ ⎝ 75 75 ⎠⎦

5. Adjustment Factor for Non-Dynamometer Effects 

There are a large number of factors which affect vehicle fuel economy which are not 

addressed by any of the five current dynamometers tests. These include roadway roughness, 

road grade (hills), large vehicle loads (e.g., trailers, cargo, multiple passengers), wind, 

precipitation, to name just a few. Even when a factor is addressed by a dynamometer test, such 

as driving pattern or air conditioning, the factor is only approximately measured, as all realistic 

driving patterns cannot possibly be included in a test having a reasonable length of time. Nor 

can all the possible ambient conditions affecting air conditioner operation be tested. Thus, any 

estimate of in-use fuel economy derived from the five dynamometer tests is necessarily 

approximate. 

It would be possible to use the formulae described in Section III.B to directly estimate the 

fuel economy label values. These fuel economy values would provide drivers with an indication 

of the relative fuel economy which they should expect to achieve in-use, at least the best estimate 

that the current five dynamometer tests can provide. This would provide vehicle purchasers with 

information as to which vehicle would provide greater or lesser fuel economy than another 

vehicle. However, as discussed in section I, many vehicle owners expect to achieve the fuel 

economy label values when they drive. Often, they do this to determine if their vehicle is 

operating properly. Thus, it would be advantageous to such vehicle operators if the fuel economy 

label values accounted for all factors affecting fuel economy and not just those addressed by the 

dynamometer tests. This is the rationale for the 90% and 78% adjustment factors which are 

currently applied to the measured FTP and HFET fuel economies when determining the city and 

81 
highway label values. This cannot be done in a vehicle specific manner, since no such estimates 

are available without additional dynamometer tests and additional vehicle testing. However, it is 

possible to account for these factors on a fleet wide basis, as was done with the current 90 and 

78% adjustment factors. 

It is possible to estimate the effect of each of the untested factors mentioned above and 

then add them up. This was done as part of the 1984 label adjustment rule. That study estimated 

that the net effect of all the non-dynamometer factors was roughly 30%.31

 However, at that time, 

these factors included the impact of vehicle speed, acceleration, colder temperature and air 

conditioning, in addition to the factors described above. With the inclusion of the fuel 

consumption over the US06, SC03 and cold FTP tests in the 5-cycle formulae, the net effect of 

these factors should be much smaller. One caution is that the comparisons of dynamometer 

measured fuel economy and onroad fuel economy performed for the 1984 rule tended to find a 

smaller shortfall than the total of all the individual non-dynamometer factors. The reason for this 

was not clear. In other words, the individual factors which might have been over-estimated 

could not be determined. In this section, we will update the impact of several of these factors 

and develop a revised estimate of the overall impact of non-dynamometer factors on onroad fuel 

economy relative to an estimate based on the 5-cycle formulae. 

The easiest factor to quantify is likely that related to fuel energy density. EPA’s test fuels 

do not contain any oxygen. However, commercial gasoline can contain either of two 

oxygenates, methyl tertiary butyl ether (MTBE) and ethanol. Future levels of MTBE use are 

uncertain because of water contamination issues related to leaking underground storage tanks. 

However, the recently passed Energy Policy Act of 2005 (EPA2005) guarantees a certain level 

of ethanol use. For example, in 2008, EPA2005 requires the use of 5.4 billion gallons of 

renewable fuel, the vast majority of which is expected to be ethanol blended into gasoline. DOE 

projects that total gasoline consumption will be 146 billion gallons in 2008. Thus, ethanol would 

represent 3.7% of gasoline by volume. Ethanol contains roughly 33% less energy per gallon, so 

on average, commercial gasoline in 2008 will contain 1.2% less energy per gallon than it would 

if it were not oxygenated. Engine efficiency is unaffected by fuel energy content in this range. 

Thus, reducing the energy content of gasoline by 1.2% will reduce volumetric fuel economy by 

the same 1.2%. This is not reflected in EPA dynamometer testing. 

Currently, dynamometer testing performed for the state of California is done using a fuel 

containing 2% oxygen by weight. This fuel contains approximately 2% less energy per gallon 

than EPA’s test fuel. In many cases, dynamometer tests performed for California can also be 

used in EPA certification. Thus, it would be appropriate to divide the measured fuel economy of 

any test performed using an oxygenated California test fuel by one minus the oxygen content of 

that fuel by weight, usually 2%. However, with the passage of EPA2005, gasoline sold in 

Federal reformulated gasoline areas in California will no longer be required to contain two 

weight percent oxygen. Thus, California may no longer require its test fuels to contain oxygen. 

In any case, measured fuel economy using an oxygenated fuel should be adjusted to reflect the 

energy content of EPA’s non-oxygenated test fuel. At the same time, the average driver in 2008 

will achieve 1.2% lower fuel economy than they would if they were using a non-oxygenated 

fuel. 

82 
The volume of renewable fuel required under EPA2005 increases in later years, reaching 

7.5 billion gallons in 2012. Assuming this is all ethanol and using DOE’s projected gasoline 

volume of 147 billion gallons, average commercial gasoline would contain 1.5% less energy than 

EPA test fuel. Thus, in the early years of the revised fuel economy labels, difference in fuel 

quality will cause onroad fuel economy to be 1.2-1.5% less than that measured on the 

dynamometer. 

Another factor which has recently been studied in some detail is tire pressure. NHTSA 

recently promulgated a regulation requiring car and light truck manufacturers to install tire 

pressure monitoring systems in future vehicles. In preparation for this rule, NHTSA conducted 

a survey of the tire pressure of in-use vehicles in February 2001.32

 Tire pressures were 

measured on over 11,500 vehicles at 24 locations throughout the U.S. The results are 

summarized in the Table III.A-23. NHTSA presented data for each of the four tires separately 

(i.e., front, driver’s side tire).33

 We averaged the findings for the four tires. 

Table III.A-23. NHTSA Onroad Tire Pressure Survey 

Difference between Onroad and Manufacturer’s Recommend Tire Cumulative Frequency 

Pressure (psi) (Average of four tires) LDVs LDTs 

-12 3.0% 3.0% 

-11 5.0% 6.0% 

-10 7.5% 8.5% 

-9 9.5% 11.0% 

-8 12.0% 15.0% 

-7 16.0% 20.0% 

-6 20.0% 25.5% 

-5 26.0% 34.5% 

-4 31.5% 40.0% 

-3 40.0% 47.0% 

-2 46.5% 54.5% 

-1 54.0% 62.0% 

0 61.0% 69.0% 

1 68.0% 75.0% 

2 74.5% 80.0% 

3 79.0% 84.0% 

4 85.5% 88.0% 

5 88.0% 91.0% 

6 90.0% 92.0% 

7 94.0% 93.0% 

8 95.0% 95.0% 

9 96.0% 97.0% 

10 97.0% 98.0% 

11 98.0% 99.0% 

12 99.0% 100.0% 

83 
As can be seen from the table, 54-62% of cars and light trucks have under-inflated tires, while 

31-39% have over-inflated tires. Using these estimates, we found that the tires of the average car 

were under-inflated by 1.1 psi, while those on light trucks were under-inflated by 1.9 psi. 

NHTSA presented two estimates of the effect of tire pressure on fuel economy. A 1978 

study by Aerospace Corp. found that fuel economy decreased by 1% for every 3.3 psi decrease in 

tire pressure, while more recent test data submitted by Goodyear showed a 1% decrease in fuel 

economy for every 2.96 psi decrease in tire pressure. Using these two factors, the 1.1 psi underinflation of car tire pressure causes a 0.3-0.4% decrease in onroad fuel economy. The 1.9 psi 

under-inflation of light truck tire pressure causes a 0.6% decrease in onroad fuel economy. 

Assuming that new vehicles average close to their CAFE fuel economy standards (27.5 mpg for 

cars and 20.6 mpg for light trucks) and a 50/50 mix of the two types of vehicles, the fleet-wide 

effect of under-inflation is 0.5% using either factor. 

NHTSA recently promulgated a regulation requiring manufacturers to monitor tire 

pressure.34

 This rule requires vehicles to be equipped with sensors to detect a tire which is 

under-inflated by 25% or more. With a few exceptions, the regulation begins phasing in with the 

2006 model year and all new cars and light trucks must have the monitoring systems by the 2008 

model year. Assuming a tire’s recommended pressure is about 32 psi, this implies catching tires 

under-inflated by 8 psi or more. If we assume that the regulation is 100% effective and eliminate 

all tires under-inflated by 8 psi or more in Table III.A-23 above, passenger car tires are no longer 

under-inflated on average in-use; they exceed their specifications by roughly 0.1 psi. Light 

trucks are still under-inflated by about 0.4 psi. Across the light duty fleet, the net effect on fuel 

economy decreases to 0.1%, or roughly one-fifth the level prior to the rule. Of course, the 

effectiveness of the rule could be less than 100%. At the same time, vehicles with a single tire 

under-inflated by 8 psi could have other tires with a lower degree of under-inflation. Thus, the 

rule could have some effect on tires with smaller levels of under-inflation than assumed above. 

In any event, the effect should be less than 0.5% and could be close to zero. 

A third factor which can be quantitatively estimated is the effect of wind. Wind affects 

fuel economy by changing the road load of the vehicle. Wind can affect both rolling resistance 

and aerodynamic drag. Rolling resistance is primarily affected by a side wind, which pushes the 

vehicle sideways. The driver must compensate by turning the steering wheel into the wind. This 

increases the drag caused by the tires on the roadway surface. However, the effect of wind on 

aerodynamic drag is far the larger of the two effects. 

Aerodynamic drag is generally assumed to be the product of three factors: 1) the frontal 

area of the vehicle, 2) the air speed going by the vehicle squared, and 3) the “drag coefficient or 

Cd.” A headwind increases the speed of the air going by the vehicle directly (i.e., a 10 mph wind 

increases air speed 10 mph). A tailwind decreases air speed by the vehicle. Even if the 

frequency of a headwind and a tailwind is the same, total aerodynamic drag increases, due to the 

fact that drag is proportional to air speed squared. For example, 40 mph squared is 1600 mph2

. 

Given a headwind of 10 mph, air speed increases to 50 mph and 50 mph squared is 2500 mph2

. 

Given a tailwind of 10 mph, air speed decreases to 30 mph and 30 mph squared is 900 mph2

. 

The average of 2500 and 900 mph2

 is 1700 mph, which is more than 6% greater than 1600 mph2. 

84 
Thus, even a randomly directional wind will increase total aerodynamic drag and decrease fuel 

economy. 

An even greater effect of wind, however, is that it changes the drag coefficient, Cd, and 

increases the effective frontal area of the vehicle in the direction of the wind. For example, as 

large as the frontal area is of a semi-tractor trailer combination, its area from a side view is 5-10 

times as large. The greater the side wind relative to vehicle speed, the more the truck is actually 

driving sideways down the road as far as aerodynamic drag is concerned. With respect to cars 

and light trucks, their body shapes are designed to reduce aerodynamic drag when traveling into 

the wind. Front and rear ends are sloped. Spoilers and other rear end shapes are designed to 

minimize the creation of vortices behind the vehicle which “pull” the vehicle back as it is driving 

forward. However, as soon as a significant side wind occurs, these benefits start to diminish. 

For the 1984 label adjustment rule, EPA estimated that wind reduced onroad fuel 

economy by 3% for a small car and 2% for a large car.29,35

 These estimates were based on 

several estimates made by the Department of Transportation: 1) the effect of 10, 15, and 20 mph 

winds on aerodynamic drag at a constant speed of 55 mph as a function of wind angle, 2) the 

effect of increased aerodynamic drag on 55 mph fuel economy, and 3) a distribution of onroad 

VMT as a function of wind speed (with the national average wind speed being 9 mph). EPA 

applied these estimates directly to highway fuel economy, but reduced the fuel economy effect 

by 80% for city driving. This reduction was based on the fact that roughly 20% of the FTP test 

is at speeds near 55 mph. 

We reviewed this methodology in detail to determine if any improvement could be made. 

Two areas were identified. The first area was the fact that the effect was estimated only for cars, 

as that was the focus of the study. The second area was the assumption that wind had no effect 

on fuel economy at vehicle speeds below roughly 55 mph. While aerodynamic drag is much 

lower at city driving like speeds than highway speeds, wind speed is a higher fraction of vehicle 

speed at low vehicle speeds. The effect of wind on a vehicle’s effective drag coefficient 

increases as the effective angle of the air speed increases. Thus, the effect of a side wind can be 

significant, even at low vehicle speeds. 

In order to expand the previous analysis, we developed a model of aerodynamic drag and 

its impact on fuel economy as a function of wind speed and angle. We broke down the speed of 

the vehicle through the air in terms of its x and y coordinates (i.e., parallel and perpendicular to 

the direction of the vehicle). The parallel component is the speed of the vehicle plus the cosine 

of the wind angle times wind speed. The perpendicular component is the sine of the wind angle 

times wind speed. We then calculated the net angle of the air flowing past the vehicle and its 

speed from these two x-y components. The net angle of the air flowing past the vehicle is the 

arctangent of the ratio of perpendicular air speed to parallel air speed. Net air speed is the square 

root of the sum of the square of the perpendicular air speed and the square of the parallel air 

speed. Aerodynamic drag is the square of the net air speed times the vehicle drag coefficient. 

DOT estimated that the vehicle drag coefficient increased 1.5% for every degree increase 

in yaw angle, or angle of net air flow past the vehicle. Using this estimate, we were able to 

85 
reproduce the estimates of the change in aerodynamic drag as a function of wind speed and 

direction on a vehicle traveling at 55 mph, which were presented Figure 26 of the EPA report.33 

In order to broaden the estimate to include light trucks, we obtained estimates of the 

effect of wind angle on a vehicle’s drag coefficient from Gillespie.36

 Gillespie presents the 

estimated absolute increase in drag coefficient as a function of wind speed for four vehicle 

designs: pick-up trucks, station wagons, family sedans, and sports cars. The results are presented 

in Table III.A-24 below in tabular form. Gillespie did not present estimates for sport utility 

vehicles (SUV). We estimated the effect for SUVs by averaging the impacts for pick-up trucks 

and station wagons. 

Table III.A-24. Effect of Wind Angle on Vehicle Drag Coefficient 

Wind Angle 

(Deg) 

Pick-Up 

Truck 

Station 

Wagon 

Family 

Sedan 

Sports 

Car SUV Average 

0 0 0 0 0 0 0 

5 0.045 0.015 0.010 0.010 0.030 0.025 

10 0.120 0.050 0.040 0.025 0.085 0.070 

15 0.195 0.090 0.080 0.050 0.143 0.121 

20 0.240 0.110 0.125 0.070 0.175 0.155 

We estimated a fleet average change in the effective drag coefficient by averaging the 

estimates for the five model types. We averaged the estimates for the three types of passenger 

cars equally (33/33/33), the two estimates for light trucks equally (50/50) and then averaged the 

averages for car and light trucks equally (50.50). For a wind angle of 20 degrees, the average 

change in drag coefficient for cars is 0.102 and 0.208 for light trucks. Assuming average drag 

coefficients in still air of 0.30 for passenger cars and 0.40 for light trucks,25 these changes 

represent increases of 1.7% and 2.6% per degree of wind angle. The figure for cars matches the 

DOE estimate from 1974 quite well, while that for light trucks is much larger. We performed a 

regression of the change in drag coefficient versus wind angle in degrees and found the 

following relationship: 

Change in drag coefficient = -0.00376 + 0.006815× wind angle + 0.000065× (wind angle)

2 

We also performed a similar regression used a linear model. The linear model yielded larger 

average increase in vehicle drag coefficient. Therefore, we retained the non-linear model. 

In order to expand the estimate to include city, as well as highway driving, we again used 

PERE.25 Using PERE, we estimate that a 10% increase in aerodynamic drag or drag coefficient 

decreases city fuel economy by 0.93%. Likewise, highway fuel economy decreases 3.11%. 

Implied in the DOT estimate of the effect of wind speed on 55 mph fuel economy is a decrease 

of roughly 4%. Thus, PERE estimates a somewhat lower effect of wind speed on fuel economy 

even at highway speeds. 

We then applied our model using the DOT estimates of the national average distribution 

of wind speeds, which is shown in Table III.A-25 below. We assumed that the average wind 

86 
speed within a range of wind speeds was the average of the lower and upper limit of the range. 

We assumed that the average wind speed for winds above 25 mph was 27.5 mph. 

Table III.A-25. Frequency of Wind Speeds in the U.S. 

Wind Speed 

(mph) 

Assumed Average 

Wind Speed 

(mph) 

% of National VMT 

0-3 1.5 16% 

4-7 5.5 28% 

8-12 10 30% 

13-18 15.5 18% 

19-24 21.5 6% 

25 27.5 2% 

Using an average vehicle speed of 19.9 mph for city driving and 57.1 mph for highway 

driving (from Draft MOVES2004), the vehicle drag coefficient increases by 73.5% and 15.9%, 

respectively, on average. Assuming average city and highway fuel economy of 18.8 and 25.5 

mpg (from our database of 615 recent model year vehicles without any factor for nondynamometer effects) and city and highway VMT weights of 43% and 57%, respectively, 

composite fuel economy is 22.1 mpg in still air and 20.8 mpg with a typical distribution of wind. 

Thus, taking wind into account reduces onroad fuel economy by 6%. This is more than twice 

that estimated for the 1984 label adjustment rule. 

Roughly 60% of this 6% increase is due to the increase in drag coefficient during city 

driving. This portion of the estimate is likely the most uncertain, due to the large wind angles 

which can occur at relatively low vehicle speeds (e.g., 45% or more). This means that the 

figures taken from Gillespie are being extrapolated to a significant degree. We are not certain 

that the drag coefficient would continue to increase beyond 20 degrees wind angle at the same 

rate as below 20 degrees. However, the effective frontal area of the vehicle would continue to 

increase. Rolling resistance is also likely to increase, as the vehicle must be driven increasingly 

sideways to travel in the direction that the vehicle is pointing (i.e., down the road). It is unlikely 

that either the DOT or Gillespie estimates consider an increase in rolling resistance, as they were 

likely developed in wind tunnels where the vehicle is standing still. Thus, it is likely that the 

estimate for the effect of wind on onroad fuel economy is more uncertain than those for fuel 

quality or tire pressure. Still, the effect of wind appears to be very significant and likely larger 

than either of the other two factors. 

The final factor which we reevaluated was road roughness. Road roughness was 

estimated to reduce onroad fuel economy by 4.2% relative to that measured on the 

dynamometer.29 The model developed in 1980 included estimates of the percentage of VMT 

driven on dry (69%), wet (25%) and snow-covered (6%) roads. 33 It also included estimates of 

roadway miles which were unsurfaced, gravel, low-load asphalt, and concrete and high load 

asphalt. It also included estimates of the percentages of VMT on each roadway type, as well as 

the effect of each roadway type on fuel economy relative to that measured on a dynamometer. 

As the vehicle coast downs used to determine dynamometer road loads are conducted on dry 

concrete or high load asphalt roads, this combination of roadway type and driving condition was 

87 
assumed to have no effect on fuel economy relative to a dynamometer. All other roadway types 

and driving conditions reduced onroad fuel economy. Table III.A-26 presents the inputs to the 

model developed in 1980 (with highway and VMT estimates from FHWA’s Highway Statistics 

1977). 

Table III.A-26. Effect of Road Roughness on Onroad Fuel Economy: 1977

 Unsurfaced Gravel Low-load 

Asphalt 

Concrete or Highload Asphalt 

Percent of Roadway 

Miles 

18.2% 31.1% 27.3% 23.4% 

Percent of VMT 1.8% 9.7% 30.2% 58.3% 

Effect on Onroad Fuel Economy 

Dry -20% -15% -4% 0% 

Wet -30% -18% -5% -3% 

Snowy -35% -20% -10% -7% 

The net effect of the inputs shown in Table III.A-26 and the percentages of VMT with various 

road conditions cited above was an average fuel economy shortfall of 4.4%. 

We updated the model using more recent estimates of VMT by roadway type contained 

in FHWA’s Highway Statistics 2003. We had to combine estimates from a set of tables in order 

to estimate VMT by cars and light-duty trucks by roadway surface. We began with estimates of 

national VMT by cars and 2-axle, 4 tire trucks by roadway type in 2003 from Table VM-1. We 

then converted the VMT by 2-axle, 4 tire trucks to the VMT by EPA-defined light-duty trucks by 

multiplying by 0.9234, which was derived from an Oak Ridge National Laboratory study. 

According to this study, 7.66% of the VMT by 2-axle, 4 tire trucks is by trucks which have a 

curb weight above 6000 pounds or a gross vehicle weight rating of above 8500 pounds, which 

put these vehicles into EPA’s heavy-duty vehicle class. 

We then obtained estimates of the length of roadway by surface type for each roadway 

class from Table HM-12. The surface types used in Table HM-12 differ somewhat from those 

cited in the EPA fuel economy study and shown in Table III.A-26. Table HM-12 uses two major 

classes of roadway surface: Unpaved and Paved. There are five sub-classes of paved roadway 

surfaces: low, intermediate, high-flexible, high-composite, and high-rigid, where low, 

intermediate and high refer to the weight carrying capacity of the roadway. According to 

FHWA, Paved includes the following categories: 

Low type-- an earth, gravel, or stone roadway which has a bituminous surface course less 

than 1” thick suitable for occasional heavy loads; 

Intermediate Type-- a mixed bituminous or bituminous penetration roadway on a flexible 

base having a combined surface and base thickness of less than 7”; 

High-Type Flexible-- a mixed bituminous or bituminous penetration roadway on a 

flexible base having a combined surface and base thickness of 7” or more; also 

includes brick, block, or combination roadways; 

88 
High-Type Composite-- a mixed bituminous or bituminous penetration roadway of more 

than 1” compacted material on a rigid base with a combined surface and base 

thickness of 7” or more; 

High-Type Rigid-- a Portland Cement Concrete roadway with or without a bituminous 

wearing surface of less than 1”. 

From these definitions, it seemed that the gravel referred to in the definition of the Low 

Paved class was the support for the bituminous surface. This would imply that both unsurfaced 

and gravel surfaced roadways of Table III.A-26 fell into the unpaved categories. However, it 

also seemed possible that the gravel roadways referred to in Table III.A-26 were included in the 

low paved category. It also seemed uncertain whether the high, flexible paved roadway fell into 

the low-load asphalt or high-load asphalt. In order to reflect these uncertainties, we developed to 

mappings of these six roadway surfaces onto the four types used in the EPA study in order to 

bracket the potential fuel economy impact. Table III.A-27 shows these two mappings. 

Table III.A-27. Mapping of Roadway Surfaces 

Unsurfaced Gravel Low-load Asphalt Concrete or Highload Asphalt 

Low Fuel Economy Unpaved Low Paved Intermediate + HighFlexible Paved High-Composite + 

High-Rigid Paved 

High Fuel Economy None Unpaved + 

Low Paved 

Intermediate Paved High-Flexible + 

High-Composite + 

High-Rigid Paved 

Using these two sets of roadway mappings, we converted the total roadway lengths for each 

roadway surface class within each highway class from Table HM-12 into lengths of roadway by 

the surface classes shown in Table III.A-26 within each highway class. 

We then estimated the effect of roadway condition on the fuel economy of vehicles 

driving on each roadway surface type. We did this by multiplying the estimates of the 

percentage of VMT driven on dry (69%), wet (25%) and snow-covered (6%) roads to the 

changes in fuel economy for each roadway condition for each roadway surface (shown in Table 

III.A-26). The result is that the average fuel economy on unpaved roads, gravel roads, low load 

paved road and high load paved roads are 72.5%, 83.9%, 95.1%, and 98.8% of that on dry high 

load paved roads like those simulated during dynamometer testing. 

We then applied these effects of roadway conditions to the distribution of roadway 

surfaces for each highway class in order to develop estimates of the average effect of roadway 

conditions for each highway class. We then weighted these effects by the distribution of car and 

light truck VMT by highway class in order to develop a national average effect of roadway 

surface and condition on fuel economy. Using the two mappings, we estimate that the national 

average impact of roadway surface and condition on fuel economy is 1.4-3.2%. Based on the 

decisions underlying the two mappings, we believe that the low end of this range is more likely 

than the high end. Thus, it appears that the effect of roadway surface and condition is lower 

today than it was in 1977. 

89 
The other non-dynamometer factors are more difficult to estimate. Fortunately, they 

appear to be smaller in magnitude. The following table shows the breakdown of the impact of all 

the non-dynamometer factors as estimated for the 1984 rule. It also updates the impacts for the 

four factors discussed above, as well as eliminating those factors which are now addressed by the 

US06, SC03 and cold FTP tests. 

Table III.A-28. Effect of Non-Dynamometer Factors on Onroad Fuel Economy 

Factor Analysis for 1984 Rule Effect Applicable to 5-Cycle Fuel Economy 

Ambient temperature -5.3% Included 

Fuel Quality 0% -1.1 to -1.5% 

Altitude -0.1% -0.1% 

Wind -2.3% -6% 

Road grade -1.9% -1.9% 

Road surface -4.2% -1.4% to -3.2% 

Road curvature -0.1% -0.1% 

Trip length 0.8% Included 

Average vehicle speed 10.6% Included 

Cold starts -0.7% Included 

Acceleration intensity -11.8% Included 

Brake drag -0.3% -0.3% 

Wheel alignment -0.3% -0.3% 

Tire switching -0.4% -0.4% 

Tire pressure -3.3% -0.5% 

Vehicle load -0.4% -0.4% 

Dynamometer loading -2.7% 

Revised test procedures may have removed 

most of these effects 

Tire effects -5.1% 

Weight classification -1.0% 

Manual transmissions -1.8% 

Power accessories, air 

conditioning 

~0% Air conditioning included 

Sum -30% -12% to -15% 

As can be seen, the net impact of non-dynamometer factors applicable to a 5-cycle fuel economy 

estimate is 12-15%. The four factors evaluated in detail above comprise the majority of the 

impact. Together, other factors like road grade, road curvature, altitude and vehicle condition 

add only 3.5% to the overall estimate. 

We received little comment on the estimates of the impacts of the individual untested 

factors on onroad fuel economy. One commenter indicated that he thought that the effect of 

90 
wind seemed high, but offered no specific information on how the methodology or estimate 

should be changed. 

Due to the fact that this type of analysis seemed to over-estimate the impact of these 

factors compared to onroad fuel economy estimates from owner diaries, we performed a second 

analysis starting from fleet-wide fuel economy estimates. 

As described above in section II.C above, FHWA develops annual estimates of car and 

light truck fuel economy based on estimates of total VMT and fuel consumption across the 

nation. For the NPRM, we utilized FHWA fuel economy estimates for the 2002 and 2003 

vehicle fleets. Since the time of the NPRM, FHWA has updated their estimate of onroad fuel 

economy for the 2003 fleet and published an estimate for the 2004 fleet. These latest estimates 

show lower onroad fuel economy for light trucks than those estimated recently. After adjusting 

for the difference FHWA’s and EPA’s definition of light trucks, onroad fuel economy was 19.7 

and 19.9 mpg in 2003 and 2004, respectively. Using MOBILE6.2, we estimate that fleetwide 

label fuel economy for these calendar years were 21.1 and 21.2 mpg, respectively. This indicates 

a current shortfall of roughly 6.5-7.0%. 

Absent any non-dynamometer factor, the average combined (55/45) current label value 

for the 601 conventional vehicles in our certification fuel economy database is 20.9 mpg, while 

the average combined (43/57) 5-cycle fuel economy is 21.6 mpg, or 3.5% higher. Thus, the 

shortfall between the combined 5-cycle fuel economy and the FHWA-based fleet estimates is 

roughly 10-10.5%. 

In the NPRM, we added one more factor to account for changes in FTP and HFET test 

procedures when EPA implemented the Supplemental FTP standards. Specifically, we reduced 

combined fuel economy by 3% to compensate for the removal of a 10% upward adjustment to 

the vehicle’s tractive road load horsepower setting on the dynamometer. However, we received 

a comment that, at the time of the SFTP rule, EPA had found that the net fuel economy effect of 

all the changes in test procedures was zero, not 3%. We agree with this comment. Therefore, we 

have removed this 3% adjustment from this analysis. Thus, the shortfall between the combined 

5-cycle fuel economy and the FHWA-based fleet estimates remains at roughly 10-10.5%. 

This 10-10.5% difference is slightly lower than the 12-15% estimate for the 

impact of non-dynamometer factors shown in Table III.A-28. In the NPRM, we decided to 

average the two estimates, rounding down, and include a factor of 0.89 in the 5-cycle city and 

highway formulae (i.e., a reduction of 11% in both city and highway fuel economy) to account 

for the impact of these factors. As discussed above, however, the bottom-up approach overestimated the net effect of these factors back in 1984 when the current label adjustments were 

developed. Thus, for the final 5-cycle formulae, we decided to place more emphasis on the topdown comparison. Therefore, we set the value of the non-dynamometer factor so that the 

fleetwide combined 5-cycle fuel economy matches onroad fuel economy as estimated by FHWA. 

As indicated above, the average of the current combined fuel economy label values in our 

certification fuel economy database (20.9 mpg) is slightly lower than that for the entire onroad 

fleet (21.1-21.2 mpg per MOBILE6.2). Thus, the certification database appears to be biased low 

91 
by 1.0-1.5% relative to the onroad fleet. Thus, the average of the combined 5.cycle fuel 

economy label values in our certification fuel economy database should also be 1.0-1.5% lower 

than the onroad fuel economy estimated by FHWA (19.7-19.9 mpg), or about 19.6 mpg. 

Incorporating a non-dynamometer factor of 0.905 into the 5-cycle city and highway formulae 

produces an average combined label value of 19.6 mpg. Thus, we have set the value of the nondynamometer factor in the final 5-cycle formulae to 0.905. 

6. 5-Cycle Fuel Economy Formulae 

The complete 5-cycle fuel economy formulae are developed by combining the results of 

the sections on start fuel use, running fuel use, air conditioning, cold temperature, and nondynamometer effects. The resultant formulae are described below. 

Under the final rule, a special situation could exist where the city fuel economy of a 

model type could be developed using the mpg-based formula and its highway fuel economy 

developed using an alternative 5-cycle formula based on testing over only 3 test cycles (FTP, 

HFET, and US06). This alternative 5-cycle fuel economy formula is also described below. 

a. 5-Cycle Fuel Economy Formulae 

Vehicles Tested Over a Three-Bag FTP at 75 F 

5-Cycle City Fuel Economy Formula 

The final 5-cycle city fuel economy would be calculated as follows: 

1 City FE = 0.905× , where Start FC + Running FC 

StartFC (gallons per mile) = 0.33×

⎛

⎜

(0.76 × StartFuel75 )+ (0.24 × StartFuel20 )⎞

⎟ , ⎝ 4.1 ⎠

where, 

⎡ 1 1 ⎤ Start Fuelx = 3.6× ⎢ − ⎥ , where ⎣Bag1FEx Bag 3 FEx ⎦

Bag y FEx = the fuel economy in miles per gallon of fuel during Bag 1 or Bag 3 of the FTP test 

conducted at an ambient temperature of 75 or 20°F. 

92 
Running FC =

⎡ 0.48 0.41 0.11 ⎤ ⎡ 0.5 0.5 ⎤ 0.82 × ⎢ + + ⎥ + 0.18 × ⎢ + ⎥ ⎣Bag275 FE Bag375 FE US06City FE ⎦ ⎣Bag220 FE Bag320 FE ⎦

+ 0.133 ×1.083 × [A / C FC]

where 

⎡ 1 ⎛ 0.61 0.39 ⎞⎤

A /C FC = ⎢ − ⎜

⎜ + ⎟⎟⎥ , where ⎣SC03 FE ⎝ Bag375 FE Bag275 FE ⎠⎦

US06 City FE = fuel economy in miles per gallon over the city portion of the US06 test, 

US06 Highway FE = fuel economy in miles per gallon over the Highway portion of the 

US06 test, 

HFET FE = fuel economy in miles per gallon over the HFET test, 

SC03 FE = fuel economy in miles per gallon over the SC03 test. 

5-Cycle Highway Fuel Economy Formula 

The final 5-cycle highway fuel economy would be calculated as follows: 

1 Highway FE = 0.905× , where Start FC + Running FC 

StartFC (gallons per mile) = 0.33×

⎛

⎜

(0.76× StartFuel75 )+ (0.24× StartFuel20 )⎞

⎟ , ⎝ 60 ⎠

where 

⎡ 1 1 ⎤ Start Fuelx = 3.6× ⎢ − ⎥ , and ⎣Bag1FEx Bag 3 FEx ⎦

Running FC = 1.007 × ⎢

⎡ 0.79 

+

0.21 ⎥

⎤

+ 0.133× 0.377 ×[A / C FC] ⎣US06 Highway FE HFET FE ⎦

where the various symbols have the same definitions as just described above. 

93 
Hybrid Vehicles Tested over a Four bag FTP at 75 F 

5-Cycle City Fuel Economy Formula 

The final 5-cycle city fuel economy would be calculated as follows: 

1 City FE = 0.905×

Start FC + Running FC , where 

⎛(0.76 × Start Fuel75 + 0.24 × Start Fuel20 )⎞ Start FC = 0.33×⎜ ⎟ , where ⎝ 4.1 ⎠

⎡ 1 1 ⎤ ⎡ 1 1 ⎤ Start Fuel75 = 3.6 × ⎢ − ⎥ + 3.9 × ⎢ − ⎥ ,

⎣Bag1FE75 Bag 3 FE75 ⎦ ⎣Bag 2 FE75 Bag 4 FE75 ⎦

and 

⎡ 1 1 ⎤ Start Fuel20 = 3.6 × ⎢ −

⎣Bag1FE20 Bag 3 FE20 

⎥

⎦

, where 

Running FC =

⎡ 0.48 0.41 0.11 ⎤ ⎡ 0.5 0.5 ⎤ 0.82 × ⎢ + + ⎥ + 0.18× ⎢ + ⎥ ⎣Bag475 FE Bag375 FE US06City FE ⎦ ⎣Bag220 FE Bag320 FE ⎦

+ 0.133×1.083×[A / C FC]

where 

⎡ 1 ⎛ 0.61 0.39 ⎞⎤

A /C FC = ⎢

⎣ SC03 FE − ⎜

⎜

⎝ Bag375 FE 

+

Bag475 FE ⎟

⎟

⎠

⎥

⎦

, where 

US06 City FE = fuel economy in miles per gallon over the city portion of the US06 test, 

US06 Highway FE = fuel economy in miles per gallon over the Highway portion of the 

US06 test, 

HFET FE = fuel economy in miles per gallon over the HFET test, 

SC03 FE = fuel economy in miles per gallon over the SC03 test. 

94 
5-Cycle Highway Fuel Economy Formula 

The final 5-cycle highway fuel economy would be calculated as follows: 

1 Highway FE = 0.905×

Start FC + Running FC , where 

StartFC (gallons per mile) = 0.33×⎜

⎛(0.76× StartFuel75 )+ (0.24× StartFuel20 )

⎟

⎞ , ⎝ 60 ⎠

where 

⎡ 1 1 ⎤ ⎡ 1 1 ⎤ Start Fuel75 = 3.6 × ⎢ − ⎥ + 3.9 × ⎢ − ⎥ , ⎣Bag1FE75 Bag 3 FE75 ⎦ ⎣Bag 2 FE75 Bag 4 FE75 ⎦

and 

⎡ 1 1 ⎤ Start Fuel20 = 3.6 × ⎢ −

⎣Bag1FE20 Bag 3 FE20 

⎥

⎦

, and 

Running FC = 1.007 × ⎢

⎡ 0.79 

+

0.21 ⎥

⎤

+ 0.133× 0.377 ×[A / C FC] ⎣US06 Highway FE HFET FE ⎦

where the various symbols have the same definitions as just described above. 

Hybrid Vehicles Tested over a Two-Bag FTP at 75°F 

5-Cycle City Fuel Economy Formula for Vehicles Tested Over a 2-Bag FTP at 75°F 

The final 5-cycle city fuel economy for vehicles tested over a 2-Bag FTP at 75°F would 

be calculated as follows: 

1 City FE = 0.905×

Start FC + Running FC , where 

(0.76 ×Start Fuel75 + 0.24 ×Start Fuel20 ) Start FC = 0.33×

4.1 , where 

95 
⎡ 1 1 ⎤ Start Fuel75 = 7.5× ⎢ − ⎥ , where ⎣Bag1/ 2 FE75 Bag 3/ 4 FE75 ⎦

⎡ 1 1 ⎤ Start Fuel20 = 3.6 × ⎢ − ⎥ , where ⎣Bag1FE20 Bag 3 FE20 ⎦

Bag y FEx = the fuel economy in miles per gallon of fuel during Bag 1 or Bag 3 of the FTP test 

conducted at an ambient temperature of 75 or 20°F. 

Bag x/y FEx = fuel economy in miles per gallon of fuel during Bags 1 and 2 or Bags 3 and 4 of 

the FTP test conducted at an ambient temperature of 75°F. 

Running FC =

⎡ 0.90 0.10 ⎤ ⎡ 0.5 0.5 ⎤ 0.82 × ⎢ + ⎥ + 0.18 × ⎢ + ⎥ ⎣Bag3 / 475 FE US06 City FE ⎦ ⎣Bag220 FE Bag320 FE ⎦

+ 0.133×1.083× [A / C FC]

where 

⎡ 1 ⎛ 1.0 ⎞⎤

A /C FC = ⎢

⎣SC03 FE − ⎜

⎜

⎝ Bag3/ 475 FE ⎟

⎟

⎠

⎥

⎦

, where 

US06 City FE = fuel economy in miles per gallon over the city portion of the US06 test, 

US06 Highway FE = fuel economy in miles per gallon over the Highway portion of the 

US06 test, 

HFET FE = fuel economy in miles per gallon over the HFET test, 

SC03 FE = fuel economy in miles per gallon over the SC03 test. 

5-Cycle Highway Fuel Economy Formula for Vehicles Tested Over a 2-Bag FTP at 75°F 

The final 5-cycle highway fuel economy for vehicles tested over a 2-Bag FTP at 75°F 

would be calculated as follows: 

1 Highway FE = 0.905× , where Start FC + Running FC 

96 
(0.76 × Start Fuel75 + 0.24 × Start Fuel20 ) Start FC = 0.33× , where 60

⎡ 1 1 ⎤ Start Fuel75 = 7.5× ⎢ − ⎥ , and 

⎣Bag1/ 2 FE75 Bag 3/ 4 FE75 ⎦

⎡ 1 1 ⎤ Start Fuel20 = 3.6 × ⎢

Bag1FE − Bag 3 FE ⎥ , and 

⎣ 20 20 ⎦

⎡ 0.79 0.21 ⎤ Running FC = 1.007 ×

⎣

⎢

US06 Highway FE 

+

HFET FE ⎦

⎥ + 0.133× 0.377 ×[A / C FC]

where the various symbols have the same definitions as just described above. 

b. Alternative 5-cycle Highway Fuel Economy Formula 

Beginning with the 2011 model year, manufacturers would be allowed to continue to use 

the mpg-based formulae if the available 5-cycle fuel economy estimates indicated close 

alignment with the mpg-based formulae. Fuel economy values over all five cycles will be 

available for one or more vehicle configurations within each durability data group or basic 

engine group. If the 5-cycle fuel economy values for a specific emission data vehicle are no 

more than 4% below the mpg-based estimate for city fuel economy and no more than 5% below 

the mpg-based estimate for highway fuel economy, all the vehicle configurations represented by 

that emission data vehicle would be allowed to use the mpg-based formulae in complying with 

the fuel economy label requirements. If the 5-cycle fuel economy values for a specific emission 

data vehicle are more than 4% below the mpg-based estimate for city fuel economy and more 

than 5% below the mpg-based estimate for highway fuel economy, all the vehicle configurations 

represented by that emission data vehicle would be required to use the 5-cycle formulae in 

complying with the fuel economy label requirements. 

It is possible for the 5-cycle fuel economy values to meet the above criteria for either city 

or highway fuel economy, but not the other. If the 5-cycle fuel economy values for a specific 

emission data vehicle are more than 4% below the mpg-based estimate for city fuel economy, but 

no more than 5% below the mpg-based estimate for highway fuel economy, all the vehicle 

configurations represented by that emission data vehicle would be required to use the 5-cycle 

formulae in complying with the fuel economy label requirements for both city and highway fuel 

economy. All five cycles play a significant role in the 5-cycle city fuel economy formula. Once 

the five tests have been performed for the city estimate, there is little reason not to use the same 

information to derive the highway fuel economy estimate. 

We proposed a different approach for the opposite situation. If the 5-cycle fuel economy 

values for a specific emission data vehicle are no more than 4% below the mpg-based estimate 

for city fuel economy, but more than 5% below the mpg-based estimate for highway fuel 

economy, all the vehicle configurations represented by that emission data vehicle would be 

97 
allowed to use the mpg-based formulae in deriving the city fuel economy label value. The 

highway fuel economy value, however, would be based on an alternative, simplified 5-cycle 

formula as opposed to the full 5-cycle highway fuel economy formula. This alternative 5-cycle 

highway formula would be based on fuel economy values over the FTP, HFET and US06 tests. 

The impact of the SC03 test is relatively small due to the speed adjustment factor of 0.377 and 

air conditioning usage factor of 0.133 in the 5-cycle highway fuel economy formula. The impact 

of the cold FTP test is small due to the 60 mile trip length assumed for highway driving and the 

fact that we do not use the actual cold FTP test results to adjust running fuel consumption for 

colder temperatures during highway driving. 

This approach requires that we develop a simplified 5-cycle highway fuel economy 

formula which is consistent with the full 5-cycle formula. We developed this simplified formula 

using estimates of the average impact of the SC03 and cold FTP test results on 5-cycle highway 

fuel economy. In both cases, we estimated this average impact by regressing the impact of these 

test cycles on the 5-cycle highway fuel economy for the 615 vehicles in our certification 

database against fuel economy values which would be available from FTP, HFET and US06 

testing. 

Regarding the impact of the cold FTP on highway fuel economy, we regressed start fuel 

use in highway driving under a mix of ambient temperature against start fuel use in highway 

driving at 75°F. As described above, start fuel use in highway driving under a mix of ambient 

temperature is as follows: 

(0.76 × Start Fuel + 0.24 × Start Fuel ) Start FC = 0.33× 75 20 , where 60 

⎡ 1 1 ⎤ Start Fuel = 3.6× − and x can be either 20°F or 75°F. x ⎢

⎣Bag1FEx Bag 3 FEx 

⎥

⎦

The result of the regression was: 

Start FC at ambient = 0.005515 + 1.13637 * Start FC at 75°F. 

The adjusted r-squared of the regression was very good, over 0.92. 

Regarding the impact of SC03 on highway fuel economy, we regressed fuel use due to air 

conditioning use in highway driving against several estimates of running fuel use, namely Bags 2 

and 3 of the FTP, HFET and US06. As described above, fuel use due to air conditioning use is 

as follows: 

A / C FC = 1 − ⎜

⎜

⎛ 0.61 

+

0.39 

⎟

⎟

⎞

SC03 FE ⎝ Bag 3 FE75 Bag 2 FE75 ⎠

98 
In the analysis performed for the NPRM, fuel use over US06 showed the highest level of 

correlation with air conditioning use. The same was true with the expanded certification fuel 

economy database. The result of the regression is: 

0.1357 A / C FuelUse = 0.540 +

US06 Fuel Economy 

The adjusted r-squared of this regression is much lower than that for cold start fuel use (0.15). 

However, the p-values of both coefficients were less than 0.0000001, and thus, are quite 

statistically significant. 

These two relationships can be inserted directly into the 5-cycle fuel economy formula. 

The result is: 

1 Alternative Highway FE = 0.905× , where Start FC + Running FC 

(0.005515 +1.13637 × Start Fuel75 ) Start FC = 0.33× , where 60.0 

Start Fuel = 3.6 ×

⎛

⎜

⎜ 1 − 1 ⎞

⎟

⎟ , and 75 ⎝ Bag1FE75 Bag 3 FE75 ⎠

Running FC =

⎡ ⎛ 0.79 0.21 ⎞⎤ ⎡ ⎛ 0.1357 ⎢[1.0 + (0.04 × 0.18)]× ⎜

⎜ + ⎟

⎟⎥ + ⎢0.377 × 0.133× ⎜

⎜0.00540 +

⎣ ⎝US06 Highway FE HFET FE ⎠⎦ ⎣ ⎝ US06 FE 

 Hybrid gasoline-electric vehicles using this modified 5-cycle highway calculation use one 

of the following equations for start fuel, depending upon whether the vehicle is tested on a 4-bag 

FTP or a 2-bag FTP. 

For a 4-bag FTP: 

⎡ 1 1 ⎤ ⎡ 1 1 ⎤ Start Fuel75 = 3.6× ⎢ − ⎥ + 3.9× ⎢ − ⎥ ⎣Bag1FE75 Bag 3FE75 ⎦ ⎣Bag 2 FE75 Bag 4 FE75 ⎦

For a 2-bag FTP: 

⎡ 1 1 ⎤ Start Fuel75 = 7.5× ⎢ − ⎥ ⎣Bag1/ 2 FE75 Bag 3/ 4 FE75 ⎦

⎞⎤

⎟

⎟⎥

⎠⎦

99 
where the various symbols have the same definitions as just described above. 

B. Derivation of the MPG-Based Approach 

The 5-cycle fuel economy formulae derived above assume that fuel economy estimates 

are available for specific vehicles for all five dynamometer cycles and their respective bags of 

emission measurements. As discussed in the preamble to the final rule, these estimates may be 

based on fuel economy measurements, or on estimates based on test results from a similar 

vehicle. A simplified approach to implementing the 5-cycle formulae is to apply these formulae 

to test results on recent model vehicles and develop correlations between the 5-cycle city and 

highway fuel economy estimates for these vehicles and their fuel economy over the FTP and 

HFET, respectively. This simplified approach is referred to as the mpg-based approach, since 

the resultant label adjustment will vary depending on the measured fuel economy (i.e., mpg) of a 

vehicle over the FTP and HFET tests. 

The database from which the mpg-based correlations were derived consisted of 615 

2003-2006 model year vehicles, including 14 hybrids and one diesel vehicle. All vehicles had 

been tested over all five certification test cycles. In most cases, bag-specific fuel economy 

measurements were also available, but in some cases they were not. In the latter cases, we 

estimated FTP bag-specific fuel economy using relationships between bag and whole cycle fuel 

economy which were developed from those vehicles with bag fuel economy data. The following 

table shows the relationships between bag and cycle fuel economy from our 5-cycle fuel 

economy database for the standard and cold FTP. 

Table III.B-1. Ratio of FTP Bag to Cycle Fuel Consumption 

No. of Vehicles Bag 1 Bag 2 Bag 3 

Standard FTP 

Mean 

389 

1.047 1.036 0.897 

Standard Deviation 0.040 0.029 0.036 

Coefficient of Variation 3.8% 2.8% 4.0% 

Cold FTP 

Mean 

330 

1.171 1.013 0.855 

Standard Deviation 0.055 0.028 0.036 

Coefficient of Variation 4.7% 2.8% 4.2% 

The 5-cycle formulae also require separate fuel economy estimates for the city and 

highway portions of US06. These measurements have not been taken on a regular basis. In the 

Draft Technical Support Document to the NPRM, we analyzed US06 city and US06 highway 

fuel economy data for 85 vehicles which was available. There we found that the fuel economy 

of the US06 city bag averaged 68% of that over the entire US06 cycle for conventional vehicles, 

and 77% for two hybrid vehicles. We also found that the fuel economy of the US06 highway 

bag averaged 116% of that over the entire US06 cycle for conventional vehicles, and 109% for 

two hybrid vehicles. 

100 
For the NPRM, we projected the impact of the 5-cycle approach by applying the 

relationships for conventional vehicles to all vehicles, including hybrids. For the FRM, we 

believe that it would be more accurate to apply the relationships for the two hybrids that were 

tested to all the hybrid vehicles in the certification database. While only two hybrids were tested 

over a two-bag US06 test, the fact that fuel economy over the US06 city bag was closer to that 

over the entire US06 cycle than with conventional vehicles is very consistent with the effect of 

hybrid technology on fuel economy. That is, hybrid technology is generally more effective 

during lower speed, stop and go driving than at consistently high vehicle speeds. Which 

relationship is used to project US06 city and highway fuel economy values has no effect in the 

future for vehicles whose label values are set using the 5-cycle formulae. In this case, US06 city 

and highway fuel economy values will be measured, not estimated. However, the projections 

made here can affect the mpg-based equations, as these equations are based on projected 5-cycle 

fuel economy values. These projections for hybrids with the highest fuel economy values are 

particularly important, as these vehicles can affect the shape of the mpg-based equations at high 

fuel economy values. Fortunately, the two hybrids for which we have US06 city and highway 

fuel economy testing are hybrids with very high fuel economy values (a Prius and a Civic 

hybrid). Thus, using the relationships between US06 city, US06 highway and US06 fuel 

economy values based on the testing of these two vehicles to all hybrids in the certification 

database should be most accurate in the range of fuel economy where the mpg-based equations 

are most affected by hybrids. 

One additional adjustment was made to Cold FTP fuel economy values of all vehicles. 

This adjustment is related to the new requirement that the heater or defroster be turned on during 

the Cold FTP test. In order to estimate the impact of this change on fuel economy, EPA tested 

two conventional vehicles and two hybrid vehicles at 20°F with the heater turned on and off.37 

The results are shown in Table III.B-2. 

101 
Table III.B-2. Effect of Heater/Defroster Use on Cold FTP Fuel Use 

Bag 1 Bag 2 Bag 3 FTP 

Fuel Economy: Heater off (mpg) 

Odyssey 13.5 16.3 18.5 16.2 

Trailblazer 11.4 13.8 15.6 13.6 

Prius 32.4 50.4 39.0 44.1 

Civic Hybrid 30.2 38.2 40.8 38.2 

Fuel Economy: Heater on (mpg) 

Odyssey 13.0 15.2 17.4 15.3 

Trailblazer 11.2 13.2 15.2 13.2 

Prius 28.5 34.1 37.9 36.2 

Civic Hybrid 26 29.1 34.6 31.7 

Fuel Consumption: Heater off (gallon per 100 miles) 

Odyssey 7.42 6.12 5.42 6.16 

Trailblazer 8.79 7.25 6.40 7.33 

Prius 3.086 1.984 2.564 2.268 

Civic Hybrid 3.311 2.618 2.451 2.618 

Fuel Consumption: Heater on (gallon per 100 miles) 

Odyssey 7.67 6.58 5.76 6.55 

Trailblazer 8.89 7.56 6.59 7.57 

Prius 3.509 2.933 2.639 2.762 

Civic Hybrid 3.846 3.436 2.890 3.155 

Increase in Fuel Consumption (%) 

Odyssey 3.5% 7.5% 6.3% 6.4% 

Trailblazer 1.2% 4.4% 2.9% 3.2% 

Average 2.3% 6.0% 4.6% 4.8% 

Prius 13.7% 47.8% 2.9% 21.8% 

Civic Hybrid 16.2% 31.3% 17.9% 20.5% 

Average 14.9% 39.5% 10.4% 

Increase in Fuel Consumption after adjusting for test procedure differences (%) 

Odyssey 2.3% 3.8% 3.2% N/A 

Trailblazer 1.2% 2.2% 1.4% N/A 

Average 1.7% 3.0% 2.3% N/A 

Prius 13.7% 47.8% 2.9% 21.8% 

Civic Hybrid 16.2% 31.3% 17.9% 20.5% 

Average 14.9% 39.5% 10.4% 

The test procedure used in this testing differs from that being promulgated in the final 

rule. In this testing, the defroster was turned on to the maximum position immediately at the 

start of the test and held there throughout the test. This is a more severe setting than we are 

promulgating, where the start of defrosting is being delayed two minutes and then reduced to a 

more moderate setting during Bags 2 and 3. To account for these differences, we reduced the 

impact of defrosting on fuel consumption for conventional vehicles. These reduced impacts are 

shown in the final section in Table III.B-2. Specifically, we reduced the adjusted the impacts 

during Bags 2 and 3 by a factor of two. We also reduced the impact on Bag 1 fuel consumption 

102 
for the Odyssey to reflect the relative impact for the Odyssey on Bags 2 and 3 compared to those 

for the Trailblazer. The Bag 1 impact for the Trailblazer was not adjusted, because it was tested 

with the delay in defroster start-up being promulgated. Thus, the fuel consumption of 

conventional vehicles over Bags 1, 2, and 3 of the Cold FTP test in our 5-cycle certification 

database were increased by 1.7%, 2.9%, and 2.2%, respectively, to account for defroster use in 

future testing. 

As can be seen from Table III.B-2, the effect of defroster and heater use was much larger 

for the two hybrids than for the two conventional vehicles. (The temperature control was also 

turned to hot when the defroster was turned on during the hybrid testing.) This greater impact is 

likely due to the fact that operating the heater prevents the engine from shutting off during 

certain driving modes, like idling and decelerations. The same effect likely occurs when the 

heater is turned on without the defroster. Since drivers regularly use their heater under colder 

ambient conditions, this effect is occurring currently in-use. While the final Cold FTP test 

procedure is more moderate than that used in the above testing of hybrids, we believe that the 

great majority of the impact on hybrid fuel consumption was due to the elimination of the engine 

shut-off feature, as opposed to the specific defroster/heater setting. Thus, we did not believe that 

the retesting of these vehicles with the final test procedure would produce significantly lower 

fuel consumption impacts. We expect that auto manufacturers will modify their hybrid designs 

in the future to reduce this impact. However, as the mpg-based equations will be applied to 

vehicles as early as the 2008 model year, we believe that they should reflect current technology 

as much as possible. Also, hybrids which reflect improved technology in this regard can utilize 

the 5-cycle formulae, especially since hybrid models are always tested over all five dynamometer 

cycles during certification due to their unique features. Thus, we are applying the average 

impacts on Bag 1, 2, and 3 fuel consumption, as measured in the above test program, to hybrid 

fuel consumption in our 5-cycle certification database. 

Using the fuel economy values which are now available for all bags and cycles for all 65 

vehicles, we calculated 5-cycle city and highway label values. We then developed relationships 

between the 5-cycle city and highway label values and FTP and HFET fuel economy values, 

respectively, using the least squares regression function in Excel. As we did for the NPRM, we 

performed these regressions in terms of fuel consumption (i.e., gallons per mile or the inverse of 

fuel economy). For 5-cycle city fuel economy, the best fit relationship was: 

5-cycle city FE = 1 / (0.003259 + 1.18053 / FTP FE ) . 

The adjusted r-squared for this regression was 0.990. For 5-cycle highway fuel economy, the 

best fit relationship was: 

5-cycle highway FE = 1 / (0.001376 + 1.3466 / HFET FE ) . 

. 

The adjusted r-squared for this regression was again slightly worse, 0.952. Figures III-8 and 9 

show the relationship between the inverse of 5-cycle city and highway fuel economy (i.e., fuel 

consumption) versus the inverse of FTP or HFET fuel economy. The first graph shows city fuel 

consumption, while the second shows highway fuel consumption. 

103 
Figure III-8. 5-Cycle City Versus FTP Fuel Consumption 

5-Cycle City Vs. FTP Fuel Consumption 

0.15 

Inverse of 5-

C

ycle City 

F

E

(gal/mi) 

0.10 

0.05 

-

- 0.02 0.04 0.06 0.08 0.10 

Inverse of FTP Fuel Economy (gal/mi) 

Figure III-9. 5-Cycle Highway Versus HFET Fuel Consumption 

5-Cycle Highway Vs. HFET Fuel Consumption 

0.15 

Inverse 

of 5-C

ycle H

wy FE 

(gal/mi) 

0.10 

0.05 

-

- 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 

Inverse of HFET Fuel Economy (gal/mi) 

Figures III-10 and 11 show the relationship between 5-cycle city and highway fuel 

economy (i.e., fuel consumption) versus of FTP or HFET fuel economy. The first graph shows 

city fuel consumption, while the second shows highway fuel consumption. As can be seen by 

comparing the two sets of graphs, the relationships are linear in terms of fuel consumption, but 

become curved in terms of fuel economy. 

104 
Figure III-10. MPG-Based City Fuel Economy 

MPG-Based City FE 

70 

5-C

y

cle City Fu

el Econo

m

y

(m

p

g) 

60 Current Label 

50 

40 

30 

20 

10 

0 

0 10 20 30 40 50 60 70 

FTP Fuel Economy (mpg) 

Figure III-11. MPG-Based Highway Fuel Economy 

MPG Based Highway FE 

70

60

5-C

y

cle H

wy Fuel

Economy (

m

pg)

50 

40 

30 

20 

10 

0 

0 10 

Current Label 

20 30 40 50 60 70 

Current HFET Fuel Economy (mpg) 

The standard error of the difference between the mpg-based equations and the 5-cycle 

fuel economies are 0.5 mpg and 1.15 mpg for city and highway fuel economy, respectively. 

These differences represent 3% of the average 5-cycle city fuel economy and 5% of the average 

5-cycle highway fuel economy, respectively. Thus, while the mpg-based equations are able to 

reflect much of the difference in fuel economy represented by the 5-cycle formulae, differences 

105 
between the fuel economy of individual vehicles on the order of 0.5-1.1 mpg are muted by the 

mpg-based approach. 

C. Variability in Onroad Fuel Economy 

As described in the preamble to the final rule, EPA is proposing to continue to set the city 

and highway mpg estimates at a level that reflects average fuel economy. However, we desire 

the fuel economy label to indicate the range of onroad fuel economy that drivers might 

experience. Therefore, it is important to understand the variability of onroad fuel economy. 

We begin with a review of the work done in this area for the 1984 fuel economy 

adjustment rule. At that time (circa 1982), EPA conducted a systematic review of the onroad 

fuel economy experienced by over 40,000 drivers compared to the EPA fuel economy labels for 

each vehicle.38

 Vehicles were separated into 3 categories: 1) primarily city driven, 2) primarily 

highway driven, and 3) mix of driving. The percentage differences between onroad and either 

the EPA city or highway fuel economy label was determined for the first two groups. The results 

were generally normally distributed. The results for city driven vehicles are depicted in Figure 

III-12. 

Figure III-12. Onroad FE Versus Pre-1984 EPA City Label for City Driven Cars 

In-Use FE Before Adjustment: 1984 Label Rule 

0% 

5% 

10% 

15% 

20% 

-80% -60% -40% -20% 0% 20% 40% 

Difference from EPA City Label FE (FTP) 

% of Driv

ers

61% below 90% of 

label FE 

4% above 110% of 

label FE 

+10% 

The analysis of the onroad fuel economy by primarily city drivers found that average onroad fuel 

economy was 90% of the then current EPA city label (i.e., a 10% shortfall on average). At that 

time, the city label value was simply the fuel economy over the FTP. As shown in Figure III-12, 

only 4% of all drivers achieved an onroad fuel economy more than 10% of their vehicle’s city 

fuel economy label. And 61% achieved an onroad fuel economy less than 90% of their vehicle’s 

city label. 

106 
Assuming a normal distribution of the onroad fuel economy of any specific vehicle, as 

was done in the 1984 label adjustment rule, it is possible to use this information to calculate a 

coefficient of variation for the difference between onroad fuel economy and EPA’s city label. 

As shown in Figure III-12, 4% of all predominantly city drivers achieved more than 110% of the 

EPA city fuel economy label. For a normal distribution, 4% of the population exceeds the mean 

of the population plus 1.75 times the standard deviation. In this case, since the population is in 

percentage terms (onroad fuel economy divided by label fuel minus 1.0), the standard deviation 

is equal to the coefficient of variation in the ratio of onroad fuel economy to EPA label value. 

Likewise, 61% of all predominantly city drivers achieved less than 90% of the EPA city fuel 

economy label. Another way to put this is that 39% of all drivers achieved more than 90% of the 

EPA city fuel economy label. For a normal distribution, 39% of the population exceeds the 

mean of the population plus 0.279 times the coefficient of variation. Thus, 20% of the EPA city 

fuel economy label (110% minus 90%) represents 1.471 (1.75 minus 0.279) times the coefficient 

of variation. The coefficient of variation for this distribution is therefore 13.6% (20% divided by 

1.471). 

The comparison of onroad fuel economy and EPA highway label for highway driven 

vehicles yielded similar results. In the case of onroad fuel economy during highway driving, 

only 8% of all predominantly highway drivers achieved an onroad fuel economy more than 

110% of their vehicle’s highway fuel economy label. Of all predominantly highway drivers, 

34% achieved an onroad fuel economy within 10% of their vehicle’s highway label and 58% 

achieved an onroad fuel economy below 90% of their vehicle’s highway label. These 

percentages apply prior to the 22% downward adjustment to the highway fuel economy label 

implemented in that rulemaking. 

Again for a normal distribution, 8% of the population exceeds the mean of the population 

plus 1.405 times the coefficient of variation. Likewise, 42% (8% plus 38%) of all drivers 

achieved more than 90% of the EPA highway fuel economy label. For a normal distribution, 

42% of the population exceeds the mean of the population plus 0.202 times the coefficient of 

variation. Thus, 20% of the EPA highway fuel economy label (110% minus 90%) represents 

1.203 (1.405 minus 0.202) times the coefficient of variation. The coefficient of variation for this 

distribution is therefore 16.6% (20% divided by 1.203). 

The goal of the final 10% and 22% adjustments was to move the average onroad fuel 

economy closer to either the city or highway label value, as applicable. These adjustments, 

however, do not affect the underlying variability of the data. They increase the coefficients of 

variation slightly, because they reduce the denominator (the EPA label value) by 10% or 22%, 

respectively. They primarily shift the distribution over by 10% or 22%. However, EPA did 

analyze the effect of more complex adjustments, which depended on the several vehicle factors, 

such as front versus rear wheel drive, manual or automatic transmission, gasoline or diesel 

engine, etc. Applying this more complex system of adjustments reduced the variability in onroad 

versus EPA label fuel economy somewhat. For city driven vehicles, the coefficient of variation 

decreased to 13.1%, while that for highway driven vehicles decreased to 14.1%. Since the 5­

cycle formulae basically adjust fuel economy in a similar fashion (i.e., some vehicles receive 

more of an adjustment than others), we believe that it these somewhat smaller coefficients of 

variation are more indicative of what drivers would experience with vehicles labeled using the 5­

107 
cycle formulae. Thus, we will use a coefficient of variation of 13-14% to determine an 

adjustment that would convert a mean fuel economy into a 25th percentile fuel economy. As 

discussed above, 75% of the population of a normal distribution exceeds the mean minus 0.675 

times the standard deviation (or coefficient of variation in this case). Applying this factor to our 

estimate of the coefficient of variation of 13-14% yields an offset of 9-10%. 

Oak Ridge National Laboratory (ORNL), sponsored by the Department of Energy, has 

recently begun a program where drivers can submit their own fuel economy measurements via 

the Internet.

39

 The program is commonly referred to as “Your MPG.” The Your MPG data are 

similar in nature to the much larger databases analyzed for the 1984 label adjustment rule. 

Drivers measure their own fuel economy and provide a perceived split of their driving into city 

and highway categories. The strength of this type of data is the fact that the vehicle is being 

operated by the owner or regular driver in typical use. The weaknesses are the unknown 

representativeness of the sample, the unknown nature of the technique used by the owner/driver 

to measure fuel economy and the short time period over which fuel economy is generally 

assessed (e.g., as short as a tank full of fuel or two). In the particular case of the ORNL 

database, its current size is still small (8180 estimates of fuel economy for 4092 vehicles) 

compared to those available in 1984, though it is growing daily. 

We compared the fuel economy estimates submitted to the ORNL website with each 

vehicle’s fuel economy label. We combined the city and highway labels using each driver’s 

estimate of the percentage of their driving that was city-like and highway-like. If a driver did not 

provide an estimate of the breakdown of their driving pattern, we assumed that their driving was 

55% city and 45% highway. We calculated the percentage difference between the onroad fuel 

economy and the current composite EPA label value. (A more detailed discussion of these 

estimates is presented in Chapter II of this Final Technical Support Document .) The average 

difference for more than 7300 individual fuel economy estimates was -1.4%, meaning that 

onroad fuel economy was just slightly lower than the composite EPA label value using the split 

of city and highway driving estimated by the driver. This metric is analogous to those presented 

above from the early 1980’s, as vehicles were segregated then into those with predominantly city 

or highway driving. The standard deviation in this percentage difference was 13%, very 

consistent with the estimates derived above. 

Another source of onroad fuel economy data is the recent testing of over 100 vehicles in 

the Kansas City area. Valid onroad fuel economy measurements were obtained for roughly one 

day of driving from roughly 100 vehicles. The average onroad fuel economy was 30.4 mpg, 

while the average composite EPA label value was 31.4 mpg. The standard deviation of the 

percentage difference between onroad fuel economy and EPA composite fuel economy was 

14%. This is only slightly higher than the estimates from the early 1980’s and the Your MPG 

website. We would have expected a larger variability than these other sources for two reasons. 

One, we did not segregate vehicles into primarily city and highway driving categories or account 

for the predominance of one or the other type of driving. Two, driving can vary significantly 

from day to day. With only one day’s worth of driving measured, the variability in fuel economy 

would be expected to be much higher than if a week or two of driving were assessed. At the 

same time, all of these vehicles were located within a single metropolitan area, so their driving 

did not reflect much urban/rural diversity, nor the diversity likely present between urban areas. 

108 
And only 100 vehicles were assessed, including a unrepresentatively high number of hybrids. 

(The standard deviation only increased to 15% when the hybrids were excluded.) Thus, the 

small sample may be a factor. 

Finally, we evaluated the variability in the Consumer Report fuel economy measurements 

compared to the current EPA city and highway label values. (See section II.B.1 for a more 

complete discussion of the Consumer Report fuel economy estimates.) The standard deviations 

of the percentage difference between the Consumer Report and current EPA fuel economy were 

8% for city and 7% for highway. These figures are lower than the 13-16% value found during 

the 1985 label adjustment rule and in the ORNL Your MPG database. However, Consumer 

Report adjusts their fuel economy measurements to represent a single ambient temperature and 

all of their testing follows the same road routes. Thus, both the ambient conditions and the 

driving patterns are much more consistent than those experienced by the population of drivers in 

the U.S. The fact that variability is still as high as 7-8% tends to confirm that the 13% 

assumption described above is reasonable. 

All of the above estimates of the standard deviation in the percentage difference between 

onroad and EPA label fuel economy fall in the range of 13-16%. The more recent estimates fall 

towards the lower end of this range. Thus, we will select 13% as the best point estimate of 

variability. Multiplying the standard deviation by 67.5% produces an offset from mean fuel 

economy which should encompass an additional 25% of drivers. The 5-cycle formulae derived 

in section III.A and the mpg-based formulae derived in section III.B, including the 11% 

downward adjustment for non-dynamometer effects, represent estimates of mean onroad fuel 

economy. All of the inputs to the 5-cycle formulae are based on national averages of the relevant 

parameter. Thus, reducing these estimates by 9% (i.e., multiplying them by 0.91) would convert 

these figures from the mean fuel economy achieved on the road to the 25th percentile of the range 

of onroad fuel economies achieved. This would produce a label value which would be achieved 

or exceeded by 75% of all drivers. Figure III-20 depicts this graphically. 

109 
Figure III-13. Variability in Onroad FE 

Variability in Onroad FE 

0% 

5% 

10% 

15% 

20% 

-40% -20% 0% 20% 40% 

Onroad FE minus Composite EPA Label FE 

% of Drivers 

Median: 0% 

25th percentile: -9% 

10th percentile: -18% 

75% of 

drivers 

-9% 9%

25% of 

drivers 

50% of 

drivers 

The frequency distribution of onroad fuel economy shown in Figure III-13 assumes that the 5­

cycle formulae developed above match onroad fuel economy on average In this case, 25% of 

drivers achieve an onroad fuel economy below 90% of their label value and 25% achieve an 

onroad fuel economy above 110% of their label value. Half of all drivers achieve an onroad fuel 

economy within plus or minus 9% of their label value. 

The adjustment appropriate to convert mean fuel economy estimates to those 

representative of other percentiles is straightforward. For example, the 10th and 90th percentile 

fuel economy values would be 1.28 times the coefficient of variation off of the mean value, or 

17% downward and upward adjustments from the mean. The 5th and 95th percentile fuel 

economy values would be 1.645 times the coefficient of variation off of the mean value, or 21% 

downward and upward adjustments from the mean. 

D. Impact of the 5-Cycle and MPG-Based Formulae on Fuel Economy 

Labels 

The impact of the final rule on city and highway fuel economy label values was assessed 

using the same database of 615 late model year vehicles used to develop the mpg-based 

adjustments above. It should be noted that these data are not sales weighted. In fact, most 

specific vehicle models included in the database are “worst case” for emission performance 

purposes within their model group, as this is currently one of the criteria used by EPA to 

determine which vehicles should be tested over the US06, SC03 and cold FTP tests for emissions 

compliance. Table III.D-1 presents the results of this comparison for all 615 vehicles, as well as 

various sub-sets of vehicles. 

110 
Table III.D-1. Current and 5-Cycle Label Fuel Economies by Model Typei 

Current City 5-Cycle City Current Highway 5-Cycle Highway 

Conventional Vehicles 

Large car 15.7 13.8 21.9 19.7 

Midsize car 20.5 17.8 27.9 25.6 

Minivan 17.4 15.2 23.6 20.9 

Pickup 15.1 13.2 18.9 17.2 

Small car 20.7 18.1 27.3 25.3 

Station wagon 20.3 17.6 26.6 23.5 

SUV 16.8 14.6 21.6 19.5 

Van 12.5 10.9 16.0 14.3 

All conventional 18.6 16.2 24.6 22.4 

All hybrids 41.6 32.0 40.6 36.8 

Diesel (one midsize car) 26.2 22.7 35.3 31.4 

All vehicles 19.1 16.4 24.9 22.7 

The next table shows the effect of the 5-cycle formulae on conventional gasoline fueled 

vehicles with particularly high or low fuel economy. 

Table III.D-2. Current and 5-cycle Label Fuel Economy by Propulsion System 

City Highway 

Current 

(mpg) 

5-Cycle 

(mpg) 

Percent 

Change 

Current 

(mpg) 

5-Cycle 

(mpg) 

Percent 

Change 

Hybrids 42.7 33.0 -22.3% 42.8 36.9 -12.9% 

Diesel 26.2 23.4 -10.7% 35.3 32.0 -9.3% 

Conventional Gasoline-Fueled Vehicles 

12 Highest FE 30.9 26.9 -12.9% 36.6 34.0 -6.9% 

12 Lowest FE 10.2 9.5 -6.9% 14.8 14.8 -0.2% 

Average 18.6 16.5 -10.8% 24.6 22.8 -7.4% 

As can be seen from Tables III.D-1 and III.D-2, use of the 5-cycle formulae would reduce both 

current city and highway fuel economy label values. For conventional vehicles, city and 

highway fuel economy values would be reduced an average of 13% and 9%, respectively. For 

higher than average fuel economy vehicles, the reduction in city fuel economy would be slightly 

higher, while for lower than average fuel economy vehicles, the reduction in city fuel economy 

would be slightly lower. The change in highway fuel economy is essentially independent of 

current highway fuel economy. 

i

 These figures may differ from those that might appear on the EPA fuel economy window sticker, as they 

have not undergone any sales weighting. They have been derived by applying fuel economy label formulae (e.g., 

0.9 times FTP fuel economy) to fuel economy test results for individual vehicles. 

111 
The impact on hybrid vehicles would be significantly greater for city fuel economy, 

averaging a 23% reduction. However, the reduction in highway fuel economy would be the 

same as for conventional gasoline-fueled vehicles. This greater impact occurs primarily because 

a number of the fuel efficient aspects of hybrid vehicles produce their maximum benefit under 

conditions akin to the FTP tests, and are somewhat less beneficial during aggressive driving, 

colder ambient temperatures and when the air conditioner is turned on. The impacts of the 5­

cycle formulae on the single diesel vehicle in the database are very similar to those for 

conventional gasoline fueled vehicles. 

The impact of the mpg-based formulae would be very similar on average to those shown 

in Tables III.D-1 and III.D-2 above for conventional vehicles, gasoline-fueled and diesel. This is 

not surprising for conventional gasoline fueled vehicles, since the mpg-based formulae are based 

essentially on the average results of the 5-cycle formulae and the vast majority of the vehicles in 

the database are conventional gasoline-fueled vehicles. The 5-cycle fuel economy values for the 

one diesel in the database also fall very near the mpg-based curves. However, the impact of the 

mpg-based formulae on the current city fuel economy label values for hybrids would vary 

significantly from the 5-cycle values. Basically, the impact on hybrids would reflect that of 

conventional vehicles with the same current fuel economy levels. The mpg-based regressions 

therefore, represent essentially the impact of the 5-cycle formulae on conventional vehicles, 

which is less than that for hybrids. The impact on the mpg-based formulae for hybrids is shown 

in Table III.D-3 below. The impact on the city fuel economy label is still somewhat higher (­

18%) than the average for conventional gasoline vehicles, because the average FTP city fuel 

economy for hybrids is higher than that for even the top 12 conventional gasoline fueled vehicles 

(-15%). The impact of the mpg-based formula on the highway label value for hybrids is 10%, or 

just slightly higher than that for conventional gasoline-fueled (9%). With only one diesel vehicle 

in the database, no general observations about this engine type can be made. 

Table III.D-3. Effect of MPG-Based Formulae on City and Highway Fuel Economy 

City Highway 

Current 

(mpg) 

MPGBased Percent 

Change 

Current 

(mpg) 

5-Cycle 

(mpg) 

Percent 

Change 

Hybrids 41.6 34.1 -18% 40.6 36.8 -10% 

Conventional 18.6 16.2 -13% 24.6 22.4 -9% 

In addition to looking at the overall change in fuel economy estimates for all vehicles in 

the database, we also focused on those manufacturers responsible for the majority of sales in the 

U.S. This approach may better reflect the changes likely to be seen by the majority of 

consumers. In effect, Table II-3 above includes vehicles by Aston Martin and Rolls-Royce in the 

percent change, and these vehicles are weighted equally with cars made by GM, Ford, 

DaimlerChrysler, and other top-selling manufacturers. According to Autodata Corporation, the 

seven manufacturers with the greatest U.S. market share account for more than 90 percent of 

U.S. sales. Table II.D-4 shows these manufacturers, their 2005 U.S. market share, and the 

average percent change in city and highway fuel economy estimates for each of these 

manufacturers as represented in our database. It is important to note, however, that these 

112 
estimates are not intended to represent or include the entirety of a manufacturer’s product line, 

and should not be interpreted as such. These estimates are derived from our database of 615 test 

vehicles for which data on all five emission and fuel economy test procedures is available, and 

because of differing ways in which manufacturers test their vehicles and submit data to EPA, the 

database may not reflect the range of makes and models similarly across manufacturers. 

Table II.D-4. Effect of New Methods on Fuel Economy Estimates for Major 

Manufacturers 

Manufacturer 

2005 U.S. Market 

Share (%)* 

Average Change in 

City Fuel Economy 

Estimate 

Average Change in 

Highway Fuel 

Economy Estimate 

General Motors 25.9 -10% -11% 

Ford Motor Co. 17.9 -12% -10% 

DaimlerChrysler 14.9 -10% -11% 

Toyota 13.7 -11% -7% 

Honda 8.9 -13% -7% 

Nissan 6.1 -11% -7% 

Hyundai 2.9 -13% -8% 

Average -12% -8% 

* Source: Autodata Corp., Woodcliff Lake, New Jersey. 

The following table shows the effect of the various aspects of the 5-cycle formulae on 5­

cycle city and highway fuel economy relative to fuel economy over the FTP and HFET, 

respectively. 

Table III.D-5. Effect of Various Factors on 5-cycle Fuel 

Economy 

City FE Highway FE 

Conventional Hybrid Conventional Hybrid 

Start Fuel 1.2% 1.0% 

0.6% 0.7% 

Cold Temp 

Start 1.4% 4.5% 

Cold Temp 

Running 2.1% 10.1% 0.7% 0.7% 

A/C 3.2% 5.5% 2.8% 3.7% 

Running 3.8% 4.0% 16.8% 20.9% 

E. Sensitivities and Uncertainties in the 5-Cycle Fuel Economy 

Formulae 

In this section, we evaluate the impact of a series of alternative assumptions and 

approaches to developing the 5-cycle fuel economy formulae. The organization of this section 

basically follows that of section II.A above. Alternatives regarding start fuel use are addressed, 

followed by those affecting running fuel use at 75°F, fuel associated with air conditioning use 

and the impact of colder temperatures. 

113 
1. Start Fuel Use 

There are five key factors which affect start fuel use on a gallon per mile basis. These 

are: 1) the distribution of starts as a function of soak time and time of day, 2) the effect of soak 

time and ambient temperature on start fuel use, 3) fuel consumption associated with a cold start, 

4) average trip length, and 5) heater/defroster use. Each of these factors will be addressed below 

in turn. 

Regarding the distribution of starts as a function of soak time and time of day, we know 

of no other existing estimate which is as representative and extensive as the Baltimore-Spokane 

data used to develop the MOBILE6.2 and Draft MOVES2004 distributions. The recent testing in 

Kansas City covers nearly as many vehicles. However, the number of days of driving assessed 

for each vehicle is well below that achieved in Baltimore and Spokane. Georgia Tech has been 

studying driving patterns in Atlanta via vehicle instrumentation for some time. The amount of 

data which they have collected to date exceeds that obtained in Baltimore and Spokane. EPA 

has begun to obtain the start and trip related information from this study. However, the work 

involved is considerable and the results are not yet available. 

Regarding the effect of soak time and ambient temperature on start fuel use, the 

correlations used in Draft MOVES2004 and EMFAC2000 are both recent and addressed 

essentially all data available at that time. We know of no other data addressing the effect of soak 

time on start fuel use. Some information regarding the impact of ambient temperature on start 

fuel use at 50°F is available from California certification testing. California requires a small 

number of vehicles to have their emissions tested at 50°F each year. We obtained this data for 

nine conventional Honda vehicles, two Honda hybrids and two Toyota hybrids. 

The nine conventional Honda vehicles showed slightly lower sensitivity of start fuel use 

to temperature than that estimated in Draft MOVES2004. At 50°F and 20°F, the nine vehicles 

showed 1.51 and 2.30 times the start fuel use as at 75°F, respectively. (Start fuel use being 

defined as 3.59 miles times the difference in fuel consumption in Bags 1 and 3 of the FTP.) 

Draft MOVES2004 estimates ratios of 1.63 and 2.75 at 50°F and 20°F, respectively. The 90% 

confidence intervals around the means of the Honda data were roughly half of the difference 

between the means for the Honda vehicles and the Draft MOVES2004 estimates (i.e., 0.07 and 

0.20, respectively). We do not have estimates for the confidence limits around the Draft 

MOVES2004 estimates. However, given the greater number of vehicles tested, the confidence 

intervals around the Draft MOVE2004 projection are probably smaller. Thus, while 1-4 

individual Honda vehicles showed a greater sensitivity to temperature than projected by Draft 

MOVES2004, on average the Honda vehicles are less sensitive at a 90% confidence level. 

We re-estimated the weighting for start fuel use at 20°F and 75°F using the temperature 

sensitivity of the average Honda conventional vehicle. We modified the coefficients of the Draft 

MOVES2004 equation for the ratio of start fuel use as a function of temperature to match the 

ratios of 1.51 and 2.30 at 50°F and 20°F, respectively. The resulting equation was: 

Start fuel at T / Start fuel use at 75°F = 1 – 0.0170 * ( T – 75 )+ 0.00013 * ( T – 75 )2 

114 
Using this equation, we found that the weighting for start fuel use at 20°F and 75°F changed in 

the third decimal place, but remained the same when rounded to two decimal places (0.24 and 

0.76, respectively). This is likely the result of the fact that the Honda vehicles were less sensitive 

at both 50°F and 20°F. Thus, the effect of lower temperature sensitivity at 50°F is appropriately 

reflected in the 5-cycle formulae by the lower start fuel use measured during the cold FTP at 

20°F. 

All four hybrid vehicles showed greater sensitivity to temperature at 20°F than projected 

by Draft MOVES2004, but only three showed greater sensitivity at 50°F. Table III.E-1 shows 

the temperature sensitivities of these four vehicles. 

Table III.E-1. Sensitivity of Hybrid Start Fuel Use to Ambient Temperature 

Vehicle 

Ratio of Start Fuel Use at Temperature X to 75°F Weight of Start Fuel 

X = 50°F X = 20°F Use at 20°F

Honda Insight 1.99 3.68 0.24 

Honda Accord 2.02 2.93 0.30 

Toyota 2004 Prius 1.45 4.40 0.14 

Toyota RX 400H 2.55 4.68 0.26 

Draft MOVES2004 1.63 2.74 0.24 

As can be seen, the calculated 20°F cold start weights range from 0.14-0.30 for the four hybrids, 

compared to 0.24 based on Draft MOVES2004. On average, the results for the four hybrids 

essentially match that based on Draft MOVES2004. Individually, the Prius and the RX 400H 

data produce cold temperature weights very similar to that based conventional vehicles. The 

Honda Accord data produces a greater weight for cold start fuel use at 20°F, due to the fact that 

its cold start fuel use at 50°F is high relative to that at 20°F. The opposite is true for the 2004 

Prius. Using a weight of 0.30 for cold start fuel use at 20°F for the Accord hybrid would reduce 

its 5-cycle city fuel economy by 0.1 mpg from 21.6 to 21.5 mpg. Using a weight of 0.14 for cold 

start fuel use at 20°F for the 2004 Prius would increase its 5-cycle city fuel economy by 0.5 mpg 

from 43.6 to 44.1 mpg. In both cases, 5-cycle highway fuel economy would be unaffected. 

Based on this limited data, it appears unlikely that uncertainty in the effect of ambient 

temperature on start fuel use would significantly affect city 5-cycle fuel economy. It is certain to 

have no effect on 5-cycle highway fuel economy, due to the extremely low contribution of start 

fuel use in highway driving. Hybrids would likely show the greatest variability in this area, due 

to the greater number of technological factors that could be affected. Even for these vehicles, the 

5-cycle fuel economy for the vehicle reflecting the greatest difference in temperature sensitivity 

was only changed 1%. 

Regarding fuel consumption associated with a cold start, the primary issue is the 

assumption that the vehicle is fully warmed up by the end of Bag 1 of the FTP. We are not 

aware of any evidence that this is not the case at 75°F. As discussed in section III.A.4 above, 

there is some evidence that the vehicle is still warming up after Bag 1 at 20°F. However, if some 

of the difference between Bag 2 fuel consumption at 20°F and 75°F was due to continued vehicle 

warm up, then the difference after the vehicle was fully warmed up would be commensurately 

115 
smaller. In general, the second change would tend to mitigate the first. We assessed the 

sensitivity of 5-cycle city fuel economy to just the first change by assuming that the difference in 

fuel consumption (in gallons) between Bag 2 at 20°F and 75°F was associated with the cold start. 

Doing so decreased 5-cycle fuel economy on average for non-hybrid vehicles by 0.16 mpg, or 

1%. City fuel economy was reduced by 1.9 mpg for hybrids, or 5.7%. Reducing the impact of 

cold temperature on running fuel use would reduce this impact. As discussed in section III.E.5 

below, reducing the incremental running fuel use at 20°F by 60% would increase city fuel 

economy for non-hybrid vehicles by 0.7% and by 4.5% for hybrids. Thus, the net effect of this 

shift of fuel use from running fuel use to start fuel use is to decrease city fuel economy by 0.3% 

for non-hybrids and 1.2% for hybrids. 

One additional uncertainty regarding cold start fuel consumption involves the testing of 

hybrids. Most current hybrid designs include a sizeable battery with which to store energy from 

braking, provide launch power after extended idles, etc. Current EPA test procedures require 

that hybrids undergo a four-bag FTP, the fourth bag being a repeat of Bag 2. The state of battery 

charge is required to be the same at the beginning of the FTP and the end of the four bags. 

However, the state of battery charge need not be the same at the beginning and end of Bag 1, nor 

the beginning and end of Bag 3. Thus, the possibility exists that a portion of the difference in 

fuel consumption between Bags 1 and 3 is related to a change in battery charge, which may not 

occur on the road. One contributing factor towards this possibility is the fact that the driving 

pattern of Bags 1 and 3 is not representative of driving immediately following an engine start.40 

The speeds of the second hill in Bags 1 and 3 contain too much high speed driving. This affects 

the rate of engine warm-up for all vehicles. But it could also affect the net change in battery 

charge of hybrids relative to that occurring on the road. 

As was the case with potential vehicle warm up during Bag 2, any difference in battery 

use between Bags 1 and 3 should reverse in Bags 2 and 4. Thus, if the indicated cold start fuel 

use is unrepresentatively high or low, the change in running fuel use should be in the opposite 

direction. Given the difference in trip length and bag weights between the FTP and the 5-cycle 

city formula, the opposing differences do not necessarily balance exactly. However, the net 

effect is likely to be much smaller than the effect of a change in battery charge on cold start fuel 

use. 

We examined the potential impact of a change in battery capacity in Bag 1 relative to Bag 

3 using two hybrids in our 5-cycle fuel economy database: a Honda Civic hybrid and a Toyota 

Prius. In both cases, we subtracted 0.005 gallons per mile from the fuel consumption in Bag 1 

added the same fuel consumption to Bag 2. This was an increase in Bag 1 fuel consumption of 

23-25% increase for the two vehicles. Thus, these are significant shifts in battery storage and 

probably exceed any change actually occurring during the FTP. This shift in fuel consumption 

increased the 5-cycle city fuel economy of the Civic by 0.5 mpg, or 1.5%. It increased the 5­

cycle city fuel economy of the Prius by 1.0 mpg, or 2%. These changes in city fuel economy are 

likely worse case, since the degree of shift in fuel consumption are large percentages of Bag 1 

fuel consumption. Also, such a shift in Bag 1 fuel consumption would likely produce some 

degree of shift in Bag 3 fuel consumption, as well. The above analysis assumed that Bag 3 fuel 

consumption remained unchanged. Still, the potential for change in battery charge status during 

Bags 1 and 3 could have a significant effect on 5-cycle city fuel economy values that may not 

116 
reflect onroad operation. Without actual measurement of the state of battery charge after each 

bag of the FTP and on-road following vehicle start-up, it is not possible to quantify this 

uncertainty any further. 

Regarding the effect of average trip length on start fuel consumption, the three studies 

with the most extensive collections of data were addressed in section I.A above: the instrumented 

vehicle studies conducted in support of the Supplemental FTP rule, the National Household 

Travel Study (NHTS), and the data currently being collected by Georgia Tech in Atlanta. One 

obvious uncertainty in the current estimates of average trip length is the 11% downward 

adjustment to the average trip length found in the NHTS. This reduction in average trip length 

from 9.8 to 8.7 miles was applied in order to obtain consistency with the results of the various 

instrumented vehicle studies performed in support of the Supplemental FTP rule and Atlanta. It 

is also based on the belief that instruments more accurately measure short trips and short respites 

between trips, such as moving a car out of the garage or stopping to refuel. Even if it is almost 

certain that a diary-based measure of trip length should be adjusted downward, the degree of this 

adjustment is uncertain. There is also uncertainty in the average trip length for urban dwellers, 

since only three cities have been studied with vehicle instrumentation. 

In order to assess the potential consequence of this uncertainty, we removed the 11% 

adjustment from the national average trip length, leaving it at 9.8 miles. Retaining a highway 

trip length of 60 miles, this increased the city trip length to 4.6 miles. Using 4.6 miles for the 

average trip length for city driving increased the average fuel economy of the 615 vehicles in our 

5-cycle fuel economy database from 16.9 mpg to 17.0 mpg, or by less than 1%. The 5-cycle city 

fuel economy of non-hybrids increased from 16.5 mpg to 16.6 mpg, while that for hybrids 

increased from 33.0 mpg to 33.2 mpg. These increases are quite small, particularly given the 

fact that this represents removal of the entire adjustment. Since the uncertainty in the 11% 

adjustment is likely much less than +11%, the uncertainty is average trip length is not a major 

factor causing uncertainty in the 5-cycle fuel economy formulae. 

Finally, we are promulgating a change in the test procedure of the Cold FTP which 

involves activation of the heater or defroster during the test. The vast majority of drivers 

obviously utilize their heaters at 20°F, but the number that do so between 20°F and 75°F has not 

been studied. The weighting factor for cold start fuel use was developed from test data which 

did not involve heater or defroster activation. Thus, consideration of the effect of 

heater/defroster use at various temperatures could affect this factor. Also, drivers can differ in 

the way they activate their heater and defroster at colder temperatures. As described in Section 

III.B. above, EPA has tested several vehicles at 20°F with and without heater/defroster 

activation. The impact on conventional vehicle start fuel use is minimal, decreasing 5-cycle city 

fuel economy by only 0.1%. Thus, the uncertainty in this effect as estimated in the 5-cycle 

formulae should be even smaller, given some heater and defroster use obviously occurs. 

The effect is larger for hybrid vehicles, though it should be noted that our estimate of the 

effect is only based on the test results from two hybrid vehicles. Actual label values developed 

using the 5-cycle formulae will be based on actual Cold FTP fuel economy values measured with 

the heater/defroster activated. Removing the estimated effect of heater and defroster use from 

Bags 1 and 3 increases 5-cycle city fuel economy by 1%. The uncertainty in this effect as 

117 
estimated in the 5-cycle formulae should be even smaller, given some heater and defroster use 

obviously occurs. Thus, even uncertainty in the effect of heater/defroster use on start fuel use 

does not appear to be a major source of uncertainty in 5-cycle city fuel economy. 

2. Running Fuel Use At 75°F 

In this section we evaluate several alternative approaches to determining the weighting of 

the various test cycles to estimate running fuel use at 75°F. One alternative evaluates a more 

ideal split of the US06 cycle into city and highway driving. In this case, both the second and 

third hills described in Table III.A-16 are designated as highway driving. The average speed of 

the US06 city bag decreases and the average speed of the US06 highway bag increases. 

A second alternative eliminates the three highest speed freeway cycles which were not 

derived from the 3-city studies performed in support of the Supplemental FTP rule. These three 

cycles had to be developed subsequent to these instrumented vehicle studies due to the increase 

in maximum speed limit from 55 mph nationwide to 70 mph and even higher today. The basis 

for these three higher speed cycles is not as robust as that for the other 13 facility cycles. Thus, 

there is more uncertainty in the VSP distributions of these three highest speed cycles than the 

others. 

A third alternative, actually a set of alternatives, evaluates the use of alternative fuel rates 

by VSP bin in the regression of dynamometer cycles versus onroad operation. Fuel rates from 

the EPA 15 car study, fuel rates from Draft MOVES2004 extrapolated to 23 VSP bins are 

substituted for those found in the EPA Kansas City testing. The impact of using just the 17 VSP 

bins current in Draft MOVES2004 is also evaluated. 

A fourth alternative develops test cycle combinations which represent onroad VSP 

distributions and fuel rates from EPA’s recent test program in Kansas City. Test cycle 

combinations are developed for non-hybrid and hybrid vehicles separately, given that significant 

numbers of both vehicle types were tested. 

A final alternative develops a set of cycle weighting factors using only entire test cycles 

(FTP, HFET and US06) instead of allowing separate bag weights within the FTP and US06 

cycles. 

These alternatives and their effect on the cycle weighting factors are described below. 

a. Alternative Definition of US06 City and Highway Bags 

In section III.A.2, we defined the city bag of US06 to include hills number 1, 2, 4, and 5. 

However, hill 2 was placed in the city bag for practical, testing related reasons. Here, we 

redefine the city bag in a more ideal way to only include hills 1, 4, and 5. The highway bag 

includes hills 2 and 3. The description of the various hills in US06 and their assignment to the 

city and highway bags of US06 are shown in Table III.E-2. 

118 
Table III.E-2. Split of US06 Cycle into City and Highway Portions 

Hill Portion of Driving Cycle 

(cumulative seconds) 

Maximum Speed (mph) Proposed 

Designation 

Ideal 

Designation 

1 0-43 44.2 City City 

2 44-131 70.7 City Highway 

3 132-495 80.3 Highway Highway 

4 496-563 29.8 City City 

5 564-600 51.6 City City 

With this revised split, the average speed of the city bag decreases to 18.1 mph and the 

average speed of the highway bag increases to 80.3 mph. Table III.E-3 shows the VSP 

distributions for both the proposed and more ideal city and highway bags of US06. 

119 
Table III.E-3. VSP Distributions for US06 City and Highway Bags (% of time) 

VSP Proposed Definition More Ideal Definition 

Bin US06 City US06 Hwy US06 City US06 Hwy 

0 32.6% 7.1% 31.8% 11.9% 

1 14.3% 2.5% 19.6% 3.5% 

11 1.3% 0.3% 1.4% 0.4% 

12 1.7% 0.3% 2.7% 0.2% 

13 0.9% 0.0% 1.4% 0.0% 

14 1.3% 0.0% 2.0% 0.0% 

15 0.4% 0.0% 0.7% 0.0% 

16 2.2% 0.3% 2.7% 0.4% 

17 2.2% 0.0% 2.0% 0.4% 

18 1.3% 0.0% 1.4% 0.2% 

19 3.5% 0.5% 5.4% 0.4% 

21 3.3% 0.3% 5.2% 0.2% 

22 1.5% 0.1% 2.3% 0.1% 

23 1.7% 0.1% 2.7% 0.1% 

24 0.2% 0.0% 0.4% 0.0% 

25 1.1% 0.0% 1.6% 0.0% 

26 0.4% 0.0% 0.7% 0.0% 

27 1.5% 0.1% 2.5% 0.1% 

28 2.4% 0.4% 2.3% 0.7% 

29 9.1% 1.6% 9.5% 2.9% 

33 4.6% 9.9% 1.4% 10.5% 

35 3.7% 20.7% 0.0% 19.2% 

36 0.4% 13.8% 0.0% 11.4% 

37 0.9% 16.2% 0.0% 13.4% 

38 0.9% 10.2% 0.0% 8.5% 

39 6.5% 15.5% 0.7% 15.1% 

As can be seen from Table III.E-3, the changes in the VSP distribution of the highway bag 

changes are slight. This is due to the fact that hill 2 is much shorter than hill 3, so its addition 

has a smaller impact. Also, the driving in hills 2 and 3 are similar. However, the VSP 

distribution of the city bag changes dramatically. This occurs because hill 2 varies dramatically 

from hills 1, 4, and 5. In particular, the amount of time spent in VSP bins 33-39 decreases from 

17% to 2%. 

We repeated the regressions of the VSP distributions of the dynamometer bags and cycles 

(Tables III.A-15 and III.E-3) against the VSP distributions of city and highway driving (Table 

III.A-14) weighted by the square root of the fuel rate in each VSP bin from the Kansas City test 

120 
program (Table III.A-16). The results using both sets of definitions of US06 city and highway 

bags are described in Table III.E-4 below. 

Table III.E-4. Bag/Cycle Combinations for City and Highway Driving: 

Alternative US06 Splits 

Proposed Split Ideal Split 

City Driving 

Bag 2 FTP 48% 49% 

Bag 3 FTP 41% 43% 

US06 City 11% 8% 

Highway Driving 

HFET 21% 17% 

US06 Highway 79% 83% 

As seen in Table III.E-5, the more ideal breakdown of the US06 cycle results in the same 

bags and cycles being selected to represent city and highway driving as use of the proposed split 

of US06. The contribution of the US06 city bag to city driving decreases slightly, while 

contribution of the US06 highway bag to highway driving increases slightly. 

We reanalyzed the results of the 80 vehicles tested over US06 on a second by second 

basis in order to re-estimate the relative fuel economy over the US06 city and US06 highway 

bags using the more ideal split. We found that the fuel economy over the more ideal US06 city 

bag was only 50% of that over the entire US06 cycle, compared to 68% for the proposed US06 

city bag. The fuel economy over the more ideal US06 highway bag was only 111% of that over 

the entire US06 cycle, compared to 116% for the proposed US06 highway bag. Thus, hill 2 

appears to have a somewhat mid-range fuel economy compared to the lower speed hills 1, 4, and 

5 and to the higher speed hill 3. 

Using these revised cycle combinations and the revised estimates of US06 city and 

highway fuel economy relative to US06 fuel economy, we recalculated 5-cycle fuel economy 

estimates for the 615 vehicles in our 5-cycle fuel economy database. The results are summarized 

in Table III.E-5. 

Table III.E-5. Average 5-Cycle Fuel Economy: Alternative US06 Splits 

Proposed US06 Split Ideal US06 Split 

City Fuel Economy (mpg_ 16.9 16.7 

Highway Fuel Economy (mpg) 23.1 22.1 

As can be seen, 5-cycle city fuel economy decreases slightly, by roughly 1%. However, 

highway fuel economy decreases by 4.5%. This decrease in both city and highway fuel economy 

would increase the non-dynamometer factor by roughly 3%. Thus, the net change in city fuel 

economy would be an increase of roughly 1% and highway fuel economy would decrease by 

1.5%. 

121 
b. Elimination of Three Highest Speed Freeway Cycles in Draft 

MOVES2004 

As described in section III.A-2, Draft MOVES2004 uses 16 facility driving cycles to 

describe onroad driving. Most of these cycles are based on the results of the instrumented 

vehicle studies conducted in the early 1990’s in support of the Supplemental FTP rule. 

However, since the maximum speed limit at this time was 55 mph and is now much higher, 

additional high speed cycles had to be developed to describe this now common type of driving. 

During the development of MOBILE6.2, EPA developed three higher speed freeway 

cycles (High Speed Freeway 1, High Speed Freeway 2, and High Speed Freeway 3 in Table 

III.A-12). High Speed Freeway 3, the fastest of the three cycles, was based on a segment of 

onroad driving of one of the vehicles tested in the EPA 15-car study.17 While this driving 

segment was actually driven on the highway by a vehicle, the EPA 15-car study was a pilot study 

and neither the vehicle nor driver selection was designed to be random. Also, since the drivers 

were either EPA employees or contractors and were aware of the purpose of the study, the 

driving was not designed to be representative of typical vehicle use. The portion of onroad 

driving represented by High Speed Freeway 3 is based on its average speed and onroad speeds 

predicted by travel demand models and California rural chase car data, the acceleration rates are 

not. Thus, the power demands of this cycle could differ from those on the road. The 

representativeness of the specific speed-time trace of High Speed Freeway 3 and its effect on 

vehicle power is being evaluated as part of the further development of MOVES. 

The High Speed Freeway 2 cycle is a portion of the US06 cycle. This portion of the 

cycle met the desired average speed, which was slightly below that of High Speed Freeway 3. 

As discussed in section III.A.2, the US06 cycle is a concentrated version of the REP05 cycle. 

REP05 is a driving cycle which is representative of higher speed and/or higher power driving 

found onroad during the vehicle studies performed in support of the EPA Supplemental FTP 

rulemaking.12 In the early 1990’s, it represented roughly 28% of U.S. driving. US06, consisting 

of the most aggressive portions of REP05, represented a smaller percentage of driving at that 

time. However, both speed limits and the power to weight ratio of vehicles have both increased 

since that time. US06 driving represents more driving today than it did in the early 1990’s. 

However, the exact percentage is not known. As is the case with High Speed Freeway 3, the 

portion of onroad driving represented by High Speed Freeway 2 is based on its average speed 

and onroad speeds predicted by travel demand models and California rural chase car data. 

However, the power demands of the cycle derive from the specific portion of US06 which was 

selected to comprise High Speed Freeway 2. Thus, the power demands of this cycle could differ 

from those of in-use vehicles which are driving at these speeds. The representativeness of the 

specific speed-time trace of High Speed Freeway 2 and its effect on vehicle power is being 

evaluated as part of the further development of MOVES. 

High Speed Freeway 1 is the most representative of the three highest speed cycles.41 It 

was developed for use in MOBILE6.2 from data obtained during the 3-city studies. Basically, 

vehicle operation which occurred on relatively uncongested freeways (LOS A-C) which lasted at 

least 30 seconds with a minimum speed of 50 mph were binned and used to create the High 

Speed Freeway 1 cycle. Thus, the underlying driving characteristics reflect real world operation 

122 
in the early 1990’s. However, vehicles operating at these speeds today might be driven 

differently, due to a change in the types of roadways which carry vehicles at these speeds and to 

the fact that many vehicles on the same roads are driving faster and not slower. As is the case 

with High Speed Freeway 2 and 3, the representativeness of the specific speed-time trace of High 

Speed Freeway 1 and its effect on vehicle power is being evaluated as part of the further 

development of MOVES. 

We evaluated the impact of these three cycles on the proposed cycle combinations in the 

5-cycle formulae by shifting the onroad driving assigned to these cycles to the fourth highest 

speed cycle, LOS AC Freeway. This is an extreme change and goes far beyond any possible 

uncertainty related to these three high speed freeway cycles. Thus, it bounds the potential 

uncertainty in this area and likely over-estimates it. Only the highway fuel economy formula is 

affected, since we assumed in section III.A.2 that 100% of the driving over these cycles was 

highway driving. As shown in Table III.A-13, this shift represents 20% of onroad highway like 

driving. 

We repeated the regressions of the VSP distributions of the dynamometer bags and cycles 

(Tables III.A-15) against the VSP distribution of highway driving (Table III.A-14 with shift to 

LOS A-C Freeway) weighted by the square root of the fuel rate in each VSP bin from the Kansas 

City test program (Table III.A-16). The results with and without the three highest speed cycles 

are described in Table III.E-6 below. 

Table III.E-6. Bag/Cycle Combinations for Highway Driving: High 

Speed Freeway Cycles 

With 3 High Speed 

Cycles 

Without 3 High Speed Cycles 

HFET 21% 25% 

US06 Highway 79% 75% 

Using this revised cycle combination for highway driving, we recalculated 5-cycle 

highway fuel economy estimates for the 615 vehicles in our 5-cycle fuel economy database. 

Eliminating the three highest speed freeway cycles increased average 5-cycle highway fuel 

economy from 23.1 mpg to 23.3 mpg, or by less than 1%. 

c. Alternative Fuel Rates and Number of VSP Bins 

In this section, we evaluate the use of three sets of alternative fuel rates by VSP bin in the 

regression of dynamometer cycles versus onroad operation. The three sets of fuel rates are based 

on: 1) the EPA 15 car study, 2) Draft MOVES2004 extrapolated to 26 VSP bins using fuel rates 

from the EPA 15 car study, and 3) Draft MOVES2004 extrapolated to 26 VSP bins using fuel 

rates from the EPA Kansas City testing. We also evaluate the impact of using just the 17 VSP 

bins currently used in Draft MOVES2004. In this case, the fuel rates are those currently in Draft 

MOVES2004. 

These three sets of 26 VSP bin fuel rates were presented in Table III.A-16 above. Those 

currently in Draft MOVES2004 are simply those rates shown in Table III.A-16 under either of 

123 
the “Extrapolated MOVES” columns for bins 0-16, 21-26 and 33-36. The 17 bin VSP 

distributions for both the dynamometer cycles and bags and the MOVES facility cycles are the 

same as those with 26 bins, except that the x6 bin contains all of the driving in bins x6, x7, x8 

and x9. 

We repeated the regressions of the VSP distributions of the dynamometer bags and cycles 

(Tables III.A-15) against the VSP distributions of city and highway driving (Table III.A-14) 

weighted by the square root of the various fuel rates. The results under the base case and the 

alternatives are shown in Table III.E-7 below. 

Table III.E-7. Bag/Cycle Combinations for City and Highway Driving: Alternative Fuel 

Rates 

 Base Case: Kansas 

City Fuel Rates 

EPA 15 Car 

Fuel Rates 

Extrapolated MOVES MOVES 

EPA 15 Car Kansas City 17 bin

City Driving 

Bag 2 FTP 48% 50% 50% 50% 50% 

Bag 3 FTP 41% 50% 50% 50% 39% 

US06 City 11% 0% 0% 0% 11% 

Highway Driving 

HFET 21% 21% 21% 21% 20% 

US06 Highway 79% 79% 79% 79% 80% 

As seen in Table III.E-7, the three alternative sets of fuel rates yield cycle combinations 

of city driving which are identical and do not include the US06 city bag. The MOVES 17 bin 

approach yields a cycle combination of city driving which is much more similar to that of the 

base case. Regarding the cycle combinations of highway driving, all of the various alternatives 

produce essentially identical results. 

The results for city driving indicate that the contribution of the US06 city bag is the most 

uncertain of the 3 bags. This is confirmed by the p-values for the various bags in the final 

regressions. While the inclusion of the US06 city bag in the regression improves the adjusted rsquared value, the p-values for the US06 city bag are the highest of the three remaining bags, 

falling in the range of 0.2-0.3. 

Using the 50/50 combination of bag 2 and 3 (zero weight for US06 city), we recalculated 

5-cycle fuel economy estimates for the 615 vehicles in our 5-cycle fuel economy database. The 

50/50 combination of Bags 2 and 3 increases 5-cycle city fuel economy for conventional vehicles 

from 16.5 mpg to 17.2 mpg, or by 4%, compared to the proposed cycle combination. The 50/50 

combination of Bags 2 and 3 increases 5-cycle city fuel economy for hybrid vehicles from 33.0 

mpg to 34.8 mpg, or by 5.5%, compared to the proposed cycle combination. The increase in city 

fuel economy also increases combined fuel economy by 2% for conventional vehicles and 3% 

for hybrids. This increase in combined 5-cycle fuel economy would lead to a lower factor for 

non-dynamometer effects, roughly decreasing from 0.905 to 0.885. This change would decrease 

both 5-cycle city and highway fuel economy by 2%. The uncertainty related to the contribution 

of the US06 city bag to city driving is the sum of the change in city fuel economy plus the 

change in both city and highway fuel economy due to a lower non-dynamometer factor. Thus, 

124 
this uncertainty is roughly 2% in both city and highway fuel economy for conventional vehicles 

and 4% for city and 2% for highway fuel economy for hybrid vehicles. 

d. Kansas City VSP Distributions and Fuel Rates 

EPA’s recent testing of roughly 100 recent model year vehicles in Kansas City is 

described in detail in Appendix A. We aggregated the driving activity and fuel rates measured 

during this testing (basically one day’s driving for each vehicle) and developed VSP distributions 

and average fuel rates by VSP bin for two sets of vehicles, conventional vehicles and hybrids. In 

order to develop VSP distributions for city and highway driving, we segregated driving activity 

on a second by second basis into two groups: those at 45 mph or lower and those above 45 mph. 

This segregation is not exactly consistent with our definition of city and highway driving, since 

these are based on longer time frames than one second. However, this was the only method 

applicable in the near term. With additional time, each vehicle’s driving trace could be reviewed 

visually and segregated into city and highway driving. We may attempt to do this in the future. 

The city and highway VSP distributions and fuel rates are shown in Table III.E-8. 

Table III.E-8. Kansas City VSP Distributions and Fuel Rates 

VSP 

Bin 

VSP Distribution: Non-Hybrids VSP Distribution: Hybrids Fuel Rate (g/sec) 

City Highway City Highway Non-Hybrids Hybrids 

0 8.4% 0.8% 9.7% 0.7% 0.412 0.097 

1 29.1% 10.6% 0.384 0.248 

11 5.5% 7.5% 0.454 0.122 

12 9.7% 13.7% 0.641 0.188 

13 3.5% 4.9% 1.122 0.466 

14 2.1% 2.5% 1.406 0.690 

15 1.6% 2.0% 1.715 0.857 

16 0.7% 0.8% 2.006 0.976 

17 0.4% 0.4% 2.172 1.126 

18 0.2% 0.1% 2.358 1.182 

19 0.2% 0.0% 2.296 1.319 

21 7.3% 1.8% 9.5% 1.8% 0.593 0.225 

22 8.8% 0.1% 16.8% 0.833 0.359 

23 9.7% 3.6% 6.9% 5.4% 0.986 0.578 

24 2.8% 1.9% 4.8% 1.180 0.569 

25 4.0% 0.0% 4.9% 1.352 0.755 

26 2.5% 1.0% 2.4% 1.8% 1.631 0.993 

27 1.0% 1.0% 1.1% 0.2% 1.917 1.107 

28 0.8% 0.0% 0.7% 0.5% 2.272 1.327 

29 1.6% 0.7% 0.6% 0.5% 2.424 1.588 

33 14.5% 19.2% 1.069 0.645 

35 36.8% 49.8% 1.486 0.920 

36 15.7% 6.9% 1.753 1.175 

37 6.5% 3.9% 1.937 1.160 

38 4.5% 3.6% 1.948 1.210 

39 11.2% 6.1% 2.309 1.441 

125 
We repeated the regressions of the VSP distributions of the dynamometer bags and cycles 

(Tables III.A-15) against the VSP distributions of city and highway driving weighted by the 

square root of the various fuel rates shown in Table III.E-8. The results for the base case and the 

two vehicle types in Kansas City are shown in Table III.E-9 below. 

Table III.E-9. Bag/Cycle Combinations for City and Highway Driving: Kansas City 

Base Case Kansas City Non-Hybrids Kansas City Hybrids 

City Driving 

Bag 2 FTP 48% 39% 70% 

Bag 3 FTP 41% 61% 30% 

US06 City 11% 0% 0% 

Highway Driving 

HFET 21% 38% 56% 

US06 Highway 79% 62% 44% 

The cycle combinations for non-hybrid and hybrid vehicles tested in Kansas City vary 

dramatically from those of the base case and from each other. The US06 city bag does not 

appear in either of the final regressions for city driving in Kansas City. However, Bag 3 

dominates city driving by non-hybrids, while Bag 2 dominates city driving by hybrids. The 

potential causes for this difference in the driving of hybrids and conventional vehicles are 

discussed in some detail in Appendix A. First, this difference is based on limited driving; 

roughly one hour of driving per vehicle. Second, while it is possible that this difference, if real, 

will persist in the future, it seems more likely that any difference will disappear as more high 

power hybrids enter the fleet and more drivers purchase hybrids. 

The contribution of the US06 highway bag to highway driving in Kansas City is also 

lower than that predicted by MOVES for both vehicle types. The contribution of the US06 

highway bag to highway driving by hybrids in Kansas City is also lower than that by 

conventional vehicles. A more detailed review of the regression results for highway driving 

indicates that the model is having difficulty in matching the onroad VSP distributions for 

highway driving. In all the other regressions of highway driving, the coefficients for HFET and 

US06 highway from the raw regression results sum very close to 1.0 (e.g., 0.97). In Kansas City, 

the coefficients for HFET and US06 highway from the raw regression results sum to roughly 

1.25. Mathematically, this means that in order to minimize error, the model wants to use 1.25 

seconds of cycle driving to match the fuel consumption of one second of onroad driving. This 

means that the normalized coefficients presented in Table III.E-8 will under-estimate onroad fuel 

consumption significantly. The prediction of relative fuel consumption across models might be 

reasonable for the highway driving performed in Kansas City, but the absolute prediction will be 

quite low. This would necessitate use of a non-dynamometer factor well below 1.0, as opposed 

to the 0.98 factor proposed in section III.A-5. 

In general, a sum of coefficients well above or below 1.0 indicates that the model cannot 

match the onroad fuel consumption per second using the cycles made available to it. In the case 

of highway driving, the model would normally increase the contribution of US06 highway, as 

this cycle has higher average fuel consumption than HFET. However, this is creating too great 

an error in the individual VSP bins and the sum of squared errors increases. This means that the 

126 
highway driving observed in Kansas City differs dramatically (in terms of VSP) from both the 

HFET and US06 highway cycles and that another cycle is needed. 

As will be seen in the next section, driving observed in California yield very different 

cycle combinations compared to those based on the Kansas City data. This indicates the need for 

driving to be characterized in a number of urban areas in order to be representative of the country 

as a whole, and not based on just one or two areas. 

e. California Chase Car Studies 

As described in Appendix A, Sierra Research has performed several chase car studies of 

both urban and rural driving in California since 1998. We have not yet been able to perform a 

detailed analysis of the second by second data from this testing. However, we have been able to 

develop approximate VSP distributions for urban and rural driving from the speed-acceleration 

frequency distributions. As in Kansas City, we assumed that driving at or below 45 mph was 

city driving and driving above 45 mph was highway driving. However, one of the SAFD bins 

was centered at 45 mph (i.e., speeds of 42.5-47.5 mph). This bin was assigned to city driving. 

Also, all driving in bins 17-19 were assigned to bin 16. These VSP distributions are shown in 

Table III.E-10. 

Table III.E-10. California Urban and Rural VSP Distributions 

VSP 

Bin 

Urban Rural Fuel Rate (g/sec) 

City Highway City Highway Non-Hybrids Hybrids 

0 16.3% 2.9% 19.8% 2.2% 0.412 0.097 

1 14.9% 0.0% 6.8% 0.0% 0.384 0.248 

11 4.9% 0.0% 2.8% 0.0% 0.454 0.122 

12 14.6% 0.0% 9.3% 0.0% 0.641 0.188 

13 5.3% 0.0% 3.5% 0.0% 1.122 0.466 

14 4.6% 0.0% 3.0% 0.0% 1.406 0.690 

15 3.9% 0.0% 3.1% 0.0% 1.715 0.857 

16 2.8% 0.0% 4.4% 0.0% 2.006 0.976 

21 5.2% 2.6% 6.4% 1.9% 0.593 0.225 

22 4.0% 0.0% 2.4% 0.0% 0.833 0.359 

23 12.2% 0.0% 18.1% 0.0% 0.986 0.578 

24 2.1% 8.6% 1.2% 7.4% 1.180 0.569 

25 2.1% 0.0% 1.9% 0.0% 1.352 0.755 

26 4.4% 0.0% 8.9% 0.0% 1.631 0.993 

27 0.8% 3.2% 1.8% 2.6% 1.917 1.107 

28 0.9% 0.0% 2.9% 0.0% 2.272 1.327 

29 0.9% 0.7% 3.8% 0.6% 2.424 1.588 

33 0.0% 12.1% 0.0% 9.6% 1.069 0.645 

35 0.0% 19.8% 0.0% 27.0% 1.486 0.920 

36 0.0% 14.7% 0.0% 11.3% 1.753 1.175 

37 0.0% 13.4% 0.0% 11.8% 1.937 1.160 

38 0.0% 8.7% 0.0% 11.5% 1.948 1.210 

39 0.0% 13.2% 0.0% 14.1% 2.309 1.441 

127 
We repeated the regressions of the VSP distributions of the dynamometer bags and cycles 

(Tables III.A-15) against the VSP distributions of California city and highway driving in urban 

and rural (Table III.E-10) weighted by the square root of the Kansas City fuel rates (Table III.A16). We performed the same analysis for combined urban/rural VSP distributions of city and 

highway driving, using an urban/rural weighting of 60/40 (consistent with FHWA estimates of 

urban and rural VMT for the U.S.). The chase car studies showed that 75.5% of all urban 

operation was city driving and 24.5% was highway. In contrast, 30.4% of all rural operation was 

city driving and 69.6% was highway. Thus, the VSP distribution for combined urban/rural city 

driving was developed by weighting the urban city driving VSP distribution by 78.8% and the 

rural city driving VSP distribution by 21.2%. Likewise, the VSP distribution for combined 

urban/rural highway driving was developed by weighting the urban highway driving VSP 

distribution by 34.5% and the rural city driving VSP distribution by 65.5%. The results for the 

base case and the California cases are shown in Table III.E-11 below. 

Table III.E-11. Bag/Cycle Combinations for City and Highway Driving: California 

Base Case California Urban California Rural All California 

City Driving 

Bag 2 FTP 48% 42% 0% 35% 

Bag 3 FTP 41% 42% 75% 46% 

US06 City 11% 16% 25% 19% 

Highway Driving 

HFET 21% 13% 21% 18% 

US06 Highway 79% 87% 79% 82% 

Overall, the dynamometer cycle combinations for California driving tend to be more 

aggressive than those based on Draft MOVES2004 and are decidedly more aggressive than those 

found in Kansas City. City driving in California shows a 16-19% contribution for the US06 city 

bag, versus 11% based on Draft MOVES2004 and zero in Kansas City. Highway driving in 

California shows a 79-87% contribution for the US06 highway bag, versus 79% based on Draft 

MOVES2004 and 44% for hybrids and 63% for non-hybrids in Kansas City. Chase car studies 

can tend to under-represent driving on neighborhood and local roads. Thus, some increased 

percentage of US06 city driving can be expected in the cycle combinations for city driving. 

However, one would not expect over-estimation by a factor of two. The characterization of 

highway driving should not be affected by this factor, since vehicle speeds on local and 

neighborhood roads tend to be less than 45 mph. 

Thus, the results from Kansas City and California indicate the need for driving to be 

characterized in a number of urban areas in order to be representative of the country as a whole. 

The cycle combinations based on Draft MOVES2004 fall in between those found in Kansas City 

and California and thus, are not inconsistent with the driving in the two specific geographical 

areas. 

f. Alternative Splits of City/Highway Driving 

In Sections III.A.1 and III.A.2, we define city driving as that below 45 mph and highway 

driving as that above 45 mph. According to this definition, we assigned all of the driving over 

128 
the LOS D Freeway cycle to highway driving. This definition of city and highway driving 

produces a city/highway VMT split of 43/57, which differs dramatically from the current 55/45 

split. We believed it appropriate to investigate some options which yielded city/highway VMT 

splits closer to the current split of 55/45, specifically 50/50 and 55/45. This was accomplished 

by adjusting the split of driving over the LOS D freeway cycle to city and highway categories. 

The LOS D freeway cycle has an average speed of 53 mph and over three-quarters of the 

driving time of this cycle is above 45 mph. However, the cycle does include some driving below 

30 mph (about 3% in terms of driving time). Thus, there is some rationale to assigning at least a 

portion of this cycle to city driving. Also, the LA4 road route which is the basis for the FTP test 

included some freeway operation. Bag 3 of the FTP has a maximum speed of 55 mph and about 

20% of the driving time during this bag is above 45 mph. All the cycle combinations 

representing city driving include Bag 3, so all these representations of city driving include some 

driving over 45 mph regardless of how city driving is defined with respect to the Draft 

MOVES2004 facility cycles. Thus, assigning some driving over 45 mph to city driving could 

improve the representation of city driving (i.e., increase the adjusted r-squared values in the 

regression of the VSP distribution of city driving versus those of the dynamometer cycles). 

To assess this possibility, we developed two alternative assignments of driving of the 

LOS D Freeway cycle to city and highway driving. The first produced an overall city/highway 

VMT split of 50/50, while the second produced an overall city/highway VMT split of 55/45. 

The first split required assigning 26% of LOS D Freeway driving to city driving, while the 

second the required assigning 43% of LOS D Freeway driving to city driving. We recalculated 

the VSP distributions of city and highway driving with the two alternative assignments of LOS D 

Freeway driving. The base case and alternative VSP distributions are shown in Table III.E.12. 

129 
Table III.E-12. VSP Distributions for U.S. Driving with Alternative Definition of City 

Driving (% of time) 

VSP Base: 43/57 City/Highway Split 50.50 City/Highway Split 55/45 City/Highway Split 

Bin City VSP Highway VSP City VSP Highway VSP City VSP Highway VSP 

0 11.8% 3.9% 11.4% 3.6% 11.1% 3.5% 

1 20.4% 0.2% 19.1% 0.2% 18.3% 0.3% 

11 8.3% 0.1% 7.8% 0.2% 7.5% 0.2% 

12 13.0% 0.1% 12.1% 0.1% 11.7% 0.2% 

13 6.0% 0.1% 5.6% 0.2% 5.4% 0.2% 

14 3.5% 0.1% 3.3% 0.1% 3.1% 0.1% 

15 2.2% 0.0% 2.0% 0.0% 2.0% 0.0% 

16 0.6% 0.0% 0.5% 0.0% 0.5% 0.0% 

17 0.1% 0.0% 0.1% 0.0% 0.1% 0.0% 

18 0.1% 0.0% 0.1% 0.0% 0.1% 0.0% 

19 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

21 5.5% 2.8% 5.4% 2.5% 5.4% 2.3% 

22 6.2% 2.9% 6.1% 2.5% 6.1% 2.3% 

23 4.7% 3.2% 4.7% 2.8% 4.7% 2.5% 

24 4.2% 2.9% 4.3% 2.6% 4.3% 2.3% 

25 3.0% 2.6% 3.1% 2.4% 3.1% 2.1% 

26 2.2% 2.4% 2.3% 2.2% 2.3% 1.9% 

27 0.8% 0.9% 0.9% 0.8% 0.9% 0.8% 

28 0.4% 0.5% 0.4% 0.4% 0.4% 0.4% 

29 0.3% 0.9% 0.4% 0.9% 0.4% 0.8% 

33 1.9% 16.7% 2.8% 16.7% 3.4% 16.7% 

35 2.3% 21.0% 3.3% 21.5% 3.9% 21.9% 

36 1.1% 10.1% 1.6% 10.2% 1.9% 10.3% 

37 0.9% 9.8% 1.3% 10.2% 1.5% 10.5% 

38 0.5% 8.0% 0.8% 8.4% 1.0% 8.7% 

39 0.2% 10.7% 0.6% 11.4% 0.8% 11.9% 

Shifting some of the driving over the LOS D Freeway cycle to city driving increases 

operation in Bins 33-39 significantly in the VSP distribution for city driving. It also increases 

operation in Bins 35-39 and reduces operation in Bins 21-29 in the description of highway 

driving. 

We repeated the regressions of the VSP distributions of the dynamometer bags and cycles 

(Tables III.A-15) against the alternative VSP distributions of city and highway driving shown in 

Table III.E-12 weighted by the square root of the Kansas City fuel rates (Table III.A-16). As has 

been done in all such regressions, we first dropped those cycles or bags with negative 

coefficients and then dropped the least significant cycles or bags until the adjusted R-squared 

value decreased. Then, the results of the previous regression were selected as the final 

combinations of cycles. The results for the base case and the revised split of city and highway 

driving are shown in Table III.E-13 below. 

130 
Table III.E-13. Cycle Combinations for City and Highway Driving: Revised City/Highway 

Split 

Base Case 50/50 City/Highway Split 55/45 City/Highway Split 

Cycle % p-value Cycle % p-value Cycle % p-value 

City Driving 

Bag 2 FTP 48% <0.001 40% <0.001 35% <0.001 

Bag 3 FTP 41% 0.01 43% 0.003 43% 0.0015 

US06 City 11% 0.26 0% ---- 0% ---- 

US06 Highway 0% ---- 17% 0.11 22% 0.034 

Adjusted r-squared 0.7262 0.7048 0.6958 

Highway Driving 

HFET 21% <0.001 20% <0.001 18% <0.001 

US06 Highway 79% <0.001 80% <0.001 82% <0.001 

Adjusted r-squared 0.8682 0.8784 0.8855 

Including some operation over 45 mph in the definition of city driving has a significant 

effect on the cycles which represent city driving. For both alternative definitions of city driving, 

US06 city drops out of the cycle combination and US06 highway is added at roughly twice the 

previous US06 city weight. The contribution of Bag 2 drops, as well. These changes are 

progressive. The greater the percentage of city driving overall (i.e., the greater the percentage of 

LOS D Freeway driving assigned to city driving), the greater degree that the above changes 

occur. The effect on highway driving is much smaller: the weight of US06 highway increases 1­

4%, while that of HFET decreases commensurately. 

Some of the regression statistics appear to be better for the base definition of city driving, 

while others appear better for the higher city driving fraction. Again, the changes are 

progressive with respect to the two alternatives. The adjusted r-squared value for the base case 

combination of city driving is higher than that for either alternative definition of city driving. 

However, the p-values for the US06 bag with the alternative definitions of city driving are better 

than that for the base definition. The more high speed driving is included in city driving, the 

greater the significance of the US06 highway coefficient. With the base definition of city 

driving, including US06 city improves the adjusted r-squared value, but the p-value of the US06 

coefficient is relatively high, 0.26. 

For highway driving, the p-values for both HFET and US06 are very low in both cases. 

However, the adjusted r-squared value increases as the overall city driving fraction increases. In 

both alternative cases, the adjusted r-squared value is higher than that with the base definition of 

city driving. 

The impact of increasing the VMT fraction of city driving on the 5-cycle fuel economy of 

the 615 vehicles in our certification database is shown in Table III.E-14. In addition to the 

revised cycle combinations shown in Table III.E-13, increasing the city fraction of VMT also 

increases the average trip length of city driving. For a city/highway VMT split of 50/50, the 

average trip length for city driving increases from 4.1 miles to 4.7 miles, while that for a split of 

55/45 is 5.1 miles. The effect of this increased trip length is included in the 5-cycle fuel 

economy estimates with the 505/50 and 55/45 city/ highway VMT splits. 

131 
Table III.E-14. 5-Cycle Fuel Economy Values: Effect of the Definition of City Driving 

(mpg) 

City Highway Composite 

Non-Hybrid Hybrid Non-Hybrid Hybrid Non-Hybrid Hybrid 

Current EPA Label 18.6 42.7 24.6 42.8 20.9 42.6 

5-Cycle 

43/57 City/Hwy Split 16.5 33.0 22.8 36.9 19.6 35.0 

50/50 City/Hwy Split 17.7 34.2 22.7 36.8 20.2 35.5 

55/45 City/Hwy Split 17.9 34.0 22.6 36.6 20.3 35.4 

Moving some of the operation over LOS D Freeway to city driving increases 5-cycle city 

fuel economy for both non-hybrids and hybrids. This is not surprising, given that fuel economy 

over the US06 city bag is the lowest of any of the dynamometer cycles or bags. The effect is 

again progressive with the degree of the shift of driving from highway to city. Five-cycle 

highway fuel economy decreases very slightly due to the increased fraction of US06 highway in 

the 5-cycle formula. Composite fuel economy increases slightly. This implies that the effect of 

higher city fuel economy is slightly greater than the effect of the increased city VMT fraction. 

The ORNL Your MPG website, discussed in detail in section II.A, contains consumers 

estimates of their onroad fuel economy, as well as their estimate of their city and highway 

driving fractions. Across the 8180 estimates of fuel economy, the average percentage of city 

driving is 43%. This is closer to the 43% estimate resulting from placing all the operation over 

LOS D Freeway into the highway driving category than splitting this operation between city and 

highway driving. At the present time, it is the primary source of information about how typical 

drivers label their own driving. 

g. Complete Cycles 

A final set of alternatives develops cycle combinations using entire test cycles (FTP, 

HFET and US06) instead of allowing separate bag weights within the FTP and US06 cycles. 

One alternative investigates the impact of not splitting US06 into city and highway bags, but 

retaining separate bags for the FTP. The other alternative investigates the impact of not splitting 

US06 into city and highway bags, as well as using a combined Bag 2 plus Bag 3 fuel economy. 

The latter might be applicable to current hybrid testing. A hybrid FTP test consists of two 

complete LA4 driving cycles, one with a cold start and one with a hot start. This is often 

accomplished using four emission bags, with Bag 4 being similar to Bag 2. However, EPA 

regulations allow emissions to be measured in only two bags, Bag 1 consisting of the normal 

Bags 1 and 2 and Bag 2 consisting of the normal Bag 3 plus the extra Bag 4. In these cases, fuel 

economy measurements for each of the four bags would not be available. 

We repeated the regressions of the VSP distributions of the dynamometer bags and cycles 

(Tables III.A-15) against the Draft MOVES2004 VSP distributions of city and highway driving 

(Table III.A-14) weighted by the square root of the Kansas City fuel rates (Table III.A-16). The 

results for the base case and the alternative complete cycle cases are shown in Table III.E-15 

below. 

132 
Table III.E-15. Bag/Cycle Combinations for Complete Cycle Alternatives 

Base Case Complete US06 Complete US06 and 

LA4 

City Driving 

Bag 2 FTP 48% 50% ---- 

Bag 3 FTP 41% 50% ---- 

LA-4 ---- ---- 100% 

US06 City 11% ---- ---- 

Highway Driving 

HFET 21% 25% 25% 

US06 Highway 79% ---- ---- 

US06 ---- 75% 75% 

Not surprisingly, maintaining US06 as a single cycle eliminates any contribution of US06 

to city driving. The weights of Bag 2 and Bag 3 become 50% each, as was the case for the 

alternative fuel rates in Table III.E-7. This is also consistent with the modeling of city driving 

under cold temperature conditions where the US06 city bag was not allowed in the regression. 

Maintaining US06 as a single cycle reduces the contribution of US06 to highway driving 

modestly. 

The results are quite similar when the LA-4 cycle is substituted for Bags 2 and 3. The 

weighting of Bags 2 and 3 in the LA-4 is 52/48, which is very similar to the 50/50 weighting 

found when separate bag estimates are allowed into the model. The impact of using whole 

cycles on 5-cycle city and highway fuel economy values is shown in Table III.E-16. 

Table III.E-16. Effect of Using Whole Cycles on 5-Cycle Fuel Economy Values (mpg) 

 Conventional Vehicles Hybrids 

City Highway City Highway 

Current EPA label 18.6 24.6 41.6 40.6 

Base Case 5-cycle 16.5 22.8 33.0 36.9 

Complete US06 5-cycle 17.2 20.5 34.8 34.7 

Complete US06 and LA4 5-cycle 17.2 20.5 35.0 34.7 

As expected, eliminating the contribution of US06 city from the 5-cycle city formula 

increases city fuel economy (by about 4% in both alternative cases for conventional vehicles). 

While increasing the contribution of HFET increases highway fuel economy, shifting from US06 

highway to the whole US06 cycle decreases fuel economy. The latter impact predominates and 

highway fuel economy decreases substantially (~10% for conventional vehicles). City fuel 

economy for hybrids is slightly more sensitive to these changes than conventional vehicles, while 

highway fuel economy is less sensitive. Hybrid city fuel economy increases 6%, while hybrid 

highway fuel economy decreases 6%. 

133 
3. Air Conditioning Effects 

The primary factors affecting the estimation of fuel use related to air conditioning are: 1) 

the degree of compressor engagement during the SC03 test, 2) the degree of compressor 

engagement on the road, 3) the effect of vehicle speed on fuel use related to running the 

compressor, and 4) the effect of ambient conditions on the load of the compressor on the engine. 

The potential uncertainty in each of these factors and its affect on 5-cycle fuel economy will be 

addressed below. 

We assume that every vehicle’s air conditioning compressor is engaged 100% of the time 

during the SC03 test. This is based on general knowledge of the SC03 test itself. For example, 

the test only lasts 10 minutes and the vehicle is subjected to strong sunlight at 95°F during 

vehicle preconditioning operation (e.g., running a Bag 1 or 2 of the FTP, both Bags 1 and 2 of 

the FTP, a SC03 test) and during the ten minute soak prior to the test. The air conditioning 

system and fan are turned to its maximum setting and the recirculation option is chosen, if 

available. 

EPA also made the same assumption when estimating the emission benefits associated 

with the SC03 emission standards.42

 It is clear from the description of various test programs 

performed in support of this rule that the engagement of the compressor was measured over the 

SC03 and other test cycles and that the Agency had this information when it assumed that the 

compressor was engaged 100% of the time over the SC03 test. However, this data is no longer 

easily accessible and the level of compressor use which was actually measured over these tests 

was not presented in any of the official rulemaking documents. Also, it is possible that vehicles’ 

air conditioning system designs have changed since the early 1990’s. The degree of compressor 

engagement over SC03 today might differ from that measured at that time. Therefore, in order to 

assess the possible uncertainty in this assumption, we assume here that the compressor is 

engaged only 80% of the time over SC03. 

Reducing the amount of time that the compressor is engaged over SC03 from 100% to 

80% can be modeled by simply dividing the incremental fuel use over SC03 versus the 69/31 

mix of fuel use over Bags 3 and 2 of the FTP by a factor of 0.8. This change decreases the 5­

cycle city fuel economy of non-hybrid vehicles from 16.5 mpg to 16.4 mpg and that for hybrids 

decreases from 33.0 mpg to 32.6 mpg. Thus, the change in city fuel economy is roughly 1%. 

The change in compressor engagement over SC03 affects highway fuel economy even less. 

Five-cycle highway fuel economy of non-hybrid vehicles decreases from 22.8 mpg to 22.7 mpg 

and that for hybrids decreases from 36.9 mpg to 36.7 mpg. Thus, the change in highway fuel 

economy is roughly half of one percent. Overall, the changes in both city and highway fuel 

economy are small. 

Regarding the degree of compressor engagement on the road, the Phoenix study used to 

develop the 0.133 factor in the 5-cycle fuel economy formulae is based on the only instrumented 

vehicle study performed to date. As discussed above, the NREL-OAP model, based on a 

person’s comfort at a given ambient temperature and humidity, yields an estimate of drivers 

turning on the air conditioning of 29%, versus that based on the Phoenix work of 24%. 

Considering the fact that the compressor is not always engaged when the air conditioning system 

134 
is turned on, and the fact that the ambient temperature is usually less than 95 F, we estimate that 

the compressor is on 13.3% of the time at a load equivalent to that occurring at 95 F. The 

comparable percentage based on the NREL-OAP system-on estimate would be 16.1%. Defroster 

use, based on the NREL-OAP work, could add another 0.4% to overall compressor use (in terms 

of compressor load at 95°F). Thus, if both of the NREL-OAP estimates were correct, the 0.133 

factor for compressor use in the 5-cycle formulae could be as high as 0.165. This increase is 

roughly equivalent to the increase associated with assuming that the compressor is only engaged 

80% of the time over the SC03 test. Thus, the fuel economy effects of increasing the air 

conditioning usage factor to 0.152 would essentially the same as those just presented above (i.e., 

1.0% for city fuel economy and 0.5% for highway fuel economy). These are very small changes. 

Regarding the effect of vehicle speed on fuel use related to running the compressor, this 

uncertainty primarily applies to highway fuel economy. The speed of SC03 is within 10% of 

that for average city driving. So there is little uncertainty in applying the incremental fuel use of 

SC03 to city driving. The extrapolation to highway driving is much larger, the ratio of highway 

speeds to the speed over SC03 being a factor of 2.67. Our testing of six vehicles with the air 

conditioning on and off over the FTP, SC03 and HFET showed that the fuel use per unit of time 

tended to be within 10% over the various cycles. However, these tests were not conducted in an 

environmentally controlled test cell. Thus, further differences could occur under more realistic 

ambient conditions. Also, the HFET was the only highway cycle tested. The differences could 

be larger if, for example, vehicles would have been tested over the US06. Therefore, to assess 

the uncertainty in this factor, we assume here that the difference in fuel use per unit of time over 

the various cycles could vary by as much as 20%. This can be represented by applying an 

exponent of 0.8 to the ratio of speeds over the SC03 and highway driving. Thus, instead of a 

factor 0.366 (21.5 mph divided by 57.1 mph), the factor would be 0.458. With this change, 

highway fuel economy changed less than 0.5%. Therefore, this factor is not likely a significant 

source of uncertainty in the 5-cycle highway fuel economy formulae. 

Finally, regarding the effect of ambient conditions on the load of the compressor on the 

engine, consideration of this factor reduces the effective compressor on time from 15.2% to 

13.3%, or by a factor of 0.875. A reasonable estimate of the uncertainty in this factor would be 

to double its effect (i.e., double its difference from 1.0). This would mean reducing the factor to 

0.75. This change increases city fuel economy from non-hybrid vehicles by 0.1 mpg and by 0.3 

mph for hybrids, at most 1% in the latter case. The effect on highway fuel economy is even 

smaller, less than 0.5% in either case. Therefore, uncertainty in this area is unlikely to 

significantly affect 5-cycle fuel economy. 

In all, uncertainty in the four factors affecting the impact of air conditioning on onroad 

fuel economy appears to be quite small. No single factor appears to affect fuel economy by more 

than 1%, with most being significantly less than this. 

4. Cold Temperature Running Fuel Use 

The primary factors affecting the impact of colder temperatures on fuel economy are: 1) 

the assumption that vehicles are fully warmed up by the end of Bag 1 of the FTP, 2) the effect of 

heater/defroster use on warmed up fuel use, 3) the use of fuel consumption over just Bags 2 and 

135 
3 to represent city driving and 4) the assumption that fuel consumption increases 4% during 

highway driving at 20°F. 

The potential impact of the assumption that vehicles are fully warmed up by the end of 

Bag 1 of the FTP was addressed above in section III.E.1. As described there, if the vehicle 

continued to warm up during Bag 2 at 20°F relative to that at 75°F, cold start fuel use would 

increase and warmed up fuel use decrease. The net effect was an increase in the 5-cycle city fuel 

economy of conventional vehicles of 0.3% and 1.2% for hybrids. 

As described in Section III.E.1, the effect of heater/defroster use is estimated to be very 

small for conventional vehicles, but more significant for hybrids. Some effect of heater/defroster 

use is certain, given the fact that most drivers use the heater and/or the defroster under certain 

conditions. However, given the absence of precise data on their use under specific ambient 

conditions, it is difficult to establish a precise uncertainty in their effect on onroad fuel economy 

as indicated by the 5-cycle formulae. An upper bound on the uncertainty is the overall impact of 

heater/defroster use on 5-cycle fuel economy. For conventional vehicles, removing the impact of 

the heater/defroster increases 5-cycle city fuel economy by 0.4%. There is no effect on 5-cycle 

highway fuel economy, as the effect of colder temperatures on running fuel use is modeled and 

not based on the Cold FTP test. For hybrids, the effect is roughly 4%. Since heater/defroster use 

clearly affects warmed up fuel use with current hybrid systems, some loss in onroad fuel 

economy is certain. Thus, the uncertainty in the 5-cycle city fuel economy due to this factor is 

likely well below 4%. 

Regarding the absence of the US06 city cycle in the estimate of running fuel use at cold 

temperatures, this means that the driving cycles underlying our estimated fuel use at 75°F and 

20°F are inconsistent to some degree. We developed three approaches to estimate the potential 

uncertainty associated with this difference. Each approach estimates the effect of cold 

temperature on running fuel use during city driving slightly differently. For review, the approach 

included in the final 5-cycle fuel economy formula bases running fuel use at 20°F only on Bags 2 

and 3 of the FTP (Bags 3 and 4 for hybrids and other vehicles tested over a 4-bag FTP). 

Therefore, the effect of the US06 city cycle is included in the estimate of running fuel use at 

75°F (using a weighting factor of 16%), but not at 20°F. The first alternative approach calculates 

the effect of including the US06 city cycle at 75°F and then applies this effect to the running fuel 

use at 20°F, which is based only on Bags 2 and 3 of the FTP. To do this, we first remove the 

effect of cold temperature completely. We then determined the impact of replacing the 11% 

weight of the US06 city cycle by increasing the weights of Bags 2 and 3 each by 5.5%. This 

replacement increased the city fuel economy from non-hybrid vehicles from 16.7 mpg to 17.5 

mpg, or by 5%. Hybrid city fuel economy increased from 35.9 mpg to 38.6 mpg, or by 8%. 

With an 18% weighting factor for running fuel use at 20°F, this means that the current approach 

in this area could be under-estimating onroad fuel economy by 1% for non-hybrids and 2% for 

hybrids. 

The second alternative approach assumes that the effect of cold temperature on running 

fuel use over Bags 2 and 3 of the FTP applies to all city driving, including the US06 city cycle. 

Therefore, under this approach, running fuel use at 75°F is estimated using the 43/41/16% 

weights for Bag 2, Bag 3 and US06 city cycles. Running fuel use at 20°F is equal to running 

136 
fuel use at 75°F plus the difference in running fuel use over a 50/50% weight of Bags 2 and 3 at 

20°F and 75°F. This approach yields the same effect as those of the first alternative approach. 

The city fuel economy of non-hybrid vehicles decreases by 1% and that for hybrids decreases by 

2%. 

The third and final alternative approach is similar to the second approach in that it 

assumes that the effect of cold temperature on running fuel use over Bags 2 and 3 of the FTP 

applies to all city driving, including the US06 city cycle. However, rather than calculating an 

effect of cold temperature in terms of an absolute difference in fuel consumption, it calculates a 

relative difference in percentage terms and applies this percentage to relative fuel consumption at 

75°F. Therefore, under this approach, running fuel use at 75°F is estimated using the 43/41/16% 

weights for Bag 2, Bag 3 and US06 city cycles. Running fuel use at 20°F is equal to running 

fuel use at 75°F times the percentage increase in running fuel use over a 50/50% weight of Bags 

2 and 3 at 20°F and 75°F. This approach yields roughly the same effect as those of the first and 

second alternative approaches. The city fuel economy of non-hybrid vehicles decreases by 1% 

and city fuel economy for hybrids decreases by 2%. 

137 
Chapter III References 

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MOBILE6: Adjustments for Engine Start and Running LA4 Emissions for Gasoline Vehicles 

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Agency, No. EPA420-P-05-003, March 2005, pp 57-63. 

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and humidity information. Table can be accessed by downloading and installing the MOVES 

2004 model, which can be found at http://www.epa.gov/otaq/ngm.htm#moves2004. 

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table MonthVMTFraction. MonthVMTFraction is a distribution information type table that 

contains a distribution of each sourceTypeID’s VMT across the MonthsOfAnyYear. Table 

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9. Ibid. 

138 
16

10. U.S. Department of Transportation, Federal Highway Administration. Highway Statistics 

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Technical Report. U.S. Environmental Protection Agency, No. EPA420-R-93-007, May 

1993. Website: http://www.epa.gov/otaq/regs/ld-hwy/ftp-rev/ftp-tech.pdf 

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2001 National Household Travel Survey. Report No. BTS-03-05, 2003. See Tables 2 and 

A-8. Website: 

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14. Hu, P. S. and T. R. Reuscher. Summary of Travel Trends – 2001 National Household Travel 

Survey. Prepared for the U.S. Department of Transportation, Federal Highway 

Administration, December 2004. See Table 3. 

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15 Guensler, Randall, Seungju Yoon, Hainan Li, and Vetri Elango, “Atlanta Commute Vehicle 

Soak and Start Distributions and Engine Starts per Day: Impact on Mobile Source Emission 

Rates - DRAFT FOR REVIEW,” School of Civil and Environmental Engineering, Georgia 

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Atlanta,” October 11, 2006. 

17. Hu, P. S. and T. R. Reuscher. Summary of Travel Trends – 2001 National Household Travel 

Survey. Prepared for the U.S. Department of Transportation, Federal Highway 

Administration, December 2004. See Table 3. 

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18. Ibid., see Table 13-1. 

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EPA420-R-01-009, April 2001. 

Website: http://www.epa.gov/otaq/models/mobile6/r01009.pdf 

20. Rykowski, Richard A., Edward K. Nam, and George Hoffman, “On-road Testing and 

Characterization of Fuel Economy of Light-Duty Vehicles,” SAE 2005-05FL-174. 

139 
21. U.S. Environmental Protection Agency. Response to Comments for the Final Regulations for 

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213. 

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25. U.S. Environmental Protection Agency. Air Conditioning Survey Data, Phoenix AZ, 1994. 

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31. Hellman, Karl H. and J. Dillard Murrell, U.S. EPA, SAE 820791, 1982, Tables 5 and 6. 

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140 
Special Study. National Highway Traffic Safety Administration, National Center for 

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data as of September 12, 2005 is available as a Microsoft Excel spreadsheet in the public 

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Technical Report. U.S. Environmental Protection Agency, No. EPA420-R-93-007, May 

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141 
Chapter IV: Economic Impacts 

A. Testing and Facilities Burden 

This section gives additional details for the Cost Analysis under Part VI. of the Preamble, 

“Projected Cost Impacts .” 

The final rule requires calculation of fuel economy values based on the five-cycle 

formulae beginning with model year 2011 for some engine families. As discussed in detail 

elsewhere, for model years 2008 through 2010, manufacturers may use the mpg-based 

calculation for the five-cycle fuel economy values or they may conduct voluntary testing. For 

model year 2011 and after, if the five-cycle city and highway fuel economy values for an 

emission data vehicle group are within 4% and 5% of the mpg-based regression line, 

respectively, then all the vehicle configurations represented by the emission data vehicle (e.g., all 

vehicles within the vehicle test group) would use the mpg-based approach. Vehicles within a test 

group falling below the 5% tolerance band for highway fuel economy values would be required 

to conduct US06 tests; those falling below the city fuel economy band would be required to 

conduct SC03, US06, and Cold FTP tests. In addition, we expect that some of these vehicles 

falling below the tolerance levels may be eligible to estimate fuel economy for a given test 

through the application of analytically derived fuel economy (ADFE) values. Some data is 

currently available for vehicles that have conducted all five tests; based on this data, EPA has 

estimated the number of vehicles for which additional testing would be required because they fall 

below the 4 and 5 percent bands, as discussed below. 

We prepared a range of burden estimates for this analysis and the discussion will mention 

minimum and maximum burden scenarios. These low and high estimates are intended to provide 

EPA’s estimate of the outer boundaries of the likely testing and information costs, and EPA 

solicited comments on the basis of these estimates, including the number of additional tests, and 

the costs of performing such tests and of the additional tests that will be likely under the new 

regulations. EPA received no comments on the basis of these estimates, the number of additional 

tests, or the basic cost estimates for performing tests as presented in the proposal. Some 

comments were received on more specific costs issues, and these have resulted in some 

modifications to the cost estimate that will be noted below. 

1. Test Volume 

a. Testing Burden for MY 2008 through 2010 

EPA estimates no additional tests during MY 2008 through MY 2010 based on the fact 

that the mpg-based fuel economy estimates will be available for all manufacturers. No 

additional testing would be required because manufacturers simply apply the mpg-based scale of 

adjustments to the same FTP and HFET test results that they otherwise would conduct for the 

fuel economy labeling program. While manufacturers have the option of conducting and 

reporting full five-cycle test results, such tests are not required. This cost analysis is limited to 

burdens that are mandated by the final rule. 

142 
b. Testing Burden for MY 2011 and After 

The term “Test Volume” includes the labor and operations and maintenance (O&M) costs 

for running additional tests under the proposed requirements. Because of EPA’s facilities cost 

methodology, it also sets the capital cost estimates for ongoing facilities costs, as discussed 

below. 

Based on MY 2004 data, 1250 fuel economy vehicles were tested with the FTP and 

highway fuel economy tests. (The figure is approximate because the city FTP test may be used 

and recorded primarily as a fuel economy test, an emissions test, or both.) Data show that 330 

Supplemental FTP (US06 and SC03) tests were conducted and 220 Cold FTP tests. 

Consequently, if all fuel economy vehicles were required to conduct full five-cycle tests, 

approximately 920 additional Supplemental FTP tests and 1,030 Cold FTP tests would be 

required. Based on an analysis of our 615 vehicle dataset, we estimate that 8% of the test groups 

will fall outside a band of +/- 4% of the regression for the city test and 23% outside a band of 

+/- 5% of the highway regression. Taking the 2004 numbers above as a baseline, 92% of the 

additional SC03 and Cold FTP tests otherwise required would therefore be avoided for city fuel 

economy; 77% of the additional US06 tests would be avoided. Thus, for example, the initial 

estimate of increased testing burden for SC03 would be 8% of the difference between 1250 and 

330. 

This approach is retained in the final cost analysis, with one adjustment. The percent of 

vehicles triggering additional testing requirements because they fall outside the tolerance bands 

for the city and highway tests should only count those that are below the band in both cases; that 

is, only those with fuel economy lower than 4 and 5 percent below the regressions, respectively. 

With this correction applied to our updated 615-vehicle dataset, 4 percent of the test groups 

would trigger additional testing because they fall more than 4 percent below the city regression 

line, and 13 percent because they would fall more than 5 percent below the highway line. Thus, 

for example, the initial estimate of the increased testing burden for SC03 would be 4% of the 

difference between 1250 and 330, rather than 8%. The effect of this correction is that the 

baseline estimated ranges of additional tests in the proposal of 169-212 US06 tests, 59-74 SC03 

tests, and 66-82 cold FTP tests, become 96-120, 29-37, and 33-41, respectively, for the final rule. 

Finally, the high and low estimates under these assumptions are generated by differing 

estimates of the effect of another feature that will be available for MY 2011 and after: an 

expanded use of analytically derived fuel economy (ADFE) as an alternative to conducting 

vehicle tests. Current guidance (CCD-04-06) limits ADFE to 20% of the values that would 

otherwise be derived from tests; the 1250 test baseline already excludes such analytically derived 

results. Expanded ADFE guidance will be prepared in time for MY 2011 to allow for derivation 

of fuel economy values for some of the additional test cycles that otherwise would be required as 

described above. The low and high burden estimates assumes that 20% and 0% of the additional 

tests would thereby be avoided, respectively. 

143 
1) Fuel Economy Labeling for Medium-Duty Passenger Vehicles (MDPVs) 

As discussed earlier, MDPVs will be included in the labeling program beginning with 

model year 2011. This change is congruent with NHTSA’s expansion of the CAFE program to 

include MDPVs beginning the same model year. As discussed in Section II.D, we are finalizing 

fuel economy test methods for MDPVs that will not require additional testing beyond that which 

will already be required by the CAFE program beginning in model year 2011 (i.e., the FTP and 

HFET tests). Therefore, we are projecting no additional costs in this final rule to extend labeling 

to MDPVs. 

2) Cold FTP Diesel Testing 

The estimated cost impact of requiring cold FTP testing for light-duty diesel vehicles is 

small. As an example, in model year 2006, only five light-duty diesel vehicles were certified for 

sale in the U.S. A total of eight city/highway tests were performed on those vehicles to 

determine fuel economy estimates. Applied to the 2006 model year, our proposal would have 

required that an additional maximum of eight cold FTP tests be performed in addition to the 

city/highway tests. Our proposed cost analysis accounted for additional cold FTP testing across 

the entire automotive industry, including diesel vehicles. 

While anticipating the makeup of the MY2011 diesel fleet is uncertain at this point, it 

seems prudent to anticipate some addition in the number of diesel vehicles certified and, as a 

result, we have assumed a doubling in the number of light duty diesels certified by MY 2011, 

and thus increased the number of light-duty diesel test groups to 10. This has increased the 

estimated Cold FTP test volume from 66 to 82 tests (proposed) and 33-41 tests as corrected 

above, to 41 to 49 tests (final). The consequent adjustment in testing costs is approximately 

$20,000 per year. This adjustment also has an effect on the estimated capital costs for testing 

facilities (see below for the methodology for estimating facility capital costs). 

A separate capital cost addition for Cold FTP diesel testing facility upgrades is discussed 

below under Facility Burden. 

The labor and O&M costs of conducting these estimated tests are derived from prior 

Information Collection Requests submitted for EPA’s light duty certification program. Those 

estimates are based on the number of tests and the hours of labor used at EPA’s testing facility 

combined with industry data supplied in response to questionnaires; these have been somewhat 

adjusted to reflect current information. These costs are estimated to range from $1,860 to $2,441 

per test. These costs per test are applied to the numbers of tests estimated under the minimum 

and maximum scenarios above, and now amount to $343,000 to $424,000 and 5,000 to 6,200 

hours per year for MY 2011 and after, compared to $$606,000 to $757,000 and 8,800 to 11,000 

hours in the proposal. 

This analysis is summarized in the tables below: 

144 
Table IV.A-1. Estimated Cost per Test: Proposed 

and Final 

Test Cycle Cost/Test Hours/Test 

FTP/HWY $1,860 30 

US06 $1,860 30 

SC03 $2,206 30 

Cold FTP $2,441 30 

Table IV.A-2. Estimated Increase in Number of Tests for Model Years 2008-2010: 

Proposed and Final 

Test Cycle 

2004 

Model 

Year 

Number of 

Tests 

Increase In Number of Tests: MY 2008 – 2010 Increase in Hours 

Min Tests 

Min $ 

Increase Max Tests 

Max $ 

Increase Min Max 

FTP/HWY 1250 

US06 330 0 $0 0 $0 0 0 

SC03 330 0 $0 0 $0 0 0 

Cold FTP 220 0 $0 0 $0 0 0 

Min 

Increase = $0 

Max 

Increase = $0 0 0 

Table IV.A-3. Estimated Increase in Number of Tests for Model Years 2011 and Later: 

Proposed 

Test Cycle 

2004 

Model 

Year 

Number of 

Tests 

Increase In Number of Tests: MY 2010 And 

After Increase in Hours 

Min Tests 

Min $ 

Increase Max Tests 

Max $ 

Increase Min Max 

FTP/HWY 1250 

US06 330 169 $314,861 212 $393,576 5,078 6,348 

SC03 330 59 $129,907 74 $162,384 1,766 2,208 

Cold FTP 220 66 $160,904 82 $201,130 1,978 2,472 

Min 

Increase = $605,672 

Max 

Increase = $757,090 8,822 11,028 

145 
Table IV.A-4. Estimated Increase in Number of Tests for Model Years 2011 and Later: 

Final 

Test Cycle 

2004 

Model 

Year 

Number of 

Tests 

Increase In Number of Tests: MY 2010 And 

After Increase in Hours 

Min Tests 

Min $ 

Increase Max Tests 

Max $ 

Increase Min Max 

FTP/HWY 1250 

US06 330 96 $177,965 120 $222,456 2,870 3,588 

SC03 330 29 $ 64,954 37 $ 81,192 883 1,104 

Cold FTP 220 41 $ 99,979 49 $120,092 1,229 1,476 

Min 

Increase = $342,898 

Max 

Increase = $423,740 4,982 6,168 

2. Facilities Burden 

“Facilities” refers to the capital costs for constructing facilities to accommodate the 

increases in test volume estimated in the table above. For these capital costs we used an FTP 

facility cost of $4 million per facility able to perform 750 US06 tests per year, a cost of $9 

million for an environmental test facility able to conduct 300 to 428 SC03 tests per year, and $10 

million for an environmental facility able to conduct 300 to 428 Cold FTP tests per year. The 

new tests were deemed to require these facilities in proportion to the number of tests needed, and 

the costs were then annualized over ten years with a 7% discount rate. This is likely a very 

conservative assumption since it does not attempt to account for the excess capacity that exists in 

manufacturers’ current test facilities. We assume that there is no excess capacity in our analysis. 

Furthermore, consistent with other information burden analyses for the emissions and fuel 

economy programs, we have considered these as ongoing capital rather than startup capital costs 

(i.e., as the facilities depreciate they are continually being replaced). Annualized and depreciated 

over ten years at 7%, these capital costs per year under the above analysis under the proposal 

were $0 for each of model years 2008, 2009 and 2010, and range from $524,000 to $866,000 per 

year for model years 2011 and after. Under the corrected and adjusted number of tests described 

the preceding section, the final annualized and discounted capital costs for model year 2011 and 

after range from $375,000 to $560,000. 

In addition, commenters raised a number of technical issues regarding laboratory 

configurations and the difficulty of establishing cold test facility retrofits to accommodate diesel 

testing without a transition period. The 2011 effective date of the requirement is intended to 

address some of these concerns, particularly the lead time needed to implement laboratory 

adjustments, but we recognize that some facility updates will still be necessary. An additional 

capital cost of $55,000 for each of ten manufacturers has been added the proposed facilities costs 

to account for these adjustments in the final cost estimate. This figure includes the cost of flame 

ionization detectors (FIDs) as well as heated sample probes, sample lines and sample filters. 

This cold facility upgrade for diesel testing, along with the corrected and adjusted 

projected number of tests, accounts for the changes in facility capital costs from the proposal. 

146 
This analysis is summarized in the tables below: 

Table IV.A-5. Estimated Facility Costs: Proposed 

MY 2008 - 2010 MY 2011 And After 

Un-depreciated capital costs Minimum Maximum Minimum Maximum 

FTP/HW $0 $0 $0 $0 

US06 $0 $0 $902,827 $1,128,533 

SC03 $0 $0 $1,238,131 $2,208,000 

Cold FTP $0 $0 $1,540,187 $2,746,667 

Total $0 $0 $3,681,144 $6,083,200 

Depreciated, 10yrs @ 7 % $0 $0 $524,112 $866,111 

Table IV.A-6. Estimated Facility Costs: Final 

MY 2008 - 2010 MY 2011 And After 

Un-depreciated capital costs Minimum Maximum Minimum Maximum 

FTP/HW $0 $0 $0 $0 

US06 $0 $0 $510,293 $637,867 

SC03 $0 $0 $619,065 $1,104,000 

Cold FTP $0 $0 $957,009 $1,640,000 

Cold FTP facility upgrades $0 $0 $550,000 $550,000 

Total $0 $0 $2,636,368 $3,931,867 

Depreciated, 10yrs @ 7 % $0 $0 $375,359 $559,809 

3. Startup Burden 

“Startup” refers to one-time costs beginning with model year 2008 to implement the new 

requirements in the final rule. These startup burdens are primarily information technology costs 

involving familiarization with the new data reporting requirements and reformatting management 

information systems to carry out and report the necessary data and calculations. With the 

exception noted below, all these burdens are add-ons to well established reporting requirements: 

manufacturers already submit data to EPA on all five test cycles, have the option of applying 

analytically derived fuel economy numbers, and report vehicle class determinations and 

supporting information. This part of the proposed estimate assumed four weeks for four 

information technology specialists for analysis and coding, and four weeks for two IT specialists 

for testing, at $100 per hour, for 35 manufacturers, based on a count of manufacturers in 2004 

with an allowance for inter- and intra-corporate relationships. The estimate also includes 1,120 

hours industry wide for label redesign. Startup information technology costs finally also include 

one-time costs and hours for implementing US06 split phase sampling, assuming one to seven 

days of programming. The remaining startup cost is the one-time costs associated with validation 

tests for the split phase sampling, and assumes one to seven tests at the costs per test given 

above. These one-time tests are not considered to entail ongoing capital costs but are treated as 

startup capital costs. EPA’s proposal estimated all startup costs, discounted at 7% and annualized 

over ten years, as $526,000 to $615,000 and 3,815 to 4,718 hours. 

147 
a. Dual Information Systems Needed for CAFE/Gas Guzzler and Labeling 

Currently, EPA's data system has a single “path” through which fuel economy data is 

passed. Up to the point of determining the so-called "model type" fuel economy, the calculations 

performed by the database are identical for both CAFE and labeling purposes. Then, the single 

path diverges: The label value is determined by applying the 10% and 22% downward 

adjustment factors to the city and highway model-type values, whereas, for CAFE, the modeltype values are adjusted by a minor, but statutorily required, adjustment to account for test 

procedure differences since 1975. 

The new rule would now require two separate data paths, since the new derived 5-cycle 

label calculations for labeling purposes would be carried through “from the bottom up” resulting 

in a model type fuel economy that needs no adjustment for "real world" conditions (i.e., modeltype fuel economy for labeling purposes would be different from model-type fuel economy for 

CAFE purposes.). Consequently, it would no longer be possible to use the single existing 

calculation systems to determine the CAFE values. 

EPA agrees that there may be some convenience in applying the derived 5-cycle equation 

at the model-type level for manufacturers who wish to use the same data management system for 

reporting CAFE and label values. This may be particularly true for the early part of the 

transition period. However, this approach is not available for the vehicle-specific 5-cycle label 

calculations, and any manufacturers who use it during the phase-in period during the 2008-2010 

model years will encounter an information cost not contemplated in the proposal; a dual 

calculation procedure will be needed. Similarly, a dual calculating system will be needed for 

model years 2011 and after. 

The cost analysis has been updated to account for this increased information system 

startup burden. Based on a projection of EPA’s information development contract costs, and an 

estimate of the portion of those costs attributable to the dual information system possibility, we 

have increased the industry information startup costs (unamortized) by $933,450. This increases 

the annualized and discounted startup costs to $659,000 to $748,000 for the industry as a whole, 

from the proposed level of $526,000 to $615,000. 

The Final Technical Support Document has also been updated to delete labor hours 

attributed to startup costs. Startup costs are properly treated as capital costs, not labor. 

The startup burdens are summarized in the following tables: 

148 
Table IV.A-7. Estimated Startup Costs: Proposed 

Cost Hours 

Item Minimum Maximum Minimum Maximum 

Information Tech/Paperwork 

Adjustment to new FE and label value 

computations and reporting of FE data 

for SFTP and Cold FTP and new ADFE 

calculations-- analysis, code 

development, and testing; label redesign $3,472,000 $3,472,000 34,720 34,720 

Sample system changes for US06 split 

phase $28,000 $196,000 280 1,960 

O&M 

Validation testing form US06 split phase 

sampling $195,300 $651,000 3,150 10,500 

TOTAL $3,695,300 $4,319,000 38,150 47,180 

Depreciate 10 years at 7% $526,128 $614,928 3,815 4,718 

Table IV.A-8. Estimated Startup Costs: Final 

Item 

Cost Hours 

Minimum Maximum Minimum Maximum 

Information Tech/Paperwork 

Adjustment to new FE and label value 

computations and reporting of FE data 

for SFTP and Cold FTP and new ADFE 

calculations-- analysis, code 

development, and testing; label redesign $4,405,450 $4,405,450 0 0 

Sample system changes for US06 split 

phase $28,000 $196,000 0 0 

O&M 

Validation testing form US06 split phase 

sampling $195,300 $651,000 0 0 

TOTAL $4,628,750 $5,252,450 0 0 

Depreciate 10 years at 7% $659,002 $747,830 0 0 

4. Summary 

The combined results of the above tables can be summarized as follows: the final total 

estimated costs for each of Model Years 2008, 2009, and 2010 range from $659,000 to $748,000, 

and for Model Years 2011 and after, from $1,377,000 to $1,731,000. This is shown the tables 

below: 

149 
Table IV.A-9. Estimated Total Costs: Proposed 

MY 2008-2010 MY2011 and After 

Min Max Min Max 

Test Volume $0 $0 $605,672 $757,090 

Facilities (annualized 

10yrs/7%) $0 $0 $524,112 $866,111 

Startup: one-time 

IT/Paperwork and O&M 

(annualized 10yrs/7%) $526,128 $614,928 $526,128 $614,928 

Total $526,128 $614,928 $1,655,912 $2,238,129 

Table IV.A-10. Estimated Total Costs: Final 

MY 2008-2010 MY2011 and After 

Min Max Min Max 

Test Volume $0 $0 $342,898 $423,740 

Facilities (annualized 

10yrs/7%) $0 $0 $375,359 $559,809 

Startup: one-time IT and 

validation (annualized 

10yrs/7%) $659,002 $747,830 $659,002 $747,830 

Total $659,002 $747,830 $1,377,259 $1,731,380 

The final combined burden hours shown in these tables is 4,982 to 6,168 hours for each model 

years beginning 2011 and continuing thereafter, as summarized below: 

Table IV.A-11. Estimated Total Hours: Proposed 

MY 2008-2010 MY2011 and After 

Min Max Min Max 

Test Volume (Labor, 

O&M) 0 0 8,822 11,028 

Facilities (Capital, 

annualized 10yrs/7%) 0 0 0 0 

Startup: one-time 

IT/Paperwork and 

O&M (Capital, 

annualized 10yrs/7%) 3,815 4,718 3,815 4,718 

Total 3,815 4,718 12,637 15,746 

150 
Table IV.A-12. Estimated Total Hours: Final 

MY 2008-2010 MY2011 and After 

Min Max Min Max 

Test Volume (Labor, 

O&M) 0 0 4,982 6,168 

Facilities (Capital, 

annualized 10yrs/7%) 0 0 0 0 

Startup: one-time 

IT/Paperwork and 

O&M (Capital, 

annualized 10yrs/7%) 0 0 0 0 

Total 0 0 4,982 6,168 

B. Impact on Confirmatory Testing of Vehicles 

EPA can conduct confirmatory testing of any vehicle tested for emissions or fuel 

economy compliance purposes for any reason it deems necessary. EPA's fuel economy 

regulations currently specify certain conditions under which test results from manufacturers are 

to be confirmed, either by EPA at its own laboratory, or by the manufacturer. See 40 CFR 

600.008-01. These conditions are tied to the FTP and highway test procedures, but do not 

address the US06, SC03 and Cold temperature FTP tests that will now included in fuel economy 

measurement. There are separate confirmatory test provisions in EPA's emission compliance 

regulations at 40 CFR 86.1835-01, which would continue to apply to all five test cycles. 

Confirmatory testing is generally indicated for fuel economy purposes when the fuel 

economy falls near the cut point for Gas Guzzler Tax assessment, when the fuel economy (either 

city or highway) is unexpectedly high for that vehicle or falls near the leader within the 

comparable class. EPA provides guidance to manufacturers defining the actual criteria and 

cutpoints. Confirmatory testing is also required when the test results are close to or exceed the 

federal emission standard associated with the test. EPA also selects a random number of 

vehicles for confirmatory testing, and will request confirmatory testing on vehicles employing 

new or unusual technology. 

In considering the impact of confirmatory testing of US06, SC03 and Cold temperature 

tests, some of the above criteria may or may not apply. For instance, the Gas Guzzler tax is 

based only on the FTP and Highway test procedures, so there would be no impact on 

confirmatory testing for the other procedures. However, if, in the course of performing a US06, 

SC03 or Cold temperature fuel economy test, an applicable emission standard should be 

exceeded, we believe that a confirmatory test would be necessary, per the current regulations. 

Another reason to conduct additional tests is to resolve a disparity between confirmatory 

fuel economy test results. If a manufacturer's initial fuel economy test result does not compare 

closely to that of the confirmatory test, EPA requires a retest. (This requirement helps to 

establish correlation between manufacturer and EPA testing, providing assurance that 

151 
manufacturer testing is properly conducted.) This currently applies only to FTP and Highway 

testing. If this requirement was applied equally to the US06, SC03 and Cold temperature FTP 

fuel economy tests, this could result in the need for additional tests. Obviously, EPA does not 

have established fuel economy correlation data for the US06, SC03 or Cold temperature FTP 

tests, because fuel economy has never been required to be measured. There is not enough fuel 

economy data to determine what constitutes reasonable correlation from one test to the next. 

Therefore, EPA does not plan at this time to conduct retests of US06, SC03 or cold temperature 

FTP tests on the basis of test-to-test fuel economy disparities. Once the final regulations for 5­

cycle fuel economy are implemented, and more correlation data becomes available, we will 

assess whether or not we should establish retest criteria for US06, SC03 and cold temperature 

FTP tests on the basis of fuel economy test-to-test disparities. 

C. Changes to Label Format and Content 

Manufacturers are currently required to print and post fuel economy window stickers on 

all new cars and light trucks available for sale in the U.S. Our final rule does not change this 

requirement, thus no new costs for these activities will be incurred. The final changes to the 

format and content may require a one-time design change, but any additional costs are 

anticipated to be very slight, and mitigated by the design templates for the new labels which EPA 

is providing in the regulations. The reporting and recordkeeping requirements associated with 

the fuel economy label are set forth in 40 CFR sections 600.312 to 600.314. These sections 

require that manufacturers supply EPA with the label values and the data used to derive them, 

and provide schedules for the updating of this information. Under the final rule, these values will 

be recalculated and new data will be submitted. The costs for these efforts are very minimal and 

are addressed in the startup cost figures above. There will be a similar one-time set-up charge 

associated with the new label format based on the effort required for each manufacturer to apply 

the new EPA templates to the labels they must print. This cost item also has been included in the 

paperwork startup costs portion of the cost analysis. 

D. Certification Fees 

EPA collects fees from manufacturers to recover its costs associated with its emission 

and fuel economy compliance efforts. The fee amount is adjusted from time to time according to 

any changes to EPA's costs. The impact of the final rule changes is not anticipated to 

significantly increase EPA's efforts with the fuel economy labeling program; therefore, we are 

not proposing to adjust the fee amount at this time. However, we reserve the right to evaluate 

our actual incurred costs once the final rule is implemented and to propose fee changes if deemed 

appropriate. 

152 
Appendix A: EPA Kansas City Test Program 

During 2004–2005, EPA in association with the Coordinating Research Council, 

STAPPA/ALAPCO, DOT and DOE/NREL,, recruited and tested over 600 privately owned 

passenger vehicles in the Kansas City area. The vehicles included an assortment of compact 

cars, mid-size cars, pick-ups and SUVs from a variety of manufacturers. The program was split 

into 3 rounds (1, 1.5 and 2), each consisting of 120-300 vehicles. In all three rounds, vehicles 

were recruited randomly from lists of vehicle registrations in the Kansas City area. Care was 

taken to ensure that the sample were random with respect to the geographic location of the owner 

and socio-economic status. In rounds 1 and 2, the desired sample of vehicles was stratified into 

four groups of model years, with emphasis on older vehicles1,2

. The primary purpose of Rounds 

1 and 2 was the quantification of particulate emissions, particularly those from high emitters. In 

Round 1.5, only 2001 and later model year vehicles were sampled. (Details about the design and 

performance of Round 1.5 are described the study’s final report.3

) The primary purpose of 

Round 1.5 was the measurement of onroad fuel economy from vehicles for which we could 

estimate 5-cycle fuel economy. This meant that we had to have fuel economy estimates over all 

five cycles for these vehicles (i.e., that the vehicle had to be certified to the Supplemental FTP 

standards). These standards began phasing in with the 2001 model year. 

All of the vehicles In Rounds 1 and 2 were tested on a dynamometer, but only a subset of 

the vehicles were instrumented with a Portable Emissions Measurement System (PEMS) and 

tested in the hands of their owners. As these vehicles ranged in model year from 1968 to 2005, 

very few of the vehicles tested in Rounds 1 and 2 had been certified to the Supplemental FTP 

standards. 

In Round 1.5, none of the vehicles were tested on a dynamometer. However, all of these 

vehicles were instrumented with PEMS and had their fuel economy measured while being driven 

in normal use by their owners. The round 1.5 vehicle fleet consisted of approximately 120 

vehicles, including over 30 hybrid electric vehicles. The PEMS measures driving activity, as well 

as second-by-second mass emissions of CO2, CO, HC, and NOx for roughly 24 hours while the 

owners of the vehicles are utilizing their vehicles on the road under normal, real-world 

conditions. Two aspects of the PEMS limit the duration of its operation. One is the capacity of 

gas which is needed to operate the flame ionization detector used to measure HC emissions. The 

other is battery capacity needed to operate both the emissions measurement equipment and the 

onboard computer which scans vehicle activity and stores information. Using the carbon balance 

method, fuel economy can be accurately calculated on a second by second basis. 

Since the focus of this proposed rule is fuel economy labeling, we are solely interested in 

the onroad fuel economy data collected during this program. We will not present, or discuss the 

results of the dynamometer testing, nor the emission test results. Because of this, we will focus 

primarily on Round 1.5, but will also include relevant data from Rounds 1 and 2, as appropriate. 

The reader is referred to this report for further information related to vehicle recruitment, 

selection, instrumentation, data processing and delivery. 

The onroad fuel economy data will be used for several purposes: 

1) To compare the onroad fuel economy for each vehicle to its fuel economy label values, 

153 
2) To compare the driving activity of vehicles in Kansas City to the findings of past studies, and 

3) To develop 5-cycle formulae for each vehicle and compare the 5-cycle fuel economy to 

similar vehicles in our 5-cycle certification fuel economy database 

Before moving to these three tasks, the following section will describe the methods we 

used to process and quality assure the data obtained from the PEMS. 

A. Quality Assurance 

Upon initial delivery of the data, we performed a number of tests on the data to identify 

equipment malfunctions. The removal of inaccurate data is referred herein as “flagging” (i.e., 

data are “flagged” when it meets a criterion designed to identify data which are not of acceptable 

quality). Flagging was performed using the SAS statistical package. 

The initial step in quality assurance involved segregating vehicles into those with 

acceptable data and those with bad data. An entire set of bad data for a given vehicle was 

removed for a variety of reasons. Sometimes, the data collected for a given vehicle was 

completely missing vehicle speed or exhaust flow data, which are critical to proper processing of 

the data. In some cases, the vehicle was only driven for a few miles before the equipment 

stopped functioning. These data were insufficient to develop robust estimates of fuel use in the 

various VSP bins and to have confidence that the measured onroad fuel economy was indicative 

of that vehicle’s typical use. In one case, the CO2 emissions were outside a reasonable range. 

After this initial filtering of the data, there remained 9 vehicles from round 1, 97 vehicles from 

round 1.5 (including 33 hybrids), and 42 from round 2. These all constitute “good tests”. Data 

from 18 vehicles were removed in this step. 

The next step in data processing involved removing or modifying individual seconds of 

data. We often found instances where the exhaust flow meter signal was zero, or erratic. There 

were several instances where the flow meter stopped functioning properly permanently (i.e., for 

the rest of the testing of that vehicle). This could have been due to freezing in the cold 

temperatures or simply from an electronic failure. For non-hybrids, if the vehicle was moving, 

but exhaust flow dropped to a value equivalent to engine off conditions, then the data was 

flagged and omitted from the final dataset. Unfortunately, this condition was impossible to flag 

systematically with hybrids, since the engine can shut off while the vehicle is on. Obvious cases 

of lengthy equipment failures during hybrid testing were flagged by hand. However, exhaust 

flowmeters were assumed to have been functioning properly during all hybrid runs. 

Another frequent problem was that the signal from the vehicle’s onboard diagnostic 

(OBD) system, which provides measurements of vehicle speed, engine rpm, engine coolant 

temperature and other vehicle information, disappeared permanently or acted erratically. This is 

easy to spot since all of the OBD signals disappear at the same time. This can be caused by the 

driver accidentally knocking the connector, or it can be a result of manufacturer software 

settings. Fortunately, the PEMS units were equipped with a geographical positioning system 

(GPS) and vehicle speed could be calculated from the output from the GPS unit. These GPS 

estimates of vehicle speed were substituted for OBD speeds when the OBD speeds were absent 

or obviously erratic. 

154 
Sometimes the GPS speeds appeared to reflect anomalies (i.e., were clearly 

discontinuous, or drifted unrealistically over time). This was more of a concern regarding the 

calculation of vehicle acceleration than vehicle speed, due to the fact that required engine power 

is very dependent on small changes in acceleration. To identify unrealistically high 

accelerations, a cut-off was chosen at a specific power (the product of acceleration and speed, or 

v*a) of 300 mph2

/sec. Beyond this, the data were flagged (though these events were rare). 

Choosing a lower (and probably still realistic) maximum value would have resulted in excessive 

removal of data. . In addition, there were instances where the GPS speeds drifted slowly over 

time, usually when the car was not operating. These time segments were removed. 

Table A-1 summarizes the processing of the data obtained with the PEMS. 

Table A-1. Processing of Raw Data Obtained In Kansas City 

Round 1 Round 1.5 Round 2 

Vehicles tested onroad with PEMS 18 117 52 

Vehicles with some PEMS data 13 109 47 

Vehicles with acceptable PEMS data 9 97 42 

Total seconds of acceptable PEMS data while vehicle is 53,319 501,405 181,915 

in operation

Seconds of PEMS data removed 3,808 44,996 2,773 

As can be seen, a total of 187 vehicles were equipped with PEMS units in Kansas City and the 

equipment worked to some degree on 169 of these vehicles (90%). Data from an additional 22 

vehicles were rejected due to obvious equipment malfunctions, leaving 148 vehicles (78% of the 

original sample) with usable data. Of these 148 vehicles, 125 were conventional vehicles and 33 

were hybrids. This usable activity and fuel economy data were obtained for a total of 736,000 

seconds (over 200 hours) of driving from 148 vehicles, or roughly 80 minutes of driving per 

vehicle. 

B. Onroad Fuel Economy 

Total fuel consumption for each vehicle was determined from the carbon balance of the 

CO2, HC, and CO emissions. The total distance of driving was determined by summing vehicle 

speed and multiplying by total time of operation. This total distance traveled was then divided 

by total fuel consumption to determine onroad fuel economy. 

EPA city and highway label fuel economy values were obtained from EPA mileage 

guides. The test vehicles were matched to those tested in Kansas City to the closest degree 

possible. The following figure compares the measured fuel economy to the 55/45 composite 

label fuel economy from round 1.5 (newer vehicles). We segregated the vehicles into two 

groups: conventional gasoline-fueled vehicles and hybrids. A linear regression with no constant 

of the conventional vehicles showed nearly one-to-one correlation, with a slope of 1.006. The 

correlation was also quite good (r-squared value of 0.77). The largest difference was only 6 

mpg, or about 30%. Thus, the onroad fuel economy data indicate no offset from the current EPA 

label values on average. 

155 
The correlation of hybrid data shows much more scatter. This is partially explained by 

the fact that only three hybrid models were tested, the Toyota Prius and the Honda Insight and 

Civic. On average, hybrid fuel economy was 11% less than the composite EPA label values. 

The average onroad fuel economy of the Toyota Prius vehicles was closer to their composite 

label values than those for the two Honda models. On average, the onroad fuel economy of the 

hybrids tested varied more than the conventional vehicles. This could be due to hybrids’ greater 

sensitivity to operating conditions which can either take full advantage of the hybrid technology 

or essentially nullify it. The fact that most vehicles started out testing with a hot start likely 

biased onroad fuel economy upwards to some degree. Thus, the actual shortfalls found would 

have been greater to some degree if testing had begun with a cold start, which is more 

representative of a typical day of driving. 

Figure A-1. Comparison Onroad to Current Label Economy: Kansas City 

non-hybrid

hybrid

Linear (non-hybrid) y = 1.006x 

Linear (hybrid) R2

 = 0.7746 

y = 0.89402x 

R2

 = -0.18069 

0 10 20 30 40 50 60 label fuel economy (mpg) 

C. Recent Driving Activity in Kansas City and California 

There are a number of ways to evaluate and compare driving activity. We evaluate two 

measures of driving here: 1) combinations of vehicle speed and acceleration and 2) VSP 

frequency distributions. 

A common measure of driving activity is based on a speed acceleration frequency 

distribution or SAFD. This procedure divides individual seconds of driving into a 2 dimensional 

matrix of speed and acceleration. Here, we rounded accelerations to the nearest whole number 

and speeds to the nearest factor of 5 mph (i.e., 0, 5, 10, 15, etc.) If an acceleration was greater 

than 15 mph/sec, we set the acceleration to 15 mph/sec. If an acceleration was less than -15 

mph/sec, we set the acceleration to -15 mph/sec. 

0 

10 

20 

30 

40 

50 

60 

real-world fuel economy (mpg) 

156 
There are two basic ways to display a SAFD. The more complex way is to show the 

percentage of driving for each combination (or bin) of speed and acceleration. The simpler way 

is to distinguish between those speed-acceleration bins for which onroad driving was found and 

those which did not. For ease of comparison with the SAFDs of dynamometer cycles, we use the 

simpler form of display here. We developed three SAFDs for the driving monitored in Kansas 

City: one for all vehicles, one for hybrids and one for conventional, or non-hybrid vehicles. 

The SAFD for all of the vehicles tested with PEMS units in Kansas City is shown in the 

following figure. The breadth of onroad driving in Kansas City generally exceeds that of the 

FTP and HFET. Thus, for comparison, we show the SAFD for the FTP and HFET, as it 

represents an inner envelope of driving, per se. In some cases, the breadth of onroad driving in 

Kansas City generally exceeds that of the US06 cycle, as well. However, in some cases, it does 

not. Thus, in the figure, we indicate the driving in Kansas City which exceeds that of the FTP 

and HFET cycles. Then we show driving included in the US06 cycle which exceeds that found 

in Kansas City. In most cases, where the Kansas City driving forms the edge of the envelope, 

the US06 cycle also includes that type of driving. However, this is not true in all cases. Since 

the onroad data and the dynamometer cycles provide speed and acceleration rates for one second 

intervals and the HFET and US06 cycles are about 600 seconds long, the smallest amount of 

driving which can fall into a single bin in these two cases is 0.16%. Of course, smaller 

percentages of onroad driving can fall into a single bin due to the large number of observations 

available. To be comparable with the driving cycles, we only considered onroad driving to 

adequately populate a specific speed-acceleration combination if at least 0.1% of all onroad 

driving fell into that bin. 

Figure A-2. Speed-Acceleration Frequency Distribution: Kansas City Vs. Test Cycles 

ACCEL SPEED BIN 

BIN (mph) 

(mph/s) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 

14 

13 KEY 

12 Driving frequency covered by FTP/HFET style driving 

11 Driving frequency covered by Kansas City Real-World Driving 

10 Driving frequency covered by US06 style driving 

9 

8 

7 US06 

6 

5 KC 

4 

3 

2 

1 

0 FTP/HFET KC 

-1 

-2 

-3 

-4 KC 

-5 

-6 US06 KC activity cut off < 0.1% 

-7 

-8 18% of KC driving outside FTP/HFET driving envelope 

-9 

-10 0.6% of KC driving is outside US06 driving envelope 

-11 90% of US06 driving is within the 0.1% KC boundary 

157 
Overall, 18% of the onroad driving activity (time based) in Kansas City fell outside of the 

FTP/HFET envelope. This corresponds to 33% in VMT terms. However, only 0.6% fell outside 

the US06 (0.4% of the VMT). These are data in the white cells, which are populated but fall 

below 0.1% of the total. Overall, 25% of the driving activity in Kansas City fell outside the FTP 

envelope alone (40% of the VMT). Comparing to most speed limits, 28% of the VMT was 

compiled greater than 65 mph. The SAFD envelopes for hybrid and conventional vehicles did 

not differ significantly from each other. However, the percentages of driving in the various bins 

did vary, as will become more evident below when we evaluate the VSP frequency distributions 

for the two types of vehicles. 

We also show the SAFD driving activity found from a chase car study conducted in Los 

Angeles in 2000 in this figure4

. This study observed roughly 390 hours of vehicle operation. 

According to the data, 20% of California urban driving lies outside the FTP/HFET envelope 

(34% of the VMT). 1.2% fell outside the US06 envelope (1.3% of the VMT). 25% of the driving 

occurred outside of the FTP alone (42% of the VMT). However note that much more of the rural 

driving was outside the US06 envelope (3%). 

Figure A-3. Speed-Acceleration Frequency Distribution: Urban California Vs. Test 

Cycles 

ACCEL SPEED BIN 

BIN (mph) 

(mph/s) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 

14 

13 KEY 

12 Driving frequency covered by FTP/HFET style driving 

11 Driving frequency covered by real-world California urban style driving 

10 Driving frequency covered by US06 

9 Driving frequency covered by real-world CA but NOT US06 

8 

7 US06 

6 

5 CA Urban 

4 

3 

2 

1 

0 FTP/H FTP/HFET CA Urban 

-1 

-2 

-3 

-4 

-5 CA Urban 

-6 

-7 CA activity cut off < 0.1% 

-8 

-9 20% of CA URBAN driving is outside FTP/HFET driving envelope 

-10 1.2% of CA URBAN driving is outside US06 driving envelope 

-11 41% of CA RURAL driving is outside FTP/HFET driving envelope 

-12 3% of CA RURAL driving is outside US06 driving envelope 

Moving to VSP, we calculated the level of VSP for each second of each vehicle’s driving 

from the vehicle’s speed and acceleration, plus its road load characteristics and vehicle mass. 

The equation used is shown below: 

VSP = (Av + Bv2

 + Cv3 + Mva)/M, 

158 
where v is vehicle speed, a is acceleration, A, B, and C are the road load (coast-down) 

coefficients of the vehicles and M is the mass of the vehicle. 

This is the same method used in section IA.2 to convert the facility driving cycles in MOVES 

into VSP frequency distributions. The only difference is that here, the constants A, B, C and M 

are specific to the vehicle being monitored on the road. For the MOVES cycles, we used 

coefficients indicative of the average car and light truck on the road. 

Road grade can also be included in the above equation. We did not include this term, 

because the altitude data from the GPS unit appeared quite unreliable. Therefore, the effect of 

road grade is not reflected in our analysis and remains a source of uncertainty in the results. We 

obtained the specific vehicle parameters from EPA’s Inspection and Maintenance database based 

on the model year, make and model of the vehicle. The vehicles were not weighed on a scale, so 

the actual weights may vary somewhat from those in the database. 

The Kansas City driving activity was binned by VSP and vehicle speed as described in 

section IA.2. We used 26 VSP bins, which includes the addition of 9 high power bins beyond 

those used currently in Draft MOVES2004. Bin 0 contains significant decelerations, while Bin 1 

contains idle operation. Bins 11-19 include other operation below 25 mph. Bins 21-29 include 

operation between 25 and 50 mph and Bins 33-39 include operation above 50 mph. Bins x1 

contain operation with very low or negative engine power, while Bins x9 contain very high 

power operation. 

The following figure compares the Kansas City activity in terms of VSP bin to that used 

in Draft MOVES2004 and described in section IA.2 (Table IA-12). The match in trends is 

reasonable, though Draft MOVES2004 projects roughly 4% more activity in the high speed, high 

power Bins 36-39 and 4% less activity in the lower power Bin 35. 

Figure A-4. Kansas City and California VSP Frequency Distributions vs. MOVES Freq 0.16 

0.14 

0.12 

0.1 

0.08 

0.06 

0.04 

0.02 

0 

0 1 11 12 13 14 15 16 17 18 19 21 22 23 24 25 26 27 28 29 33 35 36 37 38 39 

VSP Bin 

KC Freq Measured 

MOVES 

California Urban 

159 
We also show the driving activity found from a chase car study conducted in Los Angeles in 

2000 in this figure5

. This study observed roughly 390 hours of vehicle operation. This VSP 

frequency distribution was estimated from the speed acceleration frequency distributions 

developed from the chase car data6

. The portion of driving found the Bins 36-39 in Los Angeles 

matches that in MOVES2004, though there is about 1% less operation in Bins 26-29. One must 

be cautious when comparing activity data from instrumented vehicles and chase cars. Chase cars 

do not follow drivers throughout their entire trip, so operation on local neighbor roads is often 

missed. Also, the VSP distribution from Draft MOVES2004 includes both urban and rural 

operation, while those of Kansas City and Los Angeles are primarily urban. This could 

introduce some differences, as well. 

The following figure shows the VSP frequency distributions for the three groups of 

Kansas City vehicles mentioned above: all vehicles, hybrids and non-hybrids. We removed idle 

(bin 0) prior to calculating the VSP distributions for two reasons. One, idle percentages were 

extremely high and if depicted in the figure, would have made the rest of the bars difficult to 

read. Second, given that little fuel is consumed during idle, including the percentage of time at 

idle distorts the comparison of those VSP bins where fuel consumption is more significant. 

Figure A-5. VSP Frequency Distributions in Kansas City: Hybrids vs. Non-Hybrids 

0.2 

0.18 

0.16 

0.14 

0.12 

0.1 

0.08 

0.06 

0.04 

0.02

0

0 1 11 12 13 14 15 16 17 18 19 21 22 23 24 25 26 27 28 29 33 35 36 37 38 39 

VSP Bin 

Freq 

Total Activity 

Non-Hybrid 

Hybrid 

As can be seen from a close look at the operation in the higher power bins (x6-x9), the 

driving of hybrids tends to be less aggressive than that of conventional vehicles. The percentage 

of time spent driving in bins 26-29 and 36-39 is 5-6% lower for hybrids than conventional 

vehicles. This is of interest, as this study is likely to be the first to examine the relative operation 

of conventional and hybrid vehicles. If there was something about hybrid vehicles which always 

led them to be operated less aggressively, this might need to be considered in developing their 

fuel economy label value. 

There are several possible explanations for this difference. One cause could be a 

limitation in the power of the hybrids monitored in Kansas City. All but one of the hybrids were 

160 
either a Prius or a Civic, which were clearly designed for fuel efficiency with some sacrifice in 

power. Another cause could be driver behavior. People purchasing hybrids may have an 

unusually high interest in achieving high fuel economy on the road and drive their vehicles less 

aggressively, knowing that aggressive driving reduces fuel economy. This could either represent 

a change in driving behavior towards less aggressiveness with the purchase of a hybrid, or these 

drivers may have driven their previous vehicle the same way. 

Of particular interest is whether the difference in the operation of conventional vehicles 

and hybrids will continue in the future or disappear. If the difference is due to a low power 

vehicle design, then the difference depends on the design of future hybrids. Most of the recently 

introduced hybrids match (or even exceed) the power output of their non-hybrid counterparts. 

Thus, this difference in vehicle operation found in Kansas City may soon disappear. 

If the drivers of hybrids in Kansas City always drove in the manner captured in this 

study, then this implies that people conscious of fuel efficiency tend to be the first people to buy 

hybrids. In this case, the difference in operation would continue until hybrids become the 

dominant drivetrain. As more and more people purchase hybrids, the driving of hybrids would 

likely become more aggressive, since the new hybrid purchasers are coming from the remaining, 

more aggressive pool of drivers. The driving of conventional vehicles would also become more 

aggressive as the less aggressive drivers of conventional vehicles buy hybrids. Overall driving 

operation would likely remain the same, but the driving of the two pools would shift over time, 

tending to remain distinct from each other. 

Finally, the drivers of hybrids in Kansas City may have changed their driving behavior 

when they purchased their hybrids. Some manufacturers of hybrids offer training classes or 

videos to help hybrid owners get the most from their hybrid technology. If this trend continued, 

hybrid driving activity would tend to always be less aggressive than that of conventional 

vehicles. As more hybrids enter the fleet, the overall driving of the fleet would become less 

aggressive. Even if the drivers of current hybrids have changed their driving behavior, the 

question exists whether this trend would continue as hybrids become more popular. 

This is the first systematic study of the onroad operation of hybrids in the hands of typical 

owners. Only about 45 hours of operation were studied. Thus, there is significant uncertainty in 

the difference found in the operation of conventional and hybrid vehicles. As discussed in 

section I.A, we are in the process of obtaining a large volume of operational (activity) data 

obtained in Atlanta. Some hybrid vehicles may be included in this database. We plan to 

compare the operation of conventional and hybrid vehicles in that study as soon as we receive 

the data. Still, the potential impact of the difference in driving behavior for the two types of 

vehicles is examined in section III.E.4. 

D. Evaluation of 5-Cycle Approach to Fuel Economy Estimation 

In section I.A.2.b, we developed combinations of dynamometer cycles which best 

represented the driving activity represented in Draft MOVES2004. In an earlier publication, we 

also developed a methodology by which real-world driving could be fit by 3 cycles (FTP, HFET, 

and US06).7

 We repeated this analysis using the VSP frequency distributions shown in Figure 

161 
II-5 above and additional cycle combinations. This analysis and its results are described in detail 

in section III.E.4. Because the Kansas City study measured the driving activity of each vehicle 

separately, as well as its second by second fuel consumption, we can develop dynamometer cycle 

combinations which best represent the driving of each vehicle. These cycle combinations can 

then be used in the 5-cycle formula to predict onroad fuel economy. These vehicle specific 5­

cycle fuel economies can then be compared to the measured onroad fuel economy to assess the 

accuracy of the 5-cycle methodology. 

One caveat regarding this comparison is that we need estimates of fuel economy over the 

five dynamometer cycles to use in the 5-cycle formula. These generally only exist for vehicles 

certified to the Supplemental FTP standards, which began phasing in with the 2001 model year. 

Very few of the vehicles tested with a PEMS unit on the road in Rounds 1 and 2 of the Kansas 

City test program were from the 2001 and later model years. Therefore, we only performed this 

analysis for vehicles from Round 1.5. Even with this restriction, we still do not have fuel 

economy measurements over the 5 dynamometer cycles for many of the vehicles tested in Round 

1.5. 

The first step in the analysis was to develop a VSP frequency distribution for each 

vehicle’s onroad driving. The same methods were used to do this as were used above in 

developing the VSP frequency distributions for all the Kansas City vehicles. The only difference 

is that it was done for each vehicle individually. 

The second step was to develop VSP frequency distributions for the five dynamometer 

bags and cycles for each vehicle. These distributions are very similar to those shown in Table 

IA-15. The only difference is that we used vehicle-specific values for the constants A, B, C and 

M instead of the fleet-average estimates contained in Draft MOVES2004. 

The third step was to calculate an average rate of fuel consumption for each vehicle’s 

operation in each VSP bin. This was done by calculating the rate of fuel consumption for each 

second of vehicle operation using the carbon balance method. For each vehicle, the seconds of 

operation were grouped by VSP bin and the fuel rates for these times of operation averaged. If 

no onroad operation occurred in a particular bin for a particular vehicle, the fuel consumption in 

that bin was set to the rate of fuel consumption in the nearest bin having the same level of power 

(e.g., bin 38 average fuel consumption is set to that found for bin 28). If no operation occurred 

in any of the other bins of the same power level, the rate of fuel consumption was set to the 

average rate of fuel consumption for other similar vehicles in that bin. Here, similar means 

either conventional or hybrid. In other words, if no operation occurred in bin 18, 28, or 28, then 

the rate of fuel consumption in each of these bins for a conventional vehicle was set to the rate of 

fuel consumption in each of these bins for all the conventional vehicles which operated in each 

bin. The same approach was taken for hybrid vehicles. 

We restricted the calculation of fuel rates to warmed up driving (i.e., after the effects of 

the cold start had ceased). We first had to determine when an engine start occurred. Along with 

this we determined how long the engine had been turned off (i.e., the soak time). 

162 
We used two criteria for determining the point in each trip when the engine was fully 

warmed up. The first applied to those vehicles without coolant temperature data. The second 

applied to those vehicles with coolant temperature data. Engine coolant temperature data was 

only available for some of the vehicles, so the latter criterion could be not extended to all of the 

Kansas City vehicles 

For vehicles without coolant temperature data, we assumed that the engine was fully 

warmed up after the engine had been running for 200 seconds. For those with such data, we first 

smoothed out fluctuations in the coolant temperature data by calculating a five-second average 

temperature for a time t which is the average of the coolant temperature at that time, the two 

seconds prior to time t and the two seconds subsequent to time t. For all operation beyond the 

200th second after a start, we calculated the mean and standard deviation of what we considered 

to be the warmed-up coolant temperature. The first second when the five-second average 

temperature fell within 1 standard deviation of the mean coolant temperature was estimated to be 

the time at which the engine was first fully warmed up. 

As described in section I.A.2, we regressed the VSP distributions for the 5 dynamometer 

cycles and bags against that of the onroad driving for each vehicle. We weighted the square of 

the error by the rate of fuel consumption in each bin, then minimized the weighted error. Unlike 

the analyses described in section I.A.2, here we used SAS to perform the analyses due to the 

large number of vehicles involved. In all cases, the sum of the dynamometer cycle coefficients 

was set to equal 1.0 and the intercept was set to zero. 

We developed two sets of dynamometer cycle combinations for each vehicle. One used 

the five dynamometer cycles and bags used in section I.A.2, namely: Bags 2 and 3 of the FTP, 

HFET, US06 city and US06 highway. A second set of regressions was performed using only 

three of these cycles and bags, namely Bags 2 and 3 of the FTP and HFET. This second set of 

regressions represents what is called here the “3-cycle” methodology. It represents a way of 

estimating onroad fuel economy using only the current two fuel economy tests, the FTP and 

HFET. It does not include the US06 cycle, either in whole or in part. No attempt was made to 

segregate a vehicle’s driving into city or highway modes. Thus, each regression represents all of 

the driving by an individual vehicle and the predicted fuel economy is then comparable to the 

overall fuel economy of that vehicle on the road during the duration of the PEMS testing. 

The initial weighted regression for each vehicle sometimes produced cycle coefficients 

which were negative. Those cycles with negative coefficients were removed, one-by-one, 

starting with the cycle with the coefficient of the largest magnitude. Once all regression 

coefficients were non-negative, the regression procedure was stopped and the results accepted. 

No attempt was made to further remove cycles with positive coefficients which might not pass a 

statistical significance test of some sort. 

For example, vehicle number 521, a 2003 Mitsubishi Montero Sport, was driven 46 miles 

while its fuel economy and activity were being monitored. The cycle representation of its 

driving is shown in Table A-2. 

163 
Table A-2. Cycle Combinations for the Mitsubishi Montero Sport 

Bag 3 Bag 2 HFET US06 Highway US06 City 

Time Basis 

3-cycle combination 68.6% 0% 31.4% ---- ----

5-cycle combination 66.0% 0.2% 0% 33.7% 0% 

Mileage Basis 

3-cycle combination 53.7% 0% 46.3% ---- ----

5-cycle combination 45.0% 0.1% 0% 54.9% 0% 

Fuel Economy 16.7 19.9 23.4 19.4 11.4 

The regression of VSP, weighted by fuel consumption, provides cycle combinations in 

terms of the percentage of time spent driving in each cycle. As was described in section I.A.2, 

these percentages of time are converted to percentages of miles traveled using the average speed 

of each dynamometer cycle and bag. 

As can be seen in Table A-2, without consideration of US06, about two-thirds of the 

Montero’s driving time is represented by Bag 3 and one-third by HFET. When we include US06 

in the calculation, the Bag 3 contribution changes very little. The driving previously represented 

by HFET shifts entirely to US06 highway. 

In order to use the cycle combinations to predict onroad fuel economy, we need estimates 

of each vehicle’s fuel economy over the five cycles. The vehicles tested in Kansas City were not 

tested over the dynamometer cycles. We estimated the fuel economy for each bag or cycle for 

each Kansas City vehicle from the test results of similar vehicles in our 5-cycle fuel economy 

database of 615 vehicles. The fuel economy estimates for the Montero are shown in the last row 

of Table A-2. Warmed up onroad fuel economy can be estimated by simply summing the 

product of the fuel economy of each bag and cycle and that bag’s or cycle’s contribution to 3­

cycle or 5-cycle driving. 

We were able to match up 71 of the vehicles tested in Round 1.5 to those in our 5-cycle 

fuel economy database. Of these vehicles, 53 were conventional vehicles and 18 were hybrids.j 

Generally, differences exist between the Kansas City and certification vehicles, as the latter are 

selected based on their worse case nature regarding emissions and the former are likely to be 

high sales volume vehicles. This could cause the 3-cycle and 5-cycle estimates of fuel economy 

to somewhat low. We present an analysis at the end of this section which sheds some light on 

this issue. 

In order to further refine the 3-cycle and 5-cycle estimates, we then included fuel use 

related to engine starts. This basically involved determining how many times the engine was 

started while the vehicle’s operation was being monitored, the soak time prior to each engine 

start and the ambient temperature at the time of each start. If a trip lasted less than 10 seconds, 

we assumed that it did not occur. The time associated with this trip was made part of the soak 

j

 Nearly half of the hybrids tested were pre-2004 model year Prius vehicles. We do not have 5-cycle fuel economy 

values for this model. 

164 
time prior to the next trip. We also deleted trips when more than 5% of the trip’s data were 

discarded for reasons described above. 

The ambient temperature at the time of the engine start was estimated from the time and 

day of the engine start and local meteorology data from the National Weather Service. We 

estimated the total fuel related to engine starts using the same methodology applied to the Draft 

MOVES2004 estimates of annual engine starts throughout the nation and described in section 

I.A.1. Each engine start had a previous soak time and ambient temperature associated with it. 

The start fuel use related to this engine start was estimated in terms of the start fuel use related to 

a cold start at 75°F using the equations for the effect of start fuel use as a function of soak time 

and ambient temperature presented in section I.A.1. The 75°F cold start equivalents for each 

start were summed across all of the starts for each vehicle to estimate the total number of 75°F 

cold start equivalents for each vehicle’s monitored driving. We did not have a measured soak 

time prior to the first start of monitored operation, as the equipment was by necessity installed 

while the engine was turned off. We assumed that amount of time that the equipment was 

operating prior to the first engine start was the soak time prior to that start. This tended to be 

only a matter of minutes. Practically, this was equivalent to assuming that the first engine start 

was a hot start, though in some rare cases the vehicle had sat overnight since its last operation. 

Thus, this is one certain source of uncertainty in the estimation procedure. We estimated the 

sensitivity of the estimated onroad fuel economy due to this uncertainty by evaluating the impact 

of adding one cold start to the number of equivalent cold starts. 

For example, the Montero took three trips while being monitored. The first was preceded 

by a soak of 12 minutes, the second by 99 minutes and the third by 3 minutes. The ambient 

temperature for all the starts ranged only from 24-25 F. Using the equations presented in section 

I.A.1, the total number of cold starts ignoring the effect of ambient temperature was 0.47 for the 

three starts. The total number of 75°F cold start equivalents including the effect of ambient 

temperature was 0.96 for the three starts. The latter estimate assumes that the cold start fuel use 

(the difference in fuel consumption in Bags 1 and 3 multiplied by 3.59 miles) at 20°F is 2.75 

times that at 75°F. We determined this ratio for each vehicle using the actual or estimated fuel 

economy values for Bags 1 and 3 at 20°F and 75°F from our 5-cycle fuel economy database. 

The ratio for the Montero was 2.16, meaning that the cold start fuel use only increased by a 

factor of 2.16 between 75°F and 20F, or 22% less than that assumed in Draft MOVES2004 for 

the typical vehicle. Thus, we multiplied the equivalent number of cold starts at 75°F (0.96) by 

the ratio of 2.16 to 2.75 to make the estimate of the equivalent number of cold starts at 75°F 

specific to the cold start fuel use of the Montero. This reduced the estimate of the equivalent 

number of cold starts at 75°F for the Montero to 0.75. 

We then multiplied the cold start fuel use at 75°F for the Montero by the number of cold 

start equivalents. For the 3-cycle estimate of onroad fuel economy, we used a value of 0.47 for 

the number of cold start equivalents, since the FTP and HFET only provide fuel economy 

information at 75°F. For the 5-cycle estimate of onroad fuel economy, we used a value of 0.75 

for the number of cold start equivalents, since the availability of the cold FTP provides the 

additional fuel economy information at 20°F. 

165 
Finally, for the 5-cycle estimate of onroad fuel economy, we included an estimate of the 

effect of temperature on running fuel use. We estimated the ambient temperature at the time of 

the start of each trip and then weighted these temperatures by the length of each trip in order to 

estimate an average temperature while the vehicle was operating. For the Montero, this was 24.7 

F. We then calculated a percentage indicating the degree to which this temperature represented 

the temperature drop from 75°F to 20°F; in this case, 91%. Consistent with the 5-cycle 

formulae, for the percentage of driven mileage represented by HFET and US06 highway, we 

increased running fuel use by 4% multiplied by the percentage of the temperature drop from 

75°F to 20°F. For the percentage of driven mileage represented by Bags 2 and 3 and US06 city, 

we calculated running fuel use as the sum of : 

1) 100% minus the temperature percentage (9%) times the running fuel use at 75°F (a 

function of fuel economy over Bags 2 and 3 and US06 City), plus 

2) the temperature percentage (91%) times the running fuel use at 20°F (a function of fuel 

economy over Bags 2 and 3, with half of the mileage weight of US06 City added to each Bag). 

We were able to match 71 vehicles from those successfully tested in Kansas City to 

vehicles in our 5-cycle fuel economy database. Using the procedures just described, we 

estimated 3-cycle and 5-cycle fuel economies for each vehicle and averaged the results across all 

91 vehicles. The results are shown in Table A-3. 

Table A-3. Onroad and Predicted Fuel Economy: Kansas City Test Program 

3-Cycle Predicted 5-Cycle Predicted 

Fuel Coefficient Fuel Coefficient of 

Economy of Variation Economy Variation of 

(mpg) of Error (mpg) Error 

Onroad fuel economy 28.3 ---- 28.3 ----

Predicted Fuel Economy 

Warmed up fuel economy 32.2 18% 29.9 17% 

With cold starts at 75°F 32.0 18% 29.7 17% 

With cold starts at ambient ---- ---- 29.2 17% 

temp. 

With running fuel use at ---- ---- 27.7 17% 

ambient temp 

As indicated in Table A-3, the average onroad fuel economy of the 71 vehicles (each vehicle 

weighted equally, not by mileage of travel) was 28.3 mpg. Using only the cycle combinations to 

predict onroad fuel economy over-predicted onroad fuel economy using both three cycles and 

five cycles, as would be expected. The overprediction is smaller for five cycles than three cycles 

(1.6 mpg versus 3.9 mpg), indicating the benefit of including the US06 city and highway bags in 

predicting onroad fuel economy. Also shown in Table A-5 are the coefficients of variation of the 

percentage differences in the predicted versus onroad fuel economy for each of the 71 vehicles. 

This metric provides an indication of the consistency of the prediction. A low coefficient of 

variation, even if there is a large, but consistent offset, would indicate that a significant factor 

was missing from the prediction, like cold start fuel use, but to a consistent degree across all of 

the vehicles. 

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Adding cold start fuel use as if all starts were at 75°F (as assumed in the FTP) reduces the 

difference between predicted and onroad fuel economy in both cases by the same 0.2 mpg. The 

coefficients of variation remain unchanged. 

Adding cold start fuel use at the estimated ambient temperature is only applicable to the 

5-cycle prediction. This factor reduces the difference between predicted and onroad fuel 

economy by 0.5 mpg. The coefficient of variation, however, does not change. One would have 

expected it to decrease at least slightly, since the ambient temperatures at which the vehicles 

were started varied. Three possible reasons for this outcome exist. One, the effect of these 

temperature differences was so small that total fuel use varied very little even for specific 

vehicles. Two, the data for soak times were inaccurate, particularly due to the unknown soak 

prior to the first start. Or, three, the difference in Bag 1 and Bag 3 fuel economy at 20°F is not a 

good predictor of excess fuel use at temperatures which tended to fall in the range of 20 and 

75°F. In terms of predicting onroad fuel economy, though, considering the ambient temperature 

of engine starts reduces the difference between the 5-cycle fuel economy estimate and onroad 

fuel economy by 2%. 

Adding the effect of ambient temperature on running fuel use had a larger effect on 

predicted fuel economy than the effect of cold starts at 75°F or ambient temperature. This 

reduced the predicted 5-cycle from 0.9 mpg above onroad fuel economy to 0.5 mpg below it, for 

a final difference of less than 2%. 

The difference between the 3-cycle and 5-cycle formulae is even more dramatic for nonhybrid vehicles. The difference between the best 3-cycle prediction and the onroad measurement 

for hybrids averages 24%, while that for the best 5-cycle prediction is only 3%. 

The VSP based approach to predicting onroad fuel economy was equally successful when 

applied in a 15 car study conducted by EPA in 2001. Table A-4 presents the measured onroad 

fuel economy of the 15 cars and fuel economy predictions using two different approaches. One 

approach used the VSP methodology developed for Draft MOVES2004 (see section I.A.2) to 

determine the mix of FTP, HFET and US06 driving which best matched each vehicle’s onroad 

driving pattern. This approach under-estimated onroad fuel economy by 4%. The other 

approach used average onroad speed to determine the mix of FTP and HFET driving which best 

matched each vehicle’s onroad driving pattern. This approach over-estimated onroad fuel 

economy by 24%. One advantage of the 15-car test program was that most of the 15 cars were 

tested over the FTP and US06 as part of the study. Therefore, the dynamometer fuel economy 

values used in the predictions were those for the specific vehicles tested onroad. In the Kansas 

City program, we had to match the vehicles tested onroad to those in our 5-cycle certification 

database. These matches may have been closer in some cases than others, causing the 5-cycle 

fuel economy to slightly under-predict onroad fuel economy on average in Kansas City. 

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Table A-4. On-road and Modeled Fuel Economies Using Vehicle-Specific Cycle 

Weights (mpg) 

Veh. 

No. 

Fuel Economy (mpg) % Difference 

On-road Modeled 

VSP (FTP, 

HFET, US06) 

Speed (FTP, 

HFET) 

VSP (FTP, 

HFET, US06) 

Speed (FTP, 

HFET) 

1 24.8 26.3 27.4 6.3% 10.4% 

2 26.2 23.2 34.0 -11.4% 30.0% 

3 27.2 28.7 31.5 5.5% 15.8% 

4 19.5 N/A 30.5 N/A 56.6% 

5 32.0 N/A 32.5 N/A 1.7% 

6 24.7 25.1 28.5 1.6% 15.3% 

7 40.9 N/A 40.0 N/A -2.4% 

8 29.5 28.5 38.0 -3.3% 29.0% 

11 26.0 23.8 34.6 -8.6% 33.1% 

12 27.9 25.4 40.7 -8.9% 46.0% 

13 22.5 22.8 30.7 1.0% 36.1% 

14 15.6 14.5 19.9 -7.1% 27.4% 

15 26.8 N/A 30.9 N/A 15.2% 

17 26.5 N/A 26.7 N/A 0.5% 

18 17.8 15.5 18.3 -12.8% 2.5% 

Average (10 vehicles w/ VSP based estimates) -3.8% 24.6% 

Standard Deviation (10 vehicles) 7.0% 13.2% 

Finally, in an effort to better understand the cause of the difference between predicted 

and onroad fuel economy reflected in Table A-4, we reversed this analysis. Instead of using fuel 

economy measured over dynamometer cycles to predict onroad fuel economy, we used the 

onroad fuel measurements to predict cycle fuel economy. We did this using the VSP 

methodology. Each combination of vehicle and dynamometer cycle has its own VSP frequency 

distribution. We simply weighted the measured onroad fuel consumption in each VSP bin for 

each vehicle by this cycle-specific VSP frequency distribution to estimate the average rate of fuel 

use (in gallons per second) over that cycle. We then converted this fuel rate to fuel economy 

using the average speed of each dynamometer cycle. Table A-5 shows the measured and 

predicted fuel economy values for four dynamometer bags or cycles. When comparing the 

predicted fuel economy over the dynamometer cycles to measured values, we found significant 

differences between hybrids and conventional vehicles. Thus, Table A-5 presents the results of 

this analysis separately for conventional vehicles and hybrids. 

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Table A-5. Comparison of Cycle Fuel Economy 

Bag 2 Bag 3 HFET US06 

Conventional Vehicles 

Predicted from onroad fuel rates (mpg) 16.5 20.7 28.6 24.3

Measured in lab @ 75°F (mpg) 21.5 25.0 34.0 22.5

% Difference 30% 21% 19% -7%

Measured in lab: adjusted for temperature (mpg) 20.5 24.0 --- --­

% Difference 26% 16% --- --­

Hybrids 

Predicted from onroad fuel rates (mpg) 32.4 38.4 51.5 43.3

Measured in lab @ 75°F (mpg) 61.8 53.9 61.8 41.6

% Difference 91% 40% 20% -4%

Measured in lab: adjusted for temperature (mpg) 47.6 46.9 --- --­

% Difference 46% 22% --- --­

For conventional vehicles, cycle/bag fuel economy from the 5-cycle certification 

database are higher than those predicted from second-by-second onroad fuel rates for Bags 2 and 

3 of the FTP and HFET. The differences are fairly significant, ranging from 19-30%. However, 

the situation reverses for US06, with the measured cycle fuel economy being 7% lower than that 

predicted from the onroad fuel measurements. These differences can be due to differences in 

vehicle operation on the road and on the dynamometer and to differences between the physical 

vehicles tested in both cases. As mentioned above, the vehicles which are generally included in 

our 5-cycle certification database represent the worse case vehicle configuration within their 

broader vehicle groupings. Worse case might include four wheel drive, higher inertia weight 

setting, higher TRLHP, etc. The vehicles tested in Kansas City would tend to be high sales 

volume models. This might explain the 7% difference seen for the US06 cycle, but the 

differences observed with the other cycles go in the wrong direction. Differences in ambient 

temperature could explain this difference in fuel economy, as the Kansas City testing was 

conducted during the late fall and early winter. 

We attempted to correct for the difference in temperature using the average temperature 

for each vehicle’s operation, as described above. We then used this average temperature to 

interpolate between the Bag 2 and Bag 3 fuel economy values measured and 20°F and 75°F, 

respectively, to estimate a dynamometer-measured fuel economy at the ambient temperature of 

each vehicle’s testing in Kansas City. We cannot perform a similar adjustment to the HFET and 

US06 fuel economy values as these tests are not run under cold temperature conditions. The 

results of this adjustment for temperature are shown just below the unadjusted results in Table A5. As can be seen, for conventional vehicles, this adjustment reduces the difference between the 

predicted and measured fuel economy values over Bags 2 and 3 from 21-30% to 16-26%, a 

modest decrease. As described in section II.A.4, we expect the effect of ambient temperature on 

HFET and US06 fuel economy to be relatively small. Thus, there appears to be factors which 

are causing onroad fuel consumption to be higher than dynamometer measurements during low 

speed and mild driving which is not affecting higher speed, more aggressive driving. Generally, 

this argues for including US06 fuel economy in the fuel economy label calculation. It also 

supports the inclusion of the 9.5% downward adjustment for non-dynamometer factors. 

169 
The comparison for hybrids is similar, but the differences are much more dramatic, 

particularly at low speed, stop and go driving where hybrid technology functions. Bag 2 and 3 

fuel economy measured on the dynamometer exceeds those estimated from onroad 

measurements by 40-91%. In contrast, at higher speeds, but with either mild or aggressive 

driving styles, the differences are both smaller and very similar to those found with conventional 

vehicles. Of course, at higher speeds without much stopping, hybrids operate like conventional 

vehicles. 

As for conventional vehicles, we adjusted the Bag 2 and 3 dynamometer fuel economy 

values for temperature. The effect is much more dramatic for hybrids, given their greater 

sensitivity to ambient temperature. The 40-91% difference decreases to 22-46%. These 

differences are still greater than those found for conventional vehicles. We considered the 

possibility that our analysis of the data was somehow ignoring the engine shut-off feature of the 

hybrid vehicles (i.e., the zero fuel consumption occurring during these times were being 

excluded from the average fuel rates being calculated for lower speed, low power VSP bins). 

However, we confirmed that our measurements included significant amounts of time when the 

engine was off. Overall, we measured zero carbon monoxide emissions 12% of the time from 

the hybrid vehicles. The Prius models had the highest percentage of engine off operation (19%), 

followed by the Civic (3%), following by the one Insight in the test fleet (1%). Clearly, the 

Honda hybrids turned off their engines less frequently than the Toyota Prius. This may be due to 

differences in hybrid technology utilized by the two manufacturers. 

We evaluated whether this difference in engine off time affected the comparisons shown 

in Table A-5. For both the Prius and Civic models, the dynamometer measured fuel economy 

over Bag 3 is 21% higher than those values estimated from onroad fuel rate measurements, after 

adjusting for ambient temperature. For Bag 2, the Prius models show a 63% difference, while 

that for the Civic hybrid models is 38%. Thus, the lower percentage of indicated engine off 

operation for the Civics is not likely the cause of the greater difference in hybrid fuel economy 

over Bags 2 and 3 of the FTP shown in Table A-5 for hybrids compared to conventional 

vehicles. The difference could be due to the efficiency of regenerative braking onroad versus on 

the dynamometer, as the severity of deceleration is not considered in the VSP methodology. 

Further study of the data obtained in Kansas City and additional data collected elsewhere in the 

future will be needed to better identify the cause of the difference. Overall, however, for 

hybrids, as well as conventional vehicles, the dynamometer measured fuel economy over US06 

appears to be much more directly representative of onroad fuel consumption than those measured 

over the current fuel economy cycles. 

170 
Appendix A References 

1. Eastern Research Group. Kansas City PM Characterization Study: Round 1 Testing Report. 

ERG No. 0133.18.001.001, March 7, 2005. 

2. Eastern Research Group. Kansas City Field Testing: Round 2 Testing Final Progress Report. 

ERG No. 0133.18.005.003, April 15, 2005. 

3. Eastern Research Group. Late Model Vehicle Emissions and Fuel Economy Characterization 

Study: Addendum to the Kansas City Exhaust Characterization Study-Draft Report. ERG No. 

0133.18.004.001, September 26, 2005. 

4. Sierra Research, Inc., “Task Order No. 2 SCF Improvement – Field Data Collection,” Sierra 

Report No. SR02-07-04, July, 2002. 

5. Sierra Research, Inc., “Task Order No. 2 SCF Improvement – Field Data Collection,” Sierra 

Report No. SR02-07-04, July, 2002. 

6. Brzezinski, D., E. Nam, J. Koupal, G. Hoffman. Changes in Real World Driving Behavior: 

Analysis of Recent Driving Activity Data. Proceedings of the 15th Coordinating Research 

Council On Road Vehicle Emissions Workshop, 2005. 

7. R.A. Rykowski, Nam, E.K., Hoffman, G., “On-Road testing and Characterization of Fuel 

Economy of Light-duty Vehicles,” SAE 2005-01-0677. 

171