# Final T echnical Support Document Fuel Economy Labeling of Motor V ehicle Revisions to Improve Calculation of Fuel Economy Estimates

Office of T ransport ation and Air Qualit y EP

A420-R-06-0 1 7 December 20 06

# Final T echnical Support Document Fuel Economy Labeling of Motor V ehicles: 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

EP A420-R-06-0 1 7 December 20 06

### Table of Contents

L IST O F T A B LES

............................................................................................................................... ........................ III L IST O F F IG URES

............................................................................................................................... ........................ V L IST O F A CRONYMS

............................................................................................................................... .................. VI CH APTERI : E X E C UTIVE SU M M A R Y
............................................................................................................. 1 CH APTERII : CURRENT AND PROPOS ED LABEL VALUES COMPARE D T O ONROAD ESTIMA TES
...................................................................................................................... ............. 7 A. O NROAD F UE L E CONOMY E ST IMA TES D URIN G T YPICA L O PE RATIO N

.............................................................. 7 1. ORNL “Y our M

P G” Progr a m
......................................................................................................... ............ 7 2. DOE Free dom C ar

Pr ogram
......................................................................................................... .............. 9 3. Strate gic Visions Ne w Ve hicl e Survey

........................................................................................... ........... 12 4. Kans as City Instrume nted Ve hicl e Study

......................................................................................... ......... 13 B. F UEL E CONOMY E STI MA TES B Y I NDEP E ND EN T O RGANI ZAT I ONS

................................................................... 15 1. Cons umer Re ports Estim ates of O n r o ad Fue l Economy

.......................................................................... 15 2. AAA Estimates of Onr oad Fu el Economy

........................................................................................... ...... 20 3. Edmunds

........................................................................................................................ ........................... 22 C. F LEET

- WID E E STI M AT ES O F O NROAD F UE L E CONOM Y

.................................................................................. 25 D. O VERALL C O M PARISON OF H YBRID F U EL E CONOM Y

.................................................................................... 28 C HAPT E R II R E F ERENC E S

............................................................................................................................... ......... 32 CH APTERII I : DO CU MENT ATIO N O F F I NAL AP PR OA CH FO R ESTIMATI N G O N
- R O A D FUEL ECON O M Y
........................................................................................................................ ........... 33 A. V EHICLE S PECI FIC 5-C YCLE M ETHO D F OR E S T IM AT ING O N -R OAD F UE L E CONOM Y FRO M D YNAMO M ET E R T ESTS

............................................................................................................................... .............................. 34 1. Start Fu el Use

................................................................................................................. .......................... 36
a. S t art F u e l

..................................................................................................................... ......................................... 36 b. Trip Length

.................................................................................................................... ....................................... 44 c. F o rmula for S t art F u el U s e
..................................................................................................... .............................. 50 2. Runni ng Fuel Use at 75°F W ithout Air Conditi oning

.............................................................................. 51
a. O n -Road D r ivin g P a tt erns
....................................................................................................... ............................. 51 b. Repres ent a tiv e M i x of D y na m o m eter D r iving C y c l es
............................................................................... ........... 61 3. Effect of Air C o nditioni ng on Fuel Ec onomy
..................................................................................... ....... 69 4. Effect of Cold Am bient Te m p er ature s on Runni ng Fu el Us e
.................................................................... 78 5. Adjust ment Factor for No n- D y nam o mete r Effects

.................................................................................. . 81 6. 5-Cycle Fu el Ec onomy F o r

m ulae

.................................................................................................. ........... 92
a. 5-C y c l e Fuel Ec onom y Formula e
.................................................................................................. ....................... 92 b. Alterna tive 5-c y cle Highwa y Fu el Economy Formula

............................................................................... .......... 97 B. D ERIVAT ION O F TH E MPG

- B ASED A PPRO A C H

............................................................................................. 10 0 C. V ARIABILI TY I N O NROAD F U EL E CO NOM Y

.................................................................................................. 10 6 D. I M P A C T OFTH E 5-C YCLE AN D MPG

- B ASED F ORMUL AE ON F UEL E CONO MY L ABELS

................................ 11 0 E. S ENSIT I VI TI ES AND U NCER TAI N TI ES I N THE 5- C YCLE F UEL E CONOM Y F ORM ULAE

..................................... 11 3 1. Start Fu el Use

................................................................................................................. ........................ 11 4 2. Runni ng Fuel Use At 75°F

....................................................................................................... ............... 11 8
a. A lterna tive D e fi nition o f U S 06 City and H i ghw a y Bags

........................................................................... ......... 118 b. Elim ination of T h ree High est Sp eed Fre e wa y C ycles in Draf t M OVES 2004

.................................................... 122 c. A lterna tive F u el Rates a nd N u mb er of V S P Bins

.................................................................................. ............. 123 d. K a ns as City V S P D i s tribu tions a nd F u el R a tes

................................................................................... ............... 125 e. Californi a Ch as e Car S tud ies

................................................................................................... ........................... 127 f. A lterna tive S p lit s of Ci ty /H ighw a y D r iving

..................................................................................... .................. 128 g. Comp lete C y c l e s
................................................................................................................ ................................. 132 3. Air Conditioning Eff ects

....................................................................................................... .................. 13 4 4. Cold Tem p er ature R u nni ng Fu el U s e
.............................................................................................. ....... 13 5 C HAPT E R III R EFERE NCES

............................................................................................................................... ...... 13 8

iCH APTERI V : ECON O M IC IMP ACTS
............................................................................................................ 14 2 A. T ESTI NG A ND F ACILI T IE S B URD E N

............................................................................................................... 14 2 1. Test Vol u me
.................................................................................................................... ........................ 14 2
a. Testing Burdenf o r MY 2008 th ro ugh 2010

........................................................................................ ............... 142 b. Testing Burdenf o r MY 2011 and After

........................................................................................... .................. 143 2. Facilities Bur den
.............................................................................................................. ...................... 14 6 3. Start u p Burde n
................................................................................................................. ....................... 14 7 4. Sum m ary
........................................................................................................................ ......................... 14 9 B. I MPACT O N C O NFIRM ATOR Y T ESTI NG OF V EH ICLES

.................................................................................... 15 1 C. C HANG ES TO L ABEL F OR MA T AND C ONT EN T

.............................................................................................. 15 2 D. C ERTI FICA TIO N F EES

............................................................................................................................... ..... 15 2 APPE NDI X A : EP A KA NS AS CIT Y TE ST PR OG RA M
.................................................................................. 15 3 A. Q UALITY A SSU RANCE

............................................................................................................................... ... 15 4 B. O NROAD F UE L E CONOMY

............................................................................................................................ 15 5 C. R ECEN T D RIVI NG A CTIV ITY I N K ANSAS C IT Y AND C AL I F ORN I A

.................................................................. 15 6 D. E VALUA TI ON O F 5-C YCLE A PP ROACH T O F UEL E CONO MY E STIM AT ION

..................................................... 16 1

ii

### List of Tables

T ABLE I-1 A GGREGAT E C OSTS

........................................................................................................................... 6 T ABLE II.A- 1. Y OUR MPG V E R SUS C URR E NT EPA L ABEL F UEL E CONO MY

............................................................ 8 T ABLE II.A- 2. F REEDO M CAR H YBRID F LE ET C UM ULA TIV E V ERSUS EPA C OMPO SIT E L A B EL F U EL E CO NOMY

... 10 T ABLE II.B-1. C ONSU M ER R E PORTS AND C URRENT EPA AN D MPG

- B ASED F UEL E CONO M Y : 303 V EHICLES

...... 16 T ABLE II.B-2. CRAN D C URR E NT EPA, 5- C YCLE AND MPG

- B ASED F UEL E CONOM Y : 70 V EHICLES

..................... 16 T ABLE II.B-3. C OMPAR I SON OF C ON SUM E R R EPORT S AN D EPA F UEL E CONOMY V ALU E S FOR H YBRIDS

............. 18 T ABLE II.B-4. AA A AND C UR RENT EPA, 5-C YCLE AND MPG

- B ASED F UEL E CONOM Y E ST IMA TES

..................... 20 T ABLE II.B-5. 2 003 -20 06 E DMUND S L ON G

- TERM T ES T V EH I C L ES

........................................................................ 23 T ABLE II.B-6. E DMUND S L ON G

- TERM T EST V EHICLES C OM P A RED TO EP A C OMBIN E D MPG E STIMA T ES

............ 24 T ABLE II.C-1. FH WA -B ASED E STI M AT E O F O NROAD F UE L E CONOMY

................................................................. 26 T ABLE II.C-2. B RE A K DOW N OF VM T AND F U EL U SE B Y 4- W HEE L , 2-A XLE T RUCKS

........................................... 27 T ABLE II.D- 1. O NROAD H YBRID F UE L E CONO M Y V ERS U S EP A L ABEL E ST IMA TES ( MPG )

.................................... 30 T ABLE II.D- 2. O NROAD H YBRID F UE L E CONO M Y E ST IM ATE S V ERSUS EPA L ABEL E ST I M AT ES ( MP G )

................. 31 T ABLE III.A -1

. K EY F EAT URE S OF THE F IV E C URRENT E M I S S ION AND F UEL E CONOM Y T ES TS

............................... 34 T ABLE III.A -2

. D ISTRIBUT ION OF S TAR T S BY H OUR OF T HE D AY ( IN PERC E NT )

...................................................... 40 T ABLE III.A -3

. B RE A K DOW N OF A NNUAL VM T BY M ON TH

.................................................................................... 41 T ABLE III.A -4

. D ISTRIBUT ION OF S TAR T S BY S OAK T IM E : T HREE H OURS D URING W EEKDAYS

.............................. 42 T ABLE III.A -5

. E STIM ATI ON O F D AILY A VER AGE O VER N IG HT S OA K E QU I VALE NT

................................................. 43 T ABLE III.A -6

. T RIP AND S TA RT R EL A TE D I NFORMA TIO N I N D RAFT MOVE S2004

................................................ 44 T ABLE III.A -7

. E STIM AT ES OF I N -U SE A VERAGE T RIP L ENG T H

............................................................................... 45 T ABLE III.A -8

. I NVENT ORY D RIVING C YC LE S IN D RAF T MOVE S2004

................................................................... 49 T ABLE III.A -9

. VSP

- S PEED B IN S I N D RAFT 2 0 04MO VE S

....................................................................................... 54 T ABLE III.A -1 0

. E XPAND ED S ET O F 26 VS P- S PEED B IN S

.......................................................................................... 55 T ABLE III.A -1 1

. VSP F REQUENCY D ISTRIBU T I ONS FOR O NRO AD DR IVIN G C YCLES IN MOV E S

............................... 56 T ABLE III.A -1 2

. VSP F REQUENCY D ISTRIBU T I ONS FOR O NRO AD DR IVIN G C YCLES IN MOV E S

............................... 57 T ABLE III.A -1 3

. D ISTRIBUT ION OF O NROAD D RIVING P AT TER NS : D RAFT MOVES 2 004

.......................................... 58 T ABLE III.A -1 4

. VSP D ISTRIBU T ION S FOR U.S. D RIVING (% O FTI ME )

...................................................................... 59 T ABLE III.A -1 5

. D RIVING C HAR ACTERI S TICS O F TH E C URR E N T D YNA MOM E T E R T ES TS

........................................... 62 T ABLE III.A -1 6

. S PLIT OF US06 C YCL E I NTO C ITY AND H IGH W AY P ORTI ONS

........................................................... 63 T ABLE III.A -1 7

. VSP D ISTRIBU T ION S FOR D YNAMO M E TER C YCLES (% OF T I ME )

..................................................... 64 T ABLE III.A -1 8

. 26 -B IN VSP F UEL R AT ES ( GR AM P E R S E CON D )

............................................................................... 66 T ABLE III.A -1 9

. B EST -F IT C O M BINAT IONS OF D YNAMO M ET E R C YCL E S

................................................................... 67 T ABLE III.A -2 0

. C OEFF ICIE NTS FOR A/C C O M P R ESSOR U SAG E E QUAT ION S

.............................................................. 72 T ABLE III.A -2 1

. I NCREASED F U EL U SE D UE TO A IR C OND IT IO NING AS A F UN CTION OF V E H I C LE S PE ED

................. 75 T ABLE III.A -2 2

. W ARMED U P F UEL U SE V ERS U S T EM P E RATU RE : H ON DA D ATA

..................................................... 79 T ABLE III.A -2 3

. NHT SA O NROAD T IRE P RESS URE S URV E Y

..................................................................................... 83 T ABLE III.A -2 4

. E FFECT OF W IND A NGL E O N V EHICLE D RAG C O E FFIC I EN T

............................................................. 86 T ABLE III.A -2 5

. F REQUENC Y OF W IND S P E E D S I N TH E U.S.

...................................................................................... 87 T ABLE III.A -2 6

. E FFECT OF R O AD R O UGHN ES S ON O NRO AD F UEL E CONOMY : 1977

................................................ 88 T ABLE III.A -2 7

. M A P P I NG OF R OAD WAY S URF A CES

................................................................................................. 89 T ABLE III.A -2 8

. E FFECT OF N ON -D YNAMOM E TER F ACT O RS ON O NROAD F UEL E CONO MY

...................................... 90 T ABLE III.B- 1. R ATI O OF FTP B AG TO C YCLE F UEL C ONS UM PTIO N

..................................................................... 10 0 T ABLE III.B- 2. E FFECT OF H E A TER/D EFR OST ER U SE O N C OL D FTP F UEL U SE

..................................................... 10 2 T ABLE III.D -1

. C URRENT AN D 5-C YCLE L ABE L F UE L E CONOMIES BY M OD E L T YPE

............................................. 11 1 T ABLE III.D -2

. C URRENT AN D 5- CYCLE L ABE L F UE L E CONOMY BY P ROPUL SION S YS T E M

................................... 11 1 T ABLE III.D -3

. E FFECT OF MP G

- B ASED F ORMUL AE ON C IT Y AND H IGH W AY F UE L E CONOMY

............................ 11 2 T ABLE II.D- 4. E FFECT OF N EW M ET HODS O N F UEL E CONO MY E ST IM A T E S F O R M AJOR M ANUFAC TURER S

......... 11 3 T ABLE III.D -5

. E FFECT OF V A R IOUS F AC TOR S ON 5- CYCLE F UEL E CONOMY

........................................................ 11 3 T ABLE III.E- 1. S ENSIT I VI TY O F H YBRID S TA RT F UE L U SE T O A MBIE NT T EMPERATURE

....................................... 11 5 T ABLE III.E- 2. S PLIT OF US06 C YCL E I NTO C ITY AND H IGH W AY P ORTI ONS

......................................................... 11 9 T ABLE III.E- 3. VSP D ISTRIBU T ION S FOR US0 6 C ITY AND H I GHWAY B AGS (% OF T I M E )

...................................... 12 0 T ABLE III.E- 4. B AG/C YC LE C OMBIN A T I ONS FOR C I T Y AND H IGHWA Y D RI VING : A LT ERN A TIV E US06 S PL I T S

..... 12 1 T ABLE III.E- 5. A VERAGE 5-C YCLE F UE L E CONOM Y : A LT ERN A T IV E US06 S PLIT S

............................................... 12 1

iiiT ABLE III.E- 6. B AG/C YC LE C OMBIN A T I ONS FOR H IGH W AY D RIVING : H IG H S PEE D F REEW AY C YC LES

............... 12 3

T ABLE III.E- 1 2

. VSP D ISTRIBU T ION S FOR U.S. D RIVING WIT H A LTERN A TI VE D EFINIT ION OF C ITY D RIVING (% OF

T ABLE IV.A- 2

. E STIM AT ED I NCREASE IN N UMBER OF T ES T S FOR M ODE L Y EARS 20 08 -2 01 0: P ROPOSE D AND F INA L T ABLE III.E- 7. B AG/C YC LE C OMBIN A T I ONS FOR C I T Y AND H IGHWA Y D RI VING : A LT ERN A TIV E F U EL R ATES

..... 12 4 T ABLE III.E- 8. K ANSAS C IT Y VSP D ISTRIBU T ION S AN D F UE L R A TES

................................................................... 12 5 T ABLE III.E- 9. B AG/C YC LE C OMBIN A T I ONS FOR C I T Y AND H IGHWA Y D RI VING : K ANSAS C ITY

.......................... 12 6 T ABLE III.E- 1 0

. C ALIF ORNI A U RBAN AN D R UR AL VSP D ISTRI B UTIO NS

................................................................. 12 7 T ABLE III.E- 1 1

. B AG/C YC LE C OMBIN A T I ONS FOR C I T Y AND H IGHWA Y D RI VING : C A L IF OR NIA

............................. 12 8

TIM E )

.............................................................................................................................. ............... 13 0 T ABLE III.E- 1 3

. C YCLE C O M BI NATI ONS FOR C ITY AND H IGH W AY D RIVING : R EVIS ED C IT Y/H IGHWA Y S PL I T

....... 13 1 T ABLE III.E- 1 4

. 5-C YCLE F UEL E CONOMY V ALUES : E FF ECT OF TH E D EF INI T ION OF C IT Y D RIVING ( MP G )

............ 13 2 T ABLE III.E- 1 5

. B AG/C YC LE C OMBIN A T I ONS FOR C OM P L E TE C YCL E A LT ER NATI V ES

........................................... 13 3 T ABLE III.E- 1 6

. E FFECT OF U SI NG W HOLE C Y C LES ON 5-C YCLE F U EL E CONO M Y V AL U E S ( MPG )

......................... 13 3 T ABLE IV.A- 1

. E STIM AT ED C OS T PE R T ES T : P ROPOSE D AN D F INAL

...................................................................... 14 5

.............................................................................................................................. .................... 14 5 T ABLE IV.A- 3

. E STIM AT ED I NCREASE IN N UMBER OF T ES T S FOR M ODE L Y EARS 20 11 A ND L AT ER : P R O POSE D

... 14 5 T ABLE IV.A- 4

. E STIM AT ED I NCREASE IN N UMBER OF T ES T S FOR M ODE L Y EARS 20 11 A ND L AT ER : F IN AL

.......... 14 6 T ABLE IV.A- 5

. E STIM AT ED F ACI L IT Y C OSTS : P ROPOSED

...................................................................................... 14 7 T ABLE IV.A- 6

. E STIM AT ED F ACI L IT Y C OSTS : F INAL

............................................................................................. 14 7 T ABLE IV.A- 7

. E STIM AT ED S TA RT U P C OST S : P ROPOSED

....................................................................................... 14 9 T ABLE IV.A- 8

. E STIM AT ED S TA RT U P C OST S : F INAL

.............................................................................................. 14 9 T ABLE IV.A- 9

. E STIM AT ED T OT A L C OS TS : P R O POSE D

.......................................................................................... 15 0 T ABLE IV.A- 1 0. E STIM AT ED T OT A L C OS TS : F I NAL

................................................................................................. 15 0 T ABLE IV.A- 1 1. E STIM AT ED T OT A L H OURS : P ROPOSE D

.......................................................................................... 15 0 T ABLE IV.A- 1 2. E STIM AT ED T OT A L H OURS : F I NAL

................................................................................................ 15 1 T ABLE A-1. P ROCESSING OF R AW D ATA O BTAIN E D I N K ANSAS C ITY

.............................................................. 15 5 T ABLE A-2. C YCLE C O M BI NATI ONS FOR T H E M ITS UBISH I M ON TERO S PO RT

.................................................... 16 4 T ABLE A-3. O NROAD AN D P REDICTED F U EL E CO NOM Y : K ANSAS C IT Y T EST P ROGR A M

.................................. 16 6 T ABLE A-4. O N

- ROAD AND M ODE LED F UE L E CONO MI ES U SING V EHIC L E -S PECIFIC C Y C LE W EIGHTS ( MPG )

. . 16 8 T ABLE A-5. C OMPAR I SON OF C YC LE F UEL E CONO MY

...................................................................................... 16 9

iv

### List of Figures

F IGURE II-1. D ISTRIBUT ION OF O NROAD F U EL E CO NOM Y E STIM AT ES – S TRAT EGIC V IS I O N , 2006

..................... 12 F IGURE II-2. C OMPAR I SON O NROAD TO C URRENT L A B EL E CONOM Y : K ANSAS C IT Y

.......................................... 14 F IGURE III-1

. S PEED -A CCELERATION F REQUENCY D ISTRI B UTIO N : K ANS AS C IT Y V S

. T ES T C YC LES

................... 52 F IGURE III-2

. S PEED -A CCELERATION F REQUENCY D ISTRI B UTIO N : U RBAN C A L IFOR NIA V S

. T EST C YCL E S

......... 53 F IGURE III-3

. K ANSAS C I T Y AND C ALI FORN I A VSP F REQUENCY D IS TRIB UTIO NS V S

. MOVE S

............................ 60 F IGURE III-4

. VSP F REQUENCY D ISTRIBU T I ONS I N K ANSA S C IT Y : H YBRI DS VS

. N ON -H YBRIDS

.......................... 61 F IGURE III-5

. H EAT I NDE X V S

. T EMP E RATU RE AN D H UMI D ITY

............................................................................. 71 F IGURE III-6

. A IR C ONDI TI O N ING U SE I N P HOEN I X

............................................................................................... 73 F IGURE III-7

. C OMPR ESSOR E NGAGE ME NT AS A F U NCTI O N OF A MB IEN T T EMPER A TUR E

..................................... 73 F IGURE III-8

. 5-C YCLE C IT Y V ERSUS FTP F UEL C ONS UMP T ION

......................................................................... 10 4 F IGURE III-9

. 5-C YCLE H IGH W AY V ERSUS HFET F UEL C ONSU M PT ION

.............................................................. 10 4 F IGURE III-1 0. MPG

- B ASED C ITY F UE L E CO NOMY

.............................................................................................. 10 5 F IGURE III-1 1. MPG

- B ASED H IGHWA Y F UE L E CONO MY

...................................................................................... 10 5 F IGURE III-1 2. O NROAD FE V ERSUS P RE -198 4 EPA C ITY L ABEL FOR C ITY D RIVEN C ARS

.................................. 10 6 F IGURE III-1 3. V ARIABILI TY I N O NROAD FE

......................................................................................................... 11 0 F IGURE A-1. C OMPAR I SON O NROAD TO C URRENT L A B EL E CONOM Y : K ANSAS C IT Y

........................................ 15 6 F IGURE A-2. S PEED -A CCELERATION F REQUENCY D ISTRI B UTIO N : K ANS AS C IT Y V S

. T ES T C YC LES

................. 15 7 F IGURE A-3. S PEED -A CCELERATION F REQUENCY D ISTRI B UTIO N : U RBAN C A L IFOR NIA V S

. T EST C YCL E S

....... 15 8 F IGURE A-4. K ANSAS C I T Y AND C ALI FORN I A VSP F REQUENCY D IS TRIB UTIO NS V S

. MOVE S

.......................... 15 9 F IGURE A-5. VSP F REQUENCY D ISTRIBU T I ONS I N K ANSA S C IT Y : H YBRI DS VS

. N ON -H YBRIDS

........................ 16 0

v

### List of Acronyms
AAA Am erican Autom obile Association ADFE analytically derived fuel economy AVTA Advanced Vehicle Testing Activity CAFE Corporate A verage Fuel Economy CO Carbon Monoxide CO2 Carbon Dioxide CR Consum er Reports CRC Coordinating Research Council DOE Departm e nt of Energy DOT Departm e nt of Transportation EIA Energy Inform ation Adm i nistration EPA Environm ental Protection Agency FHWA Federal High way Adm i nistration 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 (volum e of traffic) MOVES Motor Vehicle Em ission Inventory System mpg m iles per gallon MY model year NHTS National Household Travel Survey NHTSA National Highway Traffic Safety Adm i nistration NOx Oxides of Nitrogen NREL National Renewable Energy Laboratory OAP Office of Atmospheric P r ogram s ORNL Oak Ridge National Laboratory PEMS Portable Emissions Measurem ent System PERE Physical Em ission Rate Estim ator RVP Reid Vapor Pressure SAFD speed-acceleration frequ e ncy dis trib ution SFTP Supplem ental Federal T e st Procedure SUV sport utility vehicle TRLHP tractive roadload horsep ower TSD Technical Support Docum e nt UDDS Urban Dynamom e ter Driving Schedule VMT vehicle m iles traveled VSP vehicle specific power

vi

## Chapter I: Executive Summary

The EPA fuel economy estim ates have appearedon the window stickers of all new cars and light trucks since the late 1970’s and are well-recognized by consum e r s. The fuel economy estim ates es sentially serve two purp o ses: to provide consum ers with a basis on which to comp are the fuel econo my of different vehicles , and to provide consum ers with a reasonable estim ate of the range of fuel economy they can expect to achieve. W hile the estim a tes historically have been a valuable tool for comparison shopping purposes, attention has been focused recently on how closely the EPA estim at es approxim a te consum ers’ real-world fuel economy experience.
We are m aki ng changes to EPA’s fue l economy te st m ethods to bring the estim ates closer to the fuel economy consum ers are achieving in th e real-world. W e believe these estim ates will provide car buyers with useful inform ation wh e n com p aring 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 diffe rent driving s t yles, clim ates, tr affic patterns, use of accessories, loads, weath er, and veh i cle m aintena nce. Even d i f f

erent driv ers of the sa m e vehicle will experience different fuel economy as these and other factors vary. Theref ore, it is impossible to design a “p erfect” fuel economy test that wi ll pro v ide accu rate real-world fuel economy estim ates for every cons um er. W ith any estim ate, there will always be consum ers that get better or worse actual fuel eco nomy. The EPA estim ates are m eant to be a general gu ideline for consum ers, particularly to com p are the relativ e fuel economy of one vehicle to another. Nevertheles s , we do believe that today’s new fu el economy test m e thods will do a better job of giving cons um ers a m o r e accurate es tim ate of the fuel econo my they canachieve in the realworld.
It is essential that our fuel economy estim at es continue to be de rived from controlled, repeatable, laboratory tests. Ho wever, the inputs to our estim a te s are based on data f r om actual real-world driving behavior and conditions. Because the test is contro lled and repeatable, an EPA fuel economy test result can be used for comparison of differe nt vehicle m odels and types. EPA and m a nufacturers test over 1,250 vehicle m odels annually and every test is run under identical co nditions andunder a prec ise driver’s trace, which assure s that the result will be the sam e for an individual vehicle m ode l no m a tter whenand where the laboratory test is perform e d. Variations in temp erature, road grad e, driv i ng patterns, and other vari ables do not impact the result of the test. W h ile such external conditions impact fuel economy on a trip-to-trip basis, they do not change the laboratory te st resu lt. Th erefore, a repeatab le test provides a level playing field for all vehicles, which is essential for compa r ing the fuel economy of one vehicle to another. F i nally, EPA must preserve the ab ility to confirm the values achieved by the m a nufacturers’ 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 m e thods need to reflect real world co nditions as well as being a repeatable test. W hile som e cons um er groups have issued their own fuel economy numbers based on on-road driving, this approach introduces a wide num ber of variables – different drivers, dr iving patterns, weather conditions, tem p eratures, etc. – that m ake repeatability impossible. Our new fuel economy te st m e thods are m ore representative of real1world conditions than the current fuel economy te sts – yet we retain our practice of relying on controlled, repeatable , laboratory tests.
The m e thods used today for calculating the city and highway mpg estim ates were established in the 1970’s, and were adjusted in the m i d-1980’s. Since these adjustm e nts were m a de, Am erica’s driving behavior has changed. In the past 20 years, speed lim its have increased and vehicles have been designed for higher power – as a result, Am ericans are driving faster and more aggressively than ever before. Vehicle technology has ch anged m arkedly, and m a ny more vehicles are equipped with energy -co n sum i ng accesso ries like air cond itio ning. These and other factors are n o t accoun tedfor in the cu rrent tes t procedures used to dete rm ine the c ity a nd highway mp g estim ates. Our analyses indicate thatif thes e factors were b e tter accoun ted for, the city and highway fuel economy label estim ates would be generally lower and closer to the average real-world experience of consum ers. A funda m e ntal issue with today’s fuel econom y estim ates is that the underlying test procedures do not fully represent real-world driving conditions. So m e of the key lim itations are that the highway test has a t op speed of only 60 m iles per hour, both the city and highway tests are run at m ild clim atic conditions (7 5°F), both te sts have m i ld acceleratio n rates, and neithe r test is run with the use of accessories, su ch as air con d ition i ng. However, since the tim e of the last fuel economy labeling revisions in the m i d-1980’s, EPA has established several additional test procedures, used for em i ssions comp liance purpo ses, which capture a mu ch broader range of real-world driving conditions. Sp ecifically, these em issions test cycles capture the effects of higher speed s, m o re aggressive dr iv in g (i.e., h i gher acceleratio n rates ) , th e use of air conditioning at higher ambient temperatures, an d colder temp erature operation. Our analysis indicates that these factors can have a significant impact on fu el economy, and that the impacts can vary widely across different vehicles. We are now requiring that thr ee add i tional em ission tests, al ready used by m a nufacturers, will be u tiliz ed to derive more accura te fuel econ o my estim ates. These th ree test p r oc edures encomp ass a mu ch broader range of real-world driving, as they in corporate the effects of higher speeds, m ore rapid accelerations, ai r conditioning use, and cold temperatures. Our new approach will utilize these additio nal em issi on tests, togeth er with the cu rrent two fuel economy tests, so that our fuel economy test m e thods reflect a much broader range of driving conditions.
Our final rule revises the test m e thods by which the city and highway fuel economy estim ates are calcu lated. W e are rep l acing the cu rrent m etho dof adjustin g the city (F TP) test result downward by 10% and the highway (HFET) test result downward by 22%. Instead, we are finalizing a new approach that in corporates additional test m e thods that address factors that im pact fuel economy, but are m i ssing from toda y’s tests – specifically, higher speeds, m ore aggress i ve dr iving (high e r acceleration rates ) , th e use of air condition i ng, and the effect of cold tem p erature. The new test m ethods will bring in to the fuel economy estim ates the test resu lts from the five em issions tests in place t oday: FT P, HFET, US06, SC03, and Cold FTP. a Thus,

aThe U S 0 6 t e st i s desi gne d t o rep r ese n t hi gh spee d hi g h way d r i v i n g a n d a g gressi ve (i .e., r api d acc el erat i ons a n d decelerations) urba n drivi n g. The SC03 test is desi gne d t o represe n t the i m pact of air c o nditioner ope r ation at high te m p er atu r es. Th e C o ld FTP, wh ich is condu cted at 20 °F, i s d esi g n e d t o ref l ect th e im p act o f co l d tem p er atu r es.

2we refer to this as the “5-cycle” m e thod. Under this new m e thod, rather than basing the city mpg estim ate solely on the a d justed FTP test resu lt, and the highway mpg estim ate solely on the adjusted HFET test result, each estim ate will be b a sed on a “c omposite” calculation of all five tests, weighting each app r opriately to arriv e atn e w city and h i ghway mp g estim ates. The new city and h i g h way estim ates will eachbe calcu la ted accord ing to separate city and h i gh way “5­ cycle” formulae th at are based on fuel economy re sults over these five tests. The con d itions repres ented by each te st will be “we i ghted” ac cord ing to how much they occur ove r av erage realworld city or highway driving. For example, we have derived weightings to represent driving cycle effects , trip length, air cond itio ner compressor-on usage, and operation over various temperatures. The derivation of this m e thodology and the relevant weighting factors and for mulae are the principal subject of this Final T e chnical Support Document. We also are finalizing an a dditional downward adjustm e nt to fuel economy estim ates within th e 5-cycle m ethod. W e put in place a do wnward adjustm e nt to account for effects that cannot be replicated on the dynam o m e ter. There ar e m a ny factors that aff ect fuel economy that are not acco unted for in any of our existing testcycles. These includ e ro ad grade, wind, tire pressur e, he avier loads, hills, snow/ice, ef f

ects o f ethanol in gasolin e, an d others. W e are finalizing a 9.5% downward adjustm e nt to account fo r these effects. The detailed technical basis for this ad ju stm ent factor is con t ain e d in section III.A.5 of this Final Tech nical Suppo rt Docum ent. Because the 5-cycle m e thod is inherently veh i cle-specific, the difference between to day’s values and the new fuel economy estim ates could vary widely from vehicle to vehicle. Our new approach will result in city fuel econ o my estim at es that are b e tween 8 to 15 percen t lower than today’s labels for the m a jority of conventiona l vehicles. The city m pg estim ates for the m anufacturers of m o st vehicles will drop by about 12 percent on average relative to today’s estim ates. For vehicles that achieve generally better fuel economy, such as gasoline-electric hybrid veh i cles, th e new city estim ates will be abo u t 20 to 30 p e rcen t lower than today ’ s labels. The new highway f u el economy estim ates will be 5 to 15 per cent lowe r f o r the m ajor ity of vehicles, including m ost hybrids. The highway mpg estim ates for the ma nufacturers of m ost vehicles will drop on average by about 8 percent, with estim a tes for m ost hybrid vehicles dropping by 10 to 20 percent relative to today’s estim ates. In Chapter II of this Final Technical S upport Docum e nt, we comp are current EPA label fuel economy values, as well as the proposed 5-cycle and mpg-based values, to several independent estim ates of onroad fuel economy. The independent estim at es fall into several general categories, depending on the type of data involved. One type of estim ate involves the m easure m ent or estim ate of onroad fuel economy of vehicles in typical operation. T w o examples in this category are the U.S. Departm e nt of Energy (DOE) Freedom Car program and the DOE Your MPG program

. A sec ond type of estim ate involves onroad m easurement of fuel economy ac cording to som e established protocol by an independent organization. Examples in this category are fuel econo my estim a tes developed by Consum er Repo rt, Edmunds , and AAA. A third type of estim ate involves broad estim a tes of national fuel consumption and national VMT and the developm ent of fleet-w ide fuel econo my estimates. Examples in this category are fleet-wide f uel economy estim ates developed by Federal Highway Administration (FHWA) and the Energy Infor m ation Adm i nistration (EIA).

3These estimates and studies often suggest th at th ere is a shortfall betweenthe EPA estim ates an d real-worldfuel econo my. For ex ample, Consum er Reports derives city, highway, and overall fuel economy estim ates, and their m e thods clearly dem onstrat e the large degree of varia tion a cr o ss vehic l es. W h ile their city f u el e c onomy estim ates f all on average b e low the EPA label v a lues, their highway estim ates are, on average, higher than the EPA label values. Consum er Reports’ overall fuel economy estim at es range from 27% below to 20% above the EPA overall rating. The Auto m obile Association of Am erica (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 estim a tes were below the EPA com bined city/highway value, and about one half were above the EPA city/highway com b ined value. Their estim ates ranged from 40% lower than EPA’s to 22% higher, again re flecting a great deal of vehicle-to-vehicle variation.
Each of these studies dif f

ers in its test m e thods, driving cycles, sa mp ling of vehicles, and m e thods of m easuring fuel economy. There are streng ths an d weaknesses of each stu dy, which we discuss further in this T echnical Support Docum e nt. Collec tively, these studies and data indicate there are m a ny cases wh ere real-world fuel economy falls below the EPA estim a tes. The studies also ind i cate that real-world fuel econ o my vari es significantly depending on the conditions under which it is evaluated. Neverthe less, taken as a whole, these studies reflect a wide range of real-world driv ing conditions, and show that fu el economy can be mu ch lower than EPA’s estim ates if more real-world cond iti ons are considered. W here possible, we also comp are the results of th ese stud ies with the new label values that would result from the 5-cycle and mpg-ba sed m e thods, and we found that in virt ually every case the 5-cycle m e thod resulted in fuel economy values that were significantly clos er to these other estim a tes than the existing labels. In Chapter III of this Fina l Technical Support Docum e nt we describe the developm ent of the vehicle specific 5-cycle and mpg-based m e thods. W e also evaluate th e range and variability of onroad fuel economy experienced by drivers of the sam e vehicle and we develop an adjustm e nt factor that acc ounts for fuel economy impacts not reproducible on the dynamom e ter or in the testing laboratory. W e describe the final vehicle specif ic 5-cy cle for mulae and the final mpg-based formulae, followed by a discussion of how the current city and highway fuel economy va lues would change under the two m e thods. Finally, we evaluate the sensitivities and uncertainties in the veh i cle specific 5

- cycle formulae.
We describe how these different elem ents of ourfuel economy m odel are developed and assem b led from the test data. W e devel op m e thodologies for estim ating the following:

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

• Fuel use onc e the eng i ne is warm ed up at 75°F (with no a i r co ndition er op eration) ;
• Fuel use due to air conditioner use;

• Fuel use once the eng i ne is warm ed up at cold er tem p eratures ; and

• Factors that affect onroad fu el economy but which are not addressed by any of the five dynam o meter cycles.

4We describe the derivation of the mp g-based approach – a simplified m e thod that will be an interim option in the first three years of the program and an available option under certain circum stances in subsequent years. The 5- cycle fuel economy for mulae assum e that fuel economy estim ates are available for specific vehicl es for all five dynam o m e ter cycles and their respective bags of e m ission m easurem ents. As di scussed in the pream ble to the final rule, these estim ates may be based on fuel economy m easur em ents, or on estim ates based on test results from a sim i l ar vehicle. A simp lified approach to imple m enting the 5-cycle formulae is to apply these formulae to test res u lts on recen t m odel ve hicles and d e v e lop correlations between the 5­ cycle city and highway fuel economy estim ates fo r these vehicles and their fuel economy over the FTP and HFET, respectiv ely. Th is simp lif ie d approach is referred to as the mpg-based approach, since the resultant label adjustm e nt wi ll vary depending on th e m easured fuel economy (i.e., mpg) of a vehicle over the FTP and HFET tests, and will not require a ny additional tests. Following the detailed discussion of the 5-cy cle and mpg-based approaches, we present the ac tual f o rmulae and an assessm ent of the impact our n e w approache s will hav e o n f u el economy label values. The impact of today’s fina l rule on city and highway fuel economy label values was assessed using the sam e database of 615 late m odel year vehicles used to develop the mpg-based adjustm e nts. Use of the 5-cycle fo rmulae will reduce both current city and highway fuel economy label values. For conventional ve hicles, city and highway fuel economy values would be reduced an average of 11% and 8%, respectively. For higher than averag e fuel economy ve hicles, the reduction in city fuel econ o my will be slightly high er, while for lower than average fuel econo my vehicles, the redu ctio n in city fuel economy will b e slightly lower. The change in highway fuel economy is essent ially independent of current highway fuel economy. The impact on hybrid vehicles will b e signi ficantly greater fo r city fuel economy, averag ing a 22% reduction. However, the reducti on in highway fuel econ o my will be sim ilar, but toward the higher end of the rang e as for con ventional gasoline- fueled vehicles. The impacts of the 5-cycle for mulae on the single diesel vehicl e in the d atabase ar e very sim ilar to those f o r conventional gasoline fueled vehicles. In Chapter IV we detail our estim a tes of the cost impacts of our new regulation. For model years 2008 through 2010, m a nufacturers may us e the mpg-based calculation for the fivecycle fuel economy values or they m a y conduc t voluntary 5-cycle testing. For m odel years 2011 and after, if the five-cycle city and highway fuel economy va lues for an em ission data vehicle group are not m ore than 4% and 5% below the m pg-based regression line, respectively, then all the vehicle configurations represented by the em i ssion data vehicle (e.g., a ll vehicles within the vehicle test group) could continue to use the mpg-based approach. Vehicles within a test group falling m ore than 5% below the tolerance band for highway fuel economy values would be required to conduct US06 tests ; tho s e falling m ore than 4 % b e low the city fuel econo my tolerance band would be required to conduct SC03, US06, and Cold FTP tests. In addition, we expect that s o m e of these vehicl es f alling below th e tole rance levels m ay be elig ible to estim ate fuel economy for a given test through the applic ation of analytically derived fuel economy (ADFE) values. Som e data is currently available for vehicles that have conducted all five tests; based on th is data, EPA has estim a ted the num ber of vehicle s f o r which addition a l tes ting would be required because they fall belo w the 4 and 5 % tolerance bands.

5We prepared a range of burden estim ates for this analysis, estim ating m i nimu m and m a xi mu m c ost scenarios. These low and high esti m a tes are intended to be our estim a te of the outer boundaries of the likely testing and info rm a tion costs. Aggregate annual costs are estim ated to be between $1.4 and $1.7 m illion. Aco mp lete discussion of how these costs were estim ated is in Chapter IV of th is Technical Support Document.
Table I-1 Aggregate Costs MY 2008 through MY 2010 MY 2011 and After C o s tE l e me n t M i n i mu m M a x i mu m M i n i mu m M a x i mu m 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 L a bel Val u es Com p ared t o Onroad Estimates
A. Onroad Fuel Economy Estim a tes Duri ng Typical Operation In the 1984 label adjustm e nt rule, EPA was able to compare fl eetwide estim a tes of a variety of city and highway fuel economy label op tions to a num ber of i ndependent estim ates of onroad fleet fuel economy. In the late 1970' s and early 1980' s, EPA and several auto m a nufacturers had collected onroad f u el economy es tim ates from tens of thousands of drivers which could be compared to the EP A city and highway fuel economy labels. EPA prim arily used the driver-based fuel economy estim ates to develop the current 10% and 22% adjustm e nts to fuel economy over the FTP and HFET, respectively. It is not possible to repeat this type of comparison today, as the auto m a nufacturers no longer conduct the extensive m onitoring of fuel economy that was perform e d in the late 1970’s and early 1980’s. At the sam e ti m e , we ha ve discovered three new sources of sim ilar inform ation. One, Oak Ridge National Laborator y (ORNL) has recently begun a program where drivers can s u bm it their own fuel economy m easurem ents via the Internet. This program is referred to as “YourMPG.” Two, DOE has also been operating an extensive hybrid dem onstration project for a few years as part of their Freedom Car project. This program carefully m onitors bo th VMT and fuel consum ption, so accu rate fuel econ o my estim ates are available for a num ber of hybrid vehicles. Thr ee, a private survey firm , Strategic Visions, perform s two surveys of new vehicle purchasers a year to ass e ss consum er satisfactio n. The survey includes questions regarding the fuel economy being achieved to date. W e have purchased the Strategic Visions survey results for m odel years 2004-06. The results of our analysis to date are discussed below. In addition to these three program s, 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 autom obile m a nufacturers and others have been obtaining vehicle operational da ta via chase car stu d ies. All of these stud ies are discussed below.
1. ORNL “YourMPG” Program
The ORNL YourMPG data are sim i lar in natu re to the mu ch larger d atabases analy zed for the 1985 label adjustm e nt rule. Drivers measure their own fuel economy and provide a perce i ved sp lit of the i r dr iving in to city and highway catego r ie s. The stren g th of this ty pe of data is the f ac tth at the v ehic l e is being operated by the owner or regul ar dr ive r in typ i ca l u se. The weaknesses are the unknown representativeness of the sampl e , the unknown nature of the technique used by the owner/dr iver to m easure fuel economy and the unknown tim e period over which fuel economy is generally assessed (e.g., a coupl e of tanks full or the past year). In the particular case of the ORNL da tabase, its current size is stil l sm all (8180 estim ates of fuel economy for 4092 vehicles) comp ared to those ava ilable in 1985, though it is growing daily.

7W e comp ared the fuel ec onomy estim ate s subm itted to the O R NL website with each vehicle’s fuel economy label. W e com bined the city and highway labels using each driver’s estim ate of the percen tag e which was city and h i ghway. If a driver did not provide an estim ate of the breakdown of their drivi ng pattern, we assum e d that thei r driving was 55% city and 45% highway with respec t to the cur r ent label va lues and 43% city and 57% highway with respect to the mpg-based label values. W e conducted se parate comparisons for conventional gasoline vehicles, conventional vehicles with relatively high fuel econo my, hybrids and diesels. The results are shown below.
Table II.A-1. YourMPG Versus Current EPA Label Fuel Economy Vehicle

Type No. of Estim ates YourMPG C urrent Label Difference MPG-Based Label Di fference

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% * C o m b i n ed E P A L abel fuel econ o my val u e o f 32 m pg or greater, repres entin g abou t the to p 10 % fu el econ o my conv ent i onal ve hi cl es. As can be seen, diesels appear to perform the best with respect to their label fuel economy, outperform i ng the label by 4.3%. Conventional gaso line vehicles com e very close to m eeting their label, falling short by only 1.4%. Conventional vehicles with relatively high com bined fuel economy (here assum ed to be 32 m p g or m o re , representing the top 10% of conventional vehicles in term s of fuel economy) perform e d only slightly w o rse, 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 m ore related to hybrid technology than to simply high levels of fuel economy.
W ith respect to the mpg-based label values, di esels still perform the best of the four types of vehicles, now exceeding their label values by 1 8 %. b Those conventional vehicles with relatively high fuel economy fall next, followed by the typical conventional vehicle and hybrids. Thus, the YourMPG estim a tes indicate that hybrid perform a nce differs from that of conventional vehicles, including those with high fuel economy.

b T h ere i s a l ar g er ap pare nt di ffe rence bet w e e n th e m pg- bas e d l abel val u es an d th e cu rre n t l abel val u es i n Ta bl e II. A. 2 t h an th e 6% a v era g e i m pact o f t

h e m pg- base d ap p r oa ch on cu rre nt l abel val u es ci t e d el se whe r e i n th i s Fi nal Technical Support Doc u m e nt . T h is occ u rs because whe n worki n g wit h t h e YourMPG e stim a tes, we are usi n g the d r i v er’s estim a t e o f city/h ig hway driv i n g break down in all cases. When s p eaki n g of c o m b ined label values, ho we ver , we u s e a 55/ 45 b r eakd o w n f o r

th e cur r ent l a

bel v a l u es a n da 43/ 57 b r eakd o w n fo r th e m pg- ba sed val u es
. Th e l o wer city d r i v ing weigh t in th e m p g-b ased formu l a in cr eases t h e c o mbine d value rel a tiv e to t h at fo r th e cu rren t l abel val u es a n d red u ces th e d i ffere nce bet w een th e t w o ap pr oac h es.

8The YourMPG database also pr ovides us with an estim ate of drivers’ perception of the type of driving which they perfor m
. On average, they estim atedthat 43.2% of their driving was city driving and 56.8% was highwa y. These two figures are essent ially identical to the weights developed from the Draft MOVES2 004 depiction of onroad drivi ng and embedded in the 5-cycle com bined fuel economy for mula. This is encouraging. However, this change from the trad ition al 5 5 /45 city/hig hway split o f onroad driv ing also causes som e unusual re la tionships between onroad and label fuel economy as depicted in Table II.A-1 above. Focusing on conventional vehicles, which comp rise the great m a jority of the database, Table II.A-1 shows that people’s onroad fuel eco nomy is only 1.4% lower than the current label values indicates and 9.1% higher than the mpg-ba sed label values would have been. If a 55/45 split would have been u sed with th e curren tlab el values, onr oad f u el eco n omy would have been 1-2% higher than the label values for these vehicl es. This differs dram atically from the onroad fuel economy shortfall indicated by the FHWA es tim ates of onroad fuel economy discussed in Section II.C below. In other words, the Your MPG database indicates that onroad fuel economy is exceeding the standard 55/45 label fuel economy, while FHWA indicates the opposite. Exam ining the YourMPG database f u rther, we f ound that the average 55/45 label value in the database was 25.4 mpg. Per MOBILE6.2, the averag e 55/45 label value of the onroad vehicle fleet is 21.2 mpg. Thus, people subm itting their onroad fuel economy estim ates to the YourMPG database drive m ore fuel efficient vehicles than the averag e v ehicle. This could ind i cate th at those participating in the program have a greater interest in reducing fuel consumption than the average driver, but this cannot be known for certa in. However, if true, this would explain the difference seen between the YourMPG databa se and the FHWA fleetwide estim a tes.
2. DOE FreedomCar Program The Departm e nt of Energy has overseen the re al world operation of a number of electric hybrid vehicles for a period of years. Th e Advanced Vehicle Testing Activity (AVTA), conducted jointly by the Idaho National Laborat ory (INL) and the National Renewable Energy Laboratory (NREL), has been benchm arking hybrid electric vehicle perform a nce as part of the Freedom CAR & Vehicle Technologies Program

. The strength of the Freedom C AR program testing of hybrid vehicles lies in the fact that th e vehicles are operated on the road over long term periods sim ilar to what consum er-purchased ve h i cles exp e rience, albeit o f ten in comm ercial applic ation s. Over a m illion m iles of operation ha ve been ass e ssed and car ef u l f u el consum ption and m ileage records are kept. The weaknesses are that som e of the vehicles are in co mmercial use (e.g., co mpany pool vehicles) for accelerated mileage accumulation and th at the v ehicles are operated exclusively in the Southwest, m a inly in Phoenix, Arizona and surrounding areas. Nevertheles s, the veh i cles are op erated just as an y other vehicle would be in that application and the vehicles are subject to all of the environm ental and roadw a y factors w hich affect the fuel economy of typical vehicles, such as winds, rough roads, hills, tr affic congestion, etc. Because of the lim ited geographic area of the program , the vehicles are m ore likely to experience hot tem p erature s and air con d ition i ng us e than co ld tem p eratures

. The vehicles’ operators repor t m ileage and fuel usage to Freedom C AR whi c h posts the monthly andcumulative fuel economy of each el ectric hybrid fleet on a monthly sch e d ule.

9Therefore, seasonal changes in fuel economy ca n be observed. The results of the fleets are shown in Table II.A-2.
Table II.A-2. FreedomCAR Hybrid Fleet Cumu la tive Versus EPA Co mposite Label Fuel Economy Fuel Ec o nomy (m pg) Diffe re nce ( % ) Vehi cl e Accu mu lated Fleet On ro ad EPA C o m posi t e Label * Mileage Size Current 5-Cycle MPG

Based Cu rren t 5

- Cycle MPG

Based 20 0 1 Ho n d a In si ght 41 7, 0 00 6 45 .2 61 .0 50 .4 51 .5 35 % 12 % 14 % 20 0 2 T o y o t a P ri u s 45 8, 0 00 6 41 .0 48 .6

-- -- -- -- 19 % 20 0 3 Ho n d a C i vi c 37 8, 0 00 4 37 .6 46 .3 37 .9 39 .7 23 % 1% 6% 20 0 4 T o y o t a P ri u s 18 6, 0 00 2 44 .9 54 .6 44 .1 45 .5 22 % -2% 1% 2 004 C h evro let Silv er ad o 2wd 48 ,0 0 0 1 17 .7 18 .8

-- -- -- -- 6%

2 004 C h evro let Silv er ad o 4wd 53 ,0 0 0 1 17 .9 16 .9 15 .1 15 .2 -6% -1 6% -1 5%

2 005 Fo rd Escap e 2w d 70 ,0 0 0 1 28 .6 33 .6

-- -- -- -- 17 %

2 005 Fo rd Escap e 4w d 78 ,0 0 0 1 27 .0 29 .9 23 .6 26 .1 11 % -1 3% -3%

20 0 5 Ho n d a A ccor d 15 8, 0 00 2 27 .8 32 .3 25 .9 28 .3 16 % -7% 2% 20 0 5 Le x u s RX4 0 0h 67 ,0 0 0 2 24 .8 28 .1 24 .0 24 .4 13 % -3% -2% 2 006 To yo ta H igh land er 69 ,0 0 0 2 24 .7 28 .1 24 .0 24 .4 14 % -3% -1%

Ave ra g e 18 0, 0 00 2. 5 30 .7 37 .0 31 .6 32 .9 16 % -3% 1%
* C u rren t co m b in ed is a 55 /45 weigh tin g of city/h ig h w ay fu el econo my
. Co m b in ed 5

- cycle an d m p g-b a sed fu el eco no my is a 43 /57 weigh tin g of city/h ig hway fu el econ o m y. All lab el valu es fro m EPA certificatio n d atab ase. Cu rren t co m b in ed lab e l fu el eco n o my v a lu es sho w n will not m a tch o f ficial lab e l v a l u es du e to d i fferen c es in v e h i cle configurations

. T h e Free dom CAR fleet info rm ation as re p o rte d th ru A u g u st 20 0 6

. As can be seen, EPA’s current label for mul ae over-estim ate the onroad fuel economy achieved by all but one of the hybrid vehicl e fleets. It should be noted that the values for current com bined fuel economy are those from EPA’s certific ation 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 th e Supplem ental FTP cycles and the av erage vehicle sold. The largest shortfall was 35% for the Honda Insights. The Chevrolet Silverado was the only m o del which exceeded th e current lab el valu e of the tes tvehicl e in ou r certification database. This is lik ely related to the fact that its hybrid design include s lim ited fuel economy targeted features. Except for the Chevrolet Silverado, the onro a d fuel econ o my for each fleet never exceeded either the city or highway fuel economy labe l. This indicates that regardle ss of whether the vehicles were driven predom inantly in city or highway drivi ng modes, other real worl d factors reduced onroad fuel economy beyond that captured in the FT P and HFET and the current 10% and 22% adjustm ent factors.

10Table II.A-2 also presents com bined 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 com b ined 5-cycle label v alues ex ceedonroad fu el economy for two out of eight m odels, while the final mpg-based values do so for four out of eight m odels. The average of the differences is very sm all in both cases

. On averag e, the co m bined 5-cycle value is 3% lower than those m easured onroad. However, as m e ntioned above, th e specific vehicles in our 5-cycle database tend to be w o rse cas e. For exam ple, the curren t o f f i cial labe l values exceed those shown in Table II.A-2 by 3%. If we increased the com bin ed 5-cycle values com m e nsurately, they would m a tch the onroad values on average. Thus, while both of the final approaches do a much m ore reasonable job at predicting the onroad fuel economy achieved in the DOE Freedom Car program than the current lab e l formulae, the final 5-cy cl e f o rmulae appear to b e particu l arly accurate when com p ared to the Freedom Car experien ce. The close m a tch between the final 5

- cycle formulae and the Freedom Carexperience is som e what fortuitous, as the clim ate where the vehi cles were prim arily driv en is not typical of most of the U.S. The Freedom Car program focuses on the southwest U.S. There, air conditioning use is mu ch higher than average, while cold temp erature operation is muc h lower than for the U.S. on average. W h ile both fact ors reduce fuel economy, th ey 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 econo my values for individual vehicles, we have had to estim ate the impact of he ater-defroster operation on fuel economy during the cold FTP. For conventional vehicles, th e effect is likely very sm all (i.e., less than 2%). However, for a couple of hybr ids tested, the effect was much larger. The impact of heaterdefroster operation is likely to vary significantly across individua l hybrids, but without data, we cannot anticipate this vari ability. Overall, basing this imp act on the two hybrids tested reduced the com bined 5-cycle fuel economy of the hybrids in our certification database by 3%. Clearly this change is not relevant in areas like Phoeni x. This simply indicate s the lim itations involved in very direct comparisons of vehicle test p r ogram s and 5-cycle fuel econ o my estim ates. Overall, the fact that bo th the final mpg-ba sed equations and 5-cycl e formulae yield fuel economy label values quite close to the Freedom Car findings is very encouraging and the best that one could hope for without fine-tuning the 5-cycle formulae to exactly m a tch the driving activity and conditions of the FreedomCar vehicles. When analyzing m onthly reported fuel ec onomy, large seasonal fluctuations in fuel economy were observed on m ost of the hybrid fleets. The seasonal fluctuations are especially noticeable on the fleets that had been in servi ce f or over one year. The fuel economy during the hot and often hum i d summer weather m onths when heavy air conditioning usage could be expected was as mu ch as 15 mpg lower than obs erved fuel economy duri ng m ild Phoenix area winter m onths. Fuel economy over the SC03 air c onditioning test for the three hybrids with the highest rated fuel economy shown in Table II.A-2 (Prius, Insightand Civic) tends to be 15-20 mpg lower than that over the FTP. No cold w eather operation sim ilar to northern states or the Cold FTP (20°F) was reported which would likel y have resulted in further shortfalls.

113. Strategic Visions NewVehicle Survey Strategic Visions surveys roughly 100,000 purch asers of new cars and light trucks each year. The survey recipients are selected ra ndomly from a m ong the purchasers of each vehicle model and therefore represent al l regions of the country. So m e models are m o re heavily surveyed than others, particularly m o dels whic h have just been introduced or significantly redesigned. Therefore, the results should be assessed on a model by m o del basis and not averaged across m odels before averaged within each m odel. The survey asks recipients to write dow n the fuel economy which they are currently achieving. About half of the recipients respond to the request for fuel economy inform ation. Thus, about 50,000 estim a tes of onr oad fuel economy are received each year. The strengths of this survey are the large num b er of estim ates and the fact that the survey recipients are random ly selected. Th e weaknesses are th e unknown source of each consum er’s fuel economy es tim ate and the survey response rate for th is question (i.e., only 50%). Still the fact that a wide range of models are s u rveyed with each m odel having a n u m b er of independent estim ates allo ws very direct comp arison to the current, 5-cycle and mpg-based label values on a m odel by model basis. EPA purchased the Strategic Vis i ons survey results for the 2004-06 m odel years. In preparing th e data for an alysis, we n o ticed a pecu liarity. The frequency of consum ers’ estim a ted fuel economy for fuel economy values being a mu ltip le of five were mu ch higher than those with other values. Figure II-1 shows the distribution of estim ated ci ty fuel economy for 2006 m odel year vehicles. Figure II-1. Distribution of Onroad Fuel Economy Estimates – Strategic Vision, 2006



### 0% 2% 4% 6% 8% 10% 12%

0 1 0 2 0 3 0 4 0 5 0 City FE (MPG) Ar r o w s i n d i c a t e m u lt ipl e s of5

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

12frequencies of fuel economy valu es of a multiple of five with those jus tabove and below that value, we es tim ate that there is an ex cess respons e of 13% for fuel econo my values o f a mu ltiple of five comp ared to other values. This implie s that 13% of the respondents were only estim ating their onroad fuel economy to within ±5 mpg. We found the sam e effect with highway fuel economy estim ates. Thus, it is very likely that the sam e respondents rou nded both their city and highway fuel economy estim ates to a factor of five.
It is unknow n whether this tendency biases the estim ated onroad fuel economy upward or downward. However, the presence of such rough estim ates significantly reduces the value of this database to distinguish betw een two sets of fuel economy estim at es. This is particularly true for comp arisons between the mpg-based and 5- cycle formulae, which often only differ by 1-2 mpg. W e are working with Strategic Visions to better identif y the m e thod used by respondents to estim a te their onroad fuel economy so that we can focus on those who actually kept m ileage and fuel usage records. This should also avoid those respondents who estim a te, versus m easure, their actual fuel economy. W e plan to analyze this improved data ove r 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 v e hicles inclu ded an assortm e nt of compact cars, m i d-size car s, pick-ups and SUVs from a variety of m a nufa c turers. The program wa s split into 3 rounds (1, 1.5 and 2), each consisting of 120-300 vehicles. In all three rounds, vehicles were recruited random ly from lists of vehicle registrations in the Kansas City area. Care was taken to ensur e that the sa mple were random with respect to the geographic location of the owner and socio-econom i c status. In rounds 1 and 2, the desired sample of vehicles w a s stratified into four groups of m ode l years, with emphasis on older vehicles 1 , 2

. The prim ary purpose of Rounds 1 and 2 was the quantificatio n of particu l ate em ission s, partic ularly those from high em itters. In Round 1.5, only 2001 and later m odel year vehicles were samp led. (Details about the de sign and perform a nce of Round 1.5 are described th e study’s final report. 3 ) The prim ary purpose of Round 1.5 was the m easure m ent of onroad fuel economy from vehicl es for which we could estim ate 5-cycle fuel economy. This m eant that we had to have fuel economy estim a tes over all five cycles for these vehicles (i.e., that the vehicl e had to be certified to the Suppl em ental FTP standards). These standards began phasing in with the 2001 m odel year. Only a few of the vehicles tested in Rounds 1 and 2 were instrum e nted with a Portable Em issions Measurem ent System (PEMS) and test ed in the hands of their owners. As these vehicles ranged in m ode l year from 1968 to 2005, very few of the vehicles tested in Rounds 1 and 2 had been certified to the Supplem ental FTP sta ndards. However, all of the vehicles tested in Round 1.5 were instrum e nted with PEMS and had their fuel economy m easured while being driven in norm al use by their owners. The round 1.5 vehicle fleet consisted of approxim a tely 120 vehicles, including over 30 hybrid electric ve hicles. The PEMS m easur es driving activity, as well as second-by-second m a ss em is sions of CO 2 , CO, HC, and NO X for roughly 24 hours while the owners of the vehicles ar e utilizing their vehicles on th e road under norm a l, real-world conditions.

13Total fuel co nsum ption for each veh i cle was determ ined from the carbon balance of the CO 2 , HC, and CO e m issions. (Details of the test program and the m e thods used to process the data obtained are described in Appendix A of th is Final Technical Suppo rt Docum e nt.) Each vehicle’s fuel was samp led and tested for density and weight percent carbon. The total distance of driving was determ ined by summ i ng vehicle s p eed and mu ltip lying by total tim e of operation. This total distance trav eled was then divided by total fuel consumption to determ ine onroad fuel economy. EPA city and highway label fuel economy values were obtained from EPA m ileage guides. The test vehicles were m a tched to thos e tested in Ka nsas City to the clo sest d egree possible. F i gure II-2 compares the m easured fuel economy to the 55/45 composite label fuel economy from Round 1.5 (newer vehicles). W e 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-t o-one correlation, with a slope of 1.006. The correlation was also quite good (r-squared valu e 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 v alues on average. Figure II-2. Compariso n Onroad to Current Label Economy: Kans as City

The correlation of hybrid data show s much more scatter. T his is partially explained by the fact that only thr ee hybrid m odels were tested, a num ber of Toyota Prius and Honda Civic vehicles and one Honda Insight. The range of fu el economy label values for these three vehicles is very sm all, 48-56 mpg, plus one vehicle at 64 mpg. W ith the high de gree of variab ility in m easured onroad fuel economy, it is not surprisin g that the co rrela tion coefficient was sm all. On average, hybrid fuel economy was 11% le ss than the composite EPA label values. The average onroad fuel economy of the Toyota P r ius vehicles was closer to their comp osite label values than those for the two Honda m odels

. On average, the onroad fuel economy of the

14hybrids tested varied m ore than the conventional ve hicles. This could be due to hybrids’ greater sensitivity to operating conditions which can either take full advantage of the hybrid technology or essen tia lly nullif y it. The f act that m any vehi c l es started o u t tes ting with a hot s t art like l y biased onroad fuel economy upwards to som e de gree. Thus, the actual shortfalls found would have been greater to som e degree if testing had begun with a cold start. We also perform e d a regression of onroad fuel consumption per m ile versus the inverse of the current fuel economy value for hybrid ve hicles, as Honda suggested in their comments (see Section 5.5 of the Response to C o mm ents docum ent). First, we found that the intercept was not statistically signif icant (p-value of 0.684). Thus, we perform e d a new regression w ith an intercept of zero. W e found an r-s quared value of 0.18, which is not much different than that for the regression of fuel economy. The slope of the regression w a s 1.135, indicating that the hybrids consum ed 13.5% m o re fuel than predicted by the inverse of their label values. More im portantly, this slope had a p-value of 10 -3 1 , indicating that it was extr emely unlikely to be zero. The 95% confidence interval for the slope rangedfrom 1.09 to 1.18. Thus, on average, the data collected in Kansas City indicate that the hybrid vehicles testeddid not perfor m as well as the conventional vehicles compared to th eir current fuel economy label values. 

### B. Fuel Economy Estimates by Independent Organiz ations
Several consum er organizations perf orm their own fuel economy assessm e nts. Of these, the Am erican Auto m obile Association (AAA) a nd Consu m er Reports (CR) have tested the greatest number of vehicles. Th e re lative streng ths of this te sting in clud e the f act th at the vehicles are tested on actual road s, u s ually in traffic and under real environ m ental conditions. The prim ary weaknesses of this testing include: 1) The fact that the drivers or driving pa tte rns inv o lved ar e no t typically p ublished, so they m a y or m a y not be representative of average U.S. drivers or driving, 2) Vehicles are tested throughoutthe year, so som e vehicles are tested in hot weather and other in coldweather andsom e underm oderate conditions, and 3) In som e cases, the actual test procedures used to m easure the volum e of fuel consum ed during th e test are no t described, leav ing som e doubt as to their accuracy. Still, becau se of the public inte re st in th ese e s tim ates, we believ ed tha tthey shou ld be conside r ed her e. W e will begin with an analysis of the Consumer Repor ts estim ates, followed by those of Edmu nd’s and AAA.
1. Consumer Reports Estimates of Onroad Fuel Economy
Consum er Reports published their fuel economy estim ates for 303 2000-2006 m odel year vehicles. T hey publish both EPA’s current cit y, highway and com b ined fuel economy estim ates, as well as their own city, highway and com bined fuel economy estim ates. Therefore, we can comp are EPA’s curren tlabel values to those of CR for all 303 vehicles. As the mpg-based for mulae only require knowledge of fuel economy over the F T P and HFET, we can apply these for mulae to the EPA city and highway fuel econo my values presented by CR (after removing the current label adjustm e nts of 10% and 22%) and cal culate mpg-based fuel economy val ues for all 303 vehicles. W e were also able to m a tch 70 of these vehicles with those in our 5-cycle fuel

15economy da tabase. c Thus, for these 70 vehicles, we were able to calculate 5-cycle fuel economy values.
We m a de two sets of comparisons. One se t included all 303 vehicles. T h e other set included only 70 vehicles. The results of th e 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 Consum er Reports Current EP A 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% Com b ined 20.7 22.9 -9% 21.2 -3% * C o nsum er R e po rt s fuel ec o nomy com p ared t o EP A l a bel val u e. As can be seen, the CR city fuel econ o my values are well below both th e current label or mpgbased label values (21% to 30%). T he revers e is true for highway fuel economy. The CR estim ate of com b ined fuel economy is 9% lower on average than the 55/45 composite of the curren tEPA city and h i g h way label v a lues. However, th e CR estim ate of com b ined fuel economy is only 3% low e r on average than the 43/57 composite of the mpg-based city and highway fuel economy values. Thus, there is a mu ch better match up between the composite mpg-based fuel economy and the C Rcom b ined fuel economy than with current label values. Table II.B-2 presents the sam e comp arisons, except that it includes the 5-cycle estim ates and only includes the 70 m a tched vehicles.
Table II.B-2. CR and Current EPA, 5-Cycl e and MPG-Based Fuel Economy: 70 Vehicles Consum er Reports Current EP A Label 5-Cycle MPG-Based

MPG MPG Difference* MPG Di fference M PG Di fference City 14.3 20.4 -30% 18.0 -21% 17.8 -20% Highway 29.3 26.4 11% 24.3 21% 24.1 22% Com b ined 20.6 22.7 -9% 21.0 -2% 20.9 -2% * C o nsum er R e po rt s fuel ec o nomy com p ared t o EP A l a bel val u e. As can be seen, the comparisons between the CR, current E P A and mpg-based fuel econom ies are ve ry sim ilar to tho se in Table II. B-1. On average across 70 vehicles, the CR combined fuel economy estim ates differ from the current, m pg-based and 5-cycle com b ined fuel economy

In t h e Draft Tech n i cal Supp ort Do cu m e n t , we id en tif i e d1 5 1 ve hi cl es wh i c h we re b o th t e st ed by C o ns u m er R e po rt s a n di n ou r ce rt i f i cat i on dat abase

. H o we ve r, m a ny of th ese m a t c hi ng ve hi cl es we re not fr om th e sam e m odel year.

16 cvalues 9%, 2%, and 2%. The standard deviations of the percentage differs for individual m odels provides an indication of the consistency in the of f

set, or in th e ability of the various label approaches to predict relative fu el economy differences b etw een m odels. Across 70 vehicles, the standard deviation of the pe rcen tage d i fferences b e tween the CR com bined fuel econo my estim ates and the current, mpg-based and 5- cycle com bined fuel economy values are 6.5%, 6.6%, and 6.3%. These standard de viations are very sim ilar. Th e 5-cycle label values provide slightly better estim a tes of relative vehicle fuel economy tha n the other two label approaches. As m e ntioned above, the C Rtest procedures do not in clude cold starts or ai r conditioning operation. As these are two important features of the 5-cy cle for mulae, much of the potential improvem e nt associated w ith the 5-cycle approach is not reflected in CR’s fuel economy estim ates. Of particular interest here are the fuel ec onomy values for hybrid vehicles. For hybrids, the 5-cycle and mpg-based for mulae often give di fferent results. The 303 vehicles tested by Consum er Reports include six hybrid vehicles. We have 5-cycle fu el economy estimates for four of these vehicles, all except th e 2001 Prius and 2000 Insight . A comparison of the various fuel economy estim ates for the five hybrids values ar e shown in the Table II.B-3. To m ake the com p arison between the three labe l 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.

17Table II.B-3. Comparison of Consumer Repo rts and EPA Fuel Economy Values for Hybrids Consum er

Reports Current EP A Label 5-Cycle Fuel Economy MPG-Based Fuel Economy MPG MPG Difference M PG 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% Com bined 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 inf o rm ation is pr esented in T able I I .B-3. W e will f o cus f i rst on th e results f o r all five hybrids averaged together, i ndicated in bold in the table, starting with the city, then the highway, then the com bined fuel economy estim a tes. Starting with the city values, the CR city fuel economy estim a tes average 38% les s than the cur r ent EPA city lab el valu es. T h is is g r eater than for the average v e hicle, where the difference was 30%. The differences are sm aller fo r the m p g-based and 5

- cycle city v alues (26 % and 17%, respectively). W hile the 26% difference for the mpg-based approach is greater than that for th e average veh i cle (21%), th e 17% differe nce for the 5-cycle appro ach is less than that for the average vehicle (20%). Th is indica tes tha tthe 5

- cycle f o rmula f o r city driving is lik ely reflecting factors which are in cludedin CR’s city te st protocol and which ar e not inclu d ed in the FTP, nor a constant 10% adjustm e nt factor. In co ntras t , the c u rrentand mpg-based label approaches do not. W hi l e the mpg-based formul a for city driving produces fuel economy estim ates more closely resem bling those of CR t han the cu rr ent lab el va lues, the m pg-based c ity

18for mula does not pick up factors which are appa rently unique to hybrids which are included in CR’s test procedure.
W ith respect to highway fuel economy, as de scribed earlier for conventional vehicles, the CR highway fuel econo my estim ates tend to be much higher than all the label approa ches (as opposed to the CR city fuel economy estim ates, which tend to be mu ch l o wer than all the label approaches). However, except for this funda m e ntal shift, the relative pe rform ance of the three label approaches with respect to CR highway fu el economy estim ates is the sam e for city and highway fuel economy. The current and mpg-ba se d approaches predict greater relative fuel economy be nefits for hybrids that the CR highway tes ting is not finding. In contrast, the benefits of hybrid technology on highway fuel economy as indicated by the 5-cycle for mulae are reflected in the CR testin g. Again, the s t ory is s i m ilar f o r the co mbinedlabel values. The current and mpg-based label approaches average 27% highe r than CR’s com bined values for the five hybrids. This is more than twice the d i fference for th e averag e vehicle, where the difference was only 11%. Again, this difference incom b ined values indi cates that the current EPA label formulae are granting som e relative benefits to hybrid vehicles which are not reflected in CR’s combined test protoco l

. The 5-cycle com bined values averag e 5% higher than the CR com b ined values, which is only 2% more than the 3% difference found for 70 vehicles. This indi cates that the 5-cycle for mula on a com b ined driving basis is only gran ting a very slight relative benef it to hy brid vehicles compared to CR’s com bined test protocol

. In contrast, the mpg-based equations appear to be granting hybrids a greater re lative benefit compared to th e CR test protocols. The mpgbased com bined fuel economy averages 8% higher than the CR values f o r the f i ve h ybrids, while only 2% for the 70 and 303 vehicle fleets. Fourth, m oving to the comparison of com b ined fuel economy for individual hybrid vehicles, the differences between the three “EPA ” estim ates and the CR estim ates ten dto be consistent in percentage term s, with the exception of the Escape and to som e extent, the Accord. The differences for these two hybrids are not surp rising. The other hybrids have very high fuel economy va lues compared to conventional vehicles

. Thus, they essentia lly “set” the mpg-based equations for their range of fuel economy. The fuel econom ies of the Escape and the Accord fall within the range of conventional vehicle fuel economy. Here, th e mpg-based equations are “set” by the m o re num e rous conventional vehicle data. Overall, fuel economy es tim ates based on the 5-cycle and mpg-based formulae both m a tch the CR test results m ore closely than the current label values fo r conventional vehicles, which dom i nate the 70 and 303 vehicle samples. In addition, hybrid fuel economy estim ates based on the 5-cycle formulae m ore closely m a tch those of CR comp ared to either the curren t label o r m p g-based formulae. The C Restim ates do not neces sarily m atch those of the average driver. Their driving cycles, in particular, are only generally descri b ed. Still, they re present an ostensibly consistent set of estim ates. The CR test procedures find a lower benefit to hybrid technology than the FTP and HFET indicate. The 5-cycle formulae perform si m ilarly, though to a sligh tly les s exten t(i. e. , 2%).

19Of additional interest is whet her the perform a nce of hybrids is due to their relatively high fuel economy or to their hybrid technology. To shed som e light on this question, we compared the mpg-based label values to the Consum er Re ports fuel economy values for all conventional vehicles, the top ten percentile (in term s of co m b i n ed current label valu es) of all conventional vehicles and hybrids. As described above in Table II.B-1, the mpg-based com b ined label values averaged 2 % higher than the com b ined fuel economy m easured by Consum er Reports. The mpg-based com bined label values for the top 10 percentile of conventiona l vehicles m atched the com b ined fuel economy m easured by Consum er Reports, those perform i ng better than conventional vehicles as a whole, at least in terms of Consumer Reports f uel economy m easure m ents. However, as shown in Table II.B -3 above, the mpg-based com b ined label values for hybrids averaged 8% higher than the com b ined fuel economy m easured by Consum er Reports. Thus, hybrids perform e d worse in the Consum er Reports testing than the average conventional vehicles and worse to a slighter greater degree than thos e conventional vehicles with the highest fuel economy. Thus , based on the Consum er Reports testing, the differential perform a nce of hybrids appears to be more related to their hybrid tec hnology than to their relatively high fuel economy. The sam e relationships hold f or the 5-cycle fuel economy values.
2. AAA Estimates of Onroad Fuel Economy
The Am erican Autom o b ile Association ( AAA) also develops its own fuel economy estim ates. In their 2004 report, AAA presented thei r test results and the EPA label values for 163 m odels. AAA only presents a single fuel econo my estim a te, which we understand to be a composite of city and highway operation. Overall, AAA found a higher overall fuel economy than the current com b ined EPA label value for 85 models, and lower fuel economy for 73 m odels. On average, the AAA fu el economy estim ates were 1.5% lower than the cu rrent com b ined EPA label values. W e calculated an mpg-based 43/57 combined fuel economy usi ng the mpg-based equations. (FTP and HFET fuel economy values were back calculat ed from the current E P A fuel economy label values.) On average, the AAA fuel econo my estim a tes were 5.3% higher than the mpg-based com bined fuel economy values. Table II.B-4 shows these comparisons.
Table II.B-4. AAA a nd Current EPA, 5-Cycle an d MPG-Based Fuel Economy Estimates AAA Current EPA Label 5­ Cycle MPGBased
MPG MPG Difference* MPG Di fference M PG Di fference 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 m a tch 61 out of the 163 AAA- tested vehicles with sim ilar vehicles in our 5-cycle certification database. This is lower than the 98 m odels which we m a tched in the analysis described in the Draft Technical Support Docum e nt . Th e lower figure is due to the use of a m ore stringent criterion thatthe vehicles m a tch in term s of m odel year. (W e assum ed that all of the vehicles tested by AAA were 2004 m od e l year vehicles.) As was the case with the Consum er Reports data, the FTP and HFET fuel ec onom ies for the certification vehicles in our

205-cycle fuel economy da tabase were generally lower than those tested by AAA. Thus , we adjusted the 5-cycle city and hi ghway fuel economy values using the ratio of the FTP and HFET fuel economy values, respectively, fro m our certif ication database and the AAA database. On average, the AAA esti m a tes were 6.1% higher than the com bined, 5-cycle fuel economy values for these 61 vehicles. On average, the AAA es tim ates were 6.7% higher than the combined mpg-based fuel economy values for these 61 vehicles , just slightly greater than with the 5-cycle for mulae. While not shown in the table, the sta ndard 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 on road fuel econo my as m easured by AAA.
The AAA fu el economy values include two hybrid s, a Prius and an Insight. Based on the official EPA label values, the AAA estim ates we re 6.6% lower than the current EPA co mposite fuel economy values for these two vehicles, 5% lo wer than for the average vehicle. T hus, the current label adjustm e nts are indicating great er fuel economy improve m e nt due to hybrid technology than AAA is finding during their testing. In contrast, the AAA estim ates average 10.6% and 12.0% higher than the mpg-based and 5-cycle combined fuel economy val ues for these two vehicles, respectively. These differences are abou t 6 % and 4% g r eater difference than fo r the av erag e vehicle, respectively. Thus, the 5­ cycle and mpg-based formulae are indicating le ss fuel economy i mprovem e nt due to hybrid technology than AAA is finding during their testi ng. Thus, the AAA testing is finding hybridrelated benefits som e where in between those i ndicated by the current label for mulae and those indicated by the mpg-based and 5-cycle formulae. This is in contrast to the Consum er Reports estim ates, where their testing indi cated hybrid benefits m ore consistent w ith those indicated by the 5-cy cle f o rmulae. The consistency between the mpg-based equa tions and 5-cycle for mulae with respect to the AAA hy brid testing is due to the fact that AAA only tested the two hy brids with the highest fuel economy label values. In this range of fuel economy, bot h the 5-cycle for mulae and the mpg-based equations predict very sim ilar values for hybrids. Consum er Reports, on the other hand, tested four hybrids, two of whi c h have muc h lower fuel economy va lues. In this range, the mpg-based equations are dom i nate d by conventional vehicles and the 5-cycle values for hybrids tend to fall below the mp g-based lines.
The m a in reason for the difference, howe ver, is that AAA fo und much higher fuel economy va lues for the two hybrids which both orga nizations tested than Consum er Reports. Consum er Reports found that a 2000 Insight and 2004 Prius achi eved com bined fuel economy values of 51 mpg and 41 mpg, respectively. AAA found that a 2004 Insight and 2004 Prius achieved combined fuel economy va lues of 58 mpg and 52 mpg, respectively. In term s of fuel consum ption, AAA fou nd 17% less fuel use for th ese two hybrids than that found by Consu m er Reports. We developed an analogous estim ate for conventional vehicles by comparing the difference infuel econo my found by each organ i zati on for all the vehicles tested relative to the current and mpg-based label formulae. For conven tional vehicles, in term s of fuel consumption, AAA found 8% less fuel use for th ese two hybrids than that f ound by Consum er Rep orts (. Thus, the dif f

erent test procedures used by th e tw o organizations are finding much different benefits of hybrid technology rela tive to conventional vehicles. This reinforces the observation

21that the capability of hybrid tec hnology is sensitive to how and where the vehicles are operated (e.g., colder temperatures, trip le ngth, driving pattern, etc.). Th e comparison of the fuel economy values predicted by the various label approaches to the onro ad fuel economy estim ated by Consu m er R e ports, AAA and other or ganizations is investigated fu rther in Section II.D. below. As part of their comm ents on the ru le, AAA provided onroad and dynam o m e ter fuel economy estim ates for 42 additional vehicles. T he average fuel economy recorded by the owners was 25.6 mpg, while the average of the current com b ined EPA label values was 29.6 mpg, Thus, this additional dataindicates a 15% shortfall in on road fuel economy relative to the current EPA label values. In addition, AAA tested17 of these vehicles over the FTP, HFET, a hot start US06 and a US06 test with a cold start. On average, curren tEPA label valu es based th e AAA FTP a nd HFET testing was very consistent with those implie d by the vehicle labels. The average of the vehicles’ com b ined city/highway label values (26.9 mpg) and those based on the AAA testing (26.8 mpg) differed by only 0.1 mp g. The average onroad f uel economy of these vehicles as estim ated by the owners was 23.5 mp g. This indicated an onroad fuel economy shortfall of 12%, or slightly sm a ller than that for the complete set of 41 vehicles. The average cold start and hot start U S 06 fuel economy va lues for these vehicles were 23.1 and 25.0 mpg, respectively, bracketing the owne rs’ experience. In contrast, the owner’s fuel economy was lower than either the curren t city or highway label valu es for these 17 vehicles. AAA commented that the US06 test appeared to be a better predicto r of onroad fuel economy than either the current city or highw ay lab el valu es an d encourage d EPA to move f orward with its proposed 5-cycle formulae, which included fuel economy measured over the US06 test.
3. Edmunds The on-line car journal Edmunds.com m easures fuel economy on new cars they evaluate for reviews. Edmunds reviews and road tests cars in a variety of ways, but the m o st relevant data com e from their “long-term tests.” For these test s they purchase or lease vehicles directly from dealers and keep the vehicle for 1-2 years, ge nerally accumu lating as mu ch as 30,000 m iles of experience with the vehicles from se veral different reviewers. Edmunds reports the best, worst, and average fuel economy achieved during their long-term use of the vehicle. W e reviewed Edmunds data from 40 model year 2003-2006 long- term test vehicles and comp ared their average to the EPA “combined” city/highway fu el economy value. On average, the Edmunds reviewers achieved fuel economy about 14% low er than the currentEPA com bined label value. Hybrid vehicles performed even m ore poorly; the four included in the recent Edmunds long-term tests on average fell 24% below the current EPA co m b ined label value. The data from Edmunds and current EPA City, Highway, and Com bined labe l values are shown in Table II.B-5 below. As can be seen in this tabl e, the average fuel economy ach ieved by Edmunds review ers is f r equently lower than th e curr ent EP A City estim ate – in f ac tthis oc curs in m o re than half of the vehicles. And only in a m i nority of cases (8 ) do es the best achieved by Edmunds exceed the EPA highwa y estim ate, supporting the belief of many that the current highway fuel economy label v alue is a near b est-case estim a te.

22Table II.B-5. 2003-2006 Edmunds Long-term T e st Vehicles

Edmunds.co m (mpg) Curre nt EPA Label (mpg) MY MFR Model Bes t Wors t Av g. Cit y H w y Com bined 2006 Hon da Ridg eline 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 Mitsubi shi 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 Cob a lt 27.3 20.2 24.9 24.0 32.0 27.0 2005 Dod ge Magnu m 24.4 11.8 17.3 17.0 25.0 19.9 2005 Ford Esca pe Hyb r i d 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 Mustan g GT 17.8 13.0 16.5 17.0 25.0 19.9 2005 Hon da Acco rd Hybri d 35.0 14.9 23.4 29.0 37.0 32.1 2005 Hon da 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 Land rove r LR3 27.6 12.6 16.3 14.0 18.0 15.6 2005 Nissa n Frontie r 4x4 Nism o 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 Lega cy 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 Chry sle r Pacifica 20.7 9.9 15.8 17.0 22.0 18.9 2004 Ford F-15 0 17.7 9.9 13.3 14.0 18.0 15.6 2004 GMC Canyo n 21.1 13. 7 17.3 18.0 23.0 20.0 2004 Mazd a RX-8 22.5 12.0 17.5 18.0 24.0 20.3 2004 Mitsubi shi Endeavo r 25. 0 9.0 16.9 17.0 21.0 18.6 2004 Nissa n Que s t 22.9 11.7 17.8 19.0 26.0 21.6 2004 Nissa n 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 Hon da Acco rd 30.0 14.5 24.1 24.0 33.0 27.4 2003 Hon da Pilot EX 25.6 12.6 18.3 17.0 22.0 18.9 2003 Infiniti G35 Coup e 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 Mazd a Mazd a6 26.4 14.6 21.5 20.0 27.0 22.6 2003 Mitsubi shi Outland er 26.8 13.8 19.5 20.0 25.0 22.0 2003 Nissa n 350Z 25.7 13.1 19.0 20.0 26.0 22.3 2003 Subaru Fore ste r 27.7 15.0 21.6 21.0 26.0 23.0
Table II.B-6 illus trates the differences be tween the Edmunds average v a lues and the current EPA label, the MPG-specific values, and, for the hybrids, the 5-cycle values.

23Table II.B-6. Edmunds Long-term T e st Vehicles Compared to EPA Co mbined MPG Estimates
Edmunds EPA Combined Label (mpg) Differen ce: Edmunds Vs. EPA ( % )
MY MFR Model Av g. Curre nt MPGBas e d 5 - C y cle Curre nt MPGBas e d 5 - Cy c l e 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 Hon da Lexus Mitsubi shi VW Audi BMW Chevrolet Dod ge Ford Ford Ford Hon da Hon da Kia Land rove r Nissa n Scion Subaru Toyota Volvo Acura Chevrolet Chry sle r Ford GMC Mazd a Mitsubi shi Nissa n Nissa n Toyota Toyota Volvo Hon da Hon da Infiniti Lexus Mazd a Mitsubi shi Nissa n Subaru Ridg eline RX 400h Eclipse GT Jetta A4 2.0T X3 Cob a lt Magnu m Esca pe Hyb r i d GT Mustan g GT Acco rd Hybri d Odyssey Spectra LR3 Frontie r 4x4 Nism o tC Lega cy GT Solara S40 TL Malibu Pacifica F-15 0 Canyo n RX-8 Endeavo r Que s t Titan Prius Sienna XC90 Acco rd Pilot EX G35 Coup e SC 430 Mazd a6 Outland er 350Z Fore ste r 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% Av erage 19 23 21 -14% -6% -8%

24As can be seen from Table II.B-6, both th e mpg-based and 5-cycle formulae still produce fuel economy label values higher than those de veloped by Edmunds. However, the differences are sm aller than with the curren tlab e l f o rmulae. The difference for the mpg-based equations is 8%, while that for the 5-cycle formulae is 7% (t he Edmunds estim ates being the lower of the two in both cases). W hile not shown, the standard de viation of the percentage difference is 12% for the mpg-based equations and 9% for the 5-cycle f ormulae. Thus, the relative differences in vehicles’ fuel economy are slightly be tter indicated by the 5-cycle formulae.
The vehicles which Edmunds tested include four hybrids. The Edmunds fuel economy m easure m ents are on average 24%, 13%, and 9% lo wer 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 av erage for the current and mpg-base d labels, but only 2% higher than that for the 5

- cycle appro ach. T hus, the current and mpg-based approaches are indicating hybrid-related benefits which the Ed munds testing is not conf irm i ng. However, the relative benefit of hybrid technology as indicatedby Edmu nds testing and the 5-cycle approach are very sim ilar. In this sens e, the E d munds testing is m ore consistent with that of Consum er 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 ec onomy average 18%, 8%, and 8%, respectively. These latter differences are very sim ilar to those for the average conventional vehicle. The sam e findings hold for the 20% of conventional vehicl es with the highest fuel economy. Thus, the Edmunds testing is finding som e thing about hybrids wh ich is not occurring w ith either typical or high fuel economy conventional vehicles. 

### C. Fleet- w id e Estimates of Onroad Fuel Economy We begin with a comparison of the 5-cycle fu el e c onomy values with th e f l eetwid e f u el economy estim ates developed by FHWA. Because we do not have fuel economy data for all vehicles over all 5 dynamom e ter cycles, and therefore cannot de velop a 5-cycle fuel economy estim ate for the current o n road fleet d i rectly, th is comparison requires a two step process. The first step in this process compares fl eetwide fuel economy es tim ates based on EPA’s current fuel economy labels to the F H WA estimat e of onroad fuel economy. The second step in this pro cess is to comp are com b ined city -highw ay fuel economy using the 5-cycle formulae to that using the current EP A city and highway labe l procedures. This comp arison is perfor m ed for vehicles for which we have 5-cycle fuel economy data. W e will assum e that this relationship also app lies to those v e hicles for wh ich 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 acco mpanied the im plem entation of the U S 06 and SC03 testing requirem e nts. We estim ated that these changes hada positiv e impact on th e fuel econo my values measured during these tests. How e ver, as discussed in the Response to Comm ents docum e nt, we now believ e that these ch ang es had a neu tral im pact on fuel economy. Thus, this third step is no longer needed.

25Overall, the difference between 5-cycle fuel economy and FHWA onroad fuel economy is the com bi nation of the percentage differences from the two comp arisons: 1) Current E P A label fuel economy to FHWA onroad fuel economy, and 2) 5-cycle fuel economy to current E P A label fuel economy (without 10% road load adjustm e nt). FHWA publishes fleet-w i de estim ates of onroa d fuel economy for cars and light trucks in their annual Highway Statistics publication. 4 W e will f o cus o n the com b ined estim ates f o r cars and light trucks here, since various states use di fferent criteria to dist inguish between the two vehicle class e s. At the s a m e ti m e , the criteria used to distinguish between c ar s pl us li ghttruc ks and other vehicles are very consistent.
Table II.C-1 presents the FHWA estim ates of vehicle m iles traveled (VMT), fuel consumption and onroad fuel economy for passe nger cars and 4-tire, 2-w heel trucks.

Table II.C-1. FHWA-Based Estima te of Onroad Fuel Economy Year 2003 2004 VMT (m illion m iles) 1,672,07 9 1,704,98 2 Passenger c ars Fuel Use (thousand gallons) 75,455 76,007 MPG ( mpg) 22.2 22.4 VMT (m illion m iles) 984,09 4 1,014,34 2 2 axle 4 tire Trucks Fuel Use (thousand gallons) 60,758 62,626 MPG ( mpg) 16.2 16.2 VMT (m illion m iles) 908,71 2 936,64 3 Light trucks Fuel Use (thousand gallons) 55,271 56,970 MPG ( mpg) 16.4 16.4 Passenger cars and light trucks VMT (m illion m iles) 2,580,79 1 2,641,62 5 Fuel Use (thousand gallons) 130,72 6 132,97 6 MPG ( mpg) 19.7 19.9 The FHWA category of 4-tire, 2- wheel trucks includes som e vehi cles which EPA classifies as heavy-duty vehicles. We have adjusted the FH WA estim at es upward to provide a m o re direct comparison. This adjustm e nt is based on a st udy perform e d by Oak Ridge National Laboratory (ORNL). 5 ORNL estim a t ed both VMT and fuel use for several categories of light trucks. Class 1 and 2a trucks fall into EPA’s definition of li ght-duty trucks, while Class 2b trucks do not. Together, th e three class e s of trucks are approxim a tely equivale nt to FHWA’s 4-wheel, 2-axle truck class. The results of this study are shown in Table II.C-2.

26Table II.C-2. Breakdow n of VMT and Fuel Use by 4-Wheel, 2-Axle VMT (billio ns) 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 e s tim ated that 92.3% of the VMT and 91.0% of the fuel use by 4-wheel, 2-axle truck s was by vehicle s f allin g into EPA’ s light-duty truck ca tegory (and whic h are labeled for fuel economy). Therefore, we adjusted FH WA’s VMT and fuel use es tim ates for 4-wheel, 2­ axle trucks by these percentages to convert them to values app licable to E P A’s light-duty truck class. Thes e adjusted values and the resulti ng onroad fuel economy are shown in Table II.C-1. We then added the VMT and fuel use by passe nger cars and E P A light trucks together and calculated an overall fuel economy for the two vehi cle classes. These valu es are also shown at the bottom of Table II.C-1. The result is that the FHWA-based estim ate o f 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 tr ucks presented in the NPRM analysis. The difference is due to the u se of m o re recent fi gures from FHWA which considers fuel economy data indicating lower fuel economy fr om light trucks than previously estim a ted.
We then used the EPA MOBILE6.2 in-use em ission m odel to calculate f l eet-wide average EP A com b ined fuel econo my label values for these two years. MOBILE6.2 estim ates fuel economy using a sales-weighted average of th e com bined EPA city/highway label values for each m odel year of cars and ligh ttru c ks. MOBILE6.2 then estim ates anaverage fuel economy for the onroad fleet by weighting the fuel economy values for each m odel year by the fraction of vehicles on the road fro m each m o del and th eir typical annual m ileage (which decreases with age). Thus, MOBILE6.2 is an ideal tool for estimating the EP A label fuel economy using the current label for mulae for the onroad vehicle fl eet in any particular calendar year. For 2003, MOBILE6.2 e s tim ates average passenge r car and light truck fuel economy of 24.0 mpg and 17.3 mpg, respectively. For 2004, MO BILE6.2 estim ates average passenger car and light truck fuel economy of 24.0 mpg and 17.4 mpg, respectively. We weighted the fuel economy va lues for cars and light trucks together using their respective V MT from Ta ble 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 FHW A

- based onroad fuel economy was 6.5% and 6.1% lower than the curr ent c o m b ined EPA label va lu e, respe ctiv e l y. Thus, th e result of th e f i rst s t ep in this p r oce ss is an indication that the curr en t labeling formulae could be over-estimating onroad fuel economy by 6-7%. Moving to the second step, in Tables III.E

- 1 andIII.E-2 sho w n in Sectio n III.E belo w, we present city and highway fuel economy label valu es using both current an d 5-cycle formulae for 615 2003-2006 m odel year vehicles. The FHWA estim ates apply to all driving, both city and highway. Therefore, we are prim ar ily inte rested in com b ined city -highway fuel economy values. Also, we are using FHWA estim ates for the 2003 and 2004 calendar years, as these are the m o st recen t available. The nu mber of hybrid veh i cles on the road was very lo w during this tim efram e,

27much lower than the 2 % level p r esen t in our ce rt ification database. Therefore, we will only use the 5-cycle f u el economy estim ates for the 601 non- hybrid vehicles in our database. There is no need to perform this comparison separately for the m pg-based for mulae, since the av erage fuel economy from the 5-cycle and mpg-based form ul ae are identical for non-hybrid vehicles.
The com b ined fuel econ o my using the current label formulae is a 55/45 h arm onic weighting of the current city and highway fuel economy labels. The average com b ined fuel economy using the current EPA label values for th ese 601 vehicles is 20.9 mpg. For the final 5­ cycle formulae, com b ined fuel econo my is a 43/ 57 harm onic weighting of the 5-cycle city and highway fuel econom ies. This city/highway sp lit for the 5-cycle fuel econ o m i es is bas ed on: 1) the assumption that drivi ng generally less than 45 m ph is city driving and that above 45 mph is highway driving, and 2) the description of onroad driv ing patterns contained in MOVES. The m a the m atical formula for converting the 5-cy cle city and highway fuel economy values into an estim a te of average onroad fuel economy is as follows:
1 Average onroad fuel economy = ⎛ 0

. 43 ⎞ ⎛ 0

. 57 ⎞ ⎜ ⎜ 5

− cycle City FE ⎟ ⎟ + ⎜ ⎜ 5

− cycle Highway FE ⎟ ⎟ ⎝ ⎠ ⎝ ⎠ The average com bined 5-cycle fuel economy usi ng this formula for the 601 conventional vehicles is 19.6 mpg, which is 6.2% lower than that based on the curr ent label values. This is the result of the second step in the process. Overall, then, the curren t label values over-estim ate onroad fuel economy per FHWA (with som e adjustm e nts by EPA) by 6-6.5%, while the 5-cycle for mulae decrease current label values (of the 2002-2003 fleet) by 6.2%. Thus, the final 5-cycle formulae should m o ve the com bined fuel economy label values to within a fe w tenths of a percent of a comp arable estim ate of fleetwide fuel economy using FHWA technique s. This should not be surprising, since the value of the factor in the 5-cy cle formulae representing factors not represented in dynam o meter tests was set to m a tch onroad fuel economy as estim ated by FHWA (see Section III.A.5 below). 

### D. Overall Comparison of Hybrid Fuel Economy
When com p aring onroad fuel economy to EPA estim ates, it is of ten appropriate to focus on the EPA com b ined fuel economy, as it is not possible to determ ine whether a particular vehicle’s driving was city-like or highway-like. Overall, the 5-cycle formulae predict a com bined fuel economy about 6% less than the cu rrent com b ined fuel economy label for our 615 vehicle certification fuel economy da tabase. These relationships hold for the complete 615 vehicle database, as well as the 601 conventional vehicles w hich dom inate the database. However, the effect of the 5-cycle for mulae on the com b ined fuel economy of hybrids is mu ch m o re significant. Overall, the 5-cy cle for mulae predict a com b ined fuel economy about 18% less than the current com bined fuel economy label for the 14 hybrids in our certificati on fuel economy database. Thus, the 5-cycle

28for mulae reduce hybrid fuel economy roughly 12% m o re than non-hybrids comp ared to today’s labels. Two thirds of this difference occurs in c ity fuel economy. It is prim arily due to a greater impact of running fuel use at co lder temperatures and inclusion of US06 ci ty driving in the 5­ cycle formulae. This difference appears to be related m ore to hybrid technology itself , as opposed to just high fuel economy va lues. For example, the average impa ct of the 5-cycle for mulae on the com bined fuel economy of the t op 10% of conventional vehicles in term s of current combined fuel economy is 8%. This is only 2% greater than that f or all conventional vehicles and well below that for hybrids.
Som e care must be taken in relating to this d i fference between the impact of the 5-cycle for mulae on conventional and hybrid vehicles. Fi rst and foremost, the impact of the 5-cycle for mulae on label fuel economy involves a num be r of projections which may not be accurate for individual vehicles. For example, we currently lack m easured fuel econo my values o v er the US06 city and highway bags. Thus, these must be estim a ted. W e are using one relationship for conventional vehicles and another relationship for hybrids. The for m er is based on the testing of over 100 vehicles, which shows fairly consistent resu lts. The latter is ba sed on the testing of two vehicles. T h e relative perform a nce of other hybrid vehicles over the two bags of the US06 test could differ significantly from our current estim a te. Sim ilarly, we have included the effect of turning on the heate r or def r oster du ring th e cold FTP, as this will be requ ired in the f u ture. However, the data currently available on fuel economy over the cold FT P do not include this factor. Our projected impact for conventional ve hicle s is rela tive l y sm all. Thus, variab ility between vehicles is likely even sm aller. Ho wever, the testing of two hybrids showed large impacts on Bag 1 fuel economy at 20°F. Othe r hybrids m a y show different impacts. Second, m a nufacturers m a y i mprove their hybriddesigns in the future to reduce the impact of colder temp eratures, air co ndition i ng o peration, etc. W hile we believ e that m a nufacturers have always been concerned abou t fuel efficiency under realistic conditions, the fact that fuel economy labels have been based solely on operation at 75°F has inevitably focused their attentio n especially on this ty pe of operation. Hybridtechnology is s t ill relativ ely new and improve m e nts are constantly being made. W ith the 5-cycle formulae, we expect that these improve m e nts will affect vehicle operation over a mu ch wider set of in-us e conditions than m i ght have been the case with the current label for mulae.
Nonetheless , the ques tio n still ar ise s: is this greater reduction in com bined fuel economy of today’s hybrids appropriate? In this section, w e compile all the onroad fuel economy m easurem ents and consu m er organization estim ates in on e place and comp are th em to com b ined fuel economy estim ated using the curre nt, 5-cycle and mpg-based formul ae. Table II.D-1 compares three sets of onroa d fuel economy me asurem ents to both current and final EPA com bined fuel economy label valu es. Com b ined fuel eco nomy 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 fo rmulae weighs city fuel economy by 43% and highway fuel economy by 57%. It should be noted that the values for current com bined 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 ar e not the current offici al label values. The

29official 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 Econ omy Versus EPA Label Estima tes (mpg) EPA C o m b ined Fuel Ec onomy Onro ad Fuel E c onomy Measurem ents Cu rren t MPG-Based 5 -Cycle DOE Free dom C ar Yo urM P G EPA Ka nsas City 20 0 1 Ho n da In si ght 61 51 50 45 66 47 20 0 3 Ho n da C i vi c 46 40 37

- 38 38 46 40 20 0 4 T oy ot a P ri us 54 45 45 45 48 50 20 0 4 C h ev rol e t Si l v era d o 4 w d 16 15 15 18 15

-- - 20 0 5 F or d Esc ape 4w d 30 26 24 27 30

-- - 20 0 5 Ho n d a A ccor d 32 29 26 28 31

-- - 20 0 5 Le x u s RX4 0 0h 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 estim ates from the YourMP G da tabase tend to be significantly higher than those from the DOE FreedomCar project. The Ka nsas City m easure m ents tend to fall in between, ex cept for the Prius. Label fuel econo my values based on all three approaches can com e close to one of the three on roadfuel econo my estim ates, even for the sam e vehicle.
For exam ple, the current EPA label formulae appear to s i gnificantly ov er-estim ate fuel economy as m easured in the Freedom Car program , except for the Chevrolet Silverado. This vehicle has the least hybrid capab ility with respect to improved fuel economy of the vehicles listed in Table II-D.1. The mpg-based and 5-cycl e label formulae tend to perform equally well with respect to the DOE Freedom Carprogram In contrast, the current label formulae pr ovide reasonable estim a tes of onr oad fuel economy for five of the seven hybrids based on the YourMPG database. The mpg-ba sed and 5­ cycle label f o rmulae do so for four of the seve n 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 for mulae over-estim ate onroad fuel economy as m easured in the Kansas City test program

. The mpg- based and 5-cycle label f o rmulae tend to provide reasonable estim ates of onroad fuel economy as m easured in Kansas City on average, over-estim ating fuel economy for one of the three m a tched hybrid s and under-estim ating fuel economy for one hybrid.
Table II.D-2 compares onroad fuel economy estim ates by consum er organizations to EPA estim ates.

30Table II.D-2. Onroad Hybrid Fuel Econom y Estimates Versus EPA La bel Estimates (mpg) EPA C o m b ined Fuel Ec onomy On road Fuel E conomy Estimates C u r rent M P G-B a se d 5 -C y c l e C ons um er R e p o rt s Edmunds AA A 20 0 1 Ho n d a In si ght 61 51 50 51

-- - 58 20 0 3 Ho n d a C i vi c 46 40 37

- 38 36

-- - -- - 20 0 4 T oy ot a P ri us 54 45 45 44 41 52 20 0 5 F or dEsc ape 4w d 30 26 24 26 23

-- - 20 0 5 Ho n d a A ccor d 32 29 26 25 23 .4

-- - 20 0 5 Le x u s RX4 0 0h 28 24 24

-- - 25 .4

-- - Again, ther e is sign if ican t sca tte r in the data. Of the three consum er organizations, Edmunds predicts the lowest fu el economy. The Edmunds fuel economy values are lower than all three sets of EPA fuel economy es tim ates. Since the 5-cycle for mulae produce the lowest estim ates on label fuel economy, the 5-cycle fo rmulae com e closest to m atching the Edmunds’ fuel economy values. The 5-cycle fuel economy values m a tch those of Consum er Reports very closely, differing by at m ost 2 mpg for a ny individual vehicle. The mpg- based estim ates are higher for three of the five m atching hybrids. T h e curr ent label valu es exceed those found by Consum er Reports significantly in all cases.
The two AAA estim ates are high er th an the 5-cycle and mpg-based estimates, bu t low e r than (bu tclo ser to ) the current EPA estim ates. Overall, the 5-cycle fo rmulae tend to m atch the findings of the three consum er organizations m ore closely than the other two label approaches. However, there is sign if icant sc atter in the data. Over tim e, we will continue to a sses s how our 5-cycle estim ates com p are with tho se of other s tudies

. In particular, it w ould be useful to have both vehicle activity and fuel economy data, coupl ed with environm ental conditions in order to more precisely verify the ability of the five cycl es to estim a te onroad fu el economy under the full range of ambient conditions.

31

### Chapter I I References
1. Eastern Research Group. Kansas City PM Ch aracterization S t udy: Round 1 Testing R e port. 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 Em issions 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. Departm e nt of Transportation, Federa l Highway Adm i nistrati on. Highway Statistics 2004. See Table VM-1. Website: http://www.fh wa.dot.gov/policy/ohim /hs04/htm / vm1.ht m
. 5. Davis, S. C. and S. W . Diegel. T r ansportation Energy Data B ook: Edition 24. Oak Ridge National Laboratory, prepared for the U.S. Departm e nt of Ener gy, Report No. ORNL-6973, Decem ber 2004, Table 4 .3. W ebsite: http ://cta.ornl.gov/data/download24.shtm l

.

32

## Chapter III: Documentation of Final A pproach for Estimating On-Road Fuel E c on omy The current fuel economy label values util ize m easured fuel economy over city (FTP) and highway (HFET) driving cycles and ad just these values downward by 10 and 22%, respectively, to account for a vari ety of factors not addressed in EPA’s vehicle test procedures. These factors includ e differences between the way vehicles are driven on the road and over the test cycle s, a i r cond ition i ng use, wide ly vary ing a mbient tem p eratu r e and hum i dity, widely varying trip lengths, wind, precipitation, rough road condi tions, hills, etc. The purpose of the new formulae for city and highway fuel econom i es is to better account for three of these factors: 1) on-ro ad driving patterns (i.e., vehicle speeds and acceleration s), 2) air con dition i ng, an d 3) colder te mp eratures

. Vehicles are often driv en m o re aggressively and at higher speed s th an is rep r es e n ted in the F T P and HFET tests, which have m a xi mu m s peeds of 55-60 m ph and m a xi mu m a cceler ation rates of 3.2-3 .3 mph per second. The incorporatio n of m easured fuel econ o my over th e US06 test cycle into th e fuel econo my label values m akes the label values m ore realistic, as this cycle includes speeds up to 80 mp h and acceleration rates of 8.4 mph per second.
Drivers often use air con d ition i ng in warm , hum i d conditions , while the air cond ition e r is turned off during the FTP and HFET tests. Th e incorporation of m easured fuel economy over the SC03 tes tcycle into the fuel econ o my label v a lues reflects the added f uel needed to operate the air conditioning system. The SC03 test is perform e d at 95 degrees Fahrenheit ( o F ) with simulated solar heating.
Vehicles are also often dr iv en at temperatu r es below 75 o F, at which the F T P and HFET tests are perform e d. The incorpor atio n of m easured fuel econ o my over th e cold temp erature FTP test into the fuel economy label values reflects the additional f uel needed to start up a cold engine at colder temp eratures. We developed two m e thods for incorporati ng these three factors into our onroad fuel economy pr edictions. T h e first m e thod, term ed ve hicle specific 5-cycle, com b ines the fuel economy over all five dynam o meter cycles in a vehicle specific m a nner (i.e., as much inform ation as possible reflects the fuel economy perform a nce of the specific vehicle being exam ined). The second m ethod, term ed mpg-ba sed, utilizes fuel economy estim ates based on the vehicle specific 5-cycle m e thod for a large number of vehicles and develops generic adjustm e nt factors as a f unction of the vehi cle’s F T P and HFET fuel economy values. In Section III.A, we describe the developm en t of the vehicle specifi c 5-cycle m e thod. In Section III. B, we describe the mpg-based m e thod. In Sectio n III.C, we evaluate the range and variability of onroad fuel economy experienced by drivers of the sam e vehicle. In section III.D, we describe how the current city and highway fuel economy values would change under the two final m e thods. Finally, in section III.E, we eval uate the sensitivities a nd uncertainties in the vehicle specific 5-cycle formulae.

33

### A. Vehicl e S p ecific 5-Cycle Met h od for Estimating On-Road Fuel Economy from Dyn a mometer 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 achie ve while driving the vehicle during norm a l use. As such, the label valu es ar e in te nded to tak e into ac coun t the ef f

ect of seasonal and geographical variations on auto motive fuel economy, as well as the different driving habits of individual driv ers. Due to variations in cl imate 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. Howe ver, the set of fuel ec onomy values should be able to predict on-road fuel economy for the average driver under average environm ental conditions. The goal of the anal ysis presented below is to use the available fuel economy inform ation developed during em ission and fuel economy testing to predict on-road fuel economy under thes e “average” con d itions to th e gr eatest ex tent poss i ble. This onroad average fuel economy can then be adjusted to repres ent m ore worse-case conditions, such as 25 th or 10 th percen tile es tim ates (i.e., fuel econo my leve ls achieved by 75% or 90% of all drivers, respec tive l y ) , which is d i scussed 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 em ission and fuel economy test s which are already bein g perform e d. This m i ni m i zes the costs ass o ciated withthe final rule , as described below in Chapter IV. The five current em ission and fuel economy tests and thei r key aspects are described below in Table III.A-1.
Table III.A-1. Key Features of the Five Current Emission a nd Fuel Economy Tests Test Driving Am bient Temp erature Engine Condition at Start Accessories

FTP Lowspeed 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 fe atures of the five curr ent vehicle tes t s. The FTP, HFET and US06 are all perform e d at an ambien t temperature of 75°F. Each test consists of a distinctive driving pattern. In addition, the FTP test consists of three distinct m easurem ents, called bags because the em issions produced during each portion of the test are literally collected in separate plastic “bags”. Bags 1 and 3 consis t of the exact sam e driving pattern, while Bag 2 consists of a different pattern. Thus, fuel ec onomy m easurements at 75°F are available for four distinct driving cycles. Bag 1 begins with an engine start after the v e hicle has been sitting with the engine off for at least 12 hours, representing an overnight soak (soak refers to the tim e during which a vehicle sits with its engine off). This is referred to as a “cold” s t art. In this case, th e cold start occurs at

3475°F, which m a y not seem not very “cold”. Howeve r, the engine is at th e s a me t e mp e r a t u r e a s the am bient air, so it is referred to as being “co ld ”. Af ter sitting this long, the engine has essentially fully coo l ed off and sittin g longer has no effect on either em issions or fuel economy. Bag 3 begins with an engine start after the vehi cle has been sitting with the engine off for only 10 m i nutes, representing a short stop to refuel, a short shopping trip, et c. This is referred to as a “hot” s t art, s i nce th e engine is still ess entia lly a t 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 th is distribution of in-use soak tim e s and ens ure that vehicle m anufacturers design their em issi on control system s to work effi ciently under a wide range of operation. P r actically sp eaking, very little fuel is necessary to warm up the vehicle after it has been sitting with the engine off for only 10 m i nut es. Thus, the difference between fuel use over Bags 1 and 3 is that associated with a cold start. As indicated in Table II I.A-1, this e stim ate is available at both 75 and 20°F. The SC03 test is the only test performed w ith the air conditioning system operational. Therefore, its results are used to augm ent the fu e l economy fr om the five driving pattern tests for the fuel needed to operate air conditioning. Th e cold FTP is the only test perform e d a ta tem p erature below 75°F. Theref ore, its r esults ar e used to r ep r esent the ad dition al f u el needed to warm up an engine after a cold st art, as well as an y fuel needed to operate a warm ed up engine, at cold er temperatures. Conceptually, our approach to m odeling fu el economy can be depicted as follows:
Onroad fuel economy = W ar m ed up fuel economy (from Bags 2 and 3 of the FTP, HFET and US06 cycles), decreasedby the need to warm up a cold eng i ne (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 cold er tem p eratu r es (from the cold FTP). Actually, sin ce cold s t arts, air conditioning and colder temperatur es simply add fuel use over and above that needed for warm ed up driving, it is mo re straightforward to model these effects in term s of fuel consumption (e.g., gallons of fuel used per m ile) than in term s of fuel economy. In term s of fuel consum ption, the above equation looks like this:
Onroad fuel economy = 1/ Onroad fuel consumption
Onroad fuel consumption = War m ed up fuel consumption (from Bags 2 a nd 3 of the FTP, HFET and US06 cycles), plus the extra fuel associated with 1) warm ing up a cold engine (from Bags 1 and 3 of the FTP and the cold FTP), 2) use of the air cond itioner (from SC03), and 3) operation at colder temp eratures (from the cold FTP).
The rem a inder of this section is b r oken up into 6 p i eces. The f i rst, section III.A.1, develops a m ethodology for estim ating fuel use related to engine start-up, or start fuel use. The second, Section III.A.2, develops a m e thodology fo r estim ating fuel use once the engine is

35warm ed up at 75°F, assum i ng that the air conditione r is not turned on. The third, Section III.A.3, develops a m ethodology for estim ating fuel use due to air co ndition e r us e. The fourth, Section III.A.4, develops a m e thodology for estim ating fuel use once the engine is warm ed up at cold er temperatures. This fourth section also presents a com b ination of the results from the previous two sections into overall for mulae for running fuel use during city, highway and comp osite driving. Th e fifth, Section III.A.5, evaluates the impact of fa ctors that affect onro a d fuel economy but which are not addressed by any of th e five dynamom e ter cycles. The sixth, Section III.A.6, pres ents for final 5-cycle city and highway fuel econo my for mula e
. 1. Start Fuel Use
We estim ate the fuel needed to start and warm up the engine separately f r om fuel used to operate the engine after start- up, or running fuel use prim arily to be able to estim ate 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 em ission models, such as MOBILE6.2 and MOVES. We estim ate the volum e of fuel needed to start and warm up an engine first. W e then estim ate average trip len g ths for bothcity and highway driving. Finally, we com bine the two estim ates to develop a fo rmula 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 prim arily on two factors: 1) The am bient tem p erature at which the vehicle has been sitting, and 2) The length of tim e that the vehicle ha s been sitting since it w a s last used (comm only referred to as soak tim e).
Em issions during engine start up have been studied for som e tim e. 1 Most recen tly, estim ates of start fuel use as a function of a m bi ent temp erature were m a de for use in EPA’s new em ission inventory m odel, MOVES ( MO tor V ehicle E m i ssion inventory S ystem )

. d For MOVES, EPA analyzed start fuel us e from 580 gasoline fueled vehicles. 2 In this analysis, the start fuel use m easured was the d i fference betw een fuel use during Bag 1 of the FTP and Bag 3 of the FTP. The only difference between these tw o bags is the tim e prio r to th e tes tthat 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 referr ed to as a cold start. Prior to Bag 3, the engine has been off for only 10 m i nutes. 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 betw een start fuel use for a cold start an d a hot start is usually assum e d to be that of the cold start.
The resultin g rela tionsh i p between c o ld star t fuel use at o ther am bient temperatu r es rela tive to th at at 75°F (a typica l temp eratu r e f o r the standard FTP test) is as f o llows:

dA dr af t of M O V E S200 4 w a s r e leased for pu b lic co mm en ton D ec. 31 , 200 4.

36Equation 1 Cold Start Fuel Use Re lative to that at 75 F = 1

− 

## ( 0

. 01971 × 

## ( AmbientTem perature

− 75 

## ) ) + 

# ( 0

. 000219 × 

## ( AmbientTem perature

− 75 

## ) 2 

# )

At 75°F, the nom i nal te mp erature o f the FTP test, th is formula yields a value of 1.0. At 20°F, for exam ple, the tem p erature of the cold tem p erature FT P, it yields a value of 2.75. This m eans that the volum e of fuel needed to start and warm up an engine and drivetrain is 2.75 tim es as great after a 12 ho ur soak at 2 0 °F as it is at 75°F. These relationsh i ps assum e that the v e hicle had been sitting with the engine tu rned off for the sam e a m ount of tim e at each temperatu r e. It should be noted that none of the 580 vehi cles tested incorporat ed hybrid technology. Thus, the application of Equation 1 to hybrids involves greater uncertainty than for other gasolin e veh i cle s. This issue is addr e ssed in de tail in Sec tion III.E be low. EPA also analyzed s t art fuel use for diesel vehicles. Relevant da ta were only availab l e for 66 vehicles, or roughly one-tenth the num ber of gasoline vehicles. Based on these data, EPA found that start fuel use for diesels w a s roughl y 44% that of gasoline vehicles. Thus, the relationsh i p between cold start fuel u se at o t her a mbient tem p eratu r es rela tive to tha ta t75°F f o r diese l s is as f o llows: Equation 2 Diesel Cold Start Fuel Use Re lative to that at 75 F = 1

− 

## ( 0

. 00867 × 

## ( AmbientTem perature

− 75 

## ) ) + 

# ( 0

. 000096 × 

## ( AmbientTem perature

− 75 

## ) 2 

# )
For a typical diesel vehicle, start fuel use after a cold start at 20°F is only 1.77 tim es that at 75°F. Moving to the issue of soak tim e prior to engine start up, th e Draft MOVES2004 m o del does not yet include estim ates for the effect of soak tim e on start fuel use. Therefore, we obtained a relationship between st art fuel use and am bient temperature w h ich was developed by the California Air Resources Board for use intheir em ission inventory m odel, EMFAC2000. 3 These re lationships were based on the tes ting of 238 vehicle s. EPA utilizes the resu lts of this study in our current emission m odel, MOBILE6.2 , to estim a te the effect of soak tim e on regulated emissions (VOC, CO, NO X ) during start-up. The equation for fuel use versus soak tim e (in mi nutes) relative to the fuel us e after a 12 hour soak is as follows: For soaks of 90 minutes or less: Equation 3 StartFuelU se = 0

. 00433672 × SoakTime

− 0

. 000002393 × SoakTime 2
x

37For soaks greater than 90 minutes: Equation 4 StartFuelU se = 0

. 25889542 + 0

. 0014848 × SoakTime

− 0

. 0000006364 × SoakTime 2
x As is done in EMFAC2000 and MOBILE6.2, we assum e d that these relationships are independent of a m bient tem perature.
In order ob tain the combined effect of am bient tem p erature and soak time, we com b i n ed the above equations mu ltip licatively, as follows: For gasolin e vehic les : For soaks of 90 minutes or less: Equation 5

2 StartFuelU se x = 

# [ 0

. 00433672 × SoakTime

− 0

. 000002393 × SoakTime 

# ] ×
2 

# [ 1

− 0

. 01971 × 

## ( AmbientTem perature

− 75 

## ) + 0

. 000219 × 

## ( AmbientTem perature

− 75 

## ) 

# ] For soaks greater than 90 minutes: Equation 6

2 StartFuelU se = 

# [ 0

. 25889542 + 0

. 0014848 × SoakTime

− 0

. 0000006364 × SoakTime 

# ] × x 2 

# [ 1

− 0

. 01971 × 

## ( AmbientTem perature

− 75 

## ) + 0

. 000219 × 

## ( AmbientTem perature

− 75 

## ) 

# ]

For diesel vehicles: For soaks of 90 minutes or less: Equation 7

2 StartFuelU se x = 

# [ 0

. 00433672 × SoakTime

− 0

. 000002393 × SoakTime 

# ] ×
2 

# [ 1

− 0

. 00867 × 

## ( AmbientTem perature

− 75 

## ) + 0

. 000096 × 

## ( AmbientTem perature

− 75 

## ) 

# ] For soaks greater than 90 minutes: Equation 8

2 StartFuelU se x = 

# [ 0

. 25889542 + 0

. 0014848 × SoakTime

− 0

. 0000006364 × SoakTime 

# ] ×
2 

# [ 1

− 0

. 00867 × 

## ( AmbientTem perature

− 75 

## ) + 0

. 000096 × 

## ( AmbientTem perature

− 75 

## ) 

# ]

All of the ab ove equatio n s estim ate s t art fuel use in term s of t h e fraction o f start fuel u se following an overnight soak at 75°F, which are the conditions of the “cold start” contained in the FTP. W e will use these equation s to estim ate the rela tive star t f u el use f o r the rang e of starting conditions o ccurring th roughout the nation. W e will th en su m up these s t art fuel volum es and estim ate the average star t f u el use f o r an engine s t art in the U. S. Then, we will es tim ate the

38com b ination of start fuel volum es m e asured in the FTP tests perform e d at 20 and 75°F which is equal to the nationa l ave r age start fuel use.
The “hot” and “cold” s t arts con t ained in the s t an dard and co ld tem p erature FTP tests occur after 10 m i nute and 12 hour or longer hour soaks, respectively. W ith the hot start, the engine was fully warm ed up prior to the test and turned off for10 m i nutes prior to being turned on again at the start of the hot start portion of the FTP. W ith the cold start, the veh i cle has been sitting with the engine of f for at least 12 hour s in a room at the tem p erature specified. Equations 3 and 4 relating the effect of soak tim e on start fu el use indicate that the start fuel use after a 10 mi nute soak is only 4% of th at after a 12 h our soak. E quation 1 relating the effect of temperatu r e on start fuel use from gasoline vehicles indicates that start fuel use at 20°F is 2.75 tim es that at75°F

. Com b ining these ef f

ec t s, the s t art fuel use after a 10 mi nutes 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 ho t starts of both standard and cold tem p eratu r e FT P tests are relatively sm all com p ared to that o f a cold start at 75°F. The US06, SC03 and HFET tests begin with a hot start at 75°F , as is the case w ith Bag 3 of the FTP. Hereafte r, we ignore any start fuel use included in these thr ee te sts due to th eir ho t st art. (Bag 2 of the FTP does not begin with an engine start. The sam p ling equipm e n t sim p ly sw itches the em issions from Bag 1 to Bag 2 during a vehicle idle at se cond 505 of the test.)
In order to estim ate start fuel use thro ughout the U.S. under average am bient conditions, we need estim ates of the soak tim es for typical vehicle op eration, as well as the am bient tem p erature at start up. The am ount of tim e a ve hi cle has sat prior to start up varies dram atically depending on the tim e of day at wh ic h it is star te d. For example, for vehicles started up at 6 am , nearly all have sat overnight. However, for vehi cles started at noon, m ost have been driven in the past 4-5 hours. Am bient tem p eratures vary significantly during the day, so it is more accurate to evaluate start fuel use by hour of th e day rath er th an sim p ly at the daily av erage tem p erature. Am bient temperatures also vary dr am atically across th e U.S., as does the distribution of vehicle mile s trav eled (VMT). Therefore, we co m b ined estim ates of vehicle starts and prior soak tim es by hour of the day with estim a tes of a mbient tem p erature and VMT by county in order to reflect the effects of both soak tim e and ambient tem p erature on start fuel use.
W e obtainedestim ates of each of these i nput param eters from EPA’s MOBLE6.2 and MOVES2004 em ission models. The Draft MOVE S2004 model includes estim ates of a m bient tem p erature by hour of the day for each m onth of the year for each coun ty in the U.S. 4 These estim ates were obtainedfrom the National W eathe r Service and represen t 30-year av erages. The Draft MOVES2004 m o del also includes estim a tes of vehicle miles traveled (VMT) by vehicle type for every county in the U.S. during 2002. 5 W e assum ed that the d i stribution of engine starts across coun ties was the s am e as that for VMT (i.e. , that trip len g th was the sam e across the U.S.). W e used the se estim ates to dete rmine the per centa ge of total U

. S. VMT by cars (LDVs) and ligh t trucks (LDTs) occurring in each coun ty (excl udin g Puerto Rico and the Virgin Is lands ).
MOBILE6.2 includes estim a tes of the frequenc y distributions of vehicle soak tim es prior to vehicle start-up by tim e of day, as well as th e 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

39includes separate estim a tes for weekdays and week ends. Since we are prim arily concerned with fuel use on an annual average basis, we com bin ed the two sets of estim ates by weighting the percentage of vehicle starts occu rring after specific lengths of so ak 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. Distributio n of Starts by Hour of the Day (in percent) Hour of the Day W eekday W eekend 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 am ounts of VMT by m o nth of the year were taken from the Draft MOVE2004 model and are shown in Table III.A.-3 below. 7 MOVES includes estim a tes for both non leap years and leap years. W e averag ed the two esti mates in a 3:1 ratio to develop estim a tes for a typical year.

40Table III.A-3. Breakdow n of Annual VMT by M onth 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 Septem ber 0.0847 0.0845 0.0847 October 0.0865 0.0863 0.0865 Nove m ber 0.0802 0.0800 0.0802 Decem ber 0.0802 0.0800 0.0802
We obtained our estim a te of total V MT by LD Vs and LDTs by county from that used in the Draft 2002 Mobile N a tional Emission Inventory. 5 W e assum ed that trip length is independent of the season of the year.
We first estim ated the effect of soak tim e on start fuel use by hour of the day. The first step in this p r ocedur e was to estim ate the per centage of vehicle starts occu rring after s p ecific lengths of soak at each hour of the day. As is the case fo r vehicle starts by tim e of day, MOBILE6.2 includes estim a tes of soak tim es for weekdays and weekends. Again, we weighted the percentage of vehicle starts occu rring after specific lengths of soak at each hour o f the day for weekdays by five-sevenths a nd those for weekends by two-seve nths and added the two sets of percentages. MOBILE6.2 track s 69 distinct intervals of soak tim e for 14 “hours” of the day (starts between 9 pm and 5:59 am are com b ined into a single “hour”). Thus, the specific estim ates are too extensive for presentation here. However, to provide an indication of how soak tim es are distributed throughout the day, Table III. A-4 presents the distribution of starts by soak tim e for wee kdays for several aggreg ated soak tim e intervals

.

41Table III.A-4. Distributio n of St arts by Soak Time: Three Hours During Weekdays Soak Tim e ( 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 p r ocedure is to we ight the relative start fuel use for each soak tim e interva l by the per c entage of star ts occu rr ing af ter tha tra nge of soak tim es. The result is a percentage of an overnight soak equivalent for each hour of the day. These estim a tes are shown in the s econ dcolumn of Table III.A-5 for each h our of the day. For example, between6 and 7 a.m ., each start uses a vo lum e of fuel equivale nt to 68.3% of that associated with a start following anovernigh ts o ak. As can be seen, th ese estim a tes ranged from a low of 0.25 of an overnight soak at 2 pm to a high of 0.68 of an overnigh tsoak at 6 am

. This follows co mmon sense, as m o st vehicles b e ing started at 6 am in the m o rning have sat overnight, while m a ny vehicles being started in the mi ddle of the afternoon have been us ed in the past few minutes or hours. W e a ssum e that these estim ates are indep e ndent of temperature, b ecause the v ariation in tem p erature during any particular hou r of the day is rela tiv ely s m all.

42Table III.A-5. Estimation of Daily Average Overnight Soak Equivalent Hour of the Day Overnight S oak, 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 equi valents 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 colum n in Table III.A-5, the average start occu rring throughout the day uses a volum e of fuel equal to 33% of that following an overnight soak. In com p arison, the cold star t in the FTP has a weigh ting of 43%.
W h ile this analysis p r od uces a reaso n able es tim ate of the re la tive num ber of hot and cold start equivalents during real worl d driving, it ignores the effect of am bient tem p erature, which varies throughout the day, as we ll as between seasons. In order to estim ate start fuel use across the nation th roughout each day and throughout the y ear, we estim ated th e start fuel u se for each hour of the day by m ont h for each county in th e U. S. and then weighted each estim a te by the relative num ber of starts occurring in each hour of the day and by the relative am ount VMT in each m onth and county. Finally we summed the weighted start fuel use estim ates acro ss all hours of the days, m onths and counties to determine the average start fuel use in term s of a cold start at 75 o F. The national average start fuel use resulting from this process was 0.4665 of an overnight soak at 75 o Ffor gasoline vehicles and 0.4137 for diesels. W e can simu late these average start fuel use estim ates with a variety of com b inations of hot and cold starts at 20°F and 75°F. In order to select a single com b ina tion, we used the estim ate descri bed in Table III.A-5 above that the daily average start fuel use at a constant temper atu r e is 3 3 .0% of that of a cold start (i.e., th at following anovernigh ts o ak at th at tem p erature). Assumi ng that start fuel use after a h o t start

43(10 mi nute soak) is negligible and using equations (5) and (6 ) for relative start fuel us e as a function of ambient tem p eratu r e and s o ak tim e, we dete rm ined that a 24 % weighting o f a cold start at 20°Fand a 76% weighting of a cold s t art at 75°F, both mu ltiplied by 33%, has the sam e 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 th roughout the nation annually by summing the excess start fuel from the cold start for the F T P at 20°F tim e s 0.0904 (24% * 33.0%) and that for the cold start for the FTP at 75°F tim es 0.2394 (76% * 33.0%). For diesels, the approp riate weigh tfor the co ld s t art at 20°F is the sam e as that for gasolin e veh i cles, 24%. The weight is the s am e, because the shape of the curves of co ld start fuel use for both tem p erature and so ak tim e for diesel s is the sam e as that f o r gasolin e vehic l es. Thus, for diesels, start fuel use under national aver age conditions is the excess start f u el from the cold start for the FTP at 20°F tim es 0.0904 plus th e cold start for the FT P at 75°F tim e s 0.2394. As m e ntioned above, cold start fuel use is the diffe rence in total fuel use in Bags 1 and 3 of the FTP, either at 75°F or 2 0 °F. As also m e ntione dabove, we will eva l uate the app r op ria t eness of applying these weights to hybrid ve h i cles in Section III.E belo w. b. Trip Length The previou s section es tim ated start fuel use inte rm s of total f u el use per start. In this section, we address the frequency of starts per mile of typical on-road drivi ng. The inverse of starts per m iles is tr ip le ngth. The FTP im plicitly include s o n e engine s t art f o r eve r y 7.5 m iles of driving (i.e., an averag e trip le ngth of 7.5 m iles). This was the average tr ip length e stim ated f o r Los Angeles in 1969. We have updated this esti mate using several sources of infor m a tion. First, the Draft MOVES2004 m odel contains an estim ate of averag e trip length in-use. 8 These estim ates ar e sho w n in Table III.A-6.
Table III.A-6. Trip and Start Related Informatio n in Draft MOVES2004 Starts per D ay Miles p er Day Average Trip Length VMT: 2003 (billion m ile s) Vehicle Class W eekday W eekend Average Day Passenger C ars 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 estim ates of starts and miles per day are based on the results of instrum e nting 168 vehicles in Baltim ore and Spokane in 1992. The es tim ates of starts pe r day for an a v erage day were again determined by weighting the starts per day for weekdays by five-sevenths and for weekends by two-sevenths and summing. The e s tim ates of miles per day were also taken from the Draft MOVES2004 model. 9 Average trip len g ths (i. e., miles per star t) were dete rmined by dividing mi les per day f o r each veh i cle class by the num ber of starts per day for the average day.

44The num ber of miles per start ac ross all thr ee v eh i cle clas ses was determ ined by divid i ng total VMT by the total num b er of starts. From FHWA Highway Statistics 2003, total VMT for LDVs in 2003 was 1,661 billion miles. 1 0 Total VMT for 2-axle, 4 wheel trucks in 2003 was 998 billion miles. This latter ve hicle class includes trucks over 8500 pounds gross vehicle weight rating (GVWR), which are not incl uded 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. 1 1 Thus, LDT VMT in 2003 is estim a ted as 923 billion miles. Based on this sam e study, 72.8% of LDT VMT is by LDT1s and LDT2s and 27.2% by LDT3s and LDT4s. Using this break down, we estim ate 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 billionmiles. The total num ber of starts was estim ated b y dividing th e total VMT for each class by the num b er of miles per start for each class and thensumming. The result was one start for every 4.49 miles, or 0.223 starts per mile.
As m entioned above, the estim ates of starts and miles per day cam e from ve hicles operating in the Baltim ore and Spokane areas. Therefore, m o st of th is operation was likely urban (not to be confused with “city” when defining driving for fuel economy labeling purposes). These studies were perform e d along with several others in the early 1990's when EPA was de veloping the Supplem ental FTP rule , which developed and implem ented the US06 and SC03 test cycles. 1 2 Inaddition to Baltim ore and Spoka ne, vehicle ope rational inform ation was obtained in Atlanta and Los Angeles. The da ta obtained in Baltim ore and Spokane received prim ary focus, as the vehicles were recruite d f r o m centralize dinspec tion and m aintenance stations and the study involved instrum e nted vehicles. The data from At lanta involved instrum ented vehicles, b u t the veh i cles were re cruited v i a ph one contac t. W h ile the initial vehicle selection was random , the rate of people declin ing to participa t e was higher than at th e centralized inspection an d m a intena n ce sta tions. Thus, there is sligh tly m o re conce r n that the f i nal vehic l e sam p le in Atlan t a m ay not be as representative of onroa d driving as those in Baltim ore and Spokane. The findings from these studies are shown in Table III.A-7.
Table III.A-7. Estimates o f In-Use Average Trip Length Location Average Trip Length (miles ) Instrum e nted Vehicle Studies Chase Car Studies Baltim ore - Exeter 4.0 Not Availab l e Baltim ore - Rossville 5.9 Not Availab l e Baltim ore - Com b ined 4.9 7.5 Spokane 3.6 5.8 Atlanta 6.0 Not Availab l e Los Angeles Not Availab l e 7.8
As can be seen from Table III.A-7, th ere is som e variab ility in average tr ip length depending o n where peo p le res i de. People livin g in the c entral c ity s ection of Baltim ore and in the less populated city of Spokane take mu ch shorter trips than those living in the outskirts of Baltim ore and the m o re sprawling c i ty of Atlanta

.

45Table III.A-7 also shows estim ates of av erage trip lengths fro m chase car studies conducted at the sam e tim e as the instrum ented vehicle studies. W h ile chase car studies are ideally suited to study travel on selected roadways, their weakness is that they are not designed to f o llow a v ehicle f r om trip start to trip end. Thu s, they utiliz e other inf o rm ation in order to piece togeth er trip s t art and end di stances and thu s, trip leng th

. As can be seen fromTable III.A7, estim ates of average trip leng th from chase car studies tend to exceed th ose of instru m ented vehicle stud ies. The latter clearly y i eld a m o re accurate estim a te, due to th e instrum ent’s ability to accu rately and precis ely determine if the engine is runn in g or not. e However, consideration of the chas e car studies is u seful in that it brings in an additional city, Los Angeles. Based on the rela tive tr ip lengths f r om the ch as e car studies, it appears th at trips in Los Angeles ar e just slightly longer than in Baltim ore and probabl y sh orter than th ose in Atlanta. Thus, or urban/suburban dwellers, it appears that the average trip le ngth is som e where between 4 and 6 miles and could be close to 5 miles plus or mi nus a half m ile or so. This es tim ate inclu d es all trips by these people, those involving both city and highway driving.
No instrum e nted vehicle studies have been perform e d in rural areas. Chas e car s tudies have recently been perform e d in several rural Ca lifornia areas. However, chase car stu d ies do not obtain trip based inform ation, as they do not f o llow the s am e vehicle f r om start to stop. Still, it is lik ely th at trips by ru ral dweller s are long er th an those living in cities.
A second estim a te of average trip length in the U.S. is available from the National Household Travel Survey (NHTS). 1 3 The NHTS was performed in 2001 by the Departm e nt of Transportation and statistically surveyed approxim a tely 26,000 households in the U.S. This survey represents the sixth in a series of survey s dating back to 1969. (T he nam e of t he survey has changed a few tim es and the precise survey methods have varied to som e degree.) NHTS found that the average trip taken using a personal vehicl e in the U.S. was 9.8 miles long. W hile the average trip length w a s relatively constantfrom 1969 to 1990, it has increased roughly one mile since 1990. 1 4 Thus, the estim a tes shown in Table III.A-7 above could under-estim ate trip length today to som e degree. As noted in the NHTS, the 9.8 mile estim ate excludes very long trips, such as those taken on vacations , as well as comm ercial trips, such as those by tax i cabs, po lice officers, municipal workers, etc.. These excluded trips could be both shorter and longer than those covered by the survey. However, the n u m ber of commercial ve hicle op erators far exceeds those on vacation at any given tim e. Thus, t he exclusions likely bias the survey r esults upwar ds (i.e., resu lt in an over-es tim ation of nation al ave r age tr ip leng th for all light-duty vehicl es and trucks).
As the case with the instrum ented vehicle studies, the average trip length of 9.8 miles includes all driving, both city and highway oriented. NHTS does not a tte mp t to split driving into city and h i g h way catego r ies. Howev e r, unlike the instrum e nted vehicle studies, which studied the driving patterns of urban/s uburban dwellers, NHTS is intende d to cover the entire U.S.

e T h i s i s oft e n not t

h e case f o r

rece nt m odel hy b r i d ve hi cl es, w h ose e n gi ne s sh ut of f f

r e q u e nt l y du ri n g ci ty dri v i n g. H o we ver , as t h e e ngi ne i s us ual l y tu rne d of f fo r o n l y seco nds o rmi nut es, th e a d d e d fuel d u e t o th e percei veden gi ne of f i s ne gl i g i b l e b o th o n t h e r o adan d per ou r m odel i n g. Thi s i s s u e di d not a ffect t

h e s tudi es bei n g cited he re, a s t h ere we re no hybrids on t h e road in t h e early 1990' s.

46population, both urban and rural. Per the Fede ral Highway Admi nistration, 60-63% of car and light truck V MT is urban and subur ban and 37-40% is rura l. Assumi ng that urban dw ellers drive prim arily in urban areas and rura l dwellers do likewise, a mile 5 average trip length for 60-63% of

all VMT translat es into an 8 mile trip length f o r all driv ing, even if rura l dwelle rs ne ver shut off their eng i nes. In o th e r words, th ere appears to be an inco nsisten c y be tween the two sets of estim ates, with th e ins tru m ented vehicle s tudies y i eld i ng a sho r ter tr ip leng th than that which can be consistent with the estim ate from NHTS. An increase in av erage trip length of roughly one m ile since th e tim e of the instrum ented vehicle studies could explain part of this difference. However, som e difference still rem ains. Obviously , the av era g e tr ip leng th f o r rural dr iving is f i nite, not in f i nite. Based on the NHTS survey questionnaire, we be lieve that th e survey cou l d m i ss brief stops between the prim a r y trip origin ation and d e stination po ints (e.g., tho se at gas stations or convenience stores), as well as ex trem ely short trips (e.g., m oving a vehicle out of a driveway to allow another vehicle to exit, m oving from one parking place at a shopping center to another, or from one shopping center to another just across the street). Using trip infor m ation from instrum ente dvehicles inBaltim ore and Spokane (d escribed in m o re detail below), ab out 28% of all trip s f all into one of these two ca tegories. In the NPRM, we assum e d that the NHTS survey mi ssed 28% of the trips, reducing the average tr ip length 7.66 m iles (9.8 miles divided by 1.28).
Since the tim e of the NPRM, EPA obtained m o re recent data on the operation of vehicles in Atlan t a from Georgia Tech. 1 5 Georgia Tech has been ins tru m enting vehicles in the Atlanta area for som e tim e and gathering operational data. As with the previous instrum e nted vehicle studies, this type of study captu res all vehicle trips no m a tter how short. The m a jor difference between th is m o re recen t work and th e previous studies is the am ount of data being collected. To date, Georgia Tech has collected data on over 620,000 vehicle trips. In contrast, the previous instrum e nted vehicle studies involved about a week’s worth of operation f or less than a hundred vehicles per city. Thus, the recen t data represen ts alm o st two orders of m a gnitude m o re data than was evaluated in all of the instrum e nted vehicle studies described in Table III.A-7 above, albeit from one urban area and one of the m o st s p rawling at that. The average trip length f ound for these 620,000 trips around Atlanta was 7.25 miles. 1 6 The average num ber of trips per day was 4.62. Th is m eans that the v ehicles surveyed in Atlan t a drove 33 miles per day, which is very close to th e nation al av erage of 34 miles per day (per MOVES/MOBILE6.2). This is 1.2 miles, or 20% grea ter th an the ave r a g e tr ip leng th 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 nationa lly of about 1.0 m ile between 1990 and 2001. 1 7 Thus, it is likely that th e averag e trip length in the other ci ties shown in Table III.A- 7 have also increas ed, as well, since the early 1990’s when the data was collected. If the average trip length for urban driving was slightly m o re than 5 miles in the ear ly 1990’s, then it is likely m o re than 6 miles today.
A com p arison of the trip lengt hs resulting from the instrum e nted vehicle studies and the chase car stu d ies shown in Table I II. A-7 indicates that the latter cl early o v er-es tim ate trip length. This is likely because ch ase car stud ies do not focus on entire vehicle trip s, but on veh i cle operation along a specified highway segm ent. Ho wever, there is considerable consistency

47between the relative trip lengths in Baltim ore and Spokane base d on the instrum e nted vehicle data and the chase car data. Extr apolating this to Los Angeles , average trip length in Los Angeles app ears to b e slightly long er than that in Baltim ore and considerab le longe r th an that in Spokane. This is not unexpected, given the relative sizes of these cities and their geographical constra i nts. Includ ing th e Atlanta an d im plied Los Angeles e stim ates with those f r om Baltim ore and Spokane would produce an aver age trip length for urban drivi ng of considerably more than 4.49 miles. An overall estim ate closer to 5.0 miles would ap pear to b e reasonable for the early 1990’s tim efram e , given Spokane’s relatively un ique com bination of size and geographical constraints. Given the gr owth in aver age trip leng th indicated by the two A tlanta studies and the series of NHTS, we estim a te that av erage u rban tr ip length today is roughl y 6.2 miles. Assumi ng an average trip length for urban driving of 6.2 mi les, an urban VMT fraction of 0.61, and an overall national average trip length of 9.8 mile s from NHTS, the average trip length of highway driving is 13 5 m iles. W h ile no t im possible, this is still likely too high. T hus, it s t ill appears like l y thatth e NHTS is mi ssing trip s of very short length or com b ining trips with v ery short engine off tim e s, though not to the degree assu med in the NP RM analysis. Assumi ng that the NHTS mi sses 11% of all trips for th ese reasons re duces th e average trip length for all driving to 8.7 miles and that for rural drivi ng to about 24 miles. This 24 mile figure for an average rural trip is mu ch more reason able th an 13 5 miles. Th erefore, we will upda te our estim a te the national average tr ip length to be 8.7 m iles, f r o m th e estim a te of 7.7 miles in the NPRM.
This estim ate includ es all driv ing, bo th city and highway oriented. Start fuel use must be split b e twee n city and h i ghway driving. Ne ither MOBILE6.2 nor Draft M OVES2004 include s a direc tes tim ate of the sp lit b etween c ity and h i gh way driving as def i ned f o r f u el econ o my labeling pur poses. However, such a split can be derived f r o m the drivin g patte rns co ntained in Draft MOVES2004. This deri vation is described next. Table III.A-8 presents the 14 driving cycles which are used in Draft MOVES2004 to estim ate on-road emissio n s, along with each cy cle’s average speed. 1 8 W e ran the Draft MOVES2004 m odel for the entire nation using n ationa l def ault inpu ts an d determ ined the percentage of driving time which LDVs and LDTs spend in each type of driving (i.e., driving cycle). These percentag e s are show n in the thir d column of Table I II.A- 8.

48Table III.A-8. Inventory Drivin g Cycles in Draft MOVES2004 Cycle Avg. Speed (m ph) Tim e Spent During On-Road Driving Assum ed 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 Fre e way 13.1 0.1% 100 LOS F Freeway 18.6 0.5% 100 LOS E Freeway 30.5 24.7% 100 LOS D Fre e way 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 cycl e to either city or highway driving, based on its av erag e speed. If the cycle’s average sp eed was less than 45 m ph, we assum e d that it repres ented city dr iving. If the cycle’s average s p eed was greater than 4 5 m ph, we assum ed that it represented highway driving. The only ex ception was driving on freeway ram ps. Since freeway driving occurs in both ci ty and highway modes, we assum e d that driving on ram ps also occurs in bo th m odes. No inform ation exis ts con cerning the use of ram p s to access freeways with respect the average speed of the freeway driv ing at the tim e. Therefore, we assigned ram p driving to city and highway driving sim p ly in pr oportion to the percentage of tim e spent in city freeway driving and highway freeway driving. T h erefore, th e city percen tage of ram p driving was the driving percentages for the L O S E, F, and G freeway cycles (25.3 % ) div i ded by the driving percentages for all eight free way cycles (56.3%), or 44.9%. In order to calculate the pe rcentage of VMT occurring duri ng city and highway driving, we mu ltiplie dthe ave r ag e speed of e ach cycle by its driving tim e percentage. W e then sum m ed this product of speed and percentage tim e acro ss all fourteen cycles ( 31.77 m ph). W e then multiplied e ach cycle’s product of s p eed and per centage tim e by its c ity p ercen tage an d summed again (13.51 m ph). The percentage of VMT occu rring as city driving is th e ratio of the se two num bers (13.51/31.77), or 42.6%. We repeated th is procedure using the percentage of highway driving (equal to 100% mi nus the percentage of city driving). The sum of the product of speed, percentage tim e and highway percentage was 18.26 m ph. Thus, the percentage of highway driving is the ratio of 18.26 to 31.77, or 57.4% of VMT. This city/hig hway VMT split of 43/5 7 is quite dif f

erent f r om the curr ent 5 5 /45 split. Som e analysts have suggested th at the 55/45 split should be upda ted to reflect an even higher city fraction, such as 60/40 to 63/37. The curre nt 55/45 split and the s uggested updates are based

49on FHWA e s tim ates of the urban and rural fractions of natio nal VMT. This assum es that c ity driving is equivalent to urban dr iving and highway driving is equiva lent to rural driving. There is som e m e rit to this approach for the current city and highway label fuel economy estim ates, as the original Los Angeles Road Route No. 4 (the ba sis f o r the LA-4 drivin g cycle inclu d ed in the FTP) includ es som e freeway driving which reach es 55 m ph. Most of the driving us edto develop the HFET occurred on uncongested rural highways. Generally, we can expect trips dominated by low speed driving to usually involve m ore starts per m ile than h i gh speed tr ips. The curre nt highway driving cycle, the HFET, c ontains no cold starts (i.e., infinite trip length). Of course, the 22% adjust m e nt factor applied to the HFET fuel economy to calculate the highway fuel econo my label value could account for some cold starts, but n o specific trip lengt h is s p ecified. Th us, for sim p l i city purpos es, we assig n ed an average trip length of 60 miles to highway drivin g. This is approxim a te. However, once trip length is over 30-40 miles, start fuel use has a very sm all effe ct on fuel economy. Still, a finite trip length f or highway driving he lps the public to relate to th e way highway fuel economy is estim ated. Back calcu lating us ing the city and highway VMT split of 43/57 and a n overall trip length of 8.7 m iles, the a v erage trip length for city driving is 4.1 miles. f This estim ate is 17% longer than the 3.5 mile city trip length estim a ted in the NPRM. By itself, this increase intrip length increases 5-cycl e city fuel economy for the 615 vehicles in our certification database by 1%. Highway fuel economy rem a ins unchanged, since the ave r age trip length r e m ains at 60 miles. However, th is higher 5-cy cle city f u el e c onomy will indirectly cause the non-dynam o meter adjustm e nt factor to be increas ed by roughly 0.5% (see Section III. A.5). This factor applies to both c ity and highway fuel econo m y. Thus, the net effect of increasing the average trip length of city drivi ng 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 usef ul later in this sec tio n, us ing the inf o rm ation shown in Table II I.A-8, we calcu lated th e averag e sp eed of city and highway dr iving. Th e averag e sp eed of city dr iving is the sum of t he product of speed, percentage tim e and city driving percentage (13.51 mph), divided by the total percentage of city driving (68.0%), or 19.9 m ph. The average speed of highway driving is the sum of the product of speed, percentage tim e and highway driving percentage (18.26 m ph), divided by the tota l per c entage of city driving (32.0%), or 57.1 m ph. The average speed of all driving repres ented in Draft MOVES2004 is 31.8 m ph.

c. Formula for Start Fuel Use The total fuel usage in either Bag 1 or Ba g 3 can be determined by dividing the num be r of miles of driving during thes e portions of the test (3.59 mile s for either bag) by the fuel economy measured during that bag. Thus, the equati on for start fuel use at either 20°F or 75°F is as f o llows:

f Gi v en 100 mi les o f to tal VM T, 4 2 .6 mi les co n s ists of city d r i v ing an d 57 .4 miles o f h i g h way driv i n g. At 3

. 5 miles p e r city trip , th is m ean s 12 .1 4 city trip s. At 60 mi l e s per hi gh way tri p , t h i s m e ans 0. 96 hi g h way tri p s. Th e t o tal nu m b er o f t

r ip s is therefo r e 13 .1 , fo r an ov erall trip leng th of 7

. 66 miles (1 00 /13

. 1 )

.

50

### ⎛ 1 1 ⎞ StartFuel = 3

. 6 ×

− x 

### ⎜ ⎜ ⎝ Bag 1 FE x 

### Bag 3 FE x 

### ⎟ ⎟ ⎠

where x is either 20°F or 75°F. The equatio n f o r start f u el use in te r m s 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 × StartFuel 75 

## ) + 

## ( 0

. 24 × StartFuel 20 

## ) ⎞ ⎟ ⎝ 60 ⎠

2. Running F u el Use at 75°F Without Air Conditioning Running fuel use depends prim arily on how the vehicle is driven, particularly the distribution of speed and power. In this section, we develop a descriptio n of onroad driving from the Draft MOVES2004 model and th en represent this onroad driving using the available dynam o meter tests.

a. On-Road Driving Patterns On-road driving patterns have been studied for the purpose o f emissions since at least the late 1960's. The driving cycle contained in th e F T P, 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 instrum e nted vehicles opera ted over a rural road route outside of Ann Arbor, Michigan in the late 1970's. The US06 and SC03 cycles, am ong others, were developed in the early 1990's to augm ent the L A -4 and HFET cycles. These cycles were based on instrum e nte dvehicles monitored during norm a l operation in Baltim ore, Spokane and Atlanta, which were already discussed in the section on start fuel use above.
Based on th e driv ing data obtain e d in Baltim ore, Spokane and Atlanta, E P A developed three cycles which together did a reasonable job of representing the com p lete b r ead th of urban driving: SC03, REP05, and REM01. The SC03 cy cle represented driving imm e diately following engine start-up. REP05 represented high speed a nd 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 econo mi es m easured over these three cycles. However, tests o v er th e REP 05 and REM01 cycles are not regularly perform e d during certification. Perform i ng these tests would entail additiona l tes tin g costs. The SC03 test is only perform e d with the air conditioning on, so it too w ould need to be re-perform ed with the air conditioning turned off. We are prim arily interested here in approaches to estim ating

51on-road fuel economy us ing the current dynam o me ter tests. T hus, using REP05, SC03 and REM01 to project onroad fuel economy is not a practical option. The US06 cycle was developed as a concentrat ed version of the REP05 cycle. Thus, it can conceivably be used in lieu of REP05. Th e REM01 cycle is similar to the LA-4 cycle which com prises the driving in the FTP. SC03 is only perform e d with the air conditioning turned on. Thus, it cannot be used to estim ate fuel use without air conditioning. SC 03 includes higher rates of acceleration than the FTP. However, the US0 6 cycle inclu d es som e low speed driv ing with higher acceleration rates

. Thus, with correct weighting, th e US 06 test an d the warm ed up portion of the FTP test should be able to represent much or m o st of t h e drivi ng contained in the SC03, REP05 and REM01 cycles.
Our recen t testing of driving in Kans as C ity ind i cates the need to include the US06 driving pattern in our estim ation of fuel economy. This test pr ogram and our processing of the data co llec t e dare des crib ed in detail in A ppendix A. W e grouped the veh i cle operatio n monitored in Kansas City into com b inations of v ehicle speedand acceleration and co mpared it to similar comb inations re presented in the FTP, HF ET and US06 cycles. Th e bread th or envelope of operation over the FT P and HFET cycles is sh own as the innerm ost area (co l ored in dark blue in the follow i ng figure). The envelope of vehi cle operation monitored in Kansas City which exceeds that of the FTP and HFET is shown in purpl e. Fin a lly, the env e lope of the US06 cycle is shown where it exceed s that of the ve hicle operation m onitored in Kansas City. Figure III-1
. Speed-Acceleration Frequency Dist ribution: Kansas City Vs. Test Cycles

A CCEL SPEED BIN

BI N (m p h ) (m ph / s ) 0 5 10 15 20 25 30 3 5 40 45 50 55 60 65 70 75 80 85 90 14 13 KE Y 12 Dri v i ng f r eq ue ncy cov ered b y FTP/HF E T sty l e dri v i n g 11 Dri v i n g f r eq ue ncycov e red b y Kan s a s City Real- W orl d Dri v i n g 10 Dri v i n g f r eq ue ncycov e red b y US06 sty l e dri v i n g 9 8 7 US 06 6 5 KC 4 3 2 1 0 FT P / H FET KC -1 -2 -3 -4 KC -5 -6 U S06 KC acti v i ty cut of f < 0.1% -7 -8 1 8% of KC d r iv ing outsi de FTP/HFET dri v i ng en v e l op e -9 -10 0

. 6 % o f KC dri v i n g i s ou tsi d e US06 dri v i ng en v e l o p e -11 9 0% of US06 dri v i ng i s w i thi n the 0.1% KC bou nda ry Overall, 18 % of the onr oad driv ing activ ity (tim e based) in Kansas C ity f ell outs i de of the FTP/HFET envelope. T his corresponds to 33% in VMT terms. As can be seen, m ost of this operation which exceed s the FTP/HFET envelope has either a higher rate of acceleration or

52higher vehicle speed. H owever, only 0.6% of the Kansas City operation fell outside the US06 (0.4% of the VMT).
The SAFD e nvelopes for hybrid and conventio nal vehicles did not differ significantly from each other. However, the p erce n tages of driving in th e v arious b i ns d i d vary, as w ill becom e m o r e eviden t below when we evaluate th e VSP freque ncy distribu tions for the two types of vehicles.
Recent chas e car s tudies of driving in Califor nia show even more operation outside o f the FTP/HFET envelope. (The detail s of these tes tprogram s are al so discussed in Appendix A.) The com binations of speed and acceleration fo r the m onitored California urban driv in g are shown in the following figure. Figure III-2
. Speed-Acceleration Frequency Di stribution: U r ban Califo r nia Vs. Test Cycles A CCEL SP EE D B I N

BIN (m ph ) (m ph /s) 0 5 10 15 20 25 30 35 40 45 50 55 60 6 5 70 75 8 0 85 90

14 13 KEY 12 D r i v i ng fr eq uenc y c ov er e d byF TP/ H F ET s t y l e dr i v i ng 11 D r i v i ng fr eq uenc yc ov er e d by r eal

- w or l d C al i f o r ni a u r ban s t y l e dr i v i n g 10 D r i v i ng fr eq uenc yc ov er e d by U S 06 9 D r i v i ng fr eq uenc y c ov er e d byr eal

- w or l d C A b ut N O T U S06 8 7 US 06 6 5 CA Urb a n 4 3 2 1 0 FT P / H FT P/ HFET CA Urb an -1 -2 -3 -4 -5 CA Urba n -6 -7 CA a ctiv i tyc u t o f f < 0

. 1% -8 -9 20 % of C A U R BA N dr i v i ng i s ou ts i de F TP/ H F ET d r i v i ng env el ope -10 1. 2% o f C A U R BAN dr i v i ng i s out s i de U S06 d r i v i n g env el ope -11 41 % of C A R U R AL dr i v i ng i s out s i de F T P/ H F ET dr i v i ng env el o pe -12 3% of C A R U R AL dr i v i ng i s out s i de U S0 6 d r i v i n g env el ope

As can be seen, the breadth of Califor nia urb a n dr iving whichexceeds the FTP/HFET envelope is much m ore expansive than that foun d in Kansas City. Onroad driving includes much higher speed s and both h i gher and lo wer rates of acceleration. Overall, 20 % of California urban driving lies outside the FTP/HFET envelope (34% of the VMT). Just o v er 1% fell outside the US06 envelope (1.3% of the VMT). Rural driving was found to be m o re aggressive than urban driving. W h ile not shown in the above figure, 3% of the rural driving w a s outside the US06 envelope.
Both the MOBILE6.2 a nd Draft MOVES2004 models contain estim ates of on-road driving. MOBILE6.2 describes driving in a m o re traditional fashion by assigning V MT to

53various dr iv ing cycles, like those listed in Table III.A-8 above. This is very acceptable for estim ating emission inventories. However, it is not straightforward to convert driving over such cycles to driving over the five available dynam o m e ter cycles or bags. Average speed and power are availab l e for each cy cle. However, with on ly two degrees of freedom , one cannot determ ine weightings f o r five dynamom e ter cycles. A m o r e detailed distribution of speed and power is necessary. This brings us to the approach ta ken in Draft MOVES2004, which is m o re am enable to the task faced here.
Draft MOVES2004 takes a mu ch different appr oach to describing on-road driving than previous EP A e mission inventory m odels, for ex ample, MOBILE6.2. W hi l e starting with whole driving cycles, like those listed in T able III.A-8 above, it goes further by breaking down vehicle operation on a second by second basis into 17 cate gories or bins. One bin (Bin 0) contains significan t d ecelerations. Another bin (Bin 1) co ntains idling operation. The other 15 bins contain b r ief or m odest decelerations, cruis i ng op eration and acceleration s
. The 15 bins are broken down into three sets of bins by vehicle speed: Bins 11- 16 contain operation at 1-25 m ph, Bins 21-26 contain operation at 25- 50 m ph, Bins 33-36 contain ope ration at 51 m ph or faster. These three sets of bins are fu rther sub-divided according to th e power required of the engine divided by vehicle m a ss. This ratio is term ed vehicle specific power, or VSP, and has the units of kilowatt p er m egagram (kW /Mg). The VSP bins are d esc ribed in Tab l e III.A-9.
Table III.A-9. VSP-Speed Bins in Draft 2004MOVES Bin Label Vehicle Speed (m ph) 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

54The three bins with open ended power levels (bins 16, 26, and 36) have lower limits of 12 kW /Mg. Power during onroad driving can excee d 50 kW /Mg. Nearly 35% of the US06 cycle f

alls in to bin36. Theref ore, it is us e f ul to split these bins up f u rther in to sm aller power increm ents. This is be in g consider edf o r 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 rang es stay the sam e
. Howe ver, instead of bin x6 (i.e., 16, 26, and 36) inc l uding all po wer leve ls above 12 kW/Mg, bin x6, x7, and x8 will a ll include a ra nge in power of 3 kW /Mg, while bin x9 will be o p en ended. The expand ed 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 (m ph) 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

55Now that th e VSP concept has been introduced , it will be e a sier to unde rstand how Draft MOVES2004 describ e s on-road driv ing, which is accom p lished in th ree steps. First, a VSP frequency distribu tion is developed f o r each of th e fourteen in ventory cy cles lis ted in Table III.A-8 abov e. Most of these inven t o r y cycles we re deve lope d from the on-road driving data obtained in Baltim ore, Spokane and Atlanta in th e early 1980 ' s. The three highes tsp eed freeway cycles h a ve been added more recen tly to represen t driv ing at speeds high er than those typical of this e arlier tim e f r am e, since th e h i gh est speed lim it during the tim e of these studies was 55 m ph. Each VSP distribution show s the perc entage of tim e spent driv ing in each of the 26 VSP bins. Different vehicle s will p r oduce sligh tly different VSP distrib utions. Ho wever, because VSP is defined as the ratio of required power to vehicl e weight, the differences in VSP across various vehicles are relatively sm all for a given dr iving pattern. Draft MOVES2004 includes VSP distributions for typical LDVs and LDTs. W e ha ve com bined these two sets of VSP distributions using a m i x 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 se rv ice, or vo lu m e of traffic. LOS A involves the least volum e of tra ffic. LOS G is the m o st congested.)

Table III.A-11. VSP Frequency Distributions for Onroad driving Cycles in MOVES VSP Bin Low Speed New Yo rk City LOS EF Non

- Fr ee way LOS CD Non

- Fr ee way LOS AB Non

- Fr ee way LOS G Free way LOS F Free way 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%

56Table III.A-12. VSP Frequency Distributions for Onroad driving Cycles in MOVES
VSP Bin LOS E Free way LOS D Free way LOS AC Free way High Spe ed Free way 1 High Spe ed Free way 2 High Spe ed Free way 3 Free way 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 segm ent for the area of interest are developed. In urban areas, thes e es tim ates come from travel dem and models for five specific urban areas (Ada County, ID, Charlotte, NC, Chicago, IL, H ouston, TX, and New York, NY). 1 9 Travel in other urban areas is assigned to one of the five m o deled areas. In rural areas, these estim ates co m e from chase car d a ta recently obtained in California. Based on average speed and road way type, travel is ass i g n ed to two o f the f ourtee n inventory cycles (listed in Table III. A-8 above). The two cycles select edare thos e which have av erage sp eeds which most closely m a tch the average sp eed of that road way type during that hour of the day. Cycles are chosen which also represent driving on th e sam e roadway type, if th ey are av ailable. g The weighting of the two cycles are determined so that the av erage speed in-u s e is m a tched exactly. Then, the VSP distribu tions of these two cycles are com b ined using thes e sam e weightings. Third, these com binations of VSP dis tributions fo r the various cycles are aggregated o v er the geograp hical and temporal in tervals of interest, weigh tin g each by th e am ount of driving

g S o m e tim es, hi ghe r s p eedd ri v i n g on non-freeways in rural areas e x cee ds the ave r a g e s p eed of the available non-freeway dri v ing cycles. In t h is case, free w ay cycles are used.

57occurring ov er each road way segm ent and tim e in terval. Here, we are con cerned with national, annual average driving. Thus, Dr aft MOVES2004 was run for the nation as a whole for an entire year and the distribution of dr i v i ng ove r th e var i ous i nventory cycles was output. Table III.A-13 shows the resulting distributi on of on-road driving into the 14 inventory cycles. Th ree dis tribu tions are sho w n. The firs t distribu tio n repres ents all U.S. driv ing, which was the direct output from Draft MOVES2004. The second represents city driving. In this case, the all U.S. driving p erc entages we r e mu ltiplied by the city p ercen tages s hown in Table II I.A-8 above and norm alized to sum to 100%. The th ird shows the distributi on for highway driving, using 100% m i nus the city percentages shown in Ta ble III.A-8 above. Also shown is the average speed for each cycle. W e ighti ng the average speed of each cycl e by its p ercen tage of driving tim e also yields the average speed of that type of dr iving (e.g., city ). These speeds are s hown in the la st row of the table.
Table III.A-13. Distributio n of Onroa d Driving Patterns: Draft MOVES2004 Inventory Cycle Average Speed (m ph) Distribution of Driving Tim e All Driving C ity 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 Fre e way 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 Fre e way 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 (m ph) --- 19.9 57.1 31.8 The average speed of city driving, 1 9.9 m ph, is just slightly higher than that of the F T P, 19.6 m ph. The average speed of highway drivi ng is well above that of both the HFET and US06 cycles, bu t s lightly belo w the hi ghway portion of the US06 cycle. As described in section III.A.1 above, acco rding to th is m e thodology, the percentage of national VMT that is like city driv ing is 42.6% and that which is like h i ghway driving is 57.4%. As also m entioned in sec tio n III.A.1 above, we evaluate two alternatives which a ssign portions of the driving over the LO S D Freeway to city dr iving in sec tion I I I .E.2 below. These two options in cre ase the city percen tage of national VMT to 50% and 55%, respectively.

58W e then weighted the V SP distribu tions of each inventory cy cle (from Table III.A-11 and III.A-12 ) by the percenta ge of drivin g repres en te d by each cy cle (from Table III.A-13). This produced VSP distributions for all U .S. driving, city driving and highway driving. T h ese three VSP distrib u tions ar e sh own 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 repr es ent national average city and highway driving in the U.S. The next step is to determine what com b inati on of the five dynam o meter cycles best m a tches each VSP distribu tion. The following figure co mpares the VSP distri bution of vehicle operation m onitored in Kansas City to that in Draft MOVES2004.

59Figure III-3
. Kansas City and California VSP Frequency Distributio ns vs. MOVES

0 0.0 2 0.0 4 0.0 6 0.0 8

0.1 0.1 2 0.1 4 0.1 6Freq K C F r eq Me asu r e d MO VE S C al i f or n i a U r b an

0 1 11 1 2 13 14 15 16 17 18 19 2 1 2 2 2 3 2 4 2 5 26 27 28 29 33 35 36 37 38 3 9 V SP Bi n

The m a tch in trends is reasonable, though Draf t MOVES2004 projects roughly 4% more activity in the high speed, high power Bins 36-39 and 4% less ac tiv ity in the lower power Bin 35. This figure also shows the VSP distribution of the chase car data study conducted in L os Angeles in 2000 in this figure. The portion of driving found the Bins 36-39 in Los Angeles m a tches that in MOVES2004, though there is about1% less operation in Bi ns 26-29. One must be cautious when com p aring activ ity data from instrum ented vehicles andchase cars. Chase cars do not follow drivers throughout thei r entire trip, so operation on local neighbor roads is often m i ssed. Al so, the VSP distribution from Dr aft MOVES2004 includes both urban and rural operation, while those of Kansas City and Lo s Angeles are prim arily urban. This could introdu ce so m e differences, as well. The following figure shows the VSP frequency distributions for three groups of Kansas City vehicles: all vehicles, hybrids and non-hybrids. W e rem o ved idle (bin 0) prior to calculating the VSP distributions for two reasons. One, idle percen tages were extremely high andif depicted in the figure, would have made the rest of the bars difficult to read. Second, given that little fuel is consum ed during idle, including th e percen tage of tim e at idle dis t orts th e com parison of those VSP bins where fuel consu mption is m o re sign if ica n t.

60Figure III-4
. VSP Frequency Distributions in Kansas City: Hybrids vs. Non-Hybrids

0 0.0 2 0.0 4 0.0 6 0.0 8

0.1 0.1 2 0.1 4 0.1 6 0.1 8

0.2

0 1 11 1 2 13 14 15 16 17 18 19 2 1 2 2 2 3 2 4 2 5 26 27 28 29 33 35 36 37 38 3 9 V SP Bi nFreq Total Act iv i ty No n-Hy b r id Hy brid

As can be seen from a close look at the ope ration in the higher power bins (x6-x9), the driving of hybrids tends to be le ss ag gressiv e than that of con v en tional vehicles. The percentage of tim e spent driving in bins 26-29 and 36-39 is 5-6% lower for hybrids than conventional vehicles. T his is of interest, as this study is likely to be the fi rst to examine the relative operation of conventional and hybrid vehicles. If there wa s som e thing about hybrid vehicles which always led them to be opera ted less agg r ess i vely, th is m i ght need to be considered in developing their fuel economy label value. The possible explanation s for this dif f

erence are discussed in Appendi x A. Most of th e possible explanations im ply that th e difference could dimi nish or disappe ar in the f u ture. 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 uncerta inty in the difference found in the operatio n of conventional and hy brid veh i cles. As discus sed in Sectio n III.A, we are in the process of obtaining a large volum e of operationa l data obtained in Atlanta. Som e hybrid vehicles m a y be included in this database. W e plan to com p are th e operation of con v entional and hybrid vehicles in that study as soon as we receive the data. S till, the potential impact of the difference indriving b eh avior for th e two types o f vehicles is exam ined in Section III. E.4.
b. Representative Mix of Dynam o meter Driving Cycles The five current certification dynam o m e ter test s include four distinctdriving cycles, or patterns of driving. In addition, the FTP includes two distinct dr iving patterns, as emissions over the FTP is u sually collec ted in two se parate “bag s ”

. Two basic characteri s tics of the se driving patterns are depicted in Table I II.A-15: average speed and a basi c m easure of the average power required by the engine.

61Table III.A-15. Driving Characteristic s of the Current Dynamometer T e sts Cycle Average Speed (m ph) Average Power* (m ph 2 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 Tem pe r ature FTP (sam e driving cycle as FTP) 19.6 40.9

* Power d e fin e d as v e l o city ti mes th e ch ange in v e lo city p e rseco nd du r i ng cru ise oracceler atio n s
. Pow e r is set equal t o ze ro duri ng decele r ati ons and not conside r ed in t h e determination of ave r age power. The FTP and the cold temperature F T P both inv o lve the sam e driv ing cy cle, ju st a t different ambient tem p eratures. Thu s, their aver age speeds and power are identica l, both f o r the total cycle and for each bag of e missions m easured. The FTP and SC03 involve d i stinct, but similar driv ing cycles. Both are lo w speed cycles hav i ng similar averag e speeds andpower levels. As the SC03 test is only run with the air conditioning on and all the other tests are run with air con d ition i ng of f, it is no t po ssible to isol ate th e ef f

ect of the driv ing cycle d i f f

erence s between the FTP and SC03 tests directly. Thus, this le aves f i ve distinct dr iving p atterns which can be used to represen t typical U.S. driving : Bag 2 of the FTP, Bag 3 of t he 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, bu t their average pow er requ irem ents dif f

er by a f actor of 1.7. As will be seen be lo w, it m ay be usef ul to consider eac h bag separ a tely in simu lati ng typical city and highway driving. The current US06 test currently consists of 600 seconds of driving and the emissions are colle cted in one bag (i. e. , one single colle ction of pollutants e m itted durin g the te st). Thus, the fuel economy is m easured over the e n tire cycle. The US06 driving cycle consists of 5 hills, o r 5 driving segm ents which begin and e nd with the vehicle at id le. h The firs t hill of the cycle peak s at 44.2 m ph, while the la st thr ee hills peak at 28.5, 28.0 and 51.6 m ph. Al l of these hills are also rela tive l y sh ort in du ration. These hills are indic a tive of city like dr iving. The secondand third hills p eak a t70.6 and 80.3 m ph, which are m o re indicative of highway driving. The s econd hill is relatively short (roughly 90 s econds), while the third hill co mprises m o s tof the US06 test (roughly 360 seconds).

hA “h ill” wit h in a driv ing cycle is a seg m en t of d r iv i n g wh i c h starts and fin i sh es with th e v e h i cle at rest (zero spee d). The term hill com e s from the view of a dri v ing trace where vehicle spee d i s pl otted versus ti m e
. As th e v e h i cle accelerates, its sp eed in creases, cau s ing t h is trace to clim b u p a h ill. Th en as th e v e h i cle d e celerates, it p r o ceed s d o wn th e h ill.

62As discussed in the preamble to the propos ed rule, we are proposing that two separate emission m easurem ents be m ade during the US06 cy cle to be tte r estim ate city and h i g h way f u el economy. To best dis tin guish betwe en city and hi ghway lik e driv ing, we would gro up hills 1, 4, and 5 into a city bag andhills 2 and 3 into a h i gh way bag. However, the tota l leng th of tim e of hill 1 is quite short, on ly 45 seconds. In som e cas es, em issions can be co llec t ed in to a bag at the beginning of a tes t(i. e., hill 1 ) , em issions then c o llec t ed in to a second ba g (i.e., h ills 2 and 3), and then emissions collected into the first bag agai n (i.e., hills 4 and 5). In this case, there should be sufficient emission sample in eachof the two bags to m easure accu rately with tod a y’s equipm ent. However, in som e cases, a m anufacturer mi ght have to co llect the s am p le in three bags, with the f i rst bag only conta i n i ng the emissions f r om the f i rst h ill. W e are con cerned that this is not su fficient tim e to generate enough emi ssions to m easure ac curately. Inc l uding hill 2 in the firs t bag trip les the driving tim e and ensure s the ability to accurately m easure emissions and fuel use. Thus, we are proposing to p l ace the s eco nd hill into the city po rtion of the cycle, essentially separating the m easure m ent of emissi ons 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 a nd Bag 3 would consis t of

hills 4, an d 5. W h ile this inco rpor ates som e highway like driving into the “c ity” po rtion of the segregat ed US06 test, a s will be sho w n below, this is s t ill a mu ch im proved segreg ation of city and highway like dr ivin g than the U S 06 cycle as a whole.
For exam ple, even with the second hill include d in the city portion, the average speed of the city portion of US06 is only 27.7 mph. The average sp eed of the highway portion of US06 is 61.2 m ph. The average speed of the entire cycle is 48.0 m p h. Thus, separating the cycle in this way creates two dram atically d i fferent driv ing cy cl es, each of which falls much m o re clearly in to our definitions of city and highway driving th an the US06 cycle as a whole. To avoid any conf usion with th e bags of the FTP, we will re f e r

to the c ity a nd highway portions of the US06 cycle as US06 city and US06 highway. Overall, seconds 0-131 and 496-600 of the cycle would com prise the city bag and seconds 132-495 would co m prise the highway bag. The description of the hills within US06 and their designati on is summarized in the table below.
Table III.A-16. Split of US0 6 Cycle into City and Highw ay Por t ions Hill Portion of Driving Cyc l e (cumulative seconds) Maximu m Speed (m ph) 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

W e evaluate the im pact of a m o re ideal sep aration of US06 i nto city and highway driving in section III.Ebelow. There, the US06 highway bag contains hills 2 and 3 and the US06 city bag contains hills 1, 4 and 5.
W ith the split of US06 into two bags , we have av ailab l e fuel economy estim ates for five distin ct dr iving patte rns: 1) Bags 1 and 3 of the FTP; 2) Bag 2 of the FTP;

633) HFET; 4) City portion of US06; and 5) Highway portion of US06. We propose to com b ine the results of these five tests to represent ty pical city and highway driving patterns.
The VSP distributions for the four complete dynamom e ter cycl es plus individual bags of the FTP and US06 cycles are shown in Table III. A-17. As was the c ase for the Draft MOVES2004 inventory cycles, thes e VSP distributions represent a 50-50 mi x of cars and light trucks.

Table III.A-17. VSP Distributions for Dynamometer Cycles (% of time)

BinID LA4 HFET US06 US06 Cit y US06 Hw y 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%
W e then perfor m ed a set of linear regressi ons of the VSP distributions of the various dynam o meter cycles against the c ity and highway VSP distributions

. To m a xi mi ze the ability of the dynam o m e ter cycles to predict on-road fuel ec onomy, we weighted the squared error in each VSP bin by its av erage rate of fuel consum pti on in each b i n. W e could not use th e Draft MOVES2004 fuel rates directly , s i n ce they on ly exist f o r 17 VSP bins. Given this, we

64developed s e veral sets o f 26-bin VSP fuel rates, as each app r oach had it s relative strengths and weaknesses.
One set of fuel rates was based on the re sults of an EPA study perform e d on 15 cars in 2001. 2 0 This study instrum e nted 15 passenger cars w ith portable emission m easurem e n t devices and m easured 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 selecti on of vehicles and drivers were not necess arily repres entative of thos e in the U.S. The weakness is that o n ly cars were tes t ed; no light trucks.
Another set of fuel rates was taken from a recent testing prog ram conducted in Kansas City. This p r ogram is described in de tail in A ppendix A. W e used the av erage fuel rates for the 63 non-hybrid vehicles tested in the pr ogram , 30 cars and 33 light trucks. A final set of fuel rates was developed by extrapolating the Draf t MOVES2004 fuel rates (average of a 50/50 m i x 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 dete rmined by mu ltiplying the x 5 bin fuel ra tes from Draft MOVES2004 by the ratios of the fuel rates in the higher power bins to the fu el rates of the analogous x5 bins from the EPA 15 car study.
These four alternative se ts of fuel rates are show n in Table III.A-18.

65Table III.A-18. 26-Bin VSP Fuel Rates (gram per second)
VSP Bin EPA 15 Car Kansas City ExtrapolatedMOVES 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
W e selected the fuel rates from the Kansas City te st progr am over the o ther two sets of fuel rates. The Kansas City fuel rates are bas e d on a m i x of c ars and light trucks which were selected random ly from vehicle registrations of 2001 and later m odel year vehicles. T h e vehicles were also driven by their owners in norm al operation. The 15 car te st program only included cars, no light trucks. Also, the operators of the vehicles were EPA e m ploy ees or con tracto r s who were aware of the purpose of the test program

. The fuel rates from Draft MOVES2004 include data from a large num ber of cars and light trucks. However, the extrapolation of the fuel rates for the 12 highest power bins is based on data fr o m the 15 cars. Thus, the fuel rates from the Kansas City program are the m o st balanced and representative of actua l onroad operation of the three s ets of f

u el rate s. We perfor med the regressions using the regr essio n f unction inExcel. The inte rcept wa s set to zero. This function does not provide for weighting the resi duals. T hus, we incorporated this weigh t ing by mu ltip lying th e VSP f r equencie s in both Tables I II.A-1 2 and III.A- 15 by the square roo tof the fuel rate for each VSP bin from the mi ddl e column of Table III.A-18. W e also

66perform e d the regressio n s in a step wise fashion

. For both city and highway driving, we started with all five dynam o meter driving cycles in the regression. A ny driving cycle with a negative coefficient was dropped from the re gression. If two or m o re cycl es had negative coefficients, the cycle (of this group of cycles with negative co efficients) with the lowest p-value (m ost statistically significant) was droppe d first. The regression was then rerun. Once all cycles with negativ e coe f f i cients wer e dele ted, we continu e d to drop the cycle with the highest p-value until the adjusted r-squared value began to decrease. W e then selectedthe set of cycles an d their coefficients which produ ced the g r eatest ad justed r-squared value. The fin a l set of coefficients generally did not sum to 1.0. Therefore, we norm a lized the m to sum to 1.0. The res u lts of the final regressions for city and hi ghway driving are shown in the fi rst two column s of num bers in Table III.A-19 below. Cycle coefficients were rounded to the nearest percentage.
Table III.A-19. Best-Fit Co mbinations of Dynamometer Cycles Cycles Tim e Basis Mileage Bas i s 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 num b er of conclus i ons can be draw n from Table III.A-19. First, a s mi ght be expec t ed, the two high er speed cycles are absen tfrom th e MOVES description of city driving; vice versa for highway driving. Second, the relative we ighting for Bags 2 and 3 of the FTP in the description of city drivi ng are sim i lar, but no t id entic al to th os e inher e nt in the FTP. Third, FTP driving is in dicative of the grea t m ajority of th e MOVES representation of city driving, nearly 90%. Fourth, the m odeling indicate s a strong p r e f erence to sp lit the low a nd high spee d driving of US06 into city and highway driving, resp ectively. In contrast, HFET driving is only indicative of 21% of the MOVES representation of highway driving. Even m o re interesting is the fact th at the percen tage of averag e U.S. drivin g as a w hole which is represented by the HFET is only 9%. Based on th e results sho w n in Tabl e III.A-19, the HFET is the leas t rep r e senta tive and presum ably contributes the least am ount of predictive inform ation 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, sepa rating this low speed aggressive driving out of US06 appears to help focus the contribution of high speed, aggressive dr iving over the highway and overall, given the large contributions assigned to the US06 highway bag. Because VSP is defined on a second by second b asis, th e VSP distributio ns used in th e above regression analyses are on a tim e basis, not a mileage basis. For exam ple, 60% of the tim e spent city driving is lik e that of Bag 2 of the FTP. However, because this Bag’s driv in g is the slowest of all the cy cles, only 48% of the mileage spent c ity dr iving is like Bag 2. Since f u el economy is most commonly reported on a mileage basi s, and not on a tim e basis, it is useful to convert the cycle com b inations sho w n in Table III. A-19 abo v e to a mileage basis. T h is is done

67by mu ltiply ing the per ce n tages show n in Table II I.A-19 by th e averag e sp eed of the dynam o meter cycle or b ag presen tedin Table III. A-15 above. The changes in the cy cle weights for city and h i g h way are relatively sm all, becaus e th e average speed of the cy cles which p r edominate the city and highway weights all have similar (e.g., either low or high) speeds. Mo st of th e percentages on a tim e basi s change sign ificantly when converted to a mileage basis for all U.S. dr iving, as every cycle other than US 06 city has either mu ch lower or mu ch higher av erag e sp eed than the ov erall U.S. average.
In their comm e nts, Honda expressed con cern over the high percentage of highway driving represented by the US06 highway bag given their im pression of the extrem e nature of the US06 highway driving cycle. As discussed in de tail in the Response to C o mm ents docum ent, we used second by second fuel economy values of 80 ve hicles tested over the FTP and US06 tests to estim ate fuel economy over the HFET, US06 highw ay and onroad highway driving per the Draft MOVES2004 m odel. We found that the 5-cy cle 79%/21% w e ighting of the US06 and HFET fuel consumption values predicted on road highw ay fuel consumption mu ch m o re accu rately on an individual vehicle basis thanthe H F ET fuel consum ption alone (an alog ous to the cu rrent highway label formula). Thus, Honda’s concern doe s not appear to be a r eal problem with the 5­ cycle formulae. Practically, the fuel econ o mi es of the various cy cles or bags are com b ined harm onically, as is done today with the com bination of city and highway fuel economies to estim a te the 55/45 com posite fuel economy. Mathem atically, the formulae for running fuel use without consider atio n of air cond ition i ng or c o ld tem p erature ar e: City Driving : 

### RunningFC = ⎜ ⎜ ⎛ 0

. 48 ⎟ ⎟ ⎞ + ⎜ ⎜ ⎛ 0

. 41 ⎟ ⎟ ⎞ + ⎜ ⎜ ⎛ 0

. 11 ⎟ ⎟ ⎞ ⎝ Bag 2 75 

### FE ⎠ ⎝ Bag 3 75 

### FE ⎠ ⎝ US 06 City FE ⎠
Highway Driving: 

### ⎛ 0

. 21 ⎞ ⎛ 0

. 79 ⎞ RunningFC = ⎜ ⎜ ⎟ ⎟ + ⎜ ⎜ ⎟ ⎟ ⎝ HFET FE ⎠ ⎝ US 06 Highway FE ⎠
All U.S. Driving: 

### RunningFC = ⎜ ⎜ ⎛ 0

. 24 ⎟ ⎟ ⎞ + ⎜ ⎜ ⎛ 0

. 19 ⎟ ⎟ ⎞ + ⎜ ⎜ ⎛ 0

. 09 ⎟ ⎟ ⎞ + ⎜ ⎜ ⎛ 0

. 48 ⎟ ⎟ ⎞ ⎝ Bag 3 75 

### FE ⎠ ⎝ Bag 2 75 

### FE ⎠ ⎝ HFET FE ⎠ ⎝ US 06 Highway FE ⎠

For the NPRM, we developed a number of a lte rn ative s to th e se com b inations. These alternatives were developed usi ng: 1) different estim ates of fu el consum ption as a function of VSP, 2) whole cycles in stead of bags, 3) a different split of the US06 test into city and highway portions, etc. W e receiv e d very little comment on these alternatives. No new vehicle activ ity

68data has b ecom e availab l e sin ce the NPRM. Th us, we are retain ing the cycle com b inations shown in Table III.A-19 in th e final 5

- cycle formulae. Hybrid vehicles are required to be test ed over a full 4-bag FTP. Som e m a nufacturers pref er to pe r f o rm this tes tas a two bag test, with Bags 1 and 2 being co mb ined in to a single b ag and Bags 3 and 4 being similarly combined. We developed cycle weighting factors f o r this case using the sam e methodology as described in S ection III.A.2 of the Draft Technical Support Docu m e nt to the NPRM. The result was a wei ghting factor of 0.90 for Ba g 2 of a 2-Bag FTP and 0.10 for the US06 City Bag. These factor s are only slightly different from a si m p le com bination 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 econ o mi es of the various cy cles or bags are com b ined harm onically, as is done today with the com bination of city and highway fuel economies to estim a te the 55/45 com posite fuel economy. Mathem atically, the formulae for running fuel use without consider atio n of air cond ition i ng or c o ld tem p erature ar e: City Driving : 

### ⎛ 0

. 48 ⎞ ⎛ 0

. 41 ⎞ ⎛ 0

. 11 ⎞ RunningFC = ⎜ ⎜ ⎟ ⎟ + ⎜ ⎜ ⎟ ⎟ + ⎜ ⎜ ⎟ ⎟ ⎝ Bag 2 75 

### FE ⎠ ⎝ Bag 3 75 

### FE ⎠ ⎝ US 06 City FE ⎠
Highway Driving: 

## ⎛ 0

. 21 ⎞ ⎛ 0

. 79 ⎞ RunningFC = ⎝ ⎜ ⎜ HFET FE ⎠ ⎟ ⎟ + ⎝ ⎜ ⎜ US 06 Highway FE ⎠ ⎟ ⎟

For hybrids tested over a four bag FTP, th e fuel consum ption m easured 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 f o llows: City Driving (Two-Bag FTP):


## RunningFC = ⎜ ⎜ ⎛ 0

. 90 ⎟ ⎟ ⎞ + ⎜ ⎜ ⎛ 0

. 10 ⎟ ⎟ ⎞ ⎝ Bag 2 75 

## FE ⎠ ⎝ US 06 City FE ⎠

3. Effect of Air Conditioning on Fuel Economy The perf orm ance of emission controls while th e a i r cond ition i ng system is operating is assessed via the SC03 test. The SC03 test begins with a hot start (i .e., the engine has b een turn ed off for 10 minutes after having been fully warm e d up prior to engine shutdow n). The test cell is at 95°F and 40% relative humi dity, with a so lar load of 850 Watts per square m e ter on the vehicle. The vehicle is also pre- hea t ed at this so lar load f o r 10 m i nutes prior to the test, so th e air

69conditioning com pressor is genera lly engaged throughout the entire test. The driving pattern of the SC03 test is designed to represent driving pe rform ed i m m e diately after vehicle start-up, so it is a relatively low speed cycle. 2 1, 2 2 The driving p atte r n conta i nedin the SC03 test is simi lar to that of the F T P, but not identical.
We estim ate the im pact of air conditi oning operation on fuel economy ba sed on the difference in fuel use over the SC03 and Bags 2 and 3 of the FTP. The m o st sign ifican t difference between these two tests from the persp ective of fuel economy is the operation of the air conditioning system

. However, differences in the driving p atte rn betwe en the two tests should also be considered. Also , the air conditioning system is not always on in-use, so this needs to be accounted f or. In addition, when the air conditioning system is on in-use, the tem p erature and relativ e humi dity are not always 95°F and 40%, re spectiv ely. Tem p erature, in particular, can affect the load applied by the co mpressor on the engine. Thus, the effect of different ambient conditions should be consid ered, as well. Finally, the air conditioning com p ressor can also b e engaged when the def r o ster is tu rne don. Each of these f actors will b e assessed b e low, starting with the d i fference in driving pattern. Using the sam e methodology for m o deling fuel use described above, we determ ined the com bination of Bags 2 and 3 of the FTP and the US06 city cycle which m a tches the fuel use over the SC03 cycle with the air cond itioning 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 com b ination. Overall, a com b in ation of 39% of Bag 2 and 61% of Bag 3 on a mileage basis best represents the speed and pow er distribution of SC03. Thus, we propose to estim ate the increm ental fuel use due to the operation of the air condi tioner at 95°F and 40% relative humidity at an average sp eed of 21.5 m p h as the d i fference in fuel consum ption m easured over the SC03 versus this com b ination of fuel consum ption over Bags 2 and 3 of the standard FT P. The f o llowing equation depicts th is m athem atically:
Excess f uel use due to ai r conditioni n g at 95 F = ⎤ ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ 

## ( 1 1

− test 

## ) Fuel economy over the SC 03 ⎛ ⎞ + ⎛ ⎜ ⎝ ⎜ ⎞ ⎟ ⎠

⎟ 0

. 39 0

. 61 ⎜ ⎜ ⎝ ⎟ ⎟ ⎠ 

## ( 2 

## ) ( 3 

## ) Fueleconom yoverBag Fuel economy over Bag

The next factor to address is that of com pressor operation. T he length of the SC03 test is 10 mi nutes. Since the v ehicle has been sitting at 95°F for som e ti m e and has been under a solar load of 850 W atts per sq uare m eter for 10 mi nutes, the a i r con d ition i ng co mpressor is usually engaged th roughout the test. W e assum e here th at the air co ndition i ng c o m p ressor is engaged during 100% of the SC03 test. Howe ver, this estim ate could be too high. The effect of a lower estim ate will be eva l uate d in sec tion III.E be low.
This is not the case in-use. The air conditioning com p resso r generally cycles on and off depending o n the am bient tem p eratu r e, hum idity, solar load a nd the leng th of tim e that the vehicle has been operating. The greater the te mperatu r e andhumi dity, the m o re often drivers ⎥ ⎥ ⎥ ⎥ ⎥ ⎦

70turn th e air c ondition i ng on. The gre ater the te mp eratu r e andhum i dity, the m o re f r equent the com p ressor needs to ope rate in orde r to keep th e cabin at a co mfortable temperature; the shorter the trip, th e m o re relev a nt any solar loading of th e vehic l e. T hus, it is app r opria te to a d just th e excess fuel use determined from the SC03 test to account for the tim es when the air conditioning com p ressor is not engaged. The Draft MOVES2004 model contains an algorithm which estim ates the percentage of tim e which the com p ressor is engag e d as a functi on of heat index. Heat index is a com plex com b ination of a m bient tem p erature and humidity which was developed to predict degrees of personal comfort. 2 3 Heat index is used by the National W eather Service to quantify discomfort caused by the com bined effects of temperature a nd relative humi dity. Fi gure III-5 is reproduced from the MOBILE6 report and shows how heat inde x varies with both temp erature and humi dity. Figure III-5
. Heat Index vs. Temperature and Humidity

Re l Hu m 8 0% 6 0% 40%

N ot e : H eat I nd ex v

a l u es ba s ed o n s ha dy c o nd i t i on s Li nes r e pr es e nt c ur v

e f i t o f t a bu l a r da t a 

### T

e m p er atur e ( F) 10 5 10 0 95 90 85 80 75Heat Index(F) 180

160

140

12 0

100

80
60

The Draft MOVES2004 algorithm of com pre ssor on fraction versus heat index was developed from the direc tm easurem ent of air con d ition i ng op eration of over 1000 trips by 20 vehicles in P hoenix, Arizona during the summ er a nd fall of 1992. 2 4 The alg o rithm considers both the frequency that the system is turned on by the driver and the frequency that the com p ressor is engaged o n ce the sys t e m is turned on. The alg o rithm is of the f o rm :
A / C compressor on fraction = A + B × HeatIndex + C × HeatIndex 2

71The coefficients vary depending on tim e of day, basically causing the p r edicted compressor u se to increase when the su n rises h i gher in th e 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 U s age Equations

Heat In de x 7- 1 0 am , 5- 9 p m 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 6 5 -7 4 - 4 .101 0

. 0 864

- 0 .000 367

- 4 .101 0

. 0 864

- 0 .000 37 0

. 0 0 0 0

. 0 000 0

. 0 000 00 7 4 -7 6 - 2 .930 0

. 0 591

- 0 .000 213

- 4 .101 0

. 0 864

- 0 .000 37

- 1 .257 0

. 0 068 0

. 0 001 43 7 6 -9 6 - 2 .930 0

. 0 591

- 0 .000 213

- 5 .307 0

. 1 140

- 0 .000 52

- 1 .257 0

. 0 068 0

. 0 001 43 9 6 -1 01 - 3 .632 0

. 0 725

- 0 .000 276

- 5 .307 0

. 1 140

- 0 .000 52

- 1 .257 0

. 0 068 0

. 0 001 43 1 01- 104 - 3 .632 0

. 0 725

- 0 .000 276

- 5 .307 0

. 1 140

- 0 .000 52

- 3 .632 0

. 0 725

- 0 .000 276 1 04- 110 - 3 .632 0

. 0 725

- 0 .000 276

- 3 .632 0

. 0 725

- 0 .000 28

- 3 .632 0

. 0 725

- 0 .000 276 >1 10 A / C co m p r e sso r is eng a g e d 10 0% o f t

h e time
Since emissions and fuel economy are affect ed by the operation of the air conditioning com p ressor and not sim p ly whether the switch is turned on or off, the MOBILE6 analysis did not develop ana l ogous corre lations for drivers turn in g their air con d ition i ng sy stem s on as a function of heat index. However, whether the switch wa s turned on or off was recorded during the test program
. In order to provide a point of com p arison with othe r studies, which have focused on the f r equenc y that the sw itch is tu rne don, we esti m ated this param eter, as well. Using the data collected during the 1992 study in P hoenix, 2 5 we perform ed a regres sion o f the averag e percen tage o f tim e during each trip that the A/C system was turned on ag ainst the am bient tem p erature at the tim e of the trip. F o r 5 de gree F interv als, we calculated the averag e percen tage o f tim e that the air conditioning sys t e m was turned on and tha tthe a i r con d ition i ng com p ressor was engaged. These da ta are p l ottedin Figure III-6.

72Figure III-6
. Air Conditioning Use in Phoenix 

### A

/ C U s e in P h oe nix

0% 20 % 40 % 60 % 80 % 10 0%

60 80 1 00 12 0 T e m p er a t u r e ( F ) AC O n

Co m p r e s s or

O n

W e then calculated the ratio of the p ercen tage of tim e that th e com p ressor was engag ed to the percentage of tim e that the air conditioning system was turned duri ng each tem perature interval. This ratio is essentially the percen tag e of tim e that the com p ressor was eng aged while the system was turned o n. These ratios are p l otted in Figure III-7. Figure III-7
. Compresso r Engagement as a Function of A m bient Temperature 

### Co m p re sso r Use W h en A/ C i s O n

y= 1. 0535Ln ( x ) - 3. 998 1

50% 60% 70% 80% 90% 10 0 %

7 0 80 9 0 100 110 T e mperat ur e ( F )

73W e then perfor m ed a simple regress i on of these percen tages as a functionof tem p erature. The result was:
Com p ressor engagem ent (fraction) = 1. 0535 ln (ambient tem p erature (F )) - 3.9981
W e then div i ded the MOBILE6 estim ate of comp ressor on fraction by this estim ate in order to convert the fraction of tim e that the com p resso r is on to the fraction of tim e that the air conditioning system is on.
W e com b ined the es tim ates of air con d ition i ng sy stem on and com p ressor on f r actions with 30-year average m eteorolog ical conditions for each hou r of the day for every m onth of the year for each county in the U.S. to estim ate th e percentage of driving tim e during which the system and com p ressor were engaged under those conditions. W e then weighted these percentages by the relative drivi ng tim e occurring during each hour of the day and month in each county to obtain an estimate of the overall percentage of the tim e which air conditioning com p ressors are engag edin the U.S. From this, we estim ate that, on average across th e nation and through out the ye ar, the air cond ition i ng sys t em is turned on 23.9% of the tim e and the com p ressor is engaged 15.2% of the tim e.
W e then adjusted th is latter percen tage to accoun t for reducedcom p ressor loads at tem p eratures less than 95°F and higher loads above 95°F. 2 6 Ed Nam , at the University of Michigan, developed a model of air conditioning load on th e engine as a function of tem p erature. 2 7 From Figure 4 of this paper, we de rived the following equation of com p ressor torque in foot-pounds versus tem p erature in degrees F.
Com p ressor torque = 1.70 + 0.084 * Am bient Temperature (°F)
At the tem p erature of the SC03 test, 95°F, com p re ssor torque is 9.68 foot -pounds. Therefore, the estim ated to rque at a spe cif ic am bient tem p eratur e was divide d by 9.68 and mu ltiplied by the estim ated compressor on fraction from Draft MOVES2004 in order to derive a com p ressor on fraction which was consistent wit h the conditions of the SC03 test

. This adjusted formula for com p ressor on fraction was again applied to each county in the U.S., accounting for diurnal and seasonal temperature and driving d i f f

erences. Adjusting f o r load, the c o m p ressor is on in-use 13.3% of the tim e, versus 15.2% without adjustm e nt for load. Thus, the av erage 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 com p ressors are belt-driven by the e ngine. The efficiency of both the engin e a nd com p ressor will v ary with engine sp eed and lo ad. This vari ation is d i f f icult to m odel, as th e speed and load of engines in vari o u s vehic l es will vary dra m atically ba sed on the vehicle’s drivetrain design, even over the sam e driving cycle. Lacking sp ecific inform ation on each vehicle’s air conditioning system design an d how engine speed and load affect it efficiency, we assum e that the ef f i ciency of the engine and a i r cond ition i ng com p ressor im plied in the SC03 test applies to other types of dr iving, as well. Ho wever, a m o re basic effect related to driving pattern is that the faster a vehi cle is moving, the shorter the amountof tim e that the vehicle needs to be cooledwhile it trav els a specific distan ce. Other factors being equa l, this reduc es the

74am ount of e nergy needed to cool the vehicle per m ile of travel. Therefore, for a specif i c set of am bient conditions, we assum e that the im pact of air conditioning on fuel use is constant with driving tim e (i.e., fuel use in term s of gallons pe r hour is constant). This means that the excess fuel use due to operating the ai r conditioner varies inversely pr oportional to vehicle speed. In other words, at low vehicle speeds, like that of th e SC03 test, excess fuel use is relatively high on a per mile basis. At high vehicle speeds, like th at of highway driving, the excess fuel use due to operating the air condition is relative ly low on a per mile basis.
We confir med this assumption by testing five vehicles over a variety of test cycles at EPA’s Ann Arbor labor atory with b o th the a i r c ondition i ng turned on an d of f

. These tests we re not run in an environm ental cham ber which si mu lates num erous aspects of higher tem p erature conditions, like humidity and solar load. Vehicles were tested as they norm ally required for an FTP at 75°F, except that the am bient tem p erature of the test ce ll was e ithe r higher or lo wer. Since the prim ary purpose of the test program was to determ ine the r ela tive load of th e com p ressor on the engin e and the relative effect on f u el econ o my , this simp lif ied tes t procedur e was suf f i cient. A f u ll re port of this te st program is contained in the docket. 2 8 The data are sum m arizedin Table III. A-21.

Table III.A-21. Increased F u el Use Due to Ai r Conditioning as a Function of Vehicle Speed 60°F 75°F 95°F Absolute increase in fuel consum ption (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 in c r ease in f u el consum ption adju sted to 21.5 m ph (ga llons 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 h alf of Table III.A-21 sho w s the incr eased fuel us e as directly m easured over the f our cycles. The lower half mu ltiplies these f u el inc r eas e s by the ratio of the aver age speed of SC03 to the averag e s p eed of th e cycle tested. As can be s een in th e u pper half of the tab l e, the increas e in fuel use v aries by rou ghly a factor of three across the 3-4 cycles at eacham bient tem p erature. The cycle with the h i g h est veh i cl e speed, HFET, showed the sm allest increas e at all three tem p eratu r es. B ag 2 of the FTP, withthe lowest average speed, s howed the largest incre ase a ta ll th ree tem p eratu r es. Th e fuel in crea ses shown inthe lower h a lf of Table III.A-21 show mu ch lower variability. Fue l in creas es now vary by les s than 20% f r o m lowest to highest at any given tem perature. These data convinc ingly confirm the assum p tion that the increased fuel needed to operate the air conditioning system is roughly cons tant with tim e, as opposed to mileage.

75Third, the air conditioning com p ressor also can be engaged in defroster m ode to dehumidify the air and keep the wi ndshield from fogging up. Due to the fact that the defroster tends to be operated at lower am bient tem p eratures than the air conditi oner, the load on the engine is generally lower. W e do not have a dire ct es tim ate of the frequency that the d efroster is turned on, nor the frequency that the com p resso r is engaged during defroster m ode. No study analogous to that perform e d in Phoe nix has yet been performed. However, a recent study by the National Renewable Energy Laboratory (NREL) a nd the Office of At m o spheric P r ogram s (OAP) within EPA estim ated th e percen tage of tim e that people tu rned on the defroster, as well as th e air cond ition i ng system , while drivin g. 2 9, 3 0 This study uses a personal com f ort model to pr edict when a driver would be likely to turn on the air co ndition er. T h e grea ter th e com b ination of am bient tem p eratu r e and hum idity, th e more uncomfortably hot a person would feel. Th e m ore the model predicted that a person would feel uncomfortably hot, the greate r the estim a ted likelihood that the driver would turn on the air conditioner. A sim p ler model was used for defroste r use. If the am bient tem p erature is between 35 and 55°F and the relative humidity is greater than 80%, th en the driver is assum ed to turn on the defroster. The NREL-OAP studies presented e stim ates of air cond ition i ng use and a i r cond ition i ng use plus defroster use by state, but not for the nation as a whole. W e com b ined these statespecific estim ates with the VMT estim ates descri bed above (aggregated by state) in order to estim ate national averages. In the NP RM, we estim a ted that the nation a l average air conditioning use was 22.9% and combined air co ndition i ng p l us defroste r use was 33.5%. However, comments provided by NREL indicate th at their m o re recen t e stim ates of air conditioning and air con d ition i ng plus defroster use are 28.1 % and 32.6 % , respe c tiv e ly. Since the conditio ns for air co ndition i ng a nd defroster use do not o v erlap, n atio nal ave r age defroster use, acco rding to this m odel is 4.5 % (versus th e 10.6% figure estim a ted in the NPRM). Thus, the NREL-OAP m odel estim ates slightly high er average air c ondition i ng use nationwide (28.1%) than MOBILE6.2 (23.9%). These two estim ates ar e still rem arkably close given the differences in m e thodology. It is also not surprising that the MOBILE6. 2 estim ate would be the lower of the two, given that the NREL-OAP m odel does not a ccount for people choosing alternatives to air conditioning use under h o t-humid conditions, suc h as putting a convertible top down, driving with the windows open, or inopera tive air conditioning system s. The NREL- OAP study also perform e d vehicle modeling in order to estim ate the im pact of air conditioning and defroster load on vehicl e fuel economy. They assum e d an average tem p erature of 81°F for air conditioning use and 61°F for defroster use. (This defroster tem p erature exceeds the range of defroster use an d appears to consider h i g h er under th e hood tem p erature s. However, the air cond ition i ng tem p eratu r e app ears r easonable withou t such an adjustm e nt.) The also us ed the FTP as their driving cycle. Assumi ng a mi x of 65% cars and 35% trucks, the load of the air conditioner increased fuel consum ption 19.8%, while that for the defroster was 4.1%. Thus, the im pact of the defroster on fuel consum ption is 20.7% of the im pact of air conditioning. This includes the im pact of a low er am bient tem p erature and the period ic cyc ling of the c o m p ressor o n and of f

. NREL-OAP’s m odeling of the air cond ition er is com p arable to our es tim ate of 13.3% air cond itio ning use ad justed for co mpressor lo ad. Thus, the 13.3% estim ate can then be scaled based on the results of the NREL-OAP study.

76Two scaling f actors ar e necessary. One, def r oster use is 13. 8% of that a i r cond ition i ng, based on the NREL-OAP results in both cases f or consistency (4.5%/32.6%)

. (Use of our own estim ate for air conditioning (23.9% ) would increase this percentage by 36%, but we believe that it would be more appropriate to use estim ates fr om the sam e source in this case.) Tw o, when turned on, d ef r oster us e has 20.7% o f the f u el ec onomy im pact as a i r con d ition i ng. Com b ining these two factors (20.7% tim es 13. 8%) produces an overall scaling fa ctor of 2.9%. Applying this to our estim ate of 13.3% for the compressor on per centage in term s of the am bient conditions of the SC03 test produces an analogous estim ate of 0.4% for defroster use. Com b ined air conditioning and defroster use woul d be 13.7% (13.3% + 0.4%), or3% higher than that of air conditioning alone. This is a ve ry small im pact. We decided not to include the im pact of defroster us e in the 5

- cy cle formulae at th is tim e for two reas ons. One, no vehicle stu d ies have yet been perfor m ed to confirm the projection th a t driver s ac tu ally turn on the def r oster as assum e d by NREL-OAP. Two, the ambient c onditions existing during defroster use differ dram atically from those of the SC03 test.
Based on these three assum p tions, the im pact of air conditioning on running fuel use is estim ated as 13.3% of the difference between fu el use per mi le over the SC03 and a com b ination of Bag 2 and Bag 3 tests tim es 21.5 mph and divi ded by the average speed of either city o r highway driving. Based on the descriptions of city and highway driving from Draft MOVES2004, the average speeds are 19.9 m ph a nd 57.2 m p h, respectively. Thus, the excess fuel use due to air conditioning operation is as follows. For city dr iv ing: Excess fue l use due to air con ditioning

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 SC 03 ⎛ ⎜ ⎝ 1 ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ × ×

770.133 = ⎤ ⎡ Excess fue l use due to air con ditioning

21.5 57.2

W e receivedseveral other comm ents on this m e thodology. However, as d i scussed in the Response to Comm ents docum e nt, these comm ents di d not lead us to revise the above equations. Thus, the 5-cycle formulae will cont inue to use th e above re lationshi ps to e stim ate the impact of air conditioning use on city and highway fuel economy. 4. Effect o f Cold Ambient Temperatu res on Running Fuel Use Finally, we added the impact of colder am bien t tem p eratures on running fuel use. As was done for start fuel use, we base our estim ate of the im pact of colder am bi ent tem p eratures on running fuel use by comparing the fuel use over the standard and cold temperature F T P tests. At 75°F, engine, drivetrain and tires are generally assum e d to be fully warm e d up by the end of Bag 1. Thus, Bag 2 of the FTP at 75°F includes only fully warm e d up operation. As was discussed above, the start fuel use associated w ith a 10 mi nute soak at 75°F is very sm all and generally considered to be negligible. Thus, Bag 3 at 75°F can also b e conside r edto consis t o f essentia lly fully warm e d up vehicle operation.
As discussed in detail in the Draft T echnical Support Docu m e nt to the NPRM, it is not clear that vehicles are fully warm ed up during Ba gs 2 and 3 at 20°F. However, as described above, the average city driving trip is only 4.1 m iles, well below that of the FTP (7.5 m iles). Thus, Bags 2 and 3 of the cold FTP provide a reasonable estim a te of war m ed up driving during city -lik e driving (i. e., th e vehicle is warm ed up t o the extent that it typ i cally reaches d u ring short trips )

. The effect of cold tem perature on fuel use during city driving can be estim a ted 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 m ake the sam e conc lusion for either longer highway-like driving trips, no r co nclude th at the effect of colder tem p eratures would be the sam e at higher vehicle speeds. Based on a number of studies which inves tigated th e impact of colder tem p eratures on fuel economy, we estim ated that running fuel use at 20°F at higher vehicle speeds would be roughly 4% higher than that at 75°F.
When we determ ined the appropriate weigh ting factors for running fuel use at 20°F and 75°F in the NPRM, we assum e d that running fuel use increased linearly with tem perature below 75°F. W e received on e comment which challen g edthis assu mption. As discussed in Section ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ ⎞

⎟ ⎟ ⎟ ⎟ ⎟ ⎛ ⎞ ⎛ ⎜ ⎜ ⎜ ⎜ ⎜ ⎠ ⎞

⎟ ⎟ ⎠ Fuel economy 3 0

. 61

over Bag ⎜ ⎜ ⎝ ⎜ ⎜ ⎜ ⎜ ⎜ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ ⎟ ⎟ ⎠ ⎛ ⎞ ⎝ + 1

Fuel economy 2 0

. 39

over Bag ⎛ ⎜ ⎜ ⎝ ⎝ − 1 ⎞ Fuel economy ⎛ ⎟ ⎟ ⎠ over the SC 03 test ⎜ ⎜ ⎝ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ × ×

785.2.4 of the Response to Comm ent docum e nt, we evaluated the running fuel use over Bags 2 and 3 of the FTP of several conven tional Honda vehicles at 20, 50 and 75°F to determine if running fuel use did in fact chan ge linearly with a decr ease in tem p erature from 75°F. These data are sum m arizedin Table III. A-22.

Table III.A-22. Warmed Up Fuel Use Versus Temperature: Honda Data Bag 2 m pg Bag 3 m pg Bag 2+3 Fuel Consum pt ion (gal/m i) 75°F Civic 38.1 41.9 0.0251 Elem ent 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 Elem ent 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 Elem ent 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 runni ng 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 at20°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 increa se in running fuel use at 50°F was 27% that at

7920°F. The sam e relationship was true when f our hybrids (two Honda vehicles and two Toyota vehicles) were added to the database. We used this relationship to evaluate the im pact of a m bient tem p erature on running fuel use throughout the U.S. using the sam e methodology that was used in conjunction with the assum ption of linearity perform e d 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 fu el use at 20°F in the 5-cycle formulae for both city and highway fuel economy to 0.18. This is s hown in the equation s be low. For city dr iv ing: 

### Excess fue l use due to colder temperatur es = ⎡ ⎛ 0

. 5 0

. 5 ⎞ ⎛ 0

. 41 0

. 48 0

. 11 ⎞ ⎤ 0

. 18 × ⎢ ⎜ ⎜ + ⎟ ⎟

− ⎜ ⎜ + + ⎟ ⎟ ⎥ ⎣ ⎝ Bag 2 20 

### FE Bag 3 20 

### FE ⎠ ⎝ Bag 3 75 

### FE Bag 2 75 

### FE US 06 City FE ⎠ ⎦

For highway driving: 

### Excess fue l use due to colder temperatur es =

0

. 18 × 0

. 04 × running fuel use without air conditioni ng at 75 F = ⎡ 0

. 21 0

. 79 ⎤ 0

. 18 × 0

. 04 × ⎢ + ⎥ ⎣ HFET FE US 06 Highway FE ⎦

Com b ining the estim a tes of running fuel use at75°F with the air c onditioning turned off with the estim a te of exce ss fuel use of runni ng the air conditioning syst em and the estim ate of fuel use due to colder ambient tem p eratures produces the following for mulae for running fuel use:

80For city dr iv ing: 

### Running Fuel Use = ⎡ 0

. 48 0

. 41 0

. 11 ⎤ ⎡ 0

. 5 0

. 5 ⎤ 0

. 82 × ⎢ + + ⎥ + 0

. 18 × ⎢ + ⎥ ⎣ Bag 2 75 

### FE Bag 3 75 

### FE US 06 City FE ⎦ ⎣ Bag 2 20 

### FE Bag 3 20 

### FE ⎦ ⎡ 1 ⎛ 0

. 61 0

. 39 ⎞ ⎤ + 0

. 133 × 1

. 083 × ⎢ ⎣ SC 03 FE − ⎜ ⎜ ⎝ Bag 3 75 

### FE + Bag 2 75 

### FE ⎟ ⎟ ⎠ ⎥

⎦

For highway driving: Running Fuel Use = 

### (1

. 007 

### ) × ⎡ 0

. 79 + 0

. 21 ⎤ 0

. 133 × 0

. 377 × ⎡ 1

− ⎛ 0

. 61 + 0

. 39 ⎞ ⎤ ⎢ US 06 Highway FE HFET FE ⎥ + ⎢ SC 03 FE ⎜ ⎜ Bag 3 FE Bag 2 FE ⎟ ⎟ ⎥ ⎣ ⎦ ⎣ ⎝ 75 75 ⎠ ⎦

5. Adjustment Factor for Non-Dynamometer Effects There are a large num ber of factors which affect vehicle fuel economy which are not addressed by any of the five current dynam o me ters tests. These include roadway roughness, road grad e ( h ills ), large vehicle load s (e.g., traile rs, cargo, mu ltip le pas se ngers), wind , precip itation , to nam e just a f ew. Even when a factor is addres sed by a dyn am ometer test, such as driving pattern or air conditi oning, the factor is only approxim ately m e asured, as all realistic driving patterns cannot possibly be included in a test having a reasonable length of time. Nor can all the possible am bient condi tions affecting air c onditioner operation be tested. T hus, any estim ate of in-use fuel economy deriv ed from the five dynam o m eter tests is necessarily approxim ate. It would be possible to u se the formulae des c rib e d in Section III.B to d i re ctly e stim ate the fuel economy label values. These fuel economy va lues would provide driv ers with an indication of the relative fuel economy which they should expect to achieve in-u se, at least the best estim a te that the current five dyn am ometer tests can p r ovi de. This would provide vehicle purchasers with inform ation as to which vehicle would provide g r eater or less er fuel econ o my than another vehicle. However, as discussed in section I, m a ny 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 vehicl e operators if the fuel economy label values accounted f or all factors affecting fuel economy and not just those addressed by the dynam o meter tests. This is the rationale for the 90% and 78% adjustm e nt factors w h ich are currently applied to the m easur ed FTP and HFET fuel economies when determining the city and

81highway label values. T his cannot be done in a vehicle specific m a nner, since no such estim ates are ava ilab l e without add ition al dyna mom eter tes t s a nd add itional vehicle testing. However, it is possible to account for these fact ors on a fleet wide basis, as was done with the current 90 and 78% adjustm e nt factors. It is po ssib l e to estim a te the effect of each of the untested factors m e ntioned above an d then add them up. This was done as part of the 1984 label adjustm e nt rule

. That study estim ated that the net effect of all the non- dynam ometer factor s was roughly 30%. 3 1 However, at that tim e, these facto r s included th e im pact of vehicle sp eed, acceleration, colder temperature and air conditioning, in addition to the factors descri bed above. W ith the inclusion of the fuel consum ption over the U S 06, SC03 a nd cold FTP test s in th e 5-cycle formul ae, the n e t effect of these factors should be much s m aller. One caution is that the com parisons of dyna mom e ter m easured fuel economy and onroad fuel economy perform e d for the 1984 rule tended to find a sm aller shortfall than the total of all the individual non-dynam o m e ter factors. The reason for this was not clear. In other words, the individual factors which mi ght have been over-estim ated could not be determined. In this sec t ion, we will update th e impact of several of the se factors and develop a revised estim a te of the overall im pact of non-dynam o meter factors on onroad fuel economy relative to an estim ate based on the 5-cycle formulae.
The easie st f actor to qua ntif y is likely that re lated to fuel energy dens ity. EPA’s test fuels do not contain any oxygen. However, comm er cial gasoline can contain either of two oxygenates, m e thyl tertiary butyl ether (MTBE) and ethanol. Fu ture levels of MTBE use are uncertain because of water contamination issues related to leaking underground storage tanks. However, the recen tly passed Energy Policy Ac t o f 2005 (EPA2005) guarant ees a certain level of ethanol use. For example, in 2008, EPA 2005 requires the use of 5.4 billion gallons of renewable f u el, the v ast m ajority of which is exp ected to be ethanol blen ded into gasoline. DOE projec ts th at tota l gasoline consum ption will b e 1 46 billion ga llons in 2 0 0 8

. Thus, eth anol would represent 3.7% of gasoline by volume. Ethanol contains roughly 33% less energy per gallon, so on average, commercial gasolin e in 2 008 will co n t ain 1.2 % le ss energy pe r gallon thanit would if it were not oxygenated. Engine efficiency is unaffected by fuel energy content in this range. Thus, reducing the ene r g y content of gasolin e by 1.2% will re duce volum etri c f u el ec onomy by the sam e 1.2%. This is not reflected in EPA dyna mom e ter testing. Currently, dynam o meter testing perf orm e d for th e state of California is done using a fuel containing 2% oxygen by weight. T his fuel c ontains approxim ately 2% less energy per gallon than EPA’s test fuel. In m a ny cases, dynam o mete r tests perform e d for Ca lifornia can also be used in EPA certification. Thus, it would be appr opriate to divide the m easured fuel economy of any test perf orm e d using an oxygenated California test fuel by one mi nus the oxygen content of that fuel by weight, usually 2%. However, w ith the passage of EPA 2005, gasoline sold in Federal ref o rmulated gasoline area s in Calif ornia will no long er be r e quir e d to conta i n two weight percent oxygen. Thus, California m a y no l onger require its test f u els to contain oxygen. In any case, m easured fuel economy using an oxyge nated fuel should be adjusted to reflect the energy content of EPA’s non-oxygenated test fuel. At the sam e ti m e , the average driver in 2008 will ach ieve 1.2% lower fuel economy than they would if the y were using a non-oxyg enated fuel

.

82The volum e of renewable fuel required under EPA2005 increases in later years, reach ing 7.5 billion gallons in 2012. Assum i ng this is all ethanol and using DOE’s projected gasoline volum e of 147 billion gallons, av erag e comm ercial gaso line would contain 1.5% less energy than EPA test fuel. Thus, in the early years of the rev i sed fuel eco nomy labels, difference in fuel quality will cause onro a d f u el econo m y to be 1.2-1.5% le ss than that m easured on the dynam o meter.
Another factor which has recen tly been studi ed in som e detail is tire pres sure. NHTSA recen tly pro mulgated a regulation requiring car a nd light truck m a nufactur ers to ins t all tire pressure m o nitoring system s in future vehicles. In preparation for this rule, NHTSA conducted a survey of the tire press ure of in-use vehicles in February 2001. 3 2 Tire p r essures we re m easured on over 11,500 vehicles at 24 locat ions throughout the U.S. The results are summarizedin the Tab l e III.A-23. N H TSA presente d data for each of the four tires se parately (i.e., front, dr iver’s side tire). 3 3 W e averaged the findings f o r th e f our tir es. Table III.A-23. NHTSA Onroad Tire Pressure Survey Difference between Onroad and Manufacturer’s R ecommend Tire Cumu lative Frequency Pressure (ps i ) (Avera ge 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%

83As can be seen from the table, 54-62% of cars and light trucks have unde r-inflated tires, while 31-39% have over-inflated tires. Using these esti m a tes, we found that the tires of the average car were under-inflated by 1.1 psi, wh ile those on light trucks were under-inflated by 1.9 psi. NHTSA presented two estim ates of the effect of tire pressure on fuel econ o my. A 1978 study by Aerospace Corp. found that fuel econo my d ecreased by 1% for every 3.3 psi decrease in tire pressu re , while m o re recen t tes td ata submitte d by Goodyear showed a 1% dec r ea se in fuel economy for every 2.96 psi decrease in tire pressu re. Using these two factors, the 1.1 psi underinflation of car tire p r ess u re causes a 0.3-0.4% decrease in onroa d fuel economy
. The 1.9 psi under-inflation of light truck tire pressure cause s a 0.6% decrease in onroad fuel economy. Assum i ng that new vehicles aver age close to their CAFE fuel economy standards (27.5 m pg for cars and 20.6 m pg for light trucks) and a 50/50 mi x of the two types of vehicles, the fleet-wide effect of under-inflation is 0.5% using either factor. NHTSA recently p r omulgated a reg u lation re qu iring m anufacturers to m onitor tire pressure. 3 4 This rule requ ires veh i c l es to be equip p ed with sen sors to de tec ta tire whic h is under-inflated by 25% or m ore. W ith a few excepti ons, the regulation begins phasing in with the 2006 m odel year and all new cars and light trucks mu st have the m onitoring system s by the 2008 model year. Assumi ng a tire’s recomm ended pressure is about 32 psi, this im plies catching tires under-inflated by 8 psi or m o re. If we assum e th at the regulation is 100% effective and eliminate all tires und er-inflated b y 8 psi or more in Tab l e III.A-23 abo v e, passenger car tires are no longer under-inflated on averag e in-use; th ey exceed th ei r specifications by roug hly 0.1 psi. Light trucks are still under-inflated by about 0.4 psi. Ac ross the light duty fleet, the net effect on fuel economy de creases to 0.1%, or r oughly one-fifth the level prior to the rule. Of course, the effectiveness of the rule could be less than 100%

. At the sam e tim e, vehicles with a single tire under-inflated by 8 psi could have other tires witha lower degree of under-i nflation. Thus, the rule could have som e effect on tires with sm a ller levels of under-inflation than assum e d above. In any event, the effect should be less th an 0.5% and could be close to zero.
A third f acto r which can be quantitatively es tim ated is th e effect of wind. W i nd affects fuel economy by changing the road load of the vehi cle. W i nd can affect both rolling resistance and aerodyn amic drag. Rolling resis t ance is prim ar ily affected by a s i de wind, which pushes the vehicle sideways. The driver must com pensate by turning the steering wheel in to the wind. This increases th e drag cau sed by the tires on the road way surface. However, the effect of wind on aerodynami c drag is far the larger of the two effects. Aerodynami c drag is generally assum e d to be the product of three fact ors: 1) the frontal area of the v ehicle, 2) th e air sp eed g o ing by the vehicle squared, and 3) the “drag co efficient or C d .” A headwind increas e s the speed of the air going by the vehicle dir ectly (i.e., a 10 mph wind increases air speed 10 mph). A tailwind decreases air sp eed b y the vehicle. Even if the frequency of a headwind and a tailwind is the sa m e , total aerodynami c drag increases, due to the fact that drag is proportional to air speed squa red. For example, 40 m ph squared is 1600 m ph 2

. Given a headwind of 10 mph, air speed increas es to 50 m ph a nd 50 m ph squared is 2500 m ph 2

. Given a tailwind of 10 mph, air speed decrea ses to 30 m ph a nd 30 m ph squared is 900 m ph 2

. The average of 2500 and 900 m ph 2 is 1700 m ph, which is m o re th an 6% greater than 1600 m ph 2

.

84Thus, even a random ly directional wind will in cre a se to tal a erodynami c drag and decre ase fuel economy. An even greater effect of wind, however, is that it changes th e drag coefficient, C d , and increases th e effective frontal area of the vehicle in the direction of the wind. For example, as large as the frontal area is of a semi -tracto r trailer com b ination, its area from a side view is 5-10 tim es as larg e. The greater the s i de w i nd relative to vehicle sp eed, the m o re the truck is actually driving sideways down the road as far as aerodynam ic drag is c oncerned. W ith respect to cars and light trucks, their body shapes are designed to reduce aerodynami c drag when traveling into the wind. Front and rear ends are sloped. Spo ilers and other rear end sh ap es are d esig n ed to mi ni mi ze the creation of vortices beh i nd the veh i cle wh ich “p ull” the vehicle back as it is driving forward. However, as soon as a significant side wind occurs, these benefits start to dim i nish. For the 1984 label adjustm e nt rule, EPA estim ated that wind reduced onroad fuel economy by 3% for a small car and 2% for a large car. 29 , 3 5 These estim a tes were based on several estim a tes m a de by the Depa rtm ent of Transportation: 1) th e effect of 10, 15, and 20 m ph winds on aerodynami c drag at a constant speed of 55 m ph as a function of wind angle, 2) the effect of increased aerodynam i c drag on 55 m ph fuel economy, and 3) a distribution of onroad VMT as a function of wind speed (with the national average w i nd speed being 9 m ph). EPA applied these estim a tes directly to highway fuel economy, but reduced the fuel economy effect by 80% for city driving. This reduction was base d on the fact that roughly 20% of the FTP test is at sp eeds near 55 m p h.
We reviewed this m e thodology in detail to de termine if any improvem e ntcould be made. Two areas were id entified. The fi rst area was the fact that the e ffect was estim ated only for cars, as that was the focus of the study. T he second area was the assum ption that wind had no effect on fuel economy at vehicle speeds below roughl y 55 m ph. While aerodynami c drag is much lower at city driving like speeds thanhighway sp eeds, wind s p eed is a h i g h er 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 in creas es. Th us, the effect of a side wind can be significan t, even at low vehicle speeds. In order to expand the previous analysis, we developed a m o del of aerodynami c drag and its im pact on fuel economy as a function of wi nd speed and angle. W e broke down the speed of the vehicle through the air in term s of its x and y coordinates (i.e., parallel and perpendicular to the directionof the vehicle). Th e par alle l com p onent is the s p eed of the vehicle plus the cosine of the wind angle tim e s wind speed. The perpendi cular component is the sine of the wind angle tim es wind speed. W e then calcu lated the net an gl e of the air flowing past the vehicle and its speed from t hese two x-y com ponents. The net an gle of the air flowing past the vehicle is the arctangent o f the ratio of perpendicular air speed to para lle l a i r speed. Net air spe ed is the squar e root of the sum of the square of the perpendicu lar air speed and the square of the parallel air speed. Aerodynami c drag is the square of the net air speed tim es the vehicle drag coefficient.
DOT estim ated that the vehicle drag coeffici ent increas ed 1.5 % for every degree increase in yaw angle, or angle of net air flow past the vehicle. Using this estim a te, we were able to

85reproduce the estim a tes of the change in aer odynam ic drag as a function of wind speed and direction on a vehicle traveling at 55 mph, which were presente d Figure 26 of the EPA report. 33
In order to broaden the estim ate to include light trucks, we obtained estim ates of the effect of wind angle on a vehicle’ s dr ag coefficient from Gillespie. 3 6 Gille spie pre sents the estim ated ab solute increase in drag co efficien t as a function of wind speed for four vehicle designs: pick-up trucks, station wa gons, fa mily sedans, and sports cars. T h e results are presen ted in Table III. A-24 below in tabu lar f o rm
. Gillesp ie did not p r e sent es tim ates f o r sport utility vehicles (S UV). W e estim ated the effect for S UVs by averaging the impacts for pick-up trucks and station wagons.

Table III.A-24. Effect o f W i nd Angle on Vehicle Drag Coefficient W i nd 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
W e estim ated a fleet average chang e in th e effective drag coef ficient by av eraging the estim ates for the five m odel types. W e averag ed the es tim ates for the th ree types of p assenger cars equally (33/33/33), the two es timates 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. Assumi ng average drag coefficients in still air of 0.30 for passenger cars and 0.40 for light trucks, 25 these chan ges repres ent in creas es of 1.7% and 2.6% per degree of wind angle. The fig u re for cars m atches the DOE estim ate f r om 1974 quite well, while thatf o r light trucks is mu ch larger. W e perform e d a regression of the change in drag coefficien t versus wind angle in degrees and found the following relationship: Change in drag coeff icient = - 0

. 00376 + 0

. 006815 × wind angle + 0

. 000065 × 

## ( wind angle 

## ) 2

W e also perform e d a si milar regression used a li near m odel. The linear model yielded larger average increase in v ehicle drag coefficient. Therefore, we retain ed the n on-lin ear m odel. In order to expand the estim ate to inc l ude city, as w ell as h i ghway driving, we again used PERE. 25 Us ing PERE, we estim ate that a 10% increas e in aerodynami c drag or drag coefficient decreases city fuel economy by 0.93%. Like wise, highway fuel economy decreases 3.11%. Im plied in the DOT estim ate of the effect of wi nd speed on 55 m ph fuel economy is a decrease of roughly 4%. Thus, PERE estim ates a som e wh at lower effect of wind speed on fuel economy even at h i gh way speeds.
W e then applied our m o del using the DOT es tim ates of the nationa l average distribution of wind speeds, which is shown in Table III.A- 25 below. W e assum e d that the av erage wind

86speed within a range of wind speeds was the average of the lower and upper limit of the range. W e assum e d that the average wind speedfor winds above 25 mph was 27. 5 m ph.
Table III.A-25. Frequency of Wind Speeds in the U.S. W i nd Speed (m ph) Assum ed Average W i nd Speed (m ph) % 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 av erage veh i cle speed of 19.9 m ph for city driving and 57.1 m p h for highway driving (from Draft MOVES2004), the vehicle drag coefficient increases by 73.5% and 15.9%, respectively, on average. Assumi ng average c ity and highway fuel economy of 18.8 and 25.5 mpg (from our datab ase of 615 recen t m odel year vehicles without any factor for nondynam o meter effects) and city and highway VMT weights of 43% and 57%, respectively, com posite fuel economy is 22.1 m p g in still air a nd 20.8 m p g with a typical distribution of wind. Thus, taking wind into account reduces onroad f u el economy by 6%. This is m o re than twice that estim ated for the 1984 label adjustm ent rule. Roughly 60% of this 6% increase is due to the increase in drag co efficient during city driving. Th is portion of the es tim ate is lik ely th e m o st uncertain, due to the larg e win dangles which can o ccur at relatively low vehicle speeds (e.g., 45% o r m o re). This m eans that the f i gures tak e n f r om G illespie ar e bein g extrapo l at ed to a s i gnif i cant degr e e. W e are n o t certain that the drag coefficient woul d continue to increase beyond 20 degrees wind angle at the sam e rate as below 20 degrees. However, the effectiv e frontal area of the ve hicle would continue to increase. R o lling res i stance is also likely to in crease, as the vehi cle mu st be driven in creas ingly sidew ays to trave l in the direc tion thatthe veh i cl e is pointing (i.e., down the road). It is unlike l y that e ithe r th e D O T or G illesp ie es tim ates cons ide r an inc r eas e in rolling re sistan ce, as they w ere likely developed in wind tunnels wh ere the vehicle is standing stil l. Thus, it is likely that the estim ate for the effect of wind on onroad fuel eco nomy is m o r e uncertain than those for fuel quality or tir e pressu re. Still, the ef f

ect of w i nd appears to be very sign if icant and lik ely larger than either o f the other two factors.
The final factor which w e reevaluate d was road roughness. Road roughness was estim ated to reduce on ro ad fuel econ o my by 4.2% relative to that m easured on the dynam o meter. 29 The m odel developed in 1980 included estim ates of the percentage of VMT driven on dry (69%), wet (25% ) and snow-covered (6%) roads. 33 It also included estim a tes of roadway miles which were unsurfaced, gravel, lo w-load asphalt, and concrete and high load asphalt. It also included estim ates of the per centages of VMT on each roadway type, as well as the effect of each roadway type on fuel economy relative to th at m easured on a dynamom eter. As the vehicle coast downs used to determ ine dynam o meter road loads are conducted on dry concrete or high load asphalt road s, this com b ination of road w a y type and driving condition was

87assum e d to have no effect on fuel economy relative to a dynamom e ter. All other roadway types and driv ing conditions reduced onro a d fuel econ o my
. Table III.A-26 p r esents th e inp u ts to th e model developed in 1980 (with highway and VMT estim ates from FHWA ’s H i ghw ay Statis tics 1977).
Table III.A-26. Effect o f Road Roughness on Onro ad 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% W et -30% -18% -5% -3% Snowy -35% -20% -10% -7% The net effect of the inputs shown in Table I II.A-26 and the percentages of VMT with various road conditions cited above was an av erage fuel economy shortfall of 4.4%.
W e updated the m odel using m o re recent estim ates of VMT by roadway type contained in FHW A ’s Highway Statis tics 2003. W e had to com b ine estim ates from a set of tables in order to estim ate VMT by cars and ligh t -d uty tru cks b y roadway s u rface. W e began with estim ates of national VM T by cars and 2-axle, 4 tire trucks by roadway type in 2003 from Table VM-1. W e then converted the VMT by 2-axle, 4 tire trucks to the VMT by EPA-defined light-duty trucks by mu ltiply ing by 0.9234, which was derived from an Oak Ridge Nationa l Laborato r y study. According to this study, 7.66% of the VMT by 2-ax le, 4 tire trucks is by trucks which have a curb weight above 6000 pounds ora gross vehicle weight rating of above 8500 pounds, which put these vehicles into E P A’s heavy-duty vehicle class.
W e then obtained es tim ates of the length of roadway by surface type for each roadway class from Table HM-12. The surface types us ed in Table H M -12 differ som e what from those cited in the EPA fuel economy study and shown in Table III. A-26. Table HM-12 uses two m a jor classes of roadway surface: Unpavedand Paved. There are five sub-class e s of paved roadway surfaces: lo w, interm ediate, high -flexible, h i gh-com posite, an d high-rigid, where low, inte rm ediate and high ref er to the w eight car ry ing capacity of the road way. According to FHW A , Paved includ es the followin g catego r ies : Low type -- an earth, g r av el, or stone roadway which has a bitu mi nous surface course less than 1” th ick suitab l e for occas ional heavy loads ; Interm edia te Type -- a mi xed bitumi nous or bitumi nous penetration roadway on a flexible base having a com b ined surface and base th ickness of less than 7”; High-Type Flexible -- a mi xed bituminous or bitu mi nous penetration roadway on a flexible base having a co m b ined surface a nd bas e thickness of 7” or m o re; also includes brick, block, or com b ination roadways;

88High-Type Composite -- a mi xed bitumi nous or bitumi nous penetration roadway of more than 1” com p acted m aterial on a rigid base with a com b ined surface and base thickness of 7” or m o re; High-Type Rigid -- a Portland Cem e nt Concrete road way with or without a bitumi nous wearing surf ace of less than 1”.
From these def i nition s, it seem ed that the g r avel r ef erred to inthe def i nitio n of the Low Paved class was the sup port for the bitumi nous su rface. This would im ply that both unsurfaced and gravel s u rfaced road ways of Table III.A-26 fell into th e u npaved categories. Ho wever, it also seem ed possible that the grav el roadways refe rred to in T able I II.A

- 2 6 w ere inc l u d ed in the low paved category. It also seem ed uncertain w h ether the high, flexible paved roadway fell into the low-loadasphalt or h i gh-lo ad asp h alt. In or der to reflect these uncertainties, we developed to m appings of these six ro adway surfaces onto the f our types u sed in th e EPA study in order to bracket the potential fuel economy impact

. Table III.A-27 s hows these two m appings.

Table III.A-27. Mapping of Roadw ay Surfaces Unsurfaced Gravel Low-load Asphalt Concrete o rHighload Asphalt Low Fuel Economy Unpaved Low Paved Interm ediate + HighFlexible Paved High-Com p osite + High-Rigid Paved High Fuel Economy None Unpaved + Low Paved Interm ediate Paved H i gh-Flexib le + High-Com p osite + High-Rigid Paved Using these two sets of roadway m appings, we co nverted the total roadway lengths for each roadway surface class within each highway class from Table HM-12 into lengths of ro adway by the surface classes shown in Table III.A-26 within each high way class.
W e then estim a ted the effect of roadway condition on the fuel economy of vehicles driving on e ach roadw ay surface type

. W e di d this by mu ltiplying the e stim ates of the percentage of VMT driven on dry (69%), we t (25 % ) and sno w -covered (6%) road s to the changes in f u el economy for each ro adway condition for eachroadway su rface (show n in Table III.A

- 26 ). The result is that the avera g e fuel econ o my 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 simu la ted during dynamom e ter testing.
W e then applied these ef fects of road wa y conditions to the distribution of roadway surfaces for each highway class in order to dev e lop estim ates of the average effect of roadway conditions for each h i gh way class. W e then wei ghted these effects by th e distribu tio n of car and light truck V M T by highway class in order to d e v e lop a natio nal average effect of roadway surface and condition onfuel econo my. Using th e two m appings, we estim ate that the nation al average im pact of roadw ay surface and conditionon fuel eco nomy is 1.4-3.2%. Based on the decisions underlying the two m appings , w e belie ve that the low end of this range is m o re likely than the h i g h end. Thus, it appears that the effect of roadway surf ace and condition is lower today than it was in 1977.

89The other non-dynam o meter factors are m o re difficult to estim ate. Fortunately, they appear to be sm aller in m agnitude. The f o llow i ng table shows the breakdown of the im pact of all the non-dynam ometer factors as estim ated for the 1984 rule. It also update s the im pacts for the four factors discussed above, as w ell as eliminating those f actor s which are now addressed by the US06, SC03 and cold FT P tests.

Table III.A-28. Effect o f Non-Dynamomete r Factors on Onroad Fuel Economy Factor Analysis for 1984 Rule Effect Applicable to 5-C y cle Fuel Economy Am bient temp erature -5.3% Included Fuel Quality 0% -1.1 to -1.5 % A ltitud e -0.1% -0.1% W i nd -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 veh i cle speed 10.6% Included Cold starts -0.7% Included Accelerationintens ity -11.8% Included Brake drag -0.3% -0.3% W h eel align m ent -0.3% -0.3% Tire sw itching -0.4% -0.4% Tire pressure -3.3% -0.5% Vehicle load -0.4% -0.4% Dyna m o m e t e r loading -2.7% Revised test procedur es m a y have removed m o st of these effects Tire effects -5.1% W eight clas sification -1.0% Manual transmissions -1.8% Power acces sories, air conditioning ~0% Air conditioning included

Sum -30% -12% to -15% As can be seen, the net impact of non-dynam o mete r factors applicable to a 5-cycle fuel economy estim ate is 12-15%. The four factors evaluated in detail above com p rise the m ajority of the im pact. Together, o ther factors like road grade, road curvature, altitude and vehicle condition add only 3.5% to the overall estim a te.
W e receivedlittle com m ent on the es tim ates of the im pacts of the ind i vidu al untested factors on onroad fuel economy. One comm ent e r in dicated that he thoughtthat the effect of

90wind seem e dhigh, but offered no specific in form ation on how the m e thodology or estim ate should be changed.
Due to the fact th at th is type of analysis seem ed to over-estim ate the impact of these factors com p ared to onroad fuel economy estimates from owner diaries, we perform e d a second analysis starting from fle e t-wi de fuel economy estim ates. As described above in section II.C above, FHW A develops annual estim ates of car and light truck f u el economy based on estim ates of total VMT and fuel consum ption across the nation. For the NPRM, we utilized FHW Afuel economy estim ates for the 2002 and 2003 vehicle fleets. Since th e tim e of the NPRM, FH W A has updated their estim ate of onroad fuel economy for the 2003 fleet and published an estimatefor the 2004 fleet. T h ese latest estim ates show lower onroad fuel economy for light trucks than those estim at ed recently. After adjusting for the difference FHW A ’s and EPA’s definition of light trucks, onroad fuel economy was 19.7 and 19.9 m p g in 2003 and 2004, respectively. Us ing MOBILE6.2, we estim ate that fleetwide label fuel economy for these cale ndar years were 21.1 and 21.2 m pg, respectiv ely. Th is indicates a current sh ortfall of roughly 6.5-7.0%. Absent any non-dynam o m eter factor, the averag e com b ined (55/45) current label value for the 601 conventional vehicles in our certification fuel economy database is 20.9 m p g, while the average com b ined (43/57) 5-cy cle fuel econo my is 21.6 mpg, or 3.5% higher. T hus, the shortfall between the co m b ined 5-cy cle fuel eco n o my and the FHW A -based fleet es tim ates is roughly 10-10.5%. In the NPRM, we added one m o re factor to acco unt for chan ges in FTP and HFET test procedures when EPA i mplem ented the Supplem en tal FTP standards. Specifically, we reduced com b ined fuel economy by 3% to compensate fo r the rem ova l of a 10% upward adjustm e nt to the vehicle’s tractive road loadhorsepower setting on the dyn am om eter. However, we received a com m ent t h at, at the tim e of the SFTP rule, EPA had found that the net fu el econo m y effect of all the chang es in test p r o cedures was zero, not 3 %

. W e agree w ith this co m m e nt. Therefore, we have rem oved this 3% adjustm ent from this anal ysis. Thus, the shortfal l between the com b ined 5-cycle fuel economy and the FHW A -based flee t estim a tes rem a ins at roughly 10-10.5%. This 10-10.5% difference is slightly lower than the 12-15% estim ate for the im pact of non-dynam o meter factors shown in Ta ble III.A-28. In the NPRM, we decided to average the two estim ates, rounding down, and include a factor of 0.89 in the 5-cycle city and highway formulae (i.e., a reducti on of 11% in both city and hi ghway fuel economy) to account for the im pact of these f actors. As d i scu ssed above, however, the bottom-up approach overestim ated the net effect of thes e factors back in 1984 when the current label adjustm e nts were developed. Thus, for the final 5-cy cle formul ae, we decided to place m o re em phasis on the top

down com p arison. Therefore, we set the valu e of the non-dynam ometer factor so that the fleetwide combined 5-cycle fuel economy m atches onroad fuel ec onomy as estim ated by FHW A

. As indicated above, the average of the curren t combined fuel economy label values in our certification fuel economy database (20.9 m pg) is slightly low e r than that f o r the entire onroad fleet (21.1-21.2 m pg per MOBILE6.2 ). Thus, the certification databa s e ap pears to b e b i ased low

91by 1.0-1.5% relative to the onroad fleet. Thus , the averag e o f the com b ined 5.cycle f u el economy label values in our certif ication fuel economy database should also be 1.0-1.5% lower than the onroad fuel economy estimated by FHW A (19.7-19.9 m pg), or about 19.6 mpg. Incorporating a non-dynam om eter factor of 0.905 into the 5- cycle city and highway formulae produces an average combined label value of 19.6 m pg. Thus, we have set the value of the nondynam o meter factor in the final 5-cycle formulae to 0.905.
6. 5-Cycle Fuel Economy Formulae
The com p lete 5-cycle fuel economy for mulae are developed by com bining the results of the sections on start fuel use, running fuel use, air conditi oning, cold tem p erature, and nondynam o meter effects. T h e resultant for mulae are described below.
U nder the f i nal ru le, a sp ecia l situatio n c ould exist where the city fuel econ o my of a model type could be develope d using the m pg-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 fu el economy formula is al so described below.

a. 5-Cycle Fuel Economy Formulae
Vehicles Tested Over aThree-Bag FTP at 75 F 5-Cycle City Fuel Econo my Formula The final 5-cycle city fuel econom y would be calculated as follows: 

## 1 City FE = 0

. 905 × , where 

## Start FC + Running FC

StartFC 

# ( gallons per mile 

# ) = 0

. 33 × ⎛ ⎜ 

# ( 0

. 76 × StartFuel 75 

# ) + 

# ( 0

. 24 × StartFuel 20 

# ) ⎞ ⎟

, 

### ⎝ 4

. 1 ⎠ where, 

### ⎡ 1 1 ⎤ Start Fuel x 

### = 3

. 6 × ⎢

− ⎥ , where 

### ⎣ Bag 1 FE x 

### Bag 3 FE x 

### ⎦
Bag y FE x = the f u el eco nomy in miles per ga llonof fuel during Bag 1 or Bag 3 of the FTP test conducted at an am bient tem p erature of 75 or 20°F.

92

### Running FC = ⎡ 0

. 48 0

. 41 0

. 11 ⎤ ⎡ 0

. 5 0

. 5 ⎤ 0

. 82 × ⎢ + + ⎥ + 0

. 18 × ⎢ + ⎥ ⎣ Bag 2 75 

### FE Bag 3 75 

### FE US 06 City FE ⎦ ⎣ Bag 2 20 

### FE Bag 3 20 

### FE ⎦

+ 0

. 133 × 1

. 083 × 

## [ A / C FC 

## ]

where 

### ⎡ 1 ⎛ 0

. 61 0

. 39 ⎞ ⎤ A / C FC = ⎢

− ⎜ ⎜ + ⎟ ⎟ ⎥ , where 

### ⎣ SC 03 FE ⎝ Bag 3 75 

### FE Bag 2 75 

### FE ⎠ ⎦

U S 06 C ity FE = f u el eco nomy in miles per gallon over the city porti on of the US06 test, US06 Highway FE = fuel economy in miles per gallon over the Hi ghway portion of the US06 test, HFET FE = fuel economy in miles per gallon over the HFET test, SC03 FE = fuel economy in mile s per gallon over the SC03 test. 5-Cycle Highway Fuel Economy Formula The final 5-cycle h i ghway fuel econ o my would be calculated as follows: 

### 1 Highway FE = 0

. 905 × , where 

### Start FC + Running FC

StartFC 

# ( gallons per mile 

# ) = 0

. 33 × ⎛ ⎜ 

# (0

. 76 × StartFuel 75 

# ) + 

# (0

. 24 × StartFuel 20 

# ) ⎞ ⎟

, 

### ⎝ 60 ⎠ where 

### ⎡ 1 1 ⎤ Start Fuel x 

### = 3

. 6 × ⎢

− ⎥ , and 

### ⎣ Bag 1 FE x 

### Bag 3 FE x 

### ⎦

Running FC = 1

. 007 × ⎢ ⎡ 0

. 79 + 0

. 21 ⎥ ⎤ + 0

. 133 × 0

. 377 × 

## [ A / C FC 

## ] ⎣ US 06 Highway FE HFET FE ⎦ where the various sym bols have the sam e definitions as just described above.

93Hybrid Vehicles Tested over a Four bag FTP at 75 F 5-Cycle City Fuel Econo my Formula The final 5-cycle city fuel econom y would be calculated as follows: 

## 1 City FE = 0

. 905 × Start FC + Running FC , where 

### ⎛ 

# ( 0

. 76 × Start Fuel 75 

### + 0

. 24 × Start Fuel 20 

# ) ⎞ Start FC = 0

. 33 × ⎜ ⎟ , where 

### ⎝ 4

. 1 ⎠ ⎡ 1 1 ⎤ ⎡ 1 1 ⎤ Start Fuel 75 

### = 3

. 6 × ⎢

− ⎥ + 3

. 9 × ⎢

− ⎥
, 

### ⎣ Bag 1 FE 75 

### Bag 3 FE 75 

### ⎦ ⎣ Bag 2 FE 75 

### Bag 4 FE 75 

### ⎦ and 

### ⎡ 1 1 ⎤ Start Fuel = 3

. 6 ×

− 20 

### ⎢ ⎣ Bag 1 FE 20 

### Bag 3 FE 20 

### ⎥ ⎦ , where



### Running F C = ⎡ 0

. 48 0

. 41 0

. 11 ⎤ ⎡ 0

. 5 0

. 5 ⎤ 0

. 82 × ⎢ + + ⎥ + 0

. 18 × ⎢ + ⎥ ⎣ Bag 4 75 

### FE Bag 3 75 

### FE US 06 City FE ⎦ ⎣ Bag 2 20 

### FE Bag 3 20 

### FE ⎦

+ 0

. 133 × 1

. 083 × 

## [ A / C FC 

## ]

where 

### ⎡ 1 ⎛ 0

. 61 0

. 39 ⎞ ⎤ A / C FC = ⎢ ⎣ SC 03 FE − ⎜ ⎜ ⎝ Bag 3 75 

### FE + Bag 4 75 

### FE ⎟ ⎟ ⎠ ⎥

⎦ , where

U S 06 C ity FE = f u el eco nomy in miles per gallon over the city porti on of the US06 test, US06 Highway FE = fuel economy in miles per gallon over the Hi ghway portion of the US06 test, HFET FE = fuel economy in miles per gallon over the HFET test, SC03 FE = fuel economy in mile s per gallon over the SC03 test.

945-Cycle Highway Fuel Economy Formula The final 5-cycle h i ghway fuel econ o my would be calculated as follows: 

## 1 Highway FE = 0

. 905 × Start FC + Running FC , where



### StartFC 

# ( gallons per mile 

# ) = 0

. 33 × ⎜ ⎛ 

# (0

. 76 × StartFuel 75 

# ) + 

# (0

. 24 × StartFuel 20 

# ) ⎟ ⎞

, 

### ⎝ 60 ⎠ where 

### ⎡ 1 1 ⎤ ⎡ 1 1 ⎤ Start Fuel 75 

### = 3

. 6 × ⎢

− ⎥ + 3

. 9 × ⎢

− ⎥
, 

### ⎣ Bag 1 FE 75 

### Bag 3 FE 75 

### ⎦ ⎣ Bag 2 FE 75 

### Bag 4 FE 75 

### ⎦ and 

### ⎡ 1 1 ⎤ Start Fuel = 3

. 6 ×

− 20 

### ⎢ ⎣ Bag 1 FE 20 

### Bag 3 FE 20 

### ⎥ ⎦ , and



### Running FC = 1

. 007 × ⎢ ⎡ 0

. 79 + 0

. 21 ⎥ ⎤ + 0

. 133 × 0

. 377 × 

## [ A / C FC 

## ] ⎣ US 06 Highway FE HFET FE ⎦
where the various sym bols have the sam e definitions as just described above.

Hybrid Vehicles Tested over a Tw o-Bag FTP at 75°F 5-Cycle City Fuel Economy Formula for Ve hicles Tested Over a 2-Bag FTP at 75°F The final 5-cycle city fuel economy for vehi cles tested over a 2- Bag FTP at 75°F would be calcu lated as follows: 

### 1 City FE = 0

. 905 × Start FC + Running FC , where 

# (0

. 76 × Start Fuel 75 

### + 0

. 24 × Start Fuel 20 

# ) Start FC = 0

. 33 × 4

. 1 , where

95

## ⎡ 1 1 ⎤ Start Fuel 75 

## = 7

. 5 × ⎢

− ⎥ , where 

## ⎣ Bag 1 / 2 FE 75 

## Bag 3 / 4 FE 75 

## ⎦ 

## ⎡ 1 1 ⎤ Start Fuel 20 

## = 3

. 6 × ⎢

− ⎥ , where 

## ⎣ Bag 1 FE 20 

## Bag 3 FE 20 

## ⎦

Bag y FE x = the f u el eco nomy in miles per ga llonof fuel during Bag 1 or Bag 3 of the FTP test conducted at an am bient tem p erature of 75 or 20°F. Bag x/y FE x = 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 am bient tem p erature of 75°F. 

### Running FC = ⎡ 0

. 90 0

. 10 ⎤ ⎡ 0

. 5 0

. 5 ⎤ 0

. 82 × ⎢ + ⎥ + 0

. 18 × ⎢ + ⎥ ⎣ Bag 3 / 4 75 

### FE US 06 City FE ⎦ ⎣ Bag 2 20 

### FE Bag 3 20 

### FE ⎦

+ 0

. 133 × 1

. 083 × 

# [ A / C FC 

# ]

where ⎡ 1 ⎛ 1

. 0 ⎞ ⎤ A / C FC = ⎢ ⎣ SC 03 FE − ⎜ ⎜ ⎝ Bag 3 / 4 75 FE ⎟ ⎟ ⎠ ⎥

⎦ , where

US06 City FE = f u el eco nomy in m iles per gallon over the city portion of the US06 test, US06 Highway FE = fuel economy in m iles per gallon over the Hi ghway portion of the US06 test, HFET FE = fuel economy in m iles per gallon over the HFET test, SC03 FE = fuel economy in m iles per gallon over the SC03 test. 5-Cycle Highway Fuel Economy Formula for Ve hicles Tested Over a 2-Bag FTP at 75°F The final 5-cycle h i ghway fuel econ o my fo r 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 Fuel 75 + 0

. 24 × Start Fuel 20 

## ) Start FC = 0

. 33 × , where 60 ⎡ 1 1 ⎤ Start Fuel 75 = 7

. 5 × ⎢

− ⎥ , and ⎣ Bag 1 / 2 FE 75 Bag 3 / 4 FE 75 ⎦ ⎡ 1 1 ⎤ Start Fuel 20 = 3

. 6 × ⎢ Bag 1 FE − Bag 3 FE ⎥ , and ⎣ 20 20 ⎦ ⎡ 0

. 79 0

. 21 ⎤ Running FC = 1

. 007 × ⎣ ⎢ US 06 Highway FE + HFET FE ⎦ ⎥ + 0

. 133 × 0

. 377 × 

## [ A / C FC 

## ]

where the various sym bols have the sam e definitions as just described above.
b. Alternative 5-cycle High way Fuel Economy Formula Beginning with the 2011 m odel year, m a nufacturers would be allow ed to continue to use the m pg-based for mulae if the available 5-cy cle f u el economy estim ates indicated close alignm ent with the m pg- based formulae. Fuel economy values over all five cycles will be available for one or m o re vehicle con f iguratio n s within ea ch durabi lity d ata group or basic engine group. If the 5-cycle fuel economy va lues for a specific emission data veh i cle are no more than 4% below the m pg-based estim ate for city fuel economy and no m ore than 5% below the m pg-based estim a te for highway fuel economy, all the vehicle confi gurations represented by that emission data vehicle would be allowed to use the m pg-based formul ae in com p lying with the fuel economy label requirem ents. If the 5- cy cle fuel econ o my values for a specific emission data vehicle are m o re than 4% below the m pg- based estim ate for city fuel economy and m o re than 5% below the m pg- based estimate for highwa y fuel economy , all the vehicle configurations represented by that emission data vehicle woul d be required to use the 5-cycle formulae in com p lying with the fuel economy label requirem ents. It is possible for the 5-cycle fuel economy valu es to m eet the above cr iteria 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 m ore than 4% below th e m pg-based estim ate for city fuel economy, but no m o re than 5% below the m pg-based estim a te for highway fuel economy, all the vehicle configurations represented by that emission data vehicle would be requ ired to use th e 5-cycle for mulae in com p lying with the fuel economy labe l requirem ents for both city and highway fuel economy. All five cycles play a significant role in the 5-cycle city fuel economy for mula. Once the five tests have been perform e d forthe city es tim ate, there is little rea so n not to use the sam e inform ation to derive the highway fuel economy estim ate. We proposed a different approach for the opposit e situation. If the 5-cycle fuel economy values for a specific emission data v ehicle are no m ore than 4% below the m pg-based estim ate for city fuel economy, but m o re than 5% below the m pg-based estim ate for highway fuel economy, all the vehicle configurations repres ented by that emission data vehicle would be

97allowed to use the m pg-based formulae in deriving the city fuel economy label value. The highway fuel economy value, however, would be based on an alternativ e, sim p lified 5-cycle for mula as opposed to the full 5-cycle highway fu el economy for mula. This alternative 5-cycle highway formula would be based on fuel economy values over the FTP, HFET and US06 tests. The im pact of the SC03 test is relatively sm all due to the speed adjustm e nt factor of 0.377 and air conditioning usage factor of 0.133 in the 5-cy cle highway fuel economy for mula. The im pact of

the cold F T P test is s m all due to the 60 mile trip leng th ass u m ed f o r highw ay driving and the fact that we do not use the actual cold FTP testresults to adjust running f u el consum ption for colder tem p eratures during highway driving. This approach requires that we devel op a sim p lified 5-cycle highway fuel economy f

o rmula w h ich is cons istent w ith the f u ll 5-cyc l e for mula. We developed this sim p lified for mula using estim ates of the av erage im pact of the SC 03 and cold F T P test results on 5-cycle highway fuel economy. In both cases, we estim ated this average im pa ct by regressing the im pa ct of these test cycles on the 5-cycle highway fuel ec onomy for the 615 vehicles in our certification database against fuel economy values which would be available from FTP, HFET and US06 testing. Regarding the im pact of the cold FT P on hi ghway fuel econo my, we regressed start f u el use in highw ay driving under a mi x of a m bient temperature against s t ar t fuel use in highway driving at 75°F. As described above, start fuel use in highway driving under a mi x of am bient tem p erature is as f o llow s : 

## ( 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 ⎢ ⎣ Bag 1 FE x Bag 3 FE x ⎥ ⎦ The result of the reg r ess i on 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 im pact of SC03 on highway fuel economy, we regressed fuel use due to air conditioning use in highway drivi ng against several estim ates of running fuel use, nam e ly Bags 2 and 3 of the FTP, HFET and US06. As describe d above, fuel use due to air conditioning use is as f o llow s :

A / C FC = 1

− ⎜ ⎜ ⎛ 0

. 61 + 0

. 39 ⎟ ⎟ ⎞ SC 03 FE ⎝ Bag 3 FE 75 Bag 2 FE 75 ⎠

98In the analysis perform e d for the NPRM, fu el use over US06 showed the highest level of corre lation w ith air con d ition i ng us e. The sam e w as true w i th the exp an d ed certif ication f u el economy da tabase. The resultof the regression is:
0

. 1357 A / C Fuel Use = 0

. 540 + US 06 Fuel Economy The adjusted r-squared of this regr ession is mu ch lower thanthat for co ld start fuel u se (0.15). However, the p-values of both coefficients were less than 0.0000001, and thus, are quite statistically significan t. These two relationships can be inserted direc tly into the 5

- cy cle fuel econ o my for mul a. The result is :
1 Alternativ e Highway FE = 0

. 905 × , where Start FC + Running FC 

## ( 0

. 005515 + 1

. 13637 × Start Fuel 75 

## ) Start FC = 0

. 33 × , where 60

. 0

Start Fuel = 3

. 6 × ⎛ ⎜ ⎜ 1

− 1 ⎞ ⎟ ⎟ , and 75 ⎝ Bag 1 FE 75 Bag 3 FE 75 ⎠
Running FC = ⎡ ⎛ 0

. 79 0

. 21 ⎞ ⎤ ⎡ ⎛ 0

. 1357 ⎢ 

## [

1

. 0 + 

## (0

. 04 × 0

. 18 

## ) 

## ] × ⎜ ⎜ + ⎟ ⎟ ⎥ + ⎢ 0

. 377 × 0

. 133 × ⎜ ⎜ 0

. 00540 + ⎣ ⎝ US 06 Highway FE HFET FE ⎠ ⎦ ⎣ ⎝ US 06 FE Hybrid gaso line

- ele ctr ic vehi cles using this m odified 5-cy cle highway calculation use one of the following equations for start fuel, depend ing upon whether the vehicl e is tested on a 4-bag FTP or a 2-bag FTP. For a 4-bag FTP: 

### ⎡ 1 1 ⎤ ⎡ 1 1 ⎤ Start Fuel 75 

### = 3

. 6 × ⎢

− ⎥ + 3

. 9 × ⎢

− ⎥ ⎣ Bag 1 FE 75 

### Bag 3 FE 75 

### ⎦ ⎣ Bag 2 FE 75 

### Bag 4 FE 75 

### ⎦
For a 2-bag FTP: ⎡ 1 1 ⎤ Start Fuel 75 = 7

. 5 × ⎢

− ⎥ ⎣ Bag 1 / 2 FE 75 Bag 3 / 4 FE 75 ⎦ ⎞ ⎤ ⎟ ⎟ ⎥ ⎠ ⎦

99where the various sym bols have the sam e definitions as just described above. 

### B. Derivation of the MPG-Based Approach The 5-cycle fuel economy for mulae derived above assum e that fuel economy estim ates are available for specific vehicles for all five dynamom eter cycles and their respective bags of em ission m easurem ents. As discusse d in the p r ea mb le to the f i nal rule, the se estim ates m ay be based on fuel economy m easure m ents, or on esti m ates based on test re sults from a similar vehicle. A sim p lified approach to implem enting the 5-cycle formulae is to apply thes e for mulae to tes tresults on recen t model vehicles and dev e lop correlations betweenthe 5-cy cle city and highway fuel economy estim ates for these vehi cles and their fuel economy over the F T P and HFET, respective l y. This sim p lif ied approach is referred to as the mpg-based approach, since the resu ltant label adjus t m e nt will vary dependin g on the m e asured fuel economy (i.e., m pg) of a vehicle over the FTP and HFET tests.
The database from which the m pg-based co rrelations were derived consisted of 615 2003-2006 model year vehicles, including 14 hybrids a nd one diesel vehicle. All vehicles had been tes t ed over all five certification test cycl es. In m ost cases, bag-specific fuel econ o my m easurem ents were also available, bu t in som e cases they were not. In the latter cases, we estim ated FTP bag-specific fuel eco nomy 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 cy cle fuel econ o my from our 5-cycle fuel economy da tabase for the standard and cold FTP.

Table III.B-1. Ratio of FTP Bag to Cycle Fuel Co nsumption No. of Vehi cles 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 for mulae also require separate fuel economy estim ates for the city and highway portions of US06. These m e asurem ents ha ve not been taken on a regular basis. In the Draft Technical Support Docum e nt to the NP RM, we analyzed US06 city and US06 highway fuel economy data for 85 vehicles which was av ailable. 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 f ound that the fuel economy of the US06 highway bag averaged 116% of that over the entire US06 cycle for conve ntional vehicles, and 109% for two hybrid vehicles.

100For the NPRM, we proje cted th e im pact of the 5-cycle approach by applying the relationships for conventional vehicles to all vehicles, including hybrids. F or the FRM, we believ e that it would be more accurate to app l y the relation s hips for the two hybrids that were tested to all the hybrid vehicles in the certifica tion database. While only two hybrids were tested over a two-bag US06 test, the fact that fuel econo my over the US06 city bag was closer to that over the entire US06 cycle than withconventional vehicles is very consistent with the effect of hybrid technology on fuel economy. That is, hyb rid technology is generally m ore effective during lower speed, stop and go driving than at consistently high vehicle speeds. W hi c h 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 usi ng the 5-cy cle formulae. In this case, US06 city and highway f u el econo m y values will b e m easured, not es tim ated. However, the p r ojec tions m a de here can affect the m pg-based equations, as these equ a tions are b a s e d on projected 5-cy cle fuel economy values. T hese projections for hybr ids with the highest fuel economy va lues are particular ly im portant, a s these veh i cles can affect the sh ape o f the mpg-ba sed equations at high fuel economy values. Fortunate ly, the two hybrids for which we have US06 city and highway fuel economy testing are hybrids with very hi gh fuel economy values (a Prius and a Civic hybrid). Thus, using the relationships be tween US06 city, US06 highway and US06 fuel economy va lues based on the testing of these two vehicles to all hybrids in the certification database sho u ld be m o st accurate in the range of fuel economy where the mpg-based equations are m o st affected by hyb rids.
One additio nal adju stm e nt was m a de to Cold FTP fuel economy values o f all vehicles

. This adjus t m ent is related to the n e w requirem ent that the h eater or defro ster be turned on during the Cold FTP test. In order to estim ate the im p act of this change on fuel economy, EPA tested two conventional vehicles and two hybrid vehicl es at 20°F with the heater turned on and off. 3 7 The results are shown in Table III.B-2.

101Table III.B-2. Effect o f Heater/Defr oster Use on Cold FTP Fuel Use

Bag 1 Bag 2 Bag 3 FTP Fuel Econo my: Heater off ( m pg) 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 (m pg) 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 m iles) 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 m iles) 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 adju sting for test procedure differen ces (% ) 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 p r o cedure us ed in this tes tin g dif f

e rs from that being promulgated in the final rule. In this testing, the def r oster wa s turn ed on to the m axi mum position imm ediately at the start of the test and held there throughout the test. This is a mo re severe setting than we are promulgating, where the start of defrosting is be ing delayed two m i nutes and then reduced to a more m oder a te setting during Bags 2 and 3. To account for these differences, we reduced the im pact of de frosting on fuel consum ption for co n v entional vehicles. Thes e 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. W e al so reduced the impact on Bag 1 fuel consum ption

102f o r the Odyssey to ref l ec t the rela tiv e impact f o r the Odyssey on Bags 2 and 3 compar ed to those for the Trailblazer. The Bag 1 im pact for the Tr ailblazer was not adjus t ed, because it was tested with the delay in defroster start-up being pr omulgated. Thus, the fuel consum ption of conventional vehicles over Bags 1, 2, and 3 of the Cold FTP test in ou r 5-cycle certification database were increased by 1.7%, 2.9%, and 2.2% , respectively, to account for defroster use in f u ture tes tin g. As can be seen from Table III.B-2, th e effect of defroster and heater use w as mu ch larger for the two hybrids than for the two conventional vehicles. (The tem p erature control was also turned to hot when the defroster was turned on dur ing the hybrid testing.) T h is grea te r impact is like l y due to the f act th at ope rating th e heater prevents th e engine from shutting off during certain driving m odes, like idling an d decelerati ons. The sam e effect likely occu rs when the heate r is tur n ed on with out the def r o ster. Sin ce drivers regularly use th eir heater und er cold er am bient conditions, this effect is occurring curr e n tly in -use. W h ile the f i nal Cold FTP test procedure is m ore m oder a te than that used in the above testing of hybrids, we believe that the great m a jority of the impact on hybrid fuel consum ption was due to the elim ination of the engine shut-off feature, as oppo sed to th e sp ecific defros te r/heater setting. Thus, we did not b e liev e that the re tes ting of these veh i cle s with th e f i na l test procedure would produce significantly lower fuel consumption im pacts. W e expect that au to m anufacturers will m odify their hybriddesigns in the f u tur e to reduc e th is im pact. However, as the m pg-based equation s will be app l ied to vehicles as early as the 2008 m odel year, we be lieve that they should reflect current technology as mu ch as possible. Also, hybrids which reflec t im proved technology in this regard can utilize the 5-cycle f o rmulae, especially since hybrid m o dels are always tested over all five dynam o meter cycles during certification due to their unique features. Thus, we are apply i ng the av erage im pacts on Bag 1, 2, and 3 fuel consum ption, as m easured in the above test program , to hybrid fuel consumption in our 5-cy cle certification database. Using the fuel economy values which are now available for all bags and cycles for all 65 vehicles, we calcu lated 5

- cycle ci ty and highway label values. W e then developed relationships between the 5-cycle city and highway label values and FTP and HFET fuel economy values, respec tive l y, using the le ast squares regression function in Excel. As we did for the NPRM, we perform e d these regressions in term s of fuel cons um ption (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 re gression was ag ain slightly wors e, 0.952. Figures III-8 and 9 show the relationship between th e inverse of 5-cycle city and highway fuel economy (i.e., fuel consum ption) versus the inverse of F T P or HFET fuel econo my. The first graph sho w s city fuel consum ption, while the second shows highway fuel consumption.

103Figure III-8
. 5-Cycle City V ersus FTP Fuel Consumption 5 - Cycl e Ci t y V s . F T P Fue l C o nsu m pt i o n 0. 15Inverse of5-Cycle CityFE(gal/mi) 0. 10

0. 05

-

- 0

. 0 2 0 .0 4 0 .0 6 0 .0 8 0

. 1 0 I n ver se o fFT P F u el E c o n o m y ( g a l/m i )

Figure III-9
. 5-Cycle Hig h w ay Versus HFET Fuel Consumption 5- Cycl e H i gh way Vs. H F ET F u el C ons um pt i o n 0. 1 5Inverseof5-Cycle Hwy FE(gal/mi) 0. 1 0

0. 0 5

-

- 0 .0 1 0 .0 2 0 .0 3 0

. 0 4 0

. 0 5 0 .0 6 0 .0 7 0 .0 8 I n ve r se o fH F E T F u e l E c o n o m y ( g al /m i )

Figures III-10 and 11 show the relationshi p between 5-cycle city and highway fuel economy (i.e., fuel consumption) versus of FT P or HFET fue l economy. The first graph shows city fuel consumption, while the second shows hi ghway fuel consum ption. As can be seen by comparing the two sets of graphs, the relationships are linear in term s of fuel consumption, but becom e curved in term s of fuel economy
.

104Figure III-1 0. MPG-Based City Fuel Economy 

### M P G - B a s e d C i ty FE 705-CycleCityFuelEconomy(mpg) 60 C u r r e n t Labe l 50 40 30 20 10

0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 F T P Fue l E c on om y (m p g )

Figure III-1 1. MPG-Based Highw ay Fuel Economy 

### M P G B a s e d H i gh w a y FE 70 605-CycleHwyFuelEconomy(mpg) 50 40 30 20 10

0 0 1 0 C u r r en tL a be l

2 0 3 0 4 0 5 0 6 0 7 0 Cu rre nt H F ET Fu e l E c on o m y ( m p g )

The standard error of the difference betw een 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. Th us, while the mpg-based equations are able to reflect much of the difference in fuel economy re presented by the 5-cycle for mulae, differences

105between the fuel economy of individual vehicles on the order of 0.5-1. 1 mpg are mu ted by the mpg-based approach. 

### C. Variability in Onroad Fuel Economy As described in the preamble to the f i nal rule , EP A is proposing to cont inue to set the city and highway m pg estimates at a le vel that reflects average fuel economy. However, we desire the fuel economy label to indicate the range of onroad fuel economy that drivers m i ght experience. Therefore, it is im portant to unde rstand the variability of onroad fuel economy. We begin with a review of the work done in this area for the 1984 fuel economy adjustm e nt rule. At that tim e (circa 1982), EP A conducted a system atic review of the onroad fuel economy experienced by over 40,000 drivers compared to the EPA fuel economy labels for each vehicle. 3 8 Vehicles were separ ated into 3 c ategories : 1) p r im arily city driven, 2) p r im arily highway driven, and 3) m i x of driving. The pe rcentage differences between onroad and either the EPA city or highway fuel ec onomy label was determined for the first two groups. The results were generally norm a lly distributed

. The results f o r city dr iv en vehicles are dep i cted in Figure III-12. Figure III-1 2. Onroad FE Versus Pre-1984 EP A City Label for City Driven Cars 

### I n -U s e F E B e fo r e Ad j u s t m e n t : 1 9 8 4 L a b e l Ru l e

0% 5% 10 % 15 % 20 %

- 8 0 %

- 6 0%

- 4 0 %

- 2 0% 0% 20 % 4 0% Di ff er en ce f r o m EPA Ci t y La bel FE ( F T P )% ofDrivers 6 1 % be l o w 90 % o f l abe l F E
4% a b ov e 1 1 0 % o f la b e l FE

+ 10 %

The analysis of the onroad fuel economy by prim a r ily city drivers found that average onroad fuel economy was 90% of the then current EPA city labe l (i.e., a 10% shortfall on average). At that tim e, the city label v alue was sim p ly the f u el eco nomy 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.

106Assum i ng a norm a l distribution of the onroad fuel economy of any specific vehicle, as was done in the 1984 label adjustm e nt rule, it is po ssible to use this info rmation to calculate a coefficient of variation for the difference betw een onroad fuel economy and EPA’s city label. As shown in Figure III-1 2, 4% of all predom inantly city driv e r s achieved m o re than 1 10% of the EPA city fuel economy label. Fo r a norm a l distribution, 4 % of the population ex ceed s the m ean of the population plus 1.75 tim es the standard deviation. In this case, si nce the population is in percentage term s (onroad fuel ec onomy divided by label fuel m i nus 1.0), the standard deviation is equal to the coefficient of variation in the ratio of onroad fuel economy to EPA label value. Likewise, 6 1 % of all pr edom inantly city dr ive r s achieved les s than 90% of the EPA city f u el economy label. Another way to put this is that 39% of all drivers achieve d m ore than 90% of the EPA city fuel economy label. For a norm a l di stribution, 39% of the population exceeds the m ean of the population plus 0.279 tim es the coeffici ent of variation. Thus, 20% of the EPA city fuel economy label (110% m i nus 90 %) represents 1.471 (1.75 m i nus 0.279) tim es the coefficient of variation. The coefficient of variation for th is distribution is theref ore 13.6% (20% divided by 1.471).
The com parison of onroad fuel economy a nd EPA highway label for highway driven vehicles yielded sim ilar results. In th e case of onroad fuel economy during highway driving, only 8% of all predom inantly highway drivers ac hieved an on road fuel economy m o re than 110% of their vehicle’s highway fuel economy labe l. Of all predom inantly highway drivers, 34% achieved an onroad fuel economy within 10% of their vehicle’s highway label and 58% achieved an onroad fuel economy be low 90% of their vehicle’s highway label. These percentages apply prior to the 22% downward adjustm e nt to the highway fuel economy label im ple m ented in that rulem aking. Again for a norm al distribution, 8 % of th e population ex ceed s the m ean of the population plus 1.405 tim es the coefficient of variation. Likewise, 42% (8% plus 38%) of all drivers achieved m ore than 90% of the EPA highway fu el economy label. For a norm al distribution, 42% of the population exceeds th e m ean of the population p l us 0.202 tim es the coefficient of variation. T hus, 20% of the EPA highway fu el economy label (110% mi nus 90%) represents 1.203 (1.405 mi nus 0.202) tim es the coefficient of vari ation. The coefficient of variation for this distribution is therefore 16. 6% (20% divided by 1.203). The goal of the final 10% and 22% adjustm e nts was to m ove the average onroad fuel economy closer to either the city or highway label value, as applicable. These adjustm e nts, however, do not affect the underlying variability of the data. T hey increase the coefficients of variation slightly, because they reduce the deno m i nator (the EPA label value) by 10% or 22%, respec tive l y. They prim arily shif t th e dist ribution over by 10% or 22%

. However, EPA did analyze the effect of m o re com p lex adjustm e nts, which depended on the several vehicle factors, such as front versus rear wheel driv e, m anual or au tom atic tra n sm ission, gasolin e or diesel engine, etc. Applying this m ore complex system of adjustm e nts reduced the variability in onroad versus EPA label fuel economy so mewhat. For city dr iven v e hicl es, the coefficient o f variation decreased to 13.1%, while that for highway driv en vehicles d ecreased to 14.1%. Since the 5­ cycle formulae basically adjust fuel economy in asim ilar fashion (i.e., som e vehicles receive more of an adjustm e nt than others), we believe that it thes e s o m e what smaller coefficients of variation are m ore indicative of what drivers would experience w ith vehicles labeled using the 5­

107cycle f o rmulae. Thus, we will us e a coef f i ci ent o f variation o f 13-14% to determ ine a n adjustm e nt that would convert a m ean fuel economy into a 25 thpercen tile fuel econo my. As discussed ab ove, 75% of the population of a nor m al distribution exceed s the m ean m i nus 0.675 tim es the standard deviation (or co efficien t of variation in this cas e). Applying this factor to our estim ate of the coefficient of variation of 13-14% yields an offset of 9-10%. Oak Ridge National Laborator y (ORNL), sponsored by the De partm e nt of Energy, has recen tly beg un a program where drivers can sub mit their own fuel econo my m easurem ents via the In terne t

. 3 9 The program is commonly referred to as “Your MPG. ” The Your MPG data are sim ilar in nature to the much larger database s analyzed for the 1984 label adjustm e nt rule. Drivers m easure the i r o w n f u el economy and provide a pe rc eived sp lit o f their dr ivin g into c ity 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 th e sam ple, the unknown nature of the t echnique used by the owner/driver to m easure fuel economy and the short tim e period over which fuel economy is generally assessed (e.g., as short as a tank full of fuel or two). In the p articular case of the ORNL database, its current size is still sm all ( 8180 estimates of fuel economy for 4092 vehicles) com p ared to those available in 1984, though it is growing daily.
W e com p ared the fuel ec onomy estim ate s subm itted to the O R NL website with each vehicle’s fu el economy label. W e com b ined th e city and h i g hway labels using each dr iver’s estim ate of the percen tag e of their dr iving that wa s city-like and highway-like. If a driver did not provide an estim ate of the breakdow n of their driv ing pattern, we assum e d that their driving was 55% city and 45% highway. W e calculated th e percen tage d i fference between the on road fuel economy and the current composite EPA label valu e. (A m o re detailed discussion of these estim ates is presented in Chapter II of this Final Technical Support Docum e nt .) The average difference for m o re than 7300 individual fuel economy estim a tes was -1.4%, m eaning that onroad fuel economy was just slightly lower thanthe composite EPA label value using the split of city and highway driving estim at edby the dr ive r
. This m etric is analogo us to those p r esented above from t he early 1980’s, as vehicles were segr egated then into those with predom inantly city or highway driving. The sta ndard deviation in this percen tage difference was 13%, very consistent w ith the estimates derived above.
Another sou r ce of onroad fuel econo my data is the recen t testing of over 100 vehicles in the Kansas City area. Valid onroad fuel ec onomy m easurements were obtained for roughly one day of driving from roughly 100 vehicles. The average onroad fuel economy was 30.4 mpg, while the av erage com posite EPA label valu e wa s 31.4 m pg. 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 es tim ates from the early 1980’s and the Your MPG website. We would have expected a larger variab ility than th ese other so urces for tw o reasons. One, we did not segregate vehicles into prim arily city and h i g h way driv in g catego r ies or accoun t for the pred om inance of one or th e other type of driving. Tw o, driving can vary significantly from day to day. W ith only one day’s worth of driving m easured, the v ariability in fuel economy would be expected to be much higher than if a week or two of driving were assessed. A tthe sam e tim e, all of these v ehicles were located wi thin a sing le m etropolitanarea, so their driv ing did not reflect mu ch urban/rural diversity, nor th e diversity likely presen t b e tween urban areas.

108And only 100 vehicles were assessed, including a unrepresentatively high nu m b er of hybrids. (The standard deviation only increased to 15% w hen the hybrids were excluded.) Thus, the sm all sam p l e m ay be a factor. Finally, we evaluated the va riability in the Consum er Report fuel economy m easurements com p ared to the current EPA city an d highway label va lues. (See sec tionII.B.1 for a m o re com p lete discussion of the Consum er Report fuel economy estim ates.) The standard deviations of the percentage difference between the Consum er Report an d curren tEPA fuel economy were 8% for city and 7% for highway. These figures are lower th an the 13- 16% value found during the 1985 label adjustm e nt rule and in the ORNL Your MPG database. However, Consum er Report adjusts their fuel economy measurem ents to represent a single ambient tem p erature and all of their testing follows the sam e road r outes. Thus, both the am bient conditions and the driving patterns are mu ch m o re consistent than those experienced by the population of drivers in the U.S. The f act tha tv ariab ility is still as high as 7-8% tends to confirm that the 13% assumption described above is reasonable.
All of the ab ove estim ates of the stan dard deviation in the percentage d i fference b e tw een onroad and EPA label fuel economy fall in th e range of 13-16%. The more recen t estim ates fall towards the lower end of this r a nge. Thus, we will se lec t13 % as the b es tpoint estim ate of variab ility. Multip lying the standard deviation by 67.5% prod uces an of f

set f r om m ean f u el economy which should encom p ass an additional 25% of drivers. The 5-cycle formulae derived in sec tion III.A and the mpg-based formulae derived in sec tio n III.B, inc l uding the 11 % downward adjustm e nt for non-dynamom e ter effects, represent estim a tes of m ean onroad fuel economy. All of the inputs to th e 5-cycle formulae are based on natio nal averages of the relevant param e ter. Thus, reducing these estim a tes by 9% (i.e., multiplying them by 0.91) would convert these figures from the mean fuel eco nomy achieved on the road to the 25 thpercen tile of the rang e of onroad fuel econom ies achieved. This woul d produce a label value which would be achieved or exceed ed by 75% of all drivers. Fi gure III-20 depicts this graphically.

109Figure III-1 3. Variability in Onroad FE 

## V a r ia b i li t y in O n r o a d F E

0% 5% 10% 15% 20%

- 4 0%

- 2 0% 0% 20% 4 0% O nroad F E mi nu s C o m posi t e EPA Lab e l F E% ofDrivers M e dia n : 0% 2 5 t h pe r c en t i l e : - 9 % 1 0 t h pe r c en t i l e : - 1 8 %

75 % of dr iv er s



### 9% -9 % 25% of dr iv er s 50 % of dr iv e r s

The frequency distribution of onroad fuel econo my shown in Figure III-13 assum e s that the 5­ cycle formulae developed above m a tch 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 m i nus 9% of their label value. The adjustm e nt appropriate to convert m ean fuel economy estim ates to those representative of other pe rcen tiles is straightforwar d. For example, the 10 thand 90 thp ercen tile fuel economy values would be 1.28 tim e s the coeffi cient of variation off of the m ean value, or 17% downward and upward adjustments from the m ean. The 5 thand 95 thpercen tile fuel economy va lues would be 1.645 tim es the coefficient of variation off of the m ean value, or 21% downward and upward adjustm e nts from the m e a n. 

### D. Impact of the 5-Cycle and MPG-Ba sed Formula e on Fuel Economy Labels
The impact of the final rule on city and hi ghway fuel economy label values was assessed using the sam e database of 615 late model y ear vehicles used to develop the m pg-based adjustm e nts above. It should be noted that these data are not sa les weighted. In fact, most specific vehicle m odels included in the databa se are “wors tcase” for em ission perform a nce purposes within their m o del 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 em issions com p liance. Table III.D-1 presents th e results of this com p aris on for all 61 5 vehicles, as well as various sub-sets of vehicles.

110Table III.D-1. Current and 5-Cycle Label Fuel Economies b y Model Type i Current City 5-Cycle City Curre nt 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 Sm all 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 m i dsize car) 26.2 22.7 35.3 31.4 All vehic l es 19.1 16.4 24.9 22.7
The next table shows the effect of the 5- cycle formulae on co nventional g a solin e fueled vehicles with particularly high or low fuel economy.

Table III.D-2. Current and 5-cycle L a bel 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-F ueled 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 se en from Tables III.D-1 a nd III.D-2, u se of the 5-c y cle formulae would red u ce 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 ve hicles, the reduc tion 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 highw ay fuel economy is esse ntially independent of current highway fuel economy.

i T h ese fi g u res m a y di ffe r f r o m those t h at m i ght appear o n th e EP A fuel e c on omy wi nd o w st i c ke r, a s th ey have n o t un de r g o n e a n y sal e s wei g ht i n g
. Th ey have bee n d e ri ve d by ap pl y i ng fuel ec o n o my l abel for mul ae (e.g., 0. 9 t i m es FTP fuel ec o nomy ) t o f u el ec on om y t e st resul t s fo r i n di vi dual ve hi cl es.

111The im pact on hybrid vehicles would be si gnificantly greater for city fuel economy, averaging a 23% reduction. However, the reduc tion in highway fuel economy would be the sam e as for conventional gasoline-fueled vehicles. This greater im pact occurs prim arily because a num ber of the fuel efficient aspects of hybrid vehicles produce their m a ximu m benefit under conditions akin to the FTP tests, and are som e wh at less beneficial during aggressive driving, colder am bient tem p eratures and wh en the a i r co ndition er is turned on. The im pacts of the 5­ cycle formulae on the s i n g le diesel vehicle in the databas e are very sim ilar to those f o r conventional gasoline fueled vehicles.
The im pact of the m pg- based formulae would be very sim ilar on average to those shown in Tables III.D-1 and III. D-2 above for conventio nal vehicles, gasolin e-fueled and d i esel. This is not surprising for conventional gasoline fueled ve hicles, since the m pg-based formula e are based essentially o n the averag e results of the 5-cycle fo rmulae and the vast m ajority of the vehicles in the database are conventional gaso line-fueled vehicles. The 5-cy cle fuel economy va lues for the one diesel in the database also fall very near the mpg-based curves. Howe ver, th e impact of the mpg-based formulae on the current city fuel eco nomy label values for hybrids would vary significantly from the 5-cycle va lues. Basically, the im pact on hybrids w ould reflect that of conventional vehicles with the sam e current fu el economy levels. The mpg-based regressions therefore, represent essentially th e impact of the 5-cycle fo rmulae on conventional vehicles, which is less than that for hybrids. T h e im pact on the m pg-based formul ae for hybrids is shown in Table III. D-3 below. The im pact on the city fuel economy label is s till som e what higher (­ 18%) than the average for convent ional gasoline vehicl es, because the average FTP city fuel economy for hybrids is higher than that for even the top 12 conventional gasoline fueled vehicles (-15%). The im pact of the m pg-based for mula on the highw ay label value for hybrids is 10%, or just s ligh tly higher than that f o r conv entiona l ga s o line

- f u eled (9%). W ith only one diesel vehicle in the database, no general observations about this engine type can be m a de.

Table III.D-3. Effect o f M P G-Based Formul ae o n City and Highw ay 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 estim ates for all vehicles in the database, we also focused on those m a nufacturer s responsible for the m a jo rity of sales in the U.S. This approach m ay bette r ref l e ct the ch ang es lik ely to be seen by the m ajority of consum ers. In effect, Table II-3 abo v e includ es vehicles by Aston Martin and Rolls- R oyce in the percen t chan ge, and thes e vehicles are wei ghted equally with cars m a de by GM, Ford, Daim lerChrysler, and other top-selling m a nufacturer s. According to Autodata Corporation, the seven m a nufacturers with the greates tU.S. m ark et share acco unt for m o re than 90 percent of U.S. sales. Table II.D-4 shows these m a nufact urers, their 20 05 U.S. m ar k et share, an d the average p e rcent change in city and h i ghway fuel economy estim ates for each of these m a nufacturers as represented in our database. It is im portant to note, however, th at th ese

112estim ates are not in tend ed to rep r esent or in clude the entirety of a m a nuf acture r’s p r o duct line, and should not be interpreted as such. These estim ates 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 d i ffering ways in which m a nufacturers te s tth eir vehicles andsubm it data to EPA, the database m a y not reflect the range of m akes a nd models sim ilarly acro ss m anufacturers.
Table II.D-4. Effect o f NewMethod s on Fuel Economy Estimates for Major Manufactu rers

Manufactu rer 2005 U.S. Market Share ( % )* Average Change in City Fuel Economy Estimate Average Change in Highw ay Fuel Economy Estimate General Motors 25.9 -10% -11% Ford Motor Co. 17.9 -12% -10% Daim lerChrysler 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%
* S o urce: A u t o dat aC o rp ., Wo odcl i f f Lake, N e w Je rsey
. The following table sho w s the effect of the vario u s aspects of the 5-cy cle for mulae on 5­ cycle city and highway fuel economy relativ e to fuel economy over the F T P and HFET, respec tive l y.

Table III.D-5. Effect o f Various Facto r s on 5-cycle Fuel Economy City FE Highway FE Conventional Hybrid Conventional H ybrid Start Fuel 1.2% 1.0%
0.6% 0.7% Cold Tem p Start 1.4% 4.5% Cold Tem p 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 Econom y Formulae
In this sec tio n, we evalua te the im pact of a series of altern ative assumptions and approaches to developin g the 5-cy cle fuel econo my for mula e
. The organization of this section basically follows that of section II.A above. Alternatives regardi ng start fuel use are addressed, followed by those affecting running fuel use at 75 °F, fuel associated with air cond ition i ng use and the im pact of colder tem p eratures.

1131. Start Fuel Use There are five key facto r s which affect s t art fuel use on a gallon per m ile basis. These are: 1) the distribution of starts as a f unction of s o ak tim e and tim e of day, 2) the effect of soak tim e and ambient tem p erature on s t art fuel use, 3) fuel consumption asso ciated with a cold start, 4) average trip leng th, an d 5) heater/d efroster us e. Each of these factors will be addr ess e d below in turn. Regarding the distribution of starts as a f unction of soak time and tim e of day, we know of no other existing estim ate which is as repres entative and extensiv e as the Baltim ore-Spokane data used to develop the MOBILE6 .2 and Draft MOVES2004 distribut io ns. The rece nt tes ting in Kansas City covers nearly as m a ny vehicles. Ho wever, the num ber of days of driving assessed for each veh i cle is well b e low that achieved in Baltim ore and Spokane. Georgia Tech has been studying driving patterns in Atla nta via vehicle instrum e ntation for som e tim e. The amount of data which they have collected to date exceed s th at obtained in Baltim ore and Spokane. EPA has begun to obtain the start and tr ip related inform ation from th is study. However, the work involved is considerable and the re sults are not yet available. Regarding the effect of soak tim e and am bient temperature o n start fuel u se, the correlations used in Draft MOVES2004 a nd EMFAC2000 are both recent and addressed essentially a ll da ta ava ilable at that tim e. W e know of no other data addres sing the effect of soak tim e on start fuel use. Som e infor m ation regard ing the im pact of am bient tem p erature on start fuel use at 5 0°F is available from Cal i fornia ce rtif ication te sting. Calif orn i a requ ire s a sm all num b er of vehicles to have their emissions test ed at 50°F each year. W e obtained this data for nine conventional Honda vehicles, two H onda hybrids and two Toyota hybrids.
The nine conventional Honda vehicles showed slightly lower sensitiv ity of start fuel use to tem p erature than that estim ated in Draft MO VES2004. At 50°F and 20°F, the nine vehicles showed 1.51 and 2.30 tim e s the start fuel use as at 75°F, resp ectiv ely. (S tart fuel use being defined as 3.59 m iles times the difference in fuel consum ption in Bags 1 and 3 of the FTP.) Draft MOVES2004 estim a tes ratios of 1.63 and 2.75 at 50°F and 20°F, respectively. The 90% confidence intervals around the m eans of the H onda data were roughly half of the difference between the m eans for the Honda vehicles a nd the Draft MOVES2004 estim a tes (i.e., 0.07 and 0.20, respectively). W e do not have estim ates for the confidence limits around the Draft MOVES2004 estim ates. However, given the grea ter num ber of vehicles tested, the co nfidence intervals around the Draft M OVE2004 projection are probably sm aller. Thus, while 1-4 individual H onda vehicles showeda greater sensitivity to temp erature than projected by Draft MOVES2004, on average the Honda vehicles ar e less sensitive at a 90% confidence level.
W e re-estimated th e weighting for start fu el use at 20°F and 75°F using the temperature sensitivity of the average Honda c onventional vehicle. W e modifiedthe coefficients of the Draft MOVES2004 equation f or the ratio of start fuel use as a functi on of tem p eratu r e to m atch the ratios of 1.51 and 2.30 at 50°F and 20°F, respec tively. The re sulting equation was:
Start fuel at T / Start fu el use at 75°F = 1 – 0.0170 * ( T – 75 )+ 0.00013 * ( T – 75 ) 2

114Using this equation, we found that the weighting for start fuel use at 20°F and 75°F changed in the th ird decim al place, but rem ained the sam e when rounded to two decim a l places (0.24 and 0.76, respectively). This is likel y the result of the fact that the Honda vehicles were less sensitive at both 50°Fand 20°F. Thus, the effect of lower tem p erature sensitivity at 50°F is app r opria te ly reflected in the 5-cycle formulae by the lower st art fuel use m easured du ring the cold FTP at 20°F. All four hybrid vehicles show ed 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 tem p erature sens itivities of the se f our vehicle s. Table III.E-1. Sensitiv ity of Hybrid S t art Fuel Use to Ambient Tempera ture
Vehicle Ratio of Start Fuel Use at Tem perature X to 75°F Weight of Start Fuel Use at 20°F X = 50°F X = 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, com pared to 0.24 based on Draft MOVES2004. On average, the results for the four hybrids essentially m a tch that based on Draft MOVES20 04. Individually, the Prius and the RX 400H data produce cold tem pe r ature weights very similar to that based conventional vehicles. The Honda Accord data produces a great er weight for cold start fuel us e 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 fu el use at 20°F for the Accord hybrid would reduce its 5-cycle city fuel econ o my 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 in crease its 5-cycle city fuel economy by 0.5 m pg from 43.6 to 44.1 m pg. In both cases, 5-cycle highway fuel economy woul d be unaffected.
Based on this lim ited data, it app ears unlike l y tha t uncertainty in the ef f

ect of am bient tem p erature on start fuel use would significantly affect city 5-cycle fu el eco nomy
. It is certain to have no effect on 5-cycle highway fuel economy, due to the extrem ely low contribution of start fuel use in highway driving. Hybrids would likel y show the greatest variab ility in this area, due to the greater num ber of technologic al factors that could be affected

. Even for these vehicles, the 5-cycle fuel economy for the vehicl e reflecting the greatest differe nce in tem p erature sensitivity was only changed 1%. Regarding fuel consum ption associated with a co ld star t, the prim ary issu e is th e assum p tion that the vehicle is fully warm ed up by the end of Bag 1 of the FTP. W e a r e not aware of any evidence that this is not the case at 75°F. As di scussed in section III.A.4 above, there is som e evidence that the v ehicle is s t ill warm ing up aft e r Bag 1 at 20°F. However, if som e of the difference between Bag 2 fuel consum pti on at 20°F and 75°F was due to continued vehicle warm up, then the difference after th e vehicle was fully warmed up would be comm ensurately

115sm aller. In general, the second chan ge woul d tend to m itigate the firs t. W e assessedthe sensitivity o f 5-cycle city fuel econo my to just th e first ch ang e by assum i ng that the d i fference in fuel consumption (in gallons) between Bag 2 at 20°Fand 75°F was associated with th e cold s t art. Doing so decreased 5-cycle fuel economy on av erage for non-hybrid vehicles by 0.16 mpg, or 1%. City fuel economy was reduced by 1.9 m pg fo r 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 increm ental running fuel us e 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 additio nal unce r ta inty regardin g cold start f u el consum ption invo lve s the testing of hybrids. Most current h ybrid design s includ e a sizeab le battery with which to store en ergy from braking, provide launch power af ter 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 req u ired to be the sam e at the begi nning of the FTP and the end of the four bags. However, the state of battery ch ar ge need not be the sam e at the beginning and end of Bag 1, nor the beginning and end of Bag 3. Thus, the possib ility exists that a portionof the dif f

erence in fuel consumption between Bags 1 and 3 is relate d to a change in battery charge, which m a y not occur on th e road. One contributing factor towards this pos sibility is the f act th at th e driving pattern of Bags 1 and 3 is not representative of driving imm e diately follow i ng an engine start. 4 0 The speeds of the secon dhill in Bag s 1 and 3 co nt ain too mu ch high spee d driving. T h is af f

ects the rate of engine warm -up for all vehicles. But it could also affect th e net change in battery charge of hy brids r ela tiv e to that occurring on the road.
As was the c a se with po tentia l 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. G i ven the difference in trip length and bag weights between the FTP and the 5-cycle city formula, the opposing differences do not ne cessarily balance exactly. However, the net effect is lik ely to be mu ch sm aller th an the effect of a change in battery ch arge on co ldstart fuel use.
W e exam ined the poten tial im pact of a change in batte ry capa city in Bag 1 rela tive to Bag 3 using two hybrids in our 5-cycle fuel economy database: a Honda Ci vic hybrid and a Toyota Prius. In both cases, we subtracted 0.005 gallons per m ile from the fuel consumption in Bag 1 added the sam e fuel consumption to Bag 2. This was an increase in Bag 1 fuel consumption of 23-25% increase for the two vehicles. Thus, thes e are s i gnificant sh ifts inbattery sto r age and probably ex ceed any ch ange actu a lly occurring duri ng th e FTP. This shift in fuel co nsumption increased th e 5-cycle city fuel econo my of the Civic by 0.5 mpg, or 1.5%. It increased the 5­ cycle city fuel economy of the Pr ius by 1.0 mpg, or 2%. These ch anges in city fuel economy are likely worse case, sin ce the degree of shift in fu el consum ption are large percentages of Bag 1 fuel consumption. Also, such a shift in Ba g 1 fuel consumption would likely produce som e degree of shift in Bag 3 fuel consum ption, as we ll. The above analysis assum e d that Bag 3 fuel consumption rem a ined unchanged. Still, the poten tial for change in battery charge status during Bags 1 and 3 could have a signifi cant effect on 5-cycle city fu el economy values that m a y not

116reflect on road operation. W ithout actual m easurem ent of the state of ba ttery charg e after each bag of the FTP and on-road following vehicle star t-up, it is n o t possib l e to quantif y th is uncertainty any further. Regarding the effect of average trip length on start fuel consumption, the three studies with the m ost extensive collections of data were addressed in section I. A above: the instrum e nted vehicle studies conducted in support of the S upplem ental FTP rule, the National Household Travel Study (NHTS), and the data currently bei ng collected by Georgia T ech in Atlanta. One obvious uncertainty in the curr ent estim ates of average trip length is the 11% downward adjustm e nt to the average trip length found in the NHTS. Thi s reduction in average trip length from 9.8 to 8.7 miles was applied in order to ob ta in consis ten cy with the r esults of th e various instrum e nted vehicle studies perform e d in support of the Supplem ental FTP rule and Atlanta. It is also b asedon the belief that ins tru m ents m o re accurately m easure short trips and s hort respites between trip s, such as moving a car out of the garage or stopping to r ef u el. Even if it is alm o st certain that a diary-based m easure of trip length should be adjust ed downward, the degree of this adjustm ent is uncertain. There is also uncertainty in the av era g e tr ip leng th f o r urban dwellers, since on ly th ree cities have been stud ied with vehicle instrumentation. In order to assess the poten tial consequence of this uncerta inty, we rem o ved the 11% adjustm ent f r om the national ave r age trip length, leaving it a t9.8 m iles. Retaining a highway trip length o f 60 m iles, this inc r eas e dthe city tr ip length to 4.6 miles. Using 4.6 miles for the average trip length for city driv ing increased the average fuel ec onomy of the 615 vehicles in our 5-cycle fuel economy da tabase from 16.9 m pg to 17. 0 m pg, 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 m p g to 33.2 m p g. These incr eases are q u ite sm all, p articular ly g i ven the f

act that this repres ents r em oval of the entire ad ju stm ent. Since the unc ertainty in the 11% adjustm ent is like l y mu ch less th an + 1 1 %, the un certainty is a v erage trip length is no t a m ajor factor caus i ng uncertain ty in th e 5-cycle fuel eco nomy for mulae. Finally, we are promulgating a change in the tes t procedur e o f the Cold FTP which involves activation of the heater or defroster during the test. T h e vast m a jority of drivers obviously utilize their heaters at 20°F , but the nu mber that do so between 20°F and 75°F has not been stud ied

. The weighting facto r for cold s t art fuel use was developed f r om test data which did not involve heater or defr oster activation. Thus, cons ideration of the effect of heater/defro ster us e at v arious tem p eratu r es cou l d affect this factor. Als o, drivers can differ in the way they activ ate their heater and defroster at colder tem p eratures. As described in Section III.B. above, EPA has tested several v e hicles at 20 °F with and without heater/defros t er activation. The im pact on convent ional vehicle start fuel use is m i ni m a l, decreasing 5-cycle city fuel economy by only 0.1%. Thus, the uncertainty in this effect as es tim a ted in the 5-cycle for mulae should be even sm aller, given som e heater and defroster use obviously occurs. The effect is larger for hybrid vehicles, though it should be noted that our estim ate of the effect is only based on the test results from two hybrid veh i cles. Actual label values developed using the 5

- cycle formulae will b e based on actu a l Cold FTP fuel economy values m easured with the heater/d efroster activated. Removing the esti m ated effect of heater and defroster use from Bags 1 and 3 increases 5-cycle city fuel econom y by 1%. The uncertainty in this effect as

117estim ated in the 5-cycle f o rmulae should be even sm aller, given som e heater and defroster use obviously occurs. Thus, even uncertainty in the e ffect of heater/defroster use on start fuel use does not appear to be a m a jor source of un certainty in 5-cycle city fuel economy. 2. Running F u el Use At 75°F
In this sec tio n we evaluate sever al a l ternat ive app r oaches to d eterm ining the weightin g of the various test cycles to estim ate ru nning fuel u se at 75°F. One alternative evaluates a m o re ideal split of the US06 cycle into city and highw ay driving. In this case, both the second and third hills described in T able III. A-1 6 are des i gn ated as h i gh way driving. The averag e speed of the US06 city bag decreases and the average sp eed of the US06 highway bag increases.
A second alternative elim inates the three hi ghest speed freeway cycles which were not derived from the 3-city studies perform e d in s upport of the Supplem ental FTP rule. These three cycles had to be developed subsequent to these instrum ented vehicle stud ies due to th e increase in m axi mu m speed lim it from 55 m p h nationwide to 70 mph and even higher today. T he basis for these three higher speed cycles is not as robust as that for the other 13 f acility cycles. Thus, there is m o re uncertain ty in the VSP distr i bution s of these th ree high est s p eed cycles than the others.
A third a lte r n ative, actua lly a s et of a lte rnativ es, evaluates the use of altern ative fuel rates by VSP bin in the regression of dynamom e ter cycles versus onroad operation. Fuel rates from the EPA 15 car study, fuel rates from Draft MOVES2004 extrapolated to 23 VSP bins are substituted f o r those f o u nd in the EP A Kansas City testing. T he impact of using just the 17 VSP bins current in Draft MOVES2004 is also evaluated. A fourth alternative develops test cycl e com binations which represent on ro ad VSP distributions and fuel rates fr om EPA’s recen t test program inKansas City. Test cycle com binations are developed for non-hybrid and hybr id vehicles separately, given that significant num bers of both vehicle types were tested.
A final alternative d e velops a set of cycle weighting factors using only entire test cycles (FTP, HFET and US06) instead of allowing se parate bag weights w ithinth e FTP and US06 cycles.
These altern atives and th eir effect on the cycle weighting factors are described below.

a. Alterna tive Def i nition o f US 06 City and Highway Bags In section III.A.2, we defined the city bag of US06 to include hills num ber 1, 2, 4, and 5. However, hill 2 was plac ed in the city bag f o r practical, tes t ing rela ted r e asons. Here , we redefine the city bag in a m ore 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 as signm ent to th e city and highway bags of US06 are shown in Table III.E-2.

118Table III.E-2. Split of US0 6 Cycle into City and Highw ay Por t ions Hill Portion of Driving Cyc l e (cumulative seconds) Maximu m Speed (m ph) 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

W ith this revised split, the averag e s p eed of the city bag decreases to 18.1 m ph and the average speed of the hig h way bag increas es to 80.3 m ph. Table III.E-3 shows the VSP distributions for both the proposed and m ore ideal city and highway bags of US06.

119Table III.E-3. VSP Distributions for US06 City and Highw a y Bags ( % of time) VSP Proposed Definition More Ide al Def i nition 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, th e cha nges in the VSP distribu tion of the highway bag changes ar e slight. Th is is due to the f act that h i ll 2 is mu ch shorter than h i ll 3, so its ad dition has a sm alle r im pact. Also, the dr iving in hi lls 2 and 3 are sim ilar. However, the VSP distribution of the city b ag changes dram atically

. This occurs because h i ll 2 varies dram atically from hills 1, 4, and 5. Inparticular, the am ount of tim e spent in VSP bins 33-39 decreases from 17% to 2%. We repeated the regressions of the VSP distributions of the dynam o meter bags and cycles (Tables III. A-15 and III.E-3) aga i nst the VSP distributions of city and highway driving (Table III.A-14 ) weighted by th e square roo tof the fuel rate in each VSP bin from the Kansas City tes t

120program (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 Combinatio ns for City and Highway Driving: Alternativ e US06 Splits Proposed Split Ideal Sp lit 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 T able III.E-5, the m o re ideal breakdown of the US06 cycle results in the sam e bags and cycles being selected to represent city and highway drivi ng as use of the proposed split of US06. The contribution of the US06 city ba g to city dr ivin g decrea ses slightly, while contribution of the US06 highway bag to highway driving increases slightly. We reanalyzed the results of the 80 vehi cles tested over US06 on a second by second basis in o r de r to re -es tim ate th e re lative f u el economy over the US06 city and US06 highway bags using the m o re ideal split. W e found that the fuel economy over the more ideal US06 city bag was only 50% of that over the entire US06 cy cle, com p ared to 68% f o r the proposed US06 city bag. The fuel economy over the more id eal US06 highway bag was only 111% of that over the entire U S 06 cycle, com p ared to 116% for th e proposed US06 highway bag. Thus, hill 2 appears to h ave a som ewhat m i d-range f u el eco nomy com p ared to the lower speed hills 1, 4, an d 5 and to the higher spe edhill 3. Using these revised cycle com b inations a nd the revised estim ates of US06 city and highway fuel economy relative to U S 06 fuel economy , we recalcu lated 5

- cycle fuel economy estim ates for the 615 vehicles in our 5-cycle fuel economy da tabase. The results are summ arized in Table III. E-5.
Table III.E-5. Average 5-Cycle Fuel Economy: Alternativ e US06 Splits Proposed US06 Split Ideal US06 Split City Fuel Economy ( mpg_ 16.9 16.7 Highway Fuel Economy (m pg) 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-dynam o meter factor by roughl y 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%.

121b. Elim ination of Three Highest Sp eed Freeway Cycles in Draft MOVES2004 As described in section III.A-2, Draft M OVES2004 uses 16 facility driv ing cycles to describe onroad driving. Most of these cycles are based on the results of the instrum e nted vehicle studies conducted in the early 1990’s in support of the Supplem e ntal FTP rule. However, since the m aximu m speed lim it at this tim e 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 developm ent of MOBILE6.2, EPA de veloped three higher speed freeway cycles (HighSpeed Freeway 1, High Speed Fr eeway 2, and High Speed Freeway 3 in Table III.A-12 ). High Speed Freeway 3, the fastest of th e thre e cycle s, was basedon a segm ent of onroad driving of one of the vehicles tested in the EPA 15-car study. 17 W h ile this dr iving segm ent wa s 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 em ployees or con tracto r s and were aware of the purpose of the stu dy, the driving was not designed to be representative of typical vehicl e use. The portion of onroad driving represented by High Speed Freeway 3 is based on its average speed and onroad speeds predicted by travel dem a nd m odels and Californi a rural chas e car data, the accelerationrates are not. Thus, the power dem a nds of this cycl e could differ from those on the road. The repres entativeness of th e speci fic sp eed-tim e trace of High SpeedFreeway 3 and its effect on vehicle power is being evaluated as partof the f urther developm ent of MOVES. The High Speed Freeway 2 cycle is a portion of the US06 cycle. This portion of the cycle m e t th e desired av erage sp eed, which was slightly belo w that of High Speed Freeway 3. As discusse d in sec tion III.A.2, the US06 cycle is a conc entrated ve rsion 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 perform e d in support of the EPA Supplem ental FTP rulem aking. 12 In the early 1990’s, it re presented roughly 28% of U.S. driving. US06, consisting of the m ost aggressive portions of REP05, repr es ented a sm aller pe rcen ta ge of drivin g at tha t tim e. However, both sp eed lim its an d the power to weight ratio of vehicl es have both increased since that tim e. US06 driving re presents m ore driving today than it did in the early 1990’s. However, the exact percentage is not known. As is the case w ith High Speed Freeway 3, the portion of onroad driving repr es ented by High Speed Freeway 2 is based on its av erag e speed and onroad speeds predicted by travel dem a nd mode ls and California ru ral chase car data. However, the power dem a nds of the cycle d e ri ve from the specific portion of US06 which was sel e ct ed t o co mpr i se Hi gh Speed Fr eeway 2. T hus, the power dem a nds of this cycle could differ from those of in-use vehicles which are driving at these speed s
. The representativenes s of the specific sp eed-tim e trace of High Speed Freeway 2 and its effect on veh i cle power is b e ing evaluated as part of the f urther developm ent of MOVES. High Speed Freeway 1 is the m o st represen tative of the three highest sp eed cycles. 4 1 It was developed for use in MOBILE6. 2 from data obtained during the 3-city studies. B a sically, vehicle operation which occurred on relatively uncongested freeways (LOS A-C) which lasted at leas t 30 seco nds with a mi ni mu m speed of 50 mph were binned and used to create the High Speed Freeway 1 cycle. Thus, the underlying driv i ng charact e ri sti c s refl ect real world operation

122in the early 1990’s. However, vehicles opera ting at these speeds today m i ght be driven differently, due to a change in th e ty pes of roadways which carry v e hicles at these s p eeds and to the fact th at m any vehicles on the sam e roads are driving faster and not slower. As is the case with High Speed Freeway 2 and 3, th e representa tiveness of th e specific sp eed-tim e trace of High Speed Freeway 1 and its effect on vehicle power is being evaluated as part of the further developm ent of MOVES.
We evaluated the im pact of these three cycl es on the proposed cycle com binations in the 5-cycle formulae by shifting the onr oad driving assigned to these cy cles to the fourth highest speed cycle, LOS AC Freeway. This is an extrem e 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 likel y over-estim ates it. Only the highway fuel economy formula is affected, sin ce we assumed in sectionIII.A.2 th at 100% of the driving ov er these cycles was highway driving. As shown in Table III.A-13, th is shift represents 20 % of onroad highway like driving. We repeated the regressions of the VSP distributions of the dynam o meter bags and cycles (Tables III. A-15) again st the VSP distribution o f highway driving (Tab le III.A-14 with shift to LOS A-C Fr eeway) weig hted by the s quare roo to f the fuel rate in each VS P bin from the Kansas City tes tpro g ram (Table III.A-16 ). The results with and without the thre e highes tsp eed cycles are described in Table III.E-6 below.

Table III.E-6. Bag/Cycle Combinatio ns for High w ay Driving: High Speed Freew ay Cycles W ith 3 High Speed Cycles W ithout 3 High Speed Cycles
HFET 21% 25% US06 Highway 79% 75% Using this revised cycle com bination for highway driving, we recalculated 5-cycle highway fuel economy estim ates for the 615 vehicl es in our 5-cycle fuel economy dat abase. Elim inating the th ree hig h est speed freeway cy cles increased average 5

- cy cle highway fuel economy from 23.1 m pg to 23.3 m pg, or by less than 1%.

c. Alterna tive Fuel Rates a nd Num b er of VSP Bins
In this sec tio n, we evalua te the u se of three s ets of alte rnative f u el ra tes by VSP bin in the regression of dyna m o m e ter cycles ve rsus 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 MOVES 2004 extrapolated to 26 VSP bins using fuel rates f r om the EPA Kansas City testing. W e al so evalua te the im pact of using jus tthe 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 we re presented in T able III.A-16 above. Those curren tly in Draft MOVES2004 ar e sim p ly those rates shown in Table III. A-16 under either of

123the “Extrapo lated MOVES” column s f o r bi ns 0-16, 21-26 and 33-36. The 17 bin VSP distributions for both the dynam o meter cycles and bags and the MOVES facility cycles are the sam e as those with 26 bins, except th at the x6 b i n contain s all of the driv ing in bins x6, x7, x8 and x9. We repeated the regressions of the VSP distributions of the dynam o meter bags and cycles (Tables III. A-15) again st the V SP distributions of city and hi ghway 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 inTable III.E-7 below.

Table III.E-7. Bag/Cycle Combinatio ns for Ci ty and Highway Driving: Alternativ e Fuel Rates Base Case: Kansas City Fuel Ra tes EPA 15 Car Fuel Rates ExtrapolatedMOVES MOVES 17 bin EPA 15 Car Kansas City 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 T able III.E-7, the th ree alternative sets of fuel rates yield cy cle com b inations of city driving which are iden tical and do not include the US06 city bag. The MOVES 17 bin approach y i elds a cy cle com b ination of city driv ing which is mu ch m o re sim ilar to th at of the base cas e. Regarding the cycle co mbinations of highway driving, all of the variou s alterna tive s produce ess entia lly iden tica l re sults.
The results f o r city driv ing indica te that th e contribution of the US06 city bag is th e m o st uncertain of the 3 bags. This is confirm e d by the p-values for the various bags in the final regressions. W hile the inclusion of the US06 city bag in the regression improves the adjusted rsquared value, the p-values for the US06 city ba g are th e high est of the th ree rem ainin g bags, falling in the range of 0.2-0.3. Using the 50/50 com bination of bag 2 and 3 (z ero weight for US06 city), w e recalculated 5-cycle fuel economy estim ates for the 615 vehicles in our 5-cycle fuel economy database. The 50/50 com bination of Bags 2 and 3 increases 5-cy cle city fuel economy for conventional vehicles from 16.5 mpg to 17.2 mpg, or by 4%, compared to the p r op osed cycle com b ination. The 50/50 com bination of Bags 2 and 3 increases 5-cycle ci ty fuel econ o my for hyb rid vehicles from 33.0 mpg to 34.8 mpg, or by 5.5%, com pared to the pro posed cycle com b ination. The increase in city fuel economy also increases com bined fuel ec onomy by 2% for conventio nal vehicles and 3% for hybrids. This increase in com bined 5-cycle fuel economy would lead to a lower factor for non-dynam o m e ter effects, roughly decreasing from 0. 905 to 0.885. This change would decrease both 5-cycle city and highway fuel economy by 2%

. The unc erta inty rela ted to the co ntribu tion of

the US06 city bag to c ity dr iving is the sum of t he change in city fuel economy plus the change in both city and highway fuel economy due to a lower non-dynam o m eter factor. Thus,

124this uncertainty is roughly 2% in both city a nd 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 F uel Rates
EPA’s recen t testing of roughly 100 recent m odel year veh i cles in Kansas City is described in detail in Appendix A. W e aggregated the drivin g activ ity an d fuel rates m easured during th is testing (bas ically one d ay ’ s driv ing for each veh i cle) and developed VSP distribu tion s and average fuel rates by VSP bin fortwo sets of vehicles, co nventional vehicles and hybrids. In order to develop VSP distributions for city and highway driving, we segr e g ated dr ivin g activ ity on a second by second basis into two groups: those at 45 m ph or lower and those above 45 m ph. This segreg a tion is no t e x actly consistent with o u r definition of city a nd highway driving, since these are based on longer tim e fra m es than one second. However, this was the only m e thod applic able in the near te rm
. W ith addition al ti m e , each vehicle’s driving trace cou l d b e reviewed visually and segregated into city and highway drivi ng. W e may attem p t to do this in the f u ture. The city andhighway VSP distribu tions a nd fuel rates are sh own in Table III.E

- 8.
Table III.E-8. Kansas City VSP Distributions an d Fuel Rates VSP Bin VSP Dis t ribut ion: Non-Hybrids VSP Dis t ribut ion: Hybrids Fuel Rate (g/s ec ) CityHigh wayCity High way Non

- Hy b r id s Hy brid s 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

125We repeated the regressions of the VSP distributions of the dynam o meter bags and cycles (Tables III. A-15) again st the V SP distributions of city and hi ghway driving weighted by the square root of the various fuel rates shown in Tabl e III.E-8. T h e results for the bas e case and the two vehicle types in Kansas City ar e shown in Table III.E-9 below.
Table III.E-9. Bag/Cycle Combinatio ns for City and Highway Driving: Kansas City Base Case Kansas City Non- Hybrids Kansas City Hybrids City Driv ing 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 f or non-hybrid and hybrid vehicles tested in Kansas City vary dram atically from those of the base case and fro m each other. The US06 city bag d o es not appear in either of the final reg r ess i o n s for city dr iving in Ka nsas City. However, Bag 3 dom i nates city driving by non-hybrids, while Ba g 2 dom i nates city driving by hybrids. The potential causes for this difference in the driving of hybrids and conventional vehicles are discussed in som e detail in Appendix A. First, this difference is based on lim ited driving; roughly one hour of driving per vehicl e. Second, while it is possible that this difference, if real, will pe rsis t in the f u ture, it se em s m o re like l y tha tany dif f

erence will d i sap p ear as m o re high power hybrids enter the fleet and m o re 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 ve hicle 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 m ore detailed review of the regressi on results f or highway driving indicates that the m odel is having difficulty in m a tching the onroad VSP distributions for highway driving. In all the other regressions of highway drivi ng, the coefficien ts for HFET and US06 highway from the raw regression results sum ve ry close to 1.0 (e.g., 0.97). In Kansas City, the coefficients for HFET and US06 highway fr om the raw regression results sum to roughly 1.25. Mathe m atically, th is m eans that in ord e r to m i ni mi ze error, the m odel wants to use 1.25 seconds of cycle driving to m a tch the fuel cons umption of one second of onroad driving. This m eans that the norm a lized coefficients pres ented in Table III.E-8 will un der-estim ate onroad fuel consum ption significantly. The prediction of re lative fuel consum ption across m odels mi ght be reasonab l e f o r the h i ghway driv ing p erf orm ed in Kansas City, but the ab so lute p r edic tion will be quite low. This would necessitate use of a non-dynam o meter factor well below 1.0, as opposed to the 0.98 f actor p r opos ed in sectionIII.A-5. In general, a sum of coefficients well above or below 1.0 indicates that the m odel cannot m a tch the onroad fuel consum ption per second using th e cycles m a de available to it. In the case of highway driving, the mode l would norm ally increas e the c ontribution of US06 hi ghway, as this cy cle has higher av erage fuel co nsum ption th an HFET. However, this is c r ea ting too grea t an error in the individual VSP bins and the sum of squared errors increas es. This m eans that the

126highway driving observed in Kansas City differs dram atically (in term s of VSP) from both the HFET and US06 highway cycles an d that ano th e r cycle is n eeded.
As will be s een in th e ne xt sec tion, dr iving obs er ved in Calif ornia y i eld v ery dif f

erent cycle com b inations compared to tho se based on the Kansas City data. T his indicates the need for driving to be characterized in a num ber 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 C h ase Car Stu d ies As described in Appendix A, Sierra Research has perform e d several chas e car s tudies of both urban and rural driving in California since 19 98. W e have not yet been able to perfor m a detailed analysis of the second by second data from th is testing. However, we have been able to develop app r oxim a te VSP distributio ns for urba n and rural driving from the speed-acceleration frequency distributions. As in Kansas City, we assum e d 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 m ph (i.e., speeds of 42.5-47.5 mph). This bin was assigned to city driving. Also, all driving in bins 17-19 we re assigned to bin 16. These VSP distributions are shown in Table III.E-10.

Table III.E-10. California Urban and Rural VSP Distributio ns VSP Bin Urb an Rural Fuel Rate (g/s e c ) CityHigh wayCity High way Non

- Hy b r id s Hy brid s 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

127We repeated the regressions of the VSP distributions of the dynam o meter bags and cycles (Tables III. A-15) again st the VSP distributions of California city and highway driving in urban and rura l (T able III.E-10 ) weighted b y the square root of the Kansas City f u el rate s (T able I II.A16). W e perfor m ed the sam e analysis for com b in ed urban/rural VSP distributions of city and highway driving, using an urban/rural weightin g of 60/40 (consistent with FHWA estim ates of urban and ru ral VMT for the U.S.). The chase car stud ies sh owed that75.5% 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 comb ined urba n/rura l c ity driving was developed by weigh ting the urban city driving V SP distribution by 78.8% and the rural city driving VSP distribu tion by 21.2%. Likewise, the V SP distribution for com b ined urban/rural highway driving was developed by weighting the urban highway driving VSP distribution by 34.5% and the rura l city driving VSP distribution by 65.5%. The results for the base cas e an d the California cas es are shown in Table III.E-11 below.
Table III.E-11. Bag/Cycle Combinatio ns fo r City and Highway Driving: California Base Case Calif ornia U rban Calif ornia Ru ral All Calif orn i a 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 dynam o meter cycle combinations for California driving tend to be m ore aggressive than those based on Draft MOVES2004 and are d ecided l y m o re aggressiv e than tho se found in Kansas City. C ity driving in Califor nia 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-hybr ids in Kansas City. Chase car studies can tend to under-represent driving on neighborh ood and local roads. Thus, som e increased percentage of US06 city driving can be expected in the cy cle com b inations for city driving. However, one would not expect over-estim ation by a factor o f two. The characterization of highway driving should not be affected by th is factor, since vehi cle speeds on local and neighborhood roads tend to be less than 45 m ph. Thus, the re sults f r om Kansas City a nd Calif ornia indicate the need for driving to be characterized in a num ber 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 inco nsisten twiththe dr iving in the two specif i c geog raphic al areas. f. Alterna tive Splits of City/Highway Driving In Sections III.A.1 and III.A.2, we define city dr iving as th at below 45 mph and highway driving as th at above 45 mph. According to th is definition, w e assigned all of the driv ing over

128the LOS D Freeway cycle to h i ghway driving. T his definition of city and highway driving produces a c ity/h ighway VMT split o f 43/57, which dif f

ers dr am atically f r o m the curre nt 55/45 split. W e believed it app r opria te to investig at e s o m e options which yield ed city /high w ay VMT splits clos er to the cu rren tsp lit of 55/45, specifically 50/50 an d 55/45. This was acco mplished by adjusting the split of driving ov er the LOS D f r eeway cycle to city an d highway categories. The LOS D freeway cycle has an av erage sp eed of 53 m ph a nd over three-quarters of the driving tim e of this cycle is above 45 mph. Ho wever, the cycle does include som e driving below 30 m ph (about 3% in term s of driving tim e). Thus, there is som e rationale to assigning at least a portion of th is cyc l e to c i ty driv ing. Also, the L A 4 road route which is the basis for the FTP test included som e freeway operation. Bag 3 of th e FTP has a m a xi mu m s peed of 55 mph and about 20% of the driving tim e during this bag is above 45 m ph. Al l the cycle com binations repres enting city dr iving include Bag 3, so all th ese representations of city driving include som e driving over 45 m ph regardless of how city drivin g is def i ned with respe ct to the Draf t MOVES2004 facility cy cles. T hus, assigning som e driving over 45 m ph to city driving could improve the representation of city driving (i.e ., increas e the adjusted r-s quared valu es in th e regression of the VSP distribution of city drivin g versus those of the dynam o meter cycles). To assess this possibility, we developed tw o alternative assignm e nts of driving of the LOS D Free way cycle to city and highway driving. The first produced an overall city/highway VMT split o f 50/50, while the secon dproduced an overall city/highway VMT split o f 55/45. The f i rst sp lit requ ired a s signi ng 26% of LOS D Freeway driving to city dr iving, while the second the required assigning 43% o f LOS D Freeway drivin g to city driving. W e recalcu lated the VSP distributions of city and highway drivin g with the two altern ative assignm ents of LOS D Freeway driving. The b ase cas e andalte rnativ e VSP distrib u tions ar e sh own in Table III.E.12.

129Table III.E-12. VSP Distributions for U.S. Dr iving w i th Alternative Definition of City Driving (% of time) VSP Base: 43/57 City/High w ay Split 50.50 City/Hi ghway Split 55/45 City/Hi ghway 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%
Shif ting so m e of the driving ove r th e LOS D Freeway cycle to city driv ing incre ases operation in Bins 33-39 significantly in the VSP distribution for city driv ing. It also increas es operation in Bins 35-39 and reduces operation in Bins 21-29 in the de scription of highway driving. We repeated the regressions of the VSP distributions of the dynam o meter bags and cycles (Tables III. A-15) again st the alte rnative 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 firs t dropped those cycles or bags w ith negative coefficients and then dro pped the least sign ifican t cycles or bags until the adjusted R-squared value decreased. Then, the results of the pr ev iou s regression were selected as the final com b inations of cycles. The results for the bas e case and the revised sp lit of city and highway driving are shown in Table III.E-13 below.

130Table III.E-13. Cycle Combinations fo r City and Highw ay Driving: Rev i sed City/Highw ay 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 som e operation over 45 mph in the de f i nition of city driving has a significant effect on the cycles which repres ent city driving. For both alternative defin itions of city driving, US06 city drops out of the cycle combination an d US06 highway is added at roughly twice the previous US 06 city weight. The contribution of Bag 2 drops, as well. These changes are progress i ve. The greater the percen tage of city driving overall (i.e ., the gre ater the pe rc entage of LOS D Free way driving assigned to city driving), the greater degree that the above changes occur. The effect on highway driving is much s m aller: the weight of US06 highway increases 1­ 4%, while th at of HFET decreases co mmensurately.
Som e of the regres sion s t atis tic s appear to be b e tter for the base definition of city driving, while others appear better for the hig h er city driv ing fraction. Again, the changes are progress i ve with respe ct to the two alterna tive s. The adjuste d r-squared value for the base case com bination of city driving is hi ghe r than tha tf o r

eithe r altern ative d ef i nition of city dr iving. However, the p-valu es f o r the US06 bag with the a lte rnative def i nition s o f city driv in g are be tte r than that for the base definition. The m ore high speed driving is included in city driving, the greater th e s i gnificance of the US06 highway coe f f i cient. W i th the ba se d ef i nition of city driving, including US06 city im proves the adjusted r-squared value, but th e p-value of the US06 coefficient is relatively high, 0.26. For highway driving, the p-values for bot h HFET and US06 are very low in both cases. However, the adjus t ed r-squared valu e increases as the over all city dr iving f r action inc r eases. In both alternative cas es, th e adjus t ed r-squared valu e is higher than that with the base definition of city dr iving.
The im pact of increasing the VMT fraction of city driving on the 5- cycle fuel economy of the 615 vehicles in our certificat ion database is shown in Table III.E-14. In addition to the revised cycle com b inations shown in Table III. E-13, increasing the city fraction of VMT also incre ases th e averag e tr ip length of c ity dr ivi ng. For a city/highway VMT split of 50 /50, the average tr ip length f o r c i ty driv i ng in creas es f r om 4.1 m iles to 4.7 m iles, while tha tf o r a split of 55/45 is 5.1 miles. The effect of this increase d trip length is included in the 5-cycle fuel economy estim ates with the 505/50 and 55/45 city/ highway VMT splits.

131Table III.E-14. 5-Cycle Fuel Economy Values: Ef fect of the Definition of City Drivin g (mpg) City Highway Com posite Non-Hybrid Hybrid Non-Hybr id Hybrid Non-Hybrid Hybrid Current EP A 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 L O S D Freeway to city driving increas es 5-cycle city fuel economy for both non-hybrids and hybrids. This is not surprising, given that fuel economy over the US 06 city bag is the lowest of any of the dynam o meter cycles or bags. The effect is again progressive with th e degree of the shift of driving fr om highway to city. Five-cycle highway fuel economy decreases very slightly du e to the increased frac tion of US06 highway in the 5-cycle f o rmula. Composite fuel economy increas es slightly. This imp lies that the effect of higher city f u el economy is slightly greater than the eff ect of the increased city VMT fraction. The ORNL Your MPG website, discussed in de tail in section II.A, contains consum ers estim ates of their onroad fuel economy, as well as their estim a te of their city and highway driving fractions. Across the 8180 estim ates of fuel economy, the average percentage of city driving is 43 %. This is closer to the 4 3 % es tim a te resulting from pl acing all the operation over LOS D Free way into the highway driving category than splitting this operation between city and highway driving. At the present tim e, it is th e prim ary source of infor m ation about how typical drivers label their own driving.

g. Com p lete Cycles
A f i nal set o f alterna tive s develops c y cle com b inations us ing entire te st cy cles (FTP, HFET and US06) instead of allowing separate ba g weights within th e FTP and US06 cycles. One alternative investig ates th e im pact of not splitting US06 into city and highway bags, but retaining separate bags for the FTP. The other al ternative inv estig ates the im pact of not splitting US06 into city and highway bags, as well as usin g a com bined Bag 2 plus Bag 3 fuel economy. The latter m i ght be applicable to current hybrid testing. A hybrid FTP test consists of two com p lete LA4 driving c y cles, one w ith a co ld star t and one w ith a ho t start. This is of ten accomplished using four em ission bags, with Bag 4 being sim ilar to Bag 2

. However, EPA regulations allow emissions to be m easured in only two bags, Bag 1 c onsisting of the norm al Bags 1 and 2 and Bag 2 consisting of the norm a l Bag 3 plus the extra Bag 4. In these cases, fuel economy measurem ents for each of the four bags would not be available. We repeated the regressions of the VSP distributions of the dynam o meter bags and cycles (Tables III. A-15) again st the Dr aft MOVES2004 VSP distributions of city and highway driving (Table III.A

- 14) weighte d by the squ are roo tof th e Kansas City fuel rates (Table III.A

- 16). The results for th e base case and th e alternative com p lete cycle ca ses are shown in Table III.E-15 below.

132Table III.E-15. Bag/Cycle Combinatio ns for Complete Cycle Alternativ es Base Case Com p lete US06 Com p lete 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, m a intaining US06 as a sing le cycle elim inates any contribution of US06 to city driv ing. The weights of Bag 2 and Bag 3 becom e 50% each, as was the cas e for the alte rnative f u el ra tes in Table I II.E- 7. This is a l so consis ten twith th e m odeling of city driv ing under cold temperature conditions where the U S 06 city bag was not allowed in the regression. Maintaining US06 as a single cy cle reduces th e contribution of US06 to highway driving modestly.
The results ar e quite sim ilar when th e 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 estim ates are allowed in to the m odel. The im pac tof using whole cycles on 5-cycle city and highway fuel ec onomy values is shown in Table III.E-16.

Table III.E-16. Effect o f Using Whole Cycles on 5-Cycle Fuel Economy Values (mpg ) Conventional Vehicles Hybrids City Highway City Highway Current EP A label 18.6 24.6 41.6 40.6 Base Case 5-cycle 16.5 22.8 33.0 36.9 Com plete 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 , elim inating the contribu tion of US06 city from the 5-cycle city formula increases city fuel economy (by about 4% in bothalternative cases for co nventional vehicles). W h ile incr e asing th e co ntribu ti on of HFET increases highway fuel economy, shifting from US06 highway to the whole US06 cycle decreases fuel economy. The latter im pact predom i n ates and highway fuel economy decreases substantially (~ 10% for conventional vehicles). City fuel economy for hybrids is slightly m o re sensitive to these changes than conventional vehicles, while highway fuel economy is less sensitive. Hybridcity fuel economy increases 6%, while hybrid highway fuel economy decreases 6%.

1333. Air Conditioning Effec t s The prim ary f actors af f

ecting the es tim ation of f

u el use r ela te d to air cond ition i ng ar e: 1) the degree of com p ressor engagem e nt during the SC03 test, 2) the degree of compressor engagem e nt on the road, 3) the effect of ve hicle speed on fuel use related to running the com p ressor, and 4) the effect of a m bient conditio ns on the load of the compressor on the engine. The potential uncertain ty in each of these factor s and its affect on 5-cy cle fuel economy will be addressed below.
W e assum e that eve r y ve hicle ’ s a i r c ondition i ng com p ressor is engaged 1 00% of the tim e during the SC03 test. T h is is ba sed on general knowledge of the SC 03 te st itself

. For exam ple, the test only lasts 10 m i nutes and the vehicle is subjected to strong sunlight at 95°F during vehicle precondition i ng 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 m i nute soak prior to the test. The air conditioning system and fan are turne dto its m aximu m setting and the r ecircula tion op tion is chos e n , if available.
EPA also made the s a me assumption when estim a ting the em ission benefits associated with the SC 03 emission standards. 4 2 Iti s cl ear from th e descri ption of various test program s perform e d in support of this rule that the engagem e nt of the com p ressor was m easured over the SC03 and other test cycles and that the Agency had this inf o r m ation when it assum ed that the com p ressor was engaged 100% of the tim e over the SC03 test. However, this data is no longer easily acces sible and the level of co mpressor us e which was actually m eas ured over these tests was not pres ented in any of the official ru lem aki ng docum ents. Also, it is possible that vehicles’ air conditioning system designs have changed si nce the early 1990’s. The degree of compressor engagem ent over SC03 today m i ght differ from that m easured at that time. Therefore, in order to assess the p o ssible un certainty in this assum p ti on, we assume here that the com p ressor is engaged only 80% of the tim e over SC03. Reducing the am ount of tim e that the com p ressor is engaged over SC03 from 100% to 80% can be modeled by sim p ly dividing the in crem ental fuel use over SC03 versus the 69/31 m i x 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 m p g to 16.4 m p g and that for hybrids decreases from 33.0 m p g to 32.6 m p g. Thus, the ch ange in city fuel economy is roughly 1%. The change in com p ressor engagem e nt over SC 03 affects highway fuel economy even less. Five-cycle highway fuel economy of non-hybrid ve hicles decreases from 22.8 m pg to 22.7 m pg and that for hybrids decreases from 36.9 m pg to 36.7 m pg. Thus, the change in highway fuel economy is roughly half of one percent. Overal l, the changes in both city and highway fuel economy are sm all. Regarding the degree of com p ressor engagem e nt on the road, the Phoenix study used to develop the 0.133 factor in the 5- cycle fuel economy for mula e is based on the only instrum e nted vehicle study perform e d to date. As disc ussed above, the NREL-OAP model, based on a person’s comfort at a given am bient tem p eratur e and hum idity, yields an estim ate of drivers turning on the air conditioning of 29%, versus that based on th e Phoenix work of 24%. Considering the f act th at the com p ressor is no t always engaged when the air conditioning system

134is turn ed on, and the fact that the ambient tem p erature is usua lly less th an 95 F, we estim ate that the com p ressor is on 13. 3% of the tim e at a loadequivalent to that occurring at 95 F. The com p arable percen tage b ased on the NREL-OAP sy stem -on estim ate would be 16.1%. Defroster use, based on the NREL-OAP work, could add a nother 0.4% to overall com p ressor use (in term s of com pressor load at 95°F). Thus, if both of the NREL-OAP estim ates were correct, the 0.133 factor for co mpressor us e in the 5

- cy cle formulae could be as high as 0.16 5. This increase is roughly equivalent to the increase associated with assum i ng that the com pr e ssor is only engaged 80% of the tim e over the SC03 test. Thus, th e fuel economy effects of increasing the air conditioning usage factor to 0.152 would essentially the sam e as those just presented above (i.e., 1.0% for city fuel econo my and 0.5% for highway fuel econ o my). These are very s m all changes. Regarding the effect of vehicle speed on fuel use related to running the com p ressor, this uncertainty prim arily ap plies to high way f u el economy. The speed of SC03 is within 10% of that for average city driv ing. So there is little uncertainty in applying th e increm ental fuel use of SC03 to city driving. The extrapolation to highw ay driv ing is mu ch larger , the ra tio of highway speeds to the speed over SC03 being a factor of 2.67. Our testing of six v e hicles with the air conditioning on and off over the FTP, SC03 and HF ET showed that the fuel use per unit of tim e tended to be within 10% over the various cycles. However, these tests w e re not conducted in an environm entally controlled test cell. Thus, fu rther differences could occur under m ore realistic am bient conditions. A l so, the HFET was the only highway cycle tested. The differences could be larg er if, for exam ple, vehicl es would have been tested over the US06. Therefore, to assess the unce r ta inty in th is f a ctor, we ass u m e here th at the d i f f

erence in f u el u se per unit o f tim e over the various cycles cou l d vary by as much as 20%. This can be represented by applying an exponent of 0.8 to the ratio of speeds over the SC 03 and highway driving. Thus, instead of a factor 0.366 (21.5 mph divided by 57.1 mph), the factor would be 0.458. W ith this change, highway fuel economy changed less than 0.5%. Ther efore, this factor is not likely a significant source of uncertainty in the 5-cy cle highway fuel economy formulae. Finally, regarding the effect of am bient conditions on the load of the com p ressor on the engine, consideration of this facto r reduces the effectiv e co m p ressor on tim e from 15. 2% to 13.3%, or by a factor of 0.875. A reasonable estim a te of the uncertainty in this factor would be to double its effect (i.e., double its difference from 1.0). This woul d m e a n reducing the factor to 0.75. This change increases city fuel economy from non-hybrid vehicles by 0.1 m pg and by 0.3 mph for hybrids, at m o st 1% in the latter case. T he effect on highway fuel economy is even sm aller, less than 0.5% in eithe r cas e
. Ther ef ore, uncertainty in this are a is unlike l y to significan tly affect 5-cycle fuel econ o my
. In all, uncertainty in the f our factors a ffecting the impact of air conditioning on onroad fuel economy appears to be quite sm a ll. No single factor appears to affect fuel economy by m ore than 1%, with m ost being si gnif i can tly less than this.
4. Cold Temperature Running Fuel Use The prim ary factors affecting the im pact of colder tem p eratures on fuel economy are: 1) the assum p tion that veh i cles are fully warm ed up by the endof Bag 1 of the FTP, 2) the effect of heater/defro ster us e on warm ed up fuel use, 3) th e use of fuel consum ption over just Bags 2 and

1353 to represent city driving a nd 4) the assumption that fuel consumption increases 4% during highway driving at 20°F

. The potential im pact of the assum p tion that veh i cles are fully warm ed up by the endof Bag 1 of the FTP was addressed abo v e in sec tionIII.E.1. As describ e d th ere, if the v e hicle continued to warm up during Bag 2 at 20°F relative to that at 75° F, cold start fuel us e would increase andwarm ed up fuel use decrease. The n e t effect was an increase in the 5

- cycle city fuel economy of conventional vehicles of 0.3% and 1.2% for hybrids.
As describe d in Section III.E.1, th e e ffect of heater/defros t er use is es tim ated to be v ery sm all for conventional vehicles, but more significant for hybrids. Som e effect of heater/defroster use is certain, given the fact th at m o st driv ers us e the heater a nd/or the defroster under certain conditions. However, given the absence of pr ecise data on their use under specific am bient conditions, it is difficultto 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 im pact of heater/defroster use on 5-cycle fuel economy. Fo r conventional vehicles, rem oving the im pact 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 fu el use is modeled and not based on the Cold FTP test. For hybrids, the effect is roug hly 4%. Sin ce heater/d efroster use clearly affects warm ed up fuel use with cu rre n th ybrid system s, so me loss in onroad fuel economy is certain. Thus, the uncertainty in the 5- cycle city fuel economy due to this factor is like l y well b e low 4%. Regarding the absence of the US06 city cycle in the estim a te of running fuel use at cold tem p eratures, this m eans that the driving cycles underlying our estim a ted fuel use at 75°F and 20°F are inconsistent to som e degree. W e deve loped three ap proaches to estim ate the potential uncertainty associa t ed w ith th is dif f

erence. Eachapproach es tim ates the effect of cold temperature on running fuel use during city driving slightly differently. F or review, the approach included in the final 5-cy cle fuel econ o my for mul a bases running fuel us e 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 estim a te of running fuel use at 75°F (using a weighting factor of 16%), but not at 20°F. The firs t a lte rn ative appr oach calcu lates the effect of includ ing th e US06 city cycle at 75° F and then applies this ef f

ect to the ru nning f u el use at 20°F, which is based only on Bags 2 and 3 of the FTP. To do this, we f i rst rem ove the effect of cold tem p erature com p letely. W e th en determ ined the im pact of replacing the 11% weight of th e US06 city cycle by increasing the weights of Bags 2 and 3 each by 5.5 %

. This replacem ent increased the city fuel economy from non-hybrid vehicles from 16.7 m p g to 17.5 mpg, or by 5%. Hybrid city fuel economy in creased from 35. 9 m pg to 38.6 m pg, or by 8%. W ith an 18% weighting factor for running fuel use at 20°F, this m eans that the curren t approach in this area could be under-e stim ating onroad fuel economy by 1% for non-hybrids and 2% for hybrids. The second alternative approach assum e s that the effect of cold tem perature on running fuel use over Bags 2 and 3 of the FTP applies to a ll c ity dr iving, includ in g the US06 city cy cle. Therefore, u nder th is ap proach , running fuel use at 75°F is estim ated 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

136fuel use at 75°F plus the difference in running fu el use over a 50/50% weight of Bags 2 and 3 at 20°F and 75°F. This approach yields the sam e eff ect as thos e of the firs t alternative approach. The city fuel economy of non-hybrid vehicles de creases by 1% and that for hybrids decreases by 2%.
The third an d f i nal altern ative approa ch is sim ilar to the s econ dapproach in that it assum e s that the effect of cold tem p erature on running fuel use over Bags 2 and 3 of the FTP applie s to a l l city dr iving , includ ing the US06 city cycle. Ho wever, ra the r than c a lcu l ating an effect of cold tem p erature in term s of an absolu te dif f

erence inf u el consumption, it c a l cula tes a rela tive d i f f

erence in pe rcentage te r m s and applies this p erc e n tage to rela tive f u el con sum ption at 75°F. Therefore, under this approach, running fu el use at 75°F is estim a ted 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 tim es 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 sam e 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 I II References
1. Glover, E. L. and D. J. Brzezinski . E xhaust Emission Tem p eratu r e Correction Facto r s for MOBILE6: Adjustm e nts for Engine Start and Running LA4 Em issions for Gasoline Vehicles (M6.STE.004). U.S. Environm ental Protecti on A gency, No. EPA420-R-01-029, April 2001. Website: http://www.epa.gov/otaq/models/m ob ile6/r01029.pdf
2. Koupal, J., and L. Landm an, E. Na m, J. W arila, C. Scarbro, E. Glover, R. Giannelli. MOVES2004 Energy and Em issions Report –DraftReport. U.S. Environmental P r otection Agency, No. EPA420-P-05-003, March 2005, pp 57-63. Website: http://www.epa.gov/otaq/models/ngm/420p05003.pdf
3. California A i r Resources Board. Public Meet ing to Consider Approva l of Revisions to the State’s On-Road Motor Vehicle Emissions Inventory – Technical Support Docum e nt. California E nvironm ental Protection Agency, March 2000. See Section 6.7 (Start Correction Factors). Website: http://www.arb.ca. gov/m sei/on-road/doctable_test.htm
4. U.S. Environm ental Protection Agency. MOVES 2004 - Software Design and Reference Manual. U.S. EPA Office of Transportation a nd Air Quality, Septem ber 8, 2004. See data table ZoneMonthHour. ZoneMonthHour is an in form ation table which contain s tem p eratu r e and hum idity inform ation. Table canbe acces sedby downloading and ins t alling th e MOVES 2004 m odel, which can be found at http ://www.epa.gov/otaq/ng m .ht m # m o ves2004.
5. U.S. Environm ental Protection Agency and E. H. Pechan & Associates, Inc. Docum e ntation for the Draft 2002 Mobile National Em issions Inve ntory. Prepared for U.S. Environmental Protection A gency/OAQPS, Research Triangle Park, NC, March 2005. Website: http://www.epa.gov/ttn/ch ief/net/2002inventory.htm l
6. Glover, E. L. and D. J. Brzezinski. S o ak Length Activity Factor s for Start Em issions (M6.FLT.003). U.S. Environm ental Protecti on A gency, No. EPA420-R-01-011, April 2001. See Tables 2a, 2b, 3a, 3b. W e bsite: http ://www.epa.gov/otaq/models/m ob ile6/r01011.pdf
7. U.S. Environm ental Protection Agency. MOVES 2004 - Software Design and Reference Manual. U.S. EPA Office of Transportation a nd Air Quality, Septem ber 8, 2004. See data table MonthVMTFraction. MonthVMTFraction is a distribution inf o rm ation type table that contain s a distribu tion of each sourceType ID’s VMT across the MonthsO f AnyYear. Table can be accessed by downloading and installing the MOVES 2004 m odel, which can be found at http://www.epa.gov/otaq/ngm .ht m# m oves2004.
8. Beardsley, M., and D. Brzezinski , B Gia nnelli, J. Koupal, S. Srivastav a
. MOVES2004 Highway Vehicle Population and Activity Data – Draft. U.S. Environm ental Protection Agency, No. EPA420-P-04-020, Decem ber 2004. See Table 1 5-1. Website: http://www.epa.gov/otaq/models/ngm/420p04020.pdf
9. Ibid.

13816 10. U.S. Departm e nt of Trans portation, Federal Highway Adm i nistration. Highway Statistics 2003. See Table VM-1. Website: http://www.fh wa.dot.gov/policy/ohim /hs03/htm / vm1.ht m
11. Davis, S. C. and S. W . Diegel. T r anspor tation Energy Data B ook: Edition 24. Oak Ridge National Laboratory, prepared for the U.S. Departm e nt of Energy, Report No. ORNL-6973, Decem ber 2004, Table 4

. 3. W ebsite: h ttp://cta.ornl.gov/data/download24.shtm l
12. U.S. Environm ental Protection Agency. Federal Test Proced ure Review Project: Prelim inary Technical Report. U.S. Environm ental Pr otection Agency, No. EPA420-R-93-007, May 1993. W e bsite: http://www.epa.gov/otaq/r egs/ld-hwy/ftp-rev/ftp-tech.pdf
13. U.S. Departm e nt of Transportation, Bureau of Transportatio n Statis tics. Highlights o f the 2001 National Household Travel Survey. Report No. BTS-03-05, 2003. See Tables 2 and A-8. W ebsite: http://www.bts.gov/publications /highlights_of_the_2001_national _household_travel_survey/
14. Hu, P. S. and T. R. Reus cher. Summary of Travel Trends – 2001 Na tional Household Travel Survey. Prepared for the U.S. Departm e nt of Transportation, Federal Highway Admi nistration, Decem ber 2004. See Table 3. W e bsite: h t tp://nh t s.orn l

. gov/2001/pu b/STT.pdf

15 Guensler, R a ndall, Seungju Yoon, Hainan Li , and Vetri Elango, “Atlanta Commute Vehicle Soak and Start Distributions a nd Engine Starts per Day: Im pact on Mobile Source Emission Rates - DRAFT FOR REVIEW ,” School of Civ il and Environm ental Engineering, Georgia Institute of Technology, Atlanta, G A , for the U.S. Environmental P r otection Agency, February 19, 2005. Rykowski, Richard, U.S. EPA, Me morandum t o the Record, “Average Trip Length in Atlanta,” October 11, 2006.
17. Hu, P. S. and T. R. Reus cher. Summary of Travel Trends – 2001 Na tional Household Travel Survey. Prepared for the U.S. Departm e nt of Transportation, Federal Highway Admi nistration, Decem ber 2004. See Table 3. W e bsite: h t tp://nh t s.orn l

. gov/2001/pu b/STT.pdf
18. Ibid., see T able 13-1.
19. U.S. Environm ental Protection Agency. Developm ent of Methodology f o r Estim ating VMT Weighting by Facility T ype (M6.SPD.003). U.S. Environm e ntal Protection Agency, No. EPA420-R-01-009, April 2001. Website: http://www.epa.gov/otaq/models/m ob ile6/r01009.pdf
20. Rykowski, Richard A., Edward K. Na m , and George Hoffm a n, “ O n-road Testing and Characterization of Fuel Economy of Light-Duty Vehicles ,” SAE 2005-05FL-174.

13921. U.S. Environm ental Protection Agency. Respons e to Comments for the Final Regulations for Revisions to the Federal Te st Procedure for Em is sions from Motor Vehicles. U.S. Environm ental Protection Agency, August 15, 1996. Website: http://www.epa.gov/otaq/reg s/ld-hwy/ftp-rev/sftp-rtc.pdf
22. U.S. Environm ental Protection Agency. Support Docum e nt to the Proposed Regulations for Revisions to the Federal TestProcedure: Detailed Discussion and Analysis. U.S. Environm ental Protection Agency, January 31, 1995. Website: http://www.epa.gov/otaq/reg s/ld-hwy/ftp-rev/ftp-supp.pdf
23. Meisner, B.N. and Graves, L.F. Appare nt Tem perature. W eatherwise, August 1985, 211­ 213.
24. Koupal, J. W . Air Condition i ng Activity Effects in MOBILE6 (M6.ACE.001). U.S. Environm ental Protection Agency, No . EPA420-R-01-054, Novem ber 2001. Website: http://www.epa.gov/otaq/models/m ob ile6/r01054.pdf
25. U.S. Environm ental Pro t ection Agency. Air Condition i ng S urvey Data, Phoenix AZ, 1994. Website: http://www.epa.gov/otaq/sftp.htm #pho enix. Excel or CSV data for m at.
26. Nam , Edward K., “Understand ing an d M odeling NOx Em issions From Air Condition e d Autom obiles,” 2000, SAE #2000-01-0858.
27. Nam , Edward K., “Understand ing an d M odeling NOx Em issions From Air Condition e d Autom obiles,” 2000, SAE #2000-01-0858.
28. Mitch a m , A., Fernandez, A., & Bochenek, D. “Impacts of Ambient Temperatu r e andAir Conditionin g Usage on Fuel Econo m y ” U.S. EPA, Of f i ce of Transporta tion & Air Quality, 2005.
29. Johnson, Valerie H., “Fuel Used for Vehicl e Air Conditionin g: A State-b y-State Therm a l Comfort-Based Approach ,” SAE 2002-01-19574, 2002.
30. Rugh, John P, Valerie H ovland, and Stephe n O. Andersen, “Significant Fuel Savings and Em ission Reductions by Im proving Vehi cle Air Conditioning,” SAE ,
31. Hellm an, Ka rl H. and J. Dillard Murrell, U.S. EPA, SAE 820791, 1982, Tables 5 and 6.
32. U.S. Departm e nt of Transportation. Fina l Regulatory Im pact Analysis: T i re Pressure Monitoring System FM VSS No. 138. Nationa l Highway Traffic Safe ty Adm i nistration, National Center for Statistics a nd Analysis, March 2005. W e bsite: http://www.nhtsa.gov/staticfiles/DO T/NHTSA/ R ulem aking /Rules/Associated%20Files/TPM S-2005-FMVSS-No138.pdf
33. U.S. Departm e nt of Transportation. Nati onal Au tom o tive Samp ling System –Tire Pressure

140Special S tudy. National Highway Traffic Sa fety Adm i nistration, National Center for Statistics and Analysis , Ju ly 26, 2001. W e bsite: http://www-nrd.nhtsa.dot.gov/pdf/nrd01/NRDmtgs/2001/0701TirePressure.pdf
34. U.S. Departm e nt of Transpor tation, National Highway Traffic Saf ety Adm i nstration. Federal Motor Vehicle Safety Standards; Tire Pressure Monitoring System s; Co ntrols and Displays – Final Rule. U.S. Governm e nt Printin g Off i ce, Federal Reg i ster Volum e 70, No. 67, p. 18136, April 8, 2005. Website: http://www.nhtsa.dot.gov/cars/rules/rulings /TPMSfinalrule.6/TPM Sfinalrule.6.htm l
35. “Passenger Car Fuel Economy ,” US EPA, EPA 460/3-80-010, Septem be r 1980.
36. Gillesp ie, T h om as D., Fundam entals of Vehicle Dyna m i cs , Society of Autom o tive Engineers, 1992 pp. 98-102.
37. Stanard, A l an P., South w est Resear ch Institu te, “VOC/PM Cold Tem p eratur e Characterization and Interior Clim ate Contro l Emissions/Fuel Economy I mpact, Final Report Volum e II, for U.S. EPA, October 2005.
38. “Analysis of In-Use Fuel Economy Data: S t age I,” Technical Report, U.S. EPA, August, 1982.
39. U.S. Departm e nt of Energy, U.S. Environm ental Protection A g ency. “YourMPG” is a feature of the http://www. fuelecono my.gov website. Sel ect “MPG Estim ates from Users.” Direct internet address: http://w ww.fueleconomy .gov/ mpg/MPG.do? action=browseList. YourMPG data as of Septem ber 12, 2005 is available as a Microsoft Excel spr eadsheet in the public docket for this rulem aking.
40. U.S. Environm ental Protection Agency. Federal Test Proced ure Review Project: Prelim inary Technical Report. U.S. Environm ental Pr otection Agency, No. EPA420-R-93-007, May 1993.
41. Sierra Research, Inc. Developm ent of Speed Correction Cycles – Draft (M6.SPD.001). Prepared for the U.S. Environm ental Prot ection A g ency, Natio nal Vehicle and Fuel Em issions L aboratory, June 26, 1997. Si erra Research No. Report No. SR97-04-01. Website: http://www.epa.gov/otaq/models/m ob ile6/m 6spd0 01.pdf
42. U.S. Environm ental Protection Agency. Regulat ory Im pact Analysis (F inal Rule) – Federal Test Procedure Revisions. U.S. EPA, August 15, 1996. See page 14. Website: http://www.epa.gov/otaq/reg s/ld-hwy/ftp-rev/sftp-ria.pdf

141

## Chapter IV: Econ omi c Impact s A. Testing and Facilities Burden
This sec tion gives add itional details f o r the Co st Analysis under PartVI. of the Preamble, “Projected Cost Im pacts .” The final rule requires calculation of fu el economy values based on the five-cycle for mulae beginning withm odel year 2011 for som e engine fam ilies. As discussed in detail elsewhere, for m odel years 2008 through 2010, m a nufacturers m a y use the mpg-based calculation for the five-cycle fu el economy values or they m a y conduct voluntary testing. For model year 2011 and after, if the five-cycle ci ty and highway fuel economy values for an em ission data vehicle group are within 4% and 5% of the m p g-based regression line, respectively, then all the vehicle configurations represented by the em issio n data veh i cle (e.g., all vehicles within the veh i cle test g r ou p) would use the m pg-based approach. Vehicle s within a tes t group fallin g below the 5% tolerance band for hi ghway fuel economy va lues would b e required to conduct US06 tests; those falling below the ci ty fuel econ o my band would be requ ired to conduct SC03, US06, and Cold FTP tests. In add ition, we expect that s o m e of these vehicles f

alling be lo w the tole ra nce leve ls m ay be eligible to es tim ate f u el econo m y f o r a given tes t through the application of analy tically derived fuel economy (ADFE) values. Som e data is currently available for vehicles that have conducted all five test s; based on this data, EPA has estim ated the num ber of vehicles for which additi onal testing would be re quired because they fall below the 4 and 5 percent bands, as discussed below.
W e prepare da range of burden estim ates f o r this analys is an d the dis cuss i on will m ention m i ni mu m and m a ximu m burden scenar ios. Thes e low and hig h estim ates are in tendedto provid e EPA’s estimate of the outer bounda ries of the likely testing a nd inform ation costs, and EPA solicited comments on the basis of these estim a tes, including the num ber of additional tests, and the costs of perform i ng such tests and of the a dditional tests that will be likely under the new regulations. EPA received no comme nts on the b a sis of these estim ates, th e num ber of additional tests, or the basic cost estim ates for perfor m i ng tests as presented in the proposal. Some comments were receiv e d on m o re specific cos t s is sues, and th ese have res u lted in some m odif i cation s to the cost estim ate tha t will be no te d below.
1. Test Volume

a. Testing Burden for MY 2008 through 2010
EPA estim ates no additional tests duri ng MY 2008 through MY 2010 based on the fact that the m p g-based fuel economy estim ates w ill be available f o r all m anufacturers. N o additional testing would be required because m a nuf acturers sim p ly apply the m pg-based scale of adjustm e nts to the sam e FTP and HFET test resu lts that they otherwise w ould conduct for the fuel economy labeling program

. While m a nuf acturers have the op tion of conducting and reporting full five-cycle test result s, s u ch tes t s ar e not requ ired . This cost a n alysis is lim ited to burdens that are m a ndated by the final rule.

142b. Testing Burden for MY 2011 and After
The term “Test Volum e” include s the labor and operations and m a intenan ce (O&M) c o sts for running additional tests under the proposed requirem e nts. Becau se of EPA’s facilities cost m e thodology, it also sets the cap ital cost estim ates for ongoing f acilities costs, as discussed below. Based on MY 2004 data, 1250 fuel economy vehi cles were tested with the FTP and highway fuel economy tests. (The figure is app r oxim a te because the city FTP test m a y be used and recorded prim arily as a fuel economy test, an em issions test, or both.) Data show that 330 Supplem ental FTP (US06 and SC03) tests were conducted and 220 Cold FTP tests. Consequently, if all fuel economy ve hicles we re required to conduct full five-cycle tests, approxim a tely 920 additional Supplem ental FTP tests and 1,030 Cold FTP tests would be required. Based on an analysis of our 615 vehicle da taset, we estim ate that 8% of the test groups will f all ou ts ide a band o f +/- 4% of the reg r ess i o n for the c ity test and 23% outside a band of +/- 5% of the highway regression. Taking the 2 004 num b ers above as a baseline, 92% of the additional S C 03 and Cold FTP tests otherwise requi red would therefore be avoided for city fuel economy; 77% of the ad dition a l US06 tests would be avoided

. Thus, for exam ple, the initial estim ate of increased testing burden for SC03 wo uld be 8% of the difference between 1250 and 330. This approa ch is re ta ine din the f i na l cost ana l ysis, with one a d justm ent. The percen t of vehicles trig gering add i tional testing requirem e nt s because th ey fall outs i de the to lerance bands for the city and highway tests s hould only count those that are be low the band in both cases; that is, only those with fuel economy lo wer than 4 and 5 percent below the regressions, respectively. W ith this correction applied to our updated 615- vehicle dataset, 4 percent of the test groups would trigg e r additional testing b ecau se they fall more than 4 percen t belo w the city regression line, and 13 percen t because they would fall m ore than 5 percen t below the highway line. Thus, for exam ple, the in itial estim ate of the increasedtesting burden for SC03 would be 4% of the difference between 1250 and 330, rather than 8%. The effect of this co rrection is that the baseline es tim ated ranges of addition al tests in th e proposal o f 169-212 US06 tests, 59 -74 SC03 tests, and 66-82 cold FT P tests, becom e 96-120, 29- 37, and 33-41, respectively, for the final rule. Finally, the high and low estim ates under th ese assum ptions are generated by differing estim ates of the effect of another feature that will be available fo r MY 2011 and after: an expanded use of analytically derived fuel economy (ADFE) as an alternative to conducting vehicle tests. Current guidan ce (CCD-04-06) lim its ADFE to 20% of the values that would otherwise be derived from tests; the 1250 test baseline already excludes such analytically derived results. Exp a nded ADFE guidance will be p r ep aredin tim e for MY 2011 to allow for deriv a tion of fuel economy values for som e of the additional test cycles that o t herw ise would be required as described above. The low and high burden estim ates assumes that 20% and 0% of the additional tests would thereby be avoided, respectively.

1431) Fuel Economy Labeling for Medium -Duty Passenger Vehicles (MDPVs)
As discusse d earlier, M D PVs will be includ ed in the lab e lin g program beginning with model year 2011. This change is congruent with NHTSA’s expansion of the CAFE program to include MDPVs beginning the sam e model year. As discussed in Section II. D, we are finalizing fuel economy test m e thods for MDPVs that will not requ ire addition al tes ting beyond that which will already be required by the CAFE program beginning in model year 2011 (i.e., th e FTP and HFET tests). Theref ore, we are pro j e cting no add ition a l cos t s in this f i n al rule to exten dlabeling to MDPVs. 2) Cold FTP Diesel Testing
The estim ated cost im pact of requiring cold FT P testing for light-duty diesel vehicles is sm all. As an example, in m odel year 2006, only fi ve light-duty diesel vehicles were certified for sale in the U

. S. A total o f eight city/h ighw ay tests were perform e d on thos e vehicles to determ ine fuel economy estim ates. Applied to the 2006 m o del year, our proposal w ould have required that an additional m a xi mu m of eight co ld FTP tests be perform e d in addition to the city/highway tests. Our proposed cost analysis accounted for additional cold FTP testing across the entire autom o tive industry, including diesel vehicles. While anticipating the makeup of the MY2011 dies el fleet is uncertain at this point, it seem s prudent to an tic ip ate som e addition in the nu m ber of diesel vehicles certified and, as a result, we have assum e d a doubling in the num be r of light duty diesels certified by MY 2011, and thus in creased the n u m b er of light-duty diesel test groups to 10. This has increased the estim ated Cold FTP test volum e from 66 to 82 tests (proposed) and 33-41 tests as corrected above, to 41 to 49 tests (final). The consequent adjustm e nt in testing costs is approxim ately $20,000 per year. This adjustm e nt also has an e ffect on the estim ated cap ital costs for testing facilities (see below for the m e thodology fo r estim ating facility capital costs). A separate c apita l cos ta ddition f o r Cold FTP diesel testing facility upgrades is discussed below under Facility Burden. The labor and O&M costs of conducting thes e estim ated tests are derived f r om prior Inf o rm ation Collection Requests sub m itted f o r EPA’s light d u ty certif ica t ion program . Those estim ates are based on the num ber of tests and the hours of labor used at EPA’s testing facility com b ined with industry data supp lied in response to questionnaires; these have been som e what adjusted to reflect current inform ation. These costs are estim a ted to ra nge from $1,860 to $2,441 per test. These costs per test are applied to the numbers of tests estim a ted under the m i nimu m and m a xi mu m scenarios above, and now a m ount to $343,000 to $424,000 and 5,000 to 6,200 hours per year for MY 2011 and after, com pared to $$606,000 to $757,000 and 8,800 to 11,000 hours in the proposal. This analy sis is summ arized in th e tables below:

144Table IV.A-1. Estimated Cost per Test: Propose d 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 Nu mber of Tests for M o del Years 2008-2010: Proposed and Final

Test Cycle 2004 Model Year Nu m b er of Tests Increase In Num b er of Tests: MY 2008 – 2010 Increase in Hours

Min Tests Min $ Incre ase Max Tests Max $ Incre ase 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 Incre ase = $0 Max Incre ase = $0 0 0

Table IV.A-3. Estimated Increase in Number of Tests for M o del Years 2011 and Later: Proposed

Test Cycle 2004 Model Year Nu m b er of Tests Increase In Num b er of Tests: MY 2010 And After Increase in Hours

Min Tests Min $ Incre ase Max Tests Max $ Incre ase 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 Incre ase = $605,672 Max Incre ase = $757,090 8,822 11,028

145Table IV.A-4. Estimated Increase in Number of Tests for M o del Years 2011 and Later: Final

Test Cycle 2004 Model Year Nu m b er of Tests Increase In Num b er of Tests: MY 2010 And After Increase in Hours

Min Tests Min $ Incre ase Max Tests Max $ Incre ase 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 Incre ase = $342,898 Max Incre ase = $423,740 4,982 6,168

2. Facilities Burden “Facilities” refers to the capital cos t s for constructing facilities to accommodate th e incre ases in test vo lum e estim ated in the tab l e abo ve. For thes e capital co s t s we used an FTP facility cost of $4 m illio n per facility able to p e rform 750 US 06 tests p er year, a co st of $9
m illion for an environm ental test facility able to conduct 300 to 428 SC03 tests p e r year, and $10
m illion for an environm ental facili ty able to cond uct 300 to 4 28 Cold FTP tests per y ear. The new tests were deem ed to require these facilities in proportion to the num ber of tests needed, and the costs were then annualized over ten years w ith a 7% discount rate. T his is likely a very conservative assum p tion since it does not attem p t to accoun t for the ex ces s capacity th at exists in m a nufacturers’ cu rren ttest facili ties. W e assum e that there is no excess capacity in our analys is. Furtherm ore, consistent with other infor m ati on burden analyses for the em issions and fuel economy pr ogram s, we have considered these as ongoing capital rather than startup capital costs (i.e., as the f acilities dep r eciate they are con ti nually being rep l aced ). A nnualized and depreciated over ten years at 7%, these capital costs per year under the above anal ysis under the proposal were $0 for each of m o del years 200 8, 2009 and 2010, and range from $524,000 to $866,000 per year for m o del years 2011 and after. Under the co rrected andadjusted nu mber of tests described the preceding section, the fina l annualized and discounted capita l costs for m odel year 2011 and after range from $375,000 to $560,000. In addition, commenters raised a nu mber of technical issues regarding laboratory configuratio ns and the d i fficulty of establishing co ld tes tfacility retrofits to accomm odate diesel testing without a transition period. T he 2011 effec tive date of the requirem ent is intended to address som e of these co ncerns, particularly the lead tim e needed to im plem ent laboratory adjustm ents, but we reco gnize th at so m e f acility u pdates will s till be nec es sary. An add ition al capital cost of $55,000 for each of ten m a nufacturers has been adde d the proposed facilities costs to accoun t f o r these adju stm ents in th e f i nal cos t estim ate. This figure includes the cos tof flam e ioniza tion d etec t ors (FI D s) as well as heated sa mp le probes , sam p le line s and sam p le f ilte rs. This cold facility upgrade for diesel tes ting, along with the co rrected and adjusted projected nu mber of tests, accounts f o r the ch ang e s in facility capital cos t s from the proposal.

146This analy sis is summ arized in th e tables below:
Table IV.A-5. Estimated Facility Cos t s: Propose d MY 2008 - 2010 MY 2011 And After Un-depreciated cap ital costs M i n i mu m M a x i mu m M i n i mu m M a x i mu m 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 Cos t s: Final MY 2008 - 2010 MY 2011 And After Un-depreciated cap ital costs M i n i mu m M a x i mu m M i n i mu m M a x i mu m 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 fa cility 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-tim e costs beginni ng with m odel year 2008 to im plem ent the new requirem e nts in the final rule. These startup bur dens are prim arily info rmation technology costs involving familiarizationwith the new data repo rting requirem e nts and refor m atting m a nage m e nt inform ation system s to carry out and report th e necessary d ata and calcu lations. W ith th e exception n o ted below, all these bu rdens are add

- ons to well established reporting requirem ents: m anufacturers already subm it data to EPA on all f i ve te st cyc l es, have the option of applying analytically derived fuel ec onomy numbers, and report vehicl e class determ inations and supporting infor m ation. This part of the pr oposed estim ate assum e d four weeks for four inform ation technology specialists fo r analysis and coding, and four weeks for two IT specialists for testing, at $100 per hour, for 35 m a nufactur ers, based on a count of manufacturers in 2004 with an allowance for inter- and intra-corporat e relationships. The estim ate also includes 1,120 hours industry wide for label rede sign. Startup infor m ation technol ogy costs finally also include one-tim e costs and hours for im ple m enting US06 split ph ase sam p ling, assum i ng one to seven days of programmi ng. The rem a ining startup cost is th e one-tim e costs as sociated with validation tests f o r the split ph ase s amp ling, and assum es on e to seven tests a tthe c o sts per tes tgiven above. These one-tim e tests are not considered to entail ongoing capital co sts but are treated as startup capital costs. EPA’s proposal estim a ted a ll startup costs, discounte d at 7% and annualized over ten years, as $526,000 to $615,000 and 3,815 to 4,718 hours.

147a. Dual Information System s Neededfor CAFE/Gas Guzzler an d Labeling Currently, EPA's data system has a single “path” through which fuel economy data is passed. Up to the po int of determ ining the so -called "m odel type" fuel economy , the calcu lations perform e d by the database are identical for both CAFE and l abeling purpos es. Then, the sing le path dive rge s: The lab el value is dete rmined by applying the 10% and 22% downward adjustm ent f actors to th e city and h i g h way m odel-type values, whereas, for CAFE, the modeltype values are adjusted by a m i nor, but statutorily required, adjustm e nt 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 pu rposes would be carried through “from the bottom up” resulting in a m odel type fuel eco nomy that n eeds no adju stm e nt for "real world" conditions (i.e., m odeltype fuel economy for labeling pu rposes would be different from m odel-type fuel economy for CAFE purposes.). Consequen tly, it would no longer be possibl e to use the single existing calcu lation s y stem s to determ ine the CAFE values. EPA agrees that there m a y be som e conveni ence in applying the de rived 5-cycle equation at the m odel-type level f o r m anufacturers who wish to use the sam e data m a nage m e ntsystem for reporting C A FE and label values. T h is m a y be particular ly true f o r the early part of the transition period. However, this approach is no t availab l e for the vehicle-s p ecific 5-cy cle label calculations, and any m a nufacturers who use it during the phase-in period during the 2008-2010 m odel years will encoun ter an inf o rm ation co st no t contem plated in the p r o posal; a dual calcu lation procedur e will b e needed . Sim ilarly, a dual ca lculating sys t em will be ne e d ed f o r model years 2011 and after. The cost analysis has been updated to account for this in creased inform ation system startup burden. Based on a projection of EPA’s in for m ation developm ent contract costs, and an estim ate of the portion o f those costs attributable to the dua l inf o rm ation s y stem possibility, we have increased the industry inform ation startup costs (unam ortized) by $933,450. This increases the annualized and discounted st artup 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 T echnical Support Docum e nt has also been updated to delete labor hours attributed to startup cos t s . St artup cos t s are p r operly treated as capital cos t s , not labo r. The startup burdens are summarizedin the f o llo wing table s:

148Table IV.A-7. Estimated Startup Costs: Proposed Cost Hours Item M i n i mu m M a x i mu m M i n i mu m Maximu m Inform ation Tech/Paperwork Adjustm e nt to new FE and label value computations and reporting of FE data for SFTP an d Cold FTP and new ADFE calcu lations-- analys is, code developm ent, and testing; label redesign $3,472,000 $3,472,000 34,720 34,720 Sam p le system changes f o r US06 split phase $28,000 $196,000 280 1,960 O&M Validation testing f o rm US06 split p h ase sam p ling $195,300 $651,000 3,150 10,500 TOTAL $3,695,300 $4,319,000 38,150 47,180 Depreciate 1 0 years at 7 % $526,128 $614,928 3,815 4,718

Table IV.A-8. Estimated Startup Costs: Final

Item Cost Hours M i n i mu m M a x i mu m M i n i mu m Maximu m Inform ation Tech/Paperwork Adjustm e nt to new FE and label value computations and reporting of FE data for SFTP an d Cold FTP and new ADFE calcu lations-- analys is, code developm ent, and testing; label redesign $4,405,450 $4,405,450 0 0 Sam p le system changes f o r US06 split phase $28,000 $196,000 0 0 O&M Validation testing f o rm US06 split p h ase sam p ling $195,300 $651,000 0 0 TOTAL $4,628,750 $5,252,450 0 0 Depreciate 1 0 years at 7 % $659,002 $747,830 0 0

4. Summary
The com b ined results of the above tables can be summ arized as follows: the final total estim ated co sts for each of Model Years 2008, 20 09, and 2010 range fro m $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:

149Table 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 -tim e 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-tim e 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 m odel 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 (C apita l, annualized 10yrs/7%) 0 0 0 0 Startup : one -tim e 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

150Table 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 (C apita l, annualized 10yrs/7%) 0 0 0 0 Startup : one -tim e 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 confirm a tory testing of a ny vehicle tested for em issions or fuel economy compliance pu rposes for any reason itdeem s necessary. EPA' s fuel econo my regulations currently specify certain conditions under which test results f r om m a nufa cturers are to be confirm e d, either by EPA at its own laboratory, or by the m a nu facturer. See 40 CFR 600.008-01. These cond itions are tied to the FTPand highway test p r oced ures, but do not address the US06, SC03 and Coldtemperature F T P tests that will now in cluded in fu el economy m easurem ent. There are separate con f irm atory test provisions in EPA's e m ission compliance regulations at 40 CFR 86.1835-01, which would conti nue to apply to all five test cycles.
Confir m a tory testing is generally indicated for fuel economy purposes when the fuel economy falls near the cut point for Gas Guzzler Tax assessm e nt, when the fuel economy (either city o r high way) is unex p ected ly hig h f o r that ve hicle o r f

alls near th e lea d er with in th e com p arable class. EPA provides gu idance to m a nufacturers defining the actual criteria and cutpoints. Confirm a tory testing is als o required when the test re sults are close to or ex ceed the federal em ission stand ard associated with the test. EPA also selects a ran d om num b er of vehicles f o r conf irm atory testing, an d will r eque st conf irm atory tes ting o n vehicles e mploying new or unusual technology. In considering the im pact of confirm a tory testing of US06, SC03 and Cold tem perature tests, som e of the above criteria m a y or m a y notapply. For instance, the Gas Guzzler tax is based only on the FTP and Highway test pr ocedures, so there would be no impact on confirm a tory testing for the othe r procedures. However, if, in the course of perfor m i ng a US06, SC03 or Cold tem perature fuel economy test, an applicable em ission standard should be exceeded, w e believe that a confirm a tory tes tw ould be necess ary, per the curren tregu lations.
Another reason to conduct additional tests is to resolve a disparity between confirm a tory fuel economy test results. If a m a nufacturer's initial fuel econ omy test result does not com p are close l y to th at of the con f irm atory test, EPA requires a re test. (This requ irem ent helps to estab lish co rrelation between m a nufacturer and EPA testing, providing assurance that

151m a nufacturer testing is properly conducted.) This cu rren tly ap plies on ly to FTP and Highway testing. If this requ irem ent was applied e qually to the US06, SC03 and Cold tem perature FTP fuel economy tests, this could re sult in the need for additional tests. Obviously, EPA does not have established fuel economy correlation data f

or the US06, SC03 or Cold temperature FTP tests, b ecaus e fuel econo my has never been requi red to be m easured. There is not enou gh fuel economy da ta to determ ine what con stitu tes reas onable co rrelation from one tes tto the next. Therefore, E P A does not plan at this tim e to c onduct 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 f u el e c onomy are implem ented, and m o re correlation da ta becom es available, w e will assess whether or not w e should establish retest criteria for US06, SC03 and cold temperature FTP tests on the basis of fuel ec onomy test-to-test disparities. 

### C. Changes to Label Format and Content Manufacturers are currently required to prin t and post fuel economy window stickers on all new cars and ligh ttru cks available f o r sale in the U.S. Our f i nal ru le d o es not chan ge this requir em ent, thus no new costs f o r th ese ac tivitie s will be inc u rred. The f i nal ch anges to the for m at and content m a y require a one-tim e de sign change, but any addition al cos t s are antic ipated to be very slight, and mitigated by th e design tem p late s f o r the new labels which EPA is providing in the regulations. The reporting andrecordk eeping requirements associated with the fuel economy label are se t forth in 40 CFR sections 600.312 to 600.314. These sections require that m a nufacturer s supply EP A with the label values a nd the data used to derive them , and provide schedules for the updatin g of this inform ation. Under th e final rule, these values will be reca lculated and new data will be subm itted. The costs f o r these ef f

o rts are ve ry m i nim al and are addressed in the startup cost figures above. There will be a sim ilar one-tim e set-up charge associated w ith th e new label form at based on th e effort required for each m a nufacturer to app l y the new EPA tem p lates to the labels they mu st prin t. This cos titem also has been in cluded in the paperwork startup costs porti on of th e cost an alys is. 

### D. Certification Fees
EPA collects fees from m anuf acturers to recover its cos t s as s o ciated withits em ission and fuel eco nomy com p liance efforts. The fee am ount is adjusted from tim e to ti m e according to any changes to EPA' s costs. The im pact of the f i nal ru le cha nges is not a n tic ipated to significantly increase EP A's e fforts with the fu el economy labeling program ; therefore, we are not proposing to adjust the fee am ou nt at this time. However, we reserve the right to evaluate our actual incurred costs once the fi nal rule is implem ented and to propose fee changes if deem ed appropriate.

152

## Appendix A: EPA Kansas City Test Program During 2004–2005, EPA in association with the Coordinating Research Council, STAPP A/ALAPC O, DOT and DOE/NREL,, recrui ted and tested over 600 privately owned passenger v e hicles in th e Kansas City area. Th e vehicles included an asso rtm e nt of compact cars, m i d-siz e cars, p i ck- ups and SUVs f r om a varie t y of m a nuf acturer s. The progra m was split into 3 round s (1, 1.5 and2), each con sisting of 120-300 vehicles. In all th ree rounds, v e hicles were recruited random ly from lists of vehicle regi strations in the Kansas City area. Care was taken to ensure that the sam p le were random with respect to the geographi c location of the owner and socio-economi c status. In r ounds 1 and 2, the desired sample of vehicles was stratified into four groups of m odel years, with em phasis on older vehicles 1 , 2

. The prim ary purpose of Rounds 1 and 2 was the quantif ic ation of part icula t e em issions, part ic ular ly those f r om high em itters. In Round 1.5, only 2001 and later m odel year vehicles were sampled. (Details about the design and perform a nce of Round 1. 5 are described the study’s final report. 3 ) The prim ary purpose of Round 1.5 was the m easurem ent of onroad fuel economy from vehicles f or which we could estim ate 5-cycle fuel eco nomy
. This m eant that we had to have fuel econ o my estim ates over all five cycles f o r these vehicles (i.e., that the ve hicle had to be certified to the Supplem ental FTP standards). These standards began phas ing in with the 2001 model year.
All of the vehicles In Rounds 1 and 2 were tested on a dynamom e ter, but only a subset of the vehicles were instru m ented with a Portab le Em issions Measurem ent System (PEMS) and tested in the hands of their ow ners. A s these vehicles ranged in m odel year from 1968 to 2005, very few of the vehicles tested in Rounds 1 a nd 2 had been certified to the Supplem ental FTP standard s. In Round 1.5, none of the vehicles w e re testedon a dynam o meter. However, all of these vehicles were instrum e nted with PEMS and had their fuel economy m easured while b eing driven in norm a l use by their owners. The round 1.5 vehicle fleet consisted of approxim a tely 120 vehicles, including over 30 hybrid el ectric vehicles. The PEMS m easur es driving activity, as well as second-by-second m a ss em issions of CO2, CO, HC, and NOx for roughly 24 hours while the owners of the vehicles are ut ilizing their vehicles on the road under norm a l, real-world conditions. Two aspects of the PEMS lim it the durati on of its operation. One is the capacity of gas which is needed to o p erate the flam e ionizati on detector used to m easure HC em i ssions. The other is battery capacity needed to op erate both the em issions m easure m ent equipm ent and the onboard computer which scans vehi cle activity and stores inform a tion. Using the carbon balance m e thod, fuel economy ca n be accurately calculated on a secon d by second basis. Since the focus of this proposed rule is fuel economy labeling, we are solely interested in the onroad f u el economy data co llected during th is program
. W e will not present, o r d i scuss the results of th e dynam o meter tes ting, nor the em issi on tes tres u lts. Becau s e of this, we will focus prim arily onRound 1.5, but will also include releva nt data from Rounds 1 and 2, as ap propriate. The reade ris referred to this report for furthe r in f o rm ation re lated to vehicle r ecru itm ent, selec tion, in strum entatio n, da ta processing and delivery. The onroad fuel economy data will be used for several purposes: 1) To com p are the on road fuel econo my for each vehicle to its fuel econo my label values,

1532) To com p are the dr iv in g activ ity of vehicles in Kansas City to the findings of past studies, and 3) To develop 5-cycle formulae for each vehi cle and com p are the 5-cycle f u el economy to sim ilar vehicles in our 5-cycle certification fuel economy dat abase
Before m oving to these three tasks, the follo wing section will describe the m e thods we used to process and quality assure th e data ob tain ed f r om the PEMS. 

### A. Quality Assurance Upon initial delivery of the data, we perform e d a num ber of tests on the data to identify equipm ent m alfunctions. The rem o val of inaccurate d ata is ref erred h er ein as “f lagg ing” ( i .e., data are “flagged” when it m eets a criterion des i g n ed to iden tify data which are not of acceptable quality). Flagging was perform e d usi ng the SA S statistical package. The initial step in quality assurance involved segregating vehicles into those with acceptable d a ta and thos e with bad d a ta. An en tire set of baddata for a given vehicle was rem oved fora variety of reasons. Som e ti m e s, the data collected for a given vehicle was completely m i ssing vehicle speed or exhaust flow data, which are critical to proper processing of the data. In som e cases, the vehicle was only driven for a few m iles before the equipm ent stopped functioning. These data were insufficient to develop robust estim ates of fuel use in the various VSP bins and to have confidence that the m easured onroad fuel economy was indicative of that vehicle’s typical use. In one case, th e CO2 em issions were outsid e a reason able range. After this initial f iltering of the data, there rem a ined 9 ve hicles 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 rem oving or m odifying i ndividual seconds of data. W e often found instances where the exhaus t flow m eter signal was zero, or erratic. There were several instan ces where th e flow m eter st opped functioning properly perm anently (i.e., for the rest of the testing of that vehicle). This could have been d u e to freezin g in the co ld tem p eratures or sim p ly from an electronic failur e. For non-hybrids, if the vehicle was moving, but exhaust flow dropped to a va lue equivalent to engine off conditions, then the data was flagged and om itted fro m the final d a taset. Unfo rtunately, this condition was im possible to flag system atically with hybrids, since the engine can shut off whi l e the vehicle is on. Obvious cases of lengthy equipm ent failures du ring hybrid testing were flagged by hand. However, exhaust flowm e ters were assumed to have been functioning properly during all hybrid runs. Another frequent problem was that the si gnal from the vehicle’s onboard diagnostic (OBD) system, which provides m easurem ents of vehicle speed, engine rpm, engine coolant tem p erature and other vehicle info rmation, d i sapp eared perm anently o racted erratically. This is easy to spot since all of the OBD signals disappear at the sam e tim e. This can be caus ed by the driver accidentally knocking the co nnector, or it can be a resu lt of m a nufacturer software settings. Fo rtuna tely, th e PEMS units were equip p ed with a g eographical position i ng system (GPS) and vehicle speed could be calculated fr om the output from the GPS unit. These GPS estim ates of vehicle speed were substituted fo r OBD speeds when the OBD speeds were absent or obviously erratic.

154Som etim es the GPS speeds appeared to re flect an om alies (i.e. , were clearly discontinuous, or drifted unrealisti cally over time). This was m ore of a concern regarding the calcu lation of vehicle acceleration th an vehicle s p eed, due to the fact th at required eng i ne power is very dep e ndent on small ch anges in acceler atio n. To identify unrealis tically h i gh acceleration s, a cut-off was chosen at a specific power (the p r oduct of acceleration and speed, or v*a) of 300 mph 2 /sec. Beyond this, the data were fla gged (though these events were rare). Choosing a lower (and probably still realistic) m a ximu m value would have resulted in excessive rem oval of data.

. In ad diti on, there were instances where th e GPS speeds drifted slowly over tim e, usually when the car was not operating. These tim e segm ents were rem oved.
Table A-1 s u mm arizes the process i n g of the data obtained with the PEMS

. Table A-1. Processing of Raw Data Obtain ed In Kansas City Round 1 Round 1.5 Round 2 Vehicles tested onroad with PEMS 18 117 52 Vehicles with som e PEMS data 13 109 47 Vehicles with acceptable PEMS data 9 97 42 Total secon ds of acceptable PEMS data while v e hicle is 53,319 501,405 181,915 in operation Seconds of PEMS data rem oved 3,808 44,996 2,773 As can be seen, a total of 187 vehicles were equi pped with PEMS units in Kansas City and the equipm ent worked to s o m e degree on 169 of these vehicles (90%). Data from an addition al 22 vehicles were rejected due to obvious equipm ent m a lfunctions, leaving 148 vehicles (78% of the original sam p le) with usable data. Of these 148 vehicles, 125 we re 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 ve hicles, or roughly 80 m i nutes of driving per vehicle. 

### B. Onroad Fuel Economy
Total fuel co nsum ption for each veh i cle was determ ined from the carbon balance of the CO 2 , HC, and CO em issions. The total dis t ance o f driving was determ ined by summ i ng vehicle speed and mu ltip lying b y tota l tim e of operation. This total d i stan ce trave l ed was thendivided by total fuel consumption to determ ine onroad fuel economy. EPA city and highway label fuel economy values were obtained from EPA mileage guides. The test vehicles were m a tched to thos e tested in Ka nsas City to the clo sest d egree possible. T h e following figure com p ares th e m easured fuel economy to the 55/45 composite label fuel economy from round 1.5 (newer vehicles). W e segregated the vehicles into two groups: conventional gasoline-fueled vehicles and hybrids. A linear regre ssion with no constant of the conventional vehicles show ed nearly one-to-one correlati on, with a slope of 1.006. The correlation was also quite good (r -squared value of 0.77). Th e largest difference was only 6 mpg, or about 30%. Thus, the onroad fuel economy data indicate no offset from the current EPA label v alues on average.

155The correlation of hybrid data show s much more scatter. T his is partially explained by the fact that only three hybrid m odels were tested, the T oyota Prius and the Honda Insight and Civic. On average, hyb rid fuel econ o my was 11% less than the com posite EPA label values. The average onroad fuel economy of the Toyota P r ius vehicles was closer to their com p osite label values than those for the two Honda m odels

. On average, the onroad fuel economy of the hybrids tested varied m ore than the conventional ve hicles. This could be due to hybrids’ greater sensitivity to operating conditions which can either take full advantage of the hybrid technology or essen tia lly nullif y it. The f act that m o st ve hicles star ted ou t tes ting with a hot start like l y biased onroad fuel economy upwards to som e de gree. Thus, the actual shortfalls found would have been greater to som e degree if testing had begun with a cold start, w hich is m ore representative of a typical day of driving. Figure A-1. Compariso n Onroad to Current Label Economy: Kans as City

no n-hy br i d hy bri d Li near (n on-hy br i d ) y = 1. 006 x Li near (h y b ri d) R 2= 0

. 77 46
y = 0.8940 2x R 2 = - 0 .1 80 69

0 1 0 2 0 3 0 4 0 5 0 6 0 l abel fu el ec on omy( m pg)



### C. Recent Driving Activity in Kansas City and California
There are a num b er of ways to evalu ate a nd com p are driving activ ity. W e evaluate two m easures of driving h ere: 1) com b inations of vehicle speed an d acceleratio n and 2) VSP frequency distributions.
A comm on m easure of driving activ ity is b a sed on a speed acceleration frequency distribution or SAFD. This proced ure divides individual seconds of driving into a 2 dim e nsional m a trix of speed and acceleration. Here, we r ounded accelerations to the n e arest whole num b er 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 mp h/sec. If an acceleration was less th an -15 mph/sec, we set th e acceleration to -15 m ph/sec. 0 10 20 30 40 50 60real-world fueleconomy(mpg)

156There are tw o basic ways to display a SAFD

. The more com pl e x way is to show the percen tage o f driving for each com b ination (or bi n) of speed and acceleration. The s i mpler way is to disting uish between thos e speed-acceleration bins for wh ich onroad driving was found and those which did not. For ease of comparison withthe SAFDs of dyna m o meter cycles, we use the sim p ler form of display here. W e developed th r ee SAFDs for the dr ivin g m onitored 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 te sted with PEMS units in Kansas City is shown in the following figure. The b r eadth of on road driv ing in Kansas City generally exceeds that of the FTP and HFET. Thus, for com parison, we show the SAFD for the FTP and HFET, as it represents an inner envelope of driving, per se
. In som e cases, the breadth of onroad driving in Kansas City generally ex ceeds th at of the US06 cy cle, as well. However, in som e cases, it does not. Thus, in the figure, we indicate the drivi ng inKansas City which exce eds tha tof the FTP and HFET c ycles. Then we show driving incl uded in the US06 cycle which exceeds th at found in Kansas City. In m ost cases, where the Kansas City driv ing f o rm s the edge of the envelope, the US06 cycle a l so inc l udes tha ttyp e of driving. However, this is no t tru e in all cas es
. Since the onroad d ata and the d ynam o meter cycles p r ovide speed an d acceleratio n rates for o n e second intervals and the HFET and US06 cycles are ab out 600 seconds long, the sm allest amount of driving which can fall into a single bin in th es e tw o cases is 0. 16%. Of course, sm aller percentages of onroad driving can fall into a single bin due to the large number of observations available. T o be com p arable with th e driv i ng cycles, we only consid ered onroad driving to adequately p opulate a sp ecific sp eed-acceleration com bination if at least 0. 1% of all on road driving fell into that bin. Figure A-2. Speed-Acceleration Frequency Dist ribution: Kansas City Vs. Test Cycles

AC C E L S P E E D BI N

BIN (m ph ) (mp h / s ) 0 5 1 0 1 5 2 0 25 30 3 5 4 0 45 50 5 5 6 0 65 70 7 5 8 0 8 5 90

14 13 KE Y 12 Dr i v i ng fr eq ue nc y c o v e re d by F T P / H F E T s t y l e dr i v i n g 11 Dr i v i n g fr eq ue nc y c o v e re d by K ans as Ci t y R e a l -W o r l d Dr i v i n g 10 Dr i v i ng fr eq ue nc y c o v e re d by U S 0 6 s t y l e d r i v i ng 9 8 7 US 06 6 5 KC 4 3 2 1 0 FT P / HFE T KC -1 -2 -3 -4 KC -5 -6 US 06 K C ac t i v i t y c u t of f < 0. 1% -7 -8 18 % o f K C dr i v i n g o u t s i de F T P / H F ET dr i v i n g e n v e l o p e -9 -1 0 0. 6% o f KC d r iv i ng i s o u ts i d e US 06 d r i v i n g e n v e l o pe -1 1 9 0% of US 06 d r i v i n g i s w i th i n t he 0. 1 % K C b ou nda ry

157Overall, 18 % of the onr oad driv ing activ ity (tim e based) in Kansas C ity f ell outs i de of the FTP/HFET envelope. T his corresponds to 33% in VMT terms. However, only 0.6% fell outside the US06 (0.4% of the VMT). These are data inthe white cells, which are populated but fall below 0.1% of the tota l. Overall, 25 % of the dr iving activity in Kansas City f e ll ou tside the FTP envelope alone (40% of the VMT). Compari ng to m ost speed lim its, 28% of the VMT was com piled greater than 65 m ph. The SAFD enve lopes for hybrid and conventional vehicles did not differ significantly from each other. However, the p e rcentages of driving in th e v arious b i ns did vary, as will becom e m o re evide n t below when we evalu a te th e VSP f r equency distribu tion s for the two types of vehicles. We also show the SAFD driving activity found from a chase car study conducted in L o s Angeles in 2000 in this figure 4

. This study observed roughly 390 hours of vehicle operation. According to the data, 20% of California urba n driving lies outside the FTP/HFET envelope (34% of the VMT). 1.2% fell outside the US06 e nvelope (1.3% of the VMT). 25% of the driving occurred outside of the F T P alone (42% of the VM T). However note that mu ch m o re of the rura l driving was outside the US06 envelope (3%). Figure A-3. Speed-Acceleration Frequency Di stribution: U r ban Califo r nia Vs. Test Cycles AC C E L SPE ED BI N

BI N (m ph ) (m ph / s ) 0 5 10 15 20 25 30 3 5 40 4 5 50 55 60 65 7 0 75 80 8 5 90 14 13 KE Y 12 D ri v ing f req ue nc y cov e red byF T P / H F E T st y l e d riv i n g 11 D ri v ing f req ue nc y cov e red byrea l

- w o rld C a l i f ornia u rban s t y l e dri v in g 10 D ri v ing f req ue nc y cov e red byU S 0 6 9 D ri v ing f req ue nc y cov e red byrea l

- w o rld C A b ut NOT U S 0 6 8 7 US 0 6 6 5 C A U rban 4 3 2 1 0 FTP / H FTP /H F E T C A Ur b a n -1 -2 -3 -4 -5 CA U r b a n -6 -7 C A a c ti v i t y cu t o ff < 0 .1 % -8 -9 20% o f C A U R B A N d r iv in g is o ut s id e F T P / H F ET d riv ing e nv e lope -1 0 1. 2 % of C AU R BA N driv in g is ou t side U S 0 6 driv in g env e l ope -1 1 41% o f C A R U R A L d ri v in g is o ut s i d e F T P / H F ET d riv ing e nv e lop e -1 2 3% of C A R U R A L driv ing i s ou t s id e U S 0 6 driv ing env e l op e
Moving to VSP, we calculated the level of VSP for each second of each vehicle’s dr iving from the vehicle’s speed and acceleration, plus it s road load characteristics and vehicle m a ss. The equation used is shown below: VSP = (Av + Bv 2 + Cv 3 + Mva)/M,

158where v is v e hicle speed , a is acceleration, A, B, and C are th e road lo ad (coast-down) coefficients of the vehicles and M is the m a ss of t he vehicle.
This is the sam e m e thod 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 m onitored on the road. For the MOVES cycles, we used coefficients indicative of the averag e car and light truck on the road. Road grade can also be included in the above equation. W e did not include this term , because the altitude data from the GPS unit appeared quite u n reliable. T h erefore, th e effect of road grade is not reflected in our analysis and re m ains a source of uncertainty in the r esults. W e obtained the specific veh i cle p aram e ters from EP A’s Inspection and Maintenance database based on the m ode l year, m ake and m odel of the vehicle. The vehicles were not weighed on a scale, so the actual w e ights m a y vary som e what from those in the database. The Kansas City driv ing activ ity was binned by VSP and vehi cle speed as describ ed in section IA.2. W e used 26 VSP bins, which in cludes the addition of 9 high power bins beyond those used currently in Draft MOVES2004. Bin 0 contain s significan t d ecelerations, while Bin 1 contains idle operation. Bins 11-19 include other operation belo w 25 m p h. Bins 21-29 include operation between 25 and 50 m ph and Bins 33-39 include operation above 50 m ph. Bins x1 contain operation with very low or negative engine power, while Bins x9 contain very high power operation. The following figure co mpares the Kansas City activ ity in te rm s of VSP bin to th at u sed in Draft MOVES2004 and described in section IA.2 (Table IA-12). The match in trends is reasonable, though Draft MOVES2004 projects ro ughly 4% more activity in the high speed, high power Bins 36-39 and 4% less activit y in the low e r power Bin 35. Figure A-4. Kansas City and California VSP Frequency Distributions vs. MOVESFreq 0. 1 6

0. 1 4

0. 1 2

0.1

0. 0 8

0. 0 6

0. 0 4

0. 0 2

0 0 1 11 12 1 3 14 1 5 16 17 1 8 19 21 22 23 2 4 25 26 2 7 28 29 3 3 35 3 6 3 7 38 3 9 VS P Bi n K C F r eq M eas u r ed M O VES C a l i f o r n i a U r ban

159We also show the driving activ ity found from a chase car stu dy conducted in Los Angeles in 2000 in this figure 5

. This study observed roughly 390 hours of vehicle operation. This VSP frequency distribu tion w as estim ated from th e speed acceleration frequen c y distribu tions developed from the chas e car data 6

. The portion of driving found the Bins 36-39 in Los Angeles m a tches that in MOVES2004, though there is about 1% less operation in Bi ns 26-29. One must be cautious when com p aring activ ity data from instrum e nted vehicl es andchase cars. Chase cars do not follow drivers throughout their entire trip, so operation on local neighbor roads is often m i ssed. Al so, the VSP distribution from Dr aft MOVES2004 includes both urban and rural operation, while those of Kansas City and Lo s Angeles are prim arily urban. This could introdu ce so m e differences, as well. The following figure shows the VSP frequenc y distributions for the three groups of Kansas City vehicles m e ntioned above: all ve hicles, hybrids and non-hybrids. W e removed idle (bin 0) p r ior to calcula ting the VSP distr i butions for two reas ons. One, idle percentag e s were extrem ely high and if depicted in the figure, woul d have m a de the rest of the bars difficult to read. Second, given that little fuel is consum ed during idle, including the percen tage of tim e at idle distorts the comparison of those VSP bins where fuel consum ption is m o re significant. Figure A-5. VSP Frequency Distributions in Kansas City: Hybrids vs. Non-Hybrids
0.2
0. 1 8
0. 1 6
0. 1 4
0. 1 2

0.1
0. 0 8
0. 0 6
0. 0 4
0. 0 2
0 0 1 11 12 1 3 14 1 5 1 6 17 1 8 1 9 21 22 2 3 24 25 2 6 2 7 28 2 9 3 3 35 3 6 3 7 38 39 VS P B i nFreq Total A c ti vi t y No n-Hyb r id Hy brid

As can be seen from a close look at the ope ration in the higher power bins (x6-x9), the driving of hybrids tends to be le ss ag gressiv e than that of con v en tional vehicles. The percentage of tim e spent driving in bins 26-29 and 36-39 is 5-6% lower for hybrids than conventional vehicles. T his is of interest, as this study is likely to be the fi rst to exam ine the relative operation of conventional and hybrid vehicles. If there wa s som e thing about hybrid vehicles which always led them to be opera ted less agg r ess i vely, th is m i ght need to be considered in developing their fuel economy label value. There are several pos sib l e explanatio ns for this difference. One cause cou l d be a lim itation in the power of the hybrids m onitored in Kansas City. All but one of the hybrids were

160either a Priu s or a Civic, which were clearly des i g n ed for fuel efficiency with som e sacrifice in power. Another cause could be driver beha vior. People purchasing hybrids m a y have an unusually high interest in achieving high fuel econo my on the road and drive their vehicles less aggressively, knowing that aggressi ve driving reduces fuel economy. This could either represent a change in driving behavior to wards less aggressiveness with th e purchase of a hybrid, or these drivers m a y have driven their prev ious vehicle the sam e way. Of particular interest is wh ether the difference in the opera tion of conventional vehicles and hybrids will con tinu e in the futu re or disapp ear. If the d i fference is due to a low power vehicle design, then the difference d e pends on th e design of future hybrid s. Most of the recen tly introdu ced h ybrids m atch (or even exceed) the p o wer output of their non-hybrid coun terparts. Thus, this difference in vehicle operation f ound in Kansas City m a y soon disappear. If the drivers of hybrids in Kansas City always drove in the m a nner 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 ope ration would continue until hybrids becom e the dom i nant drivetrain. As m ore and more peopl e purchase hybrids, the driving of hybrids would likely beco m e more aggressiv e, sin ce the new h ybrid purchasers are com i ng from the rem a ining, more aggressive pool of drivers. The driving of conventional ve hicles would also becom e m ore aggressive as the less aggressive drivers of c onventional vehicles buy hybrids. Overall driving operation would likely rem a in the sam e , but the driving of the two pools would shift over tim e , tending to rem ain distinct from each other. Finally, the drivers of hybrids in Kansas City m a y have changed their driving behavior when they purchased their hybrids. Som e m a nuf acturers of hybrids offer training classes or videos to help hybrid owners get the most from th eir hybrid technology. If this trend continued, hybrid driving activity would tend to always be less aggressive than that of conventional vehicles. As m o re hybrids enter the fleet, the overall driving of the fleet would becom e less aggressive. Even if the drivers of current hyb rids have changed their driving behavior, the question exists whether this trend would continue as hybrids becom e m ore popular. This is the first system atic study of the onroa d operation of hybrids in the hands of typical owners. Only about 45 hours of ope ration were studied. Thus, ther e is significant uncertainty in the difference found in the operation of conventional and hybrid vehicles. As discussed in section I.A, we are in th e process of obtain i ng a large volum e of opera tion a l (a ctiv ity ) data obtained in Atlanta. Som e hybrid vehicles m a y be included in this database. W e plan to compare the operation of conventional and hybrid ve hicles in that study as soon as we receive the data. Still, th e poten tial im pact of the di fference in driving behavi or f o r the two typ es of vehicles is examined in section III.E.4. 

### D. Evaluati on of 5-Cycle Approach to Fuel Economy Estimation In section I.A.2.b, we developed combin ations of dyna m o meter cycles which best represented the driving activity re presented in Draft MOVES2004. In an earlier publication, we also developed a m e thodology by which real-world driving could be fit by 3 cycles (FTP, HFET, and US06). 7 W e repeatedthis an alys is using the VSP freque ncy distributions shown in Figure

161II-5 abov e a nd additiona l cycle com b inations. Th is ana l ysis a nd its results are des crib e d in deta il in section III.E.4. Because the Kansas City study m easured the driv ing activity of eachvehicle separately, as well as its second by second fuel consum ption, we can develop dynam o m e ter cycle com b inations which best repres ent th e driv ing of each vehicle. These cycle com b inations can then be usedin the 5

- cycle formula to predic t onroad fuel eco nomy
. These vehicle sp ecific 5­ cycle fuel econom i es can then be compared to the m easured onroad fuel economy to assess the accuracy of the 5-cy cle m e thodology. One caveat regarding this com p arison is that we need estim a tes of fuel economy over the five dynam o m e ter cycles to use in the 5-cycle fo rmula. These generally only exist for vehicles certified to the Supplemental FTP standards, wh ich 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 mode l years. Therefore, we only perform e d this analysis for vehicles from Round 1. 5. Even withthis restriction, we still do not have fuel economy measurem ents over the 5 dynam o meter cycl es for m a ny of the vehi cles tested in Round 1.5.
The first s t ep in the an alysis was to develop a V SP frequency distributio n for each vehicle’s onroad driving. The sam e m e thods we re used to do this as were used above in developing the VSP frequenc y distributions for all the Kansas City vehicles. The only difference is that it was done for ea ch vehicle individually. The second step was to develop VSP freque ncy distributions for the five dynam o meter bags and cy cles for eachvehicle. These dis tri butions are very similar to those shown in Table IA-15. The only difference is that w e used vehicl e-spe cif ic v alues f o r th e constan t s A, B, C and M instead of the fleet-average estim at es contained in Draft MOVES2004. The third step was to calculate an av erage rate of fuel consumption for each vehicle’s operation in each VSP bin. This was done by cal culating the rate of fuel consum ption for each second of vehicle op eration using the carbon balan ce m e thod. For each v e hicle, the seconds of operation were grouped by VSP bin and the fuel rates for these tim es of operation averaged. If no onroad operation occurred in a particular bin for a particular ve hicle, the fuel cons um ption in that b i n was set to the r ate of fuel consum ption in the nearest bi n having the sam e level of power (e.g., bin 38 average fuel consum ption is set to that found for bin 28). If no operation occurred in any of the other bins of the sam e power leve l, the rate of fuel consum ption was set to the average rate of fuel consum ption for other sim ilar vehicles in that bin. Here, sim ilar 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 conventiona l 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 sam e approach was taken for hybrid vehicles.
W e restr i cte dthe ca lcula tion of f

u el r ates to warm ed up driv in g (i.e., af te r the ef f

ects o f the cold start had ceased). W e first had to dete rmine when an engine st art occurred. Along with this we determ ined how l ong the engine had been turned off (i.e., the soak tim e ).

162W e used two criteria for determ ining the poin tin each trip wh en the eng i n e was fully warm ed up. The first applied to those vehicles w ithout coolant tem p er ature data. The second applied to those vehicles with coolan t tem p erature data. Engin e coolan t temperature d a ta was only availab l e for som e of the vehicles, so the latt er criterion could be not extended to all of the Kansas City vehicles
For vehicles without coolant tem p erature data, w e assum e d that the engine was fully warm ed up after the engine had been running for 200 seconds. For those with such data, we first sm oothed out f l uctua tio ns in the coo l ant tem p er ature data by calcu lating a five-secondaverage tem p erature for a tim e t which is th e average of the coolan t temperature at that tim e, the two seconds prior to tim e t and the two seconds subs equent to time t. For all operation beyond the 200 thsecondafter a s t art, we calculated the m ean and standard deviation of what we considered to be the warm ed-up coolant tem p erature. The first second w hen the five-second average tem p erature f ell within 1 standard d e viation of th e m ean coolant tem p erature was estim ated to be the tim e at which the en gine was f i rs t f u lly warm ed up. As described in section I.A.2, we regresse d the VSP distributions for the 5 dynam o m e ter cycles and b ags agains t that of the on road driv in g for each vehicle. W e weighted the s quare of the error by the rate of fuel cons um ption in each bin, then m i nim i zed the weighted error. Unlike the analyses describ ed in secti on I.A.2, here we used SAS to perform the analyses due to the large num ber of vehicles involved. In all case s, the sum of the dynam o me ter cycle coefficients was set to e qual 1.0 andthe in terc ep t was set to zero. We developed two sets of dyna m o meter cycle co mbinations f o r each veh i cle. One used the five dyn am ometer cycles and b a gs used in se ction I.A.2, nam e ly: Bags 2 and 3 of the FTP, HFET, US06 city and U S 06 highway. A second set of regressions was perform e d using only three of these cycles and bags, nam e ly Bags 2 and 3 of the FTP and HFET. This second set of regressions represents what is called here the “3-cycle” m e thodol ogy. It represents a way of estim ating onroad fuel economy usi ng only the current two fuel economy tests, the F T P and HFET. It does not in clud e the US06 cycle, either in whole or in part

. No attem p t was m ade to segregate a vehicle’s dr iving into city or highway m odes. Thus, each regression rep r esents all of the driving b y an indiv i d u al vehicle and the pred icted fuel eco nomy is then com p arable to the overall fuel economy of that vehicle on the ro ad during the duration of the PEMS testing.
The initial w e ighted regression for each ve hicle s o m e tim es produced cycle coefficients which were negative. Those cycles with nega tive coefficients were rem oved, one-by-one, starting withthe cycle with the co efficient of the largest m agnitude. Once all regre ssio n coefficients were non-negative, the regression procedure was stopped andthe resu lts accepted. No attem p t was m ade to f u rther r em ove cycle s with positiv e coef f i cients which m i ght not pass a statistical significance te st of som e sort.
For exam ple, vehicle number 521, a 2003 Mits ubishi Montero Sport, was driven 46 m iles while its fuel economy and activity were being m onitored. The cycle re presentation of its driving is shown in Table A-2.

163Table A-2. Cycle Combinations fo r the Mitsu b ishi Montero Sport Bag 3 Bag 2 HFET US06 Highway US06 City Tim e Basis 3-cycle combination 68.6% 0% 31.4%

---- ---- 5-cycle combination 66.0% 0.2% 0% 33.7% 0% Mileage Bas i s 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, weightedby fuel consumption, provides cycle com binations in term s of the percen tage o f tim e spent driving in ea ch cycle. A s was descr i bed in sec tio n I.A.2, these percen tages of tim e are conv erted to percen tages of m i les traveled using the av erage sp eed of each dynamom eter cycle and bag. As can be seen in Table A-2, without cons ideration of US06, about two-thirds of the Montero’s driving tim e 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 drivi ng previously represented by HFET shifts entirely to US06 highway. In order to use the cycle com b inations to pr edict 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 dynam o m eter cycles. W e estim a t ed the fuel economy for each bag or cycle for each Kansas City vehicle from the tes tresu lts of s i m ilar vehicles in our 5

- cycle fuel eco nomy database of 615 vehicles. The fuel economy estim at es for the Montero are shown in the last row of Table A-2. W ar m ed up onroad fuel economy can be estimated by sim p ly summ ing the product of the fuel econ o my of each bag and cycle and th at b a g’s or cy cle’s contribu tion to 3­ cycle o r5-cycle driving.
We were able to m a tch 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, d i fferences exist betweenthe Kansas C ity and ce rtif ica tion ve hi cles, as th e latter are selected bas ed on their worse case n ature regard ing emissions and the form er are likely to be high sales volum e vehicles. This could cause th e 3-cycle and 5-cycle estim a tes of fuel economy to som ewhat low. W e present an ana l ysis a tth e e nd of this se ction which sheds som e light on this issue.
In order to further refine th e 3-cy cle and 5-cycle estim ates, we then in clu d ed fuel use related to engine starts. Th is basically involved determ ining how m a ny tim es the engine was started while the veh i cle’s operation was being m onitored, the soak tim e prior to each engine start and th e am bient temperature at the tim e of eachstart. If a trip lasted less th an 1 0 seconds, we assum e d that it did no t occur. The tim e associat ed with this trip was m a de partof the soak

j Near l y hal f o f th e hy b r i d s t e s t ed we re pre
- 2 0 0 4 m odel y e arP r i u s ve hi cl es. We d o not h a ve 5-cy cl e fue l econ o my v a lu es fo r th is m o d e l.

164tim e prior to the next tr ip

. W e also deleted tr ip s when m o re than 5% of the tr ip’s da ta were discarded for reasons described above.
The am bient tem p erature at the tim e of the e ngin e start was estim ated from the tim e and day of the engine start and local m e teorology data from the National W eather Service. W e estim ated the total fuel related to engine starts using the sam e m e thodology applied to the Draft MOVES2004 estim ates of annual engine starts throughout the nation and described in section I.A.1. Each engine start had a previous soak tim e and am bient tem p erature associated with it. The start f u el use re la ted to this engin e start was e stim ated in term s of the start f u el use rela ted to a cold s t art a t75°F using the equa tio ns f o r the ef f

ect of start fuel use as a function of soak tim e and am bient tem p erature presented insection I. A.1. The 75°F cold start equivalen t s for each start were su mmed across all of the starts for e ach vehicle to estim ate the total num ber of 75°F cold start eq uivalen t s for each vehicle’s m onitore d driving. W e did not have a m easured soak tim e prior to the f i rs t start of m onitor ed opera tio n , as the equ i pm ent was by necessity installed while the engine was turned off. W e assum e d that am ount of tim e that the equipm ent was operating pr ior to the f i r st engin e star t was the so ak tim e prio r to th at s t art. This tende d to be only a m atter of m i nutes. Practically, this was eq ui valen tto a ssum i ng that the f i rs t eng i ne start was a hot start, though in som e rare cases the vehi cle had sat overnight since its last operation. Thus, this is one certain s ource of unc erta inty in th e estim ation procedure. W e estim ated the sensitivity of the estim ated onroad fuel economy due to this uncertainty by evaluating the im pact of adding one cold start to the num be r of equivalent cold starts.
For example, the Montero took three trips whil e being m onitored. The first was preceded by a soak of 12 m i nutes, the second by 99 m i nutes and the third by 3 mi nutes. The ambient temp erature for all th e starts ra nged only from 24-25 F. Using th e equations presented in section I.A.1, the total num ber of cold starts ignoring th e effect of a mbient tem p erature was 0.47 for the three s t ar ts. The tota l nu mb er of 75°F cold st art equivalents including the effect of ambient tem p erature was 0.96 for the th ree s t arts. The latte r estim ate assum es that the cold start fuel use (the d i f f

erence in f u el co nsum ption in Bags 1 and 3 multiplie d by 3.59 m iles) a t20°F is 2.75 tim es that at 75°F. W e determ ined th is ra tio f o r each vehicle using the actual or es tim ated fuel economy va lues for Bags 1 and 3 at 20°F and 75° F from our 5-cycle fuel economy da tabase. The ratio for the Montero was 2.16, m eaning that the co ld start fuel use o nly increased by a factor of 2.16 between 75°F and 20F, or 22% le ss than that assum e d in Draft MOVES2004 for the typ i cal v ehicle. Thus, we mu ltip lied the equi valent num ber of cold starts a t75°F (0.96) by the ratio of 2.16 to 2.75 to m ake the estim ate of the equivalent num ber of cold starts at 75°F specific to the cold s t art fuel use of the Monter o. This reduced the es tim ate of the eq uivalen t num ber of cold starts at 75°F for the Montero to 0.75.
W e then mu ltip lied the c o ld star t f u el use at 75°F f o r the Montero by the n u m b er of cold start equiv alents. For th e 3-cycle es tim ate of onroad fuel economy, we us ed a value of 0.47 for the num ber of cold start equivalents, since the FTP and HFET only provide fuel economy inform ation at 75°F. For the 5-cycle estim ate of onroad fuel economy, we used a value of 0.75 for the number of cold start equi valents, since th e availability of the cold FTP provides the additional fuel economy inform ation at 20°F.

165Finally, for the 5-cycle estim ate of onroad fu el economy, we included an estim ate of the effect of te mperature on running fuel use. W e estim ated the ambient tem p erature at the tim e of the start of each trip and then we ighted these temperatu r es by the leng th o f each trip inorder to estim ate an average temperatu r e while the v ehicle was operating. For the Montero, this was 24.7 F. W e then calcu lated a percen tage indicating th e degree to which this temperature represented the temperature drop from 75°F to 20°F; in th is case, 91%. Consisten twith the 5

- c y cle for mulae, for the percentage of driven m il eage represented by HFET and US06 highway, we incre ased ru nning f u el u se by 4% mu ltip lied by the percen tag e of the temp eratu r e dro p f r om 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% m i nus the temp erature percentage (9 %) tim es the running fuel use at 75°F (a function of fuel economy over Bags 2 and 3 and US06 City), plus 2) the temp eratur e per ce n tage (91 %) tim es 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).
W e were able to m atch 71 vehicles f r o m those successf ully te sted in Kans as City to vehicles in our 5-cycle fuel economy database. Using the procedures just described, we estim ated 3-cycle and 5

- cycle fuel econom i es for each vehicle and averag ed the resu lts across all 91 vehicles. The results are shown in Table A-3.

Table A-3. Onroad and Predicted Fuel Economy: Kansas City Test P r ogram 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 War m ed up fuel economy 32.2 18% 29.9 17% W ith cold starts at 75°F 32.0 18% 29.7 17% W ith cold starts a tam bient

---- ---- 29.2 17% tem p
. W ith running fuel use at

---- ---- 27.7 17% am bient temp As indicated in Table A-3, the average onroad fuel economy of the 71 vehicles (each vehicle weighted eq ually, no t by m ileage of travel) was 2 8 .3 mpg. Using only the cycle combinations to predict onroad fuel economy over-predicted onroa d fuel econ o my using both three cy cles and five cycles, as would be expected. T h e overpredic tion is sm aller for five cycles than th ree cycles (1.6 m pg ve rsus 3.9 m p g), 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 percen tage d i fferences inthe pred icted versus onroad fuel eco nomy for each of the 71 vehicles. This m e tric provides an indication of the consis tency of the prediction. A low coefficient of variation, ev en if there is a larg e, but consiste nt of fset, would indicate th at a significant factor was m i ssing from the prediction, lik e cold s t art fu el use, bu t to a consis te nt degree across all of the vehicles.

166Adding coldstart fuel us e as if all starts we re at 7 5 °F (as assu m ed in the FTP) reduces the difference b e tween pred icted and on road fuel ec onomy in both cases by the sam e 0.2 mpg. The coefficients of variation rem a in unchanged. Adding coldstart fuel us e at th e estim ated am bient tem p erature is on ly applicab le to the 5-cycle pred iction. This factor redu ces th e difference between predicted and onroad fuel economy by 0.5 mpg. The coefficient of variat ion, however, does not change. One would have expected it to decrease at least s lightly, since the am bient temperatu r es at which the vehicles were started varied. Th ree possib l e reasons for th is outcom e exist. One, the effect of these temp erature dif f

erences was so sm all that tota l f u el use variedvery little even for specific vehicles. T w o, the data for soak tim es were inaccurate, particularly due to the unknown soak prior to the f i rst star t. Or , three, the d i f f

eren ce 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 term s of predicting onroad fuel econom y, though, considering the am bient tem p erature of engine starts reduces the difference betw een the 5-cycle fuel economy estim ate and onroad fuel economy by 2%. Adding the effect of a mbien t tem p erature on running fuel use had a larger effect on predicted fuel economy than the effect of cold starts at 75°F or am bien t tem p erature. This reduced the predicted 5-cycle from 0.9 mpg above onroad fuel economy to 0.5 m pg below it, for a final difference of less than 2%. The difference between the 3-cycle and 5-cycle formulae is even m ore dram atic for nonhybrid vehicles. The difference betw een the best 3-cycle prediction and the onroad m easurem ent for hybrids averages 24%, while that for th e best 5-cycle prediction is only 3%.
The VSP ba sed approach to pred icting onroa d fuel economy was equally successful w h en applied in a 15 car study conducted by EPA in 2001. Table A-4 presents the m easured onroad fuel economy of the 15 cars and fuel economy pr ed ictions using two diffe rent approaches. One approach used the VSP m e thodology developed for Draft MOVES2004 (see section I.A.2) to determ ine th e m i x of FTP, HFET and US06 driv ing which b e st m a tched each vehicle’s onroad driving pattern. This approach under-estim at ed onroad fuel economy by 4%. The other approach us ed averag e o n road speed to determ ine the m i x of FTP and HFET driving which best m atched each vehicle’s o n road driv in g pattern. T h is approachover-es tim ated onroad f u el economy by 24%. One advantage of the 15-car testprogram was that m o st of the 15 cars were tested over the FTP and US06 as part of the study. Therefore, the dynamom e ter fuel economy values used in the p r edic tions were those f o r the specific veh i cles tested o n road. In th e Kansas City program, we had to m a tch th e vehicles tested onroad to those in ou r 5-cycle certification databas e
. These m a tches m a y have been clos er in som e cases than oth e rs , causing the 5-cycle fuel economy to slightly under-predict onroad fuel economy on averag e in Kansas City.

167Table A-4. On-road and Modeled Fuel Ec onomies Using Vehicle-Spe c if ic Cycle Weights (mpg) Veh. No. Fuel Economy ( m pg) % Di fference On-road Modeled VSP (FTP, HFET, US0 6) Speed (FTP, HFET) VSP (FTP, HFET, US0 6) 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 estim ates) -3.8% 24.6% Standard Deviation (10 vehicles) 7.0% 13.2% Finally, in an effort to better understand th e cause of the difference betw een predicted and onroad fuel economy reflected in Table A-4, we reversed this analysis. Instead of using fuel economy measured over dynam o meter cycles to predict onroad fuel economy, we used the onroad fuel m easurem ents to predict cycle fuel economy. W e did this using the VSP m e thodology. Each combination of vehicle an d dynam o meter cycle has its own VSP frequency distribution. W e sim p ly weighted th e m easur ed onroad fuel consumption in each V SP bin for each vehicle by this cy cle-specific VSP frequency di stributio n to estim a te the average rate of fuel use (in gallons per second) over th at cycle. W e then converted this fuel rate to fuel economy using the av erage sp eed of each dynamom eter cy cle. Table A-5 shows the m easured and predicted fuel economy values for four dynam o me ter bags or cycles. W h en com p aring the predicted fuel economy over the dynam ometer cycl es to m e a sured valu es, we found s i gnificant differences between hybrids and co nventional vehicles. Thus, Tabl e A-5 presents the results of this analysis separately f or conve ntional vehicles and hybrids.

168Table 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 (m pg) 21.5 25.0 34.0 22.5 % Difference 30% 21% 19% -7% Measured inlab: ad juste dfor tem p erature (m pg) 20.5 24.0

--- --­ % Di fference 26% 1 6%

--- --­ Hybrids Predicted from onroad fuel rates (mpg) 32.4 38.4 51.5 43.3 Measured in lab @ 75°F (m pg) 61.8 53.9 61.8 41.6 % Difference 91% 40% 20% -4% Measured inlab: ad juste dfor tem p erature (m pg) 47.6 46.9

--- --­ % Di fference 46% 2 2%

--- --­ For conventional vehicles, cycle/bag fu el economy from the 5-cycle certification database are higher than those predicted from second-by-second onr oad 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 m easur ed cycle fuel economy be ing 7% lower than that predicted from the onro ad fuel m easurem ents. Th ese differences can be due to differences in vehicle operation on the road and on the dynam o m e ter and to differences between the physical vehicles tested in both cases. As m e ntioned ab ove, the vehicles which are generally included in our 5-cycle certification databas e rep r esent the w o rse cas e vehicle configuration with in their broader vehicle groupings. W orse case m i ght in clude four wheel drive, higher inertia weight setting, higher TRLHP, etc. Th e vehicles tested in Kansas City would tend to be high sales volum e m o dels. This m i ght explain the 7% difference seen fo r the US06 cycle, but the differences observed with the ot her cycles go in the wrong direct ion. Differences in ambient temperature could explain this difference in fu el economy, as the Kansas City testing was conducted during the late fa ll and early winter.
W e attem p ted to correct for the difference in tem p eratu r e us in g the averag e tem p erature for each veh i cle’s operation, as described above. We then used this av erag e tem p erature to interpolate between the Bag 2 and Bag 3 fuel economy values m easured and 20°F and 75°F, respectively, to estim a te a dynam o meter-m easuredfuel economy at the ambient tem p erature of each vehicle’s testing in Kansas City. W e cannot perform a sim ilar adjustm ent to the HFET and US06 fuel economy val u es as these tests are no t run under cold tem p erature conditions. The results of this adjustm e nt for tem p erature are sh own just below the unadjusted results in Table A5. As can be seen, for conventional vehicles, th is adjustm e nt reduces the difference between the predicted and m easured 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 am bient temperatu r e on HFET and US06 fuel economy to be relatively sm all. Thus, there appears to be factors which are causing onroad fuel consumpti on to be higher than dynamom eter m e asurem ents during low speed and mild driving which is not affecting higher speed, more a ggressive driving. Generally, this argues for including US06 fuel economy in the fuel economy label calc ulation. It also supports the inclusion of the 9.5% downward adjustm ent for non-dynam o m eter factors.

169The com p arison for hybrids is sim ilar, but the differences are much m o re dram atic, particularly at low speed, stop and go drivi ng where hybrid technology f unctions. Bag 2 and 3 fuel economy m easured on the dynamom e te r exceeds those estim ated from onroad m easure m ents by 40-91%. In contrast, at higher speeds, bu t with either m ild or aggressive driving styles, the differences are both sm aller an d very sim i lar to those found with conventional vehicles. Of course, at higher speeds without much stopping, hybrids operate like conventional vehicles. As for conventional vehicles, we adjust ed the Bag 2 and 3 dynam o meter fuel economy values for temperature. The effect is much m o re dram atic for hybrids, given their greater sensitivity to am bient temperature. The 40- 91% difference d ecreases to 2 2-46%. These differences are still greater th an those found for conventional vehicles. We considered the possibility that our analysis of the data was so mehow ignoring the engine shut-off feature of the hybrid vehicles (i.e., the zero fuel consum p tion occurring during th ese tim e s were being excluded fro m the average fuel rates being calc ulated for low e r speed, lo w power VSP bins). However, we confirm e d that our m e a surem ents included significant am ounts of tim e when the engine was off. Overall, we m easured zero carbon m onoxide emissions 12% of the tim e from the hybrid vehicles. The Prius m odels had the hi ghest percentage of engine off operation (19%), followed by the Civic (3%), following by the one In sight in th e tes tfleet (1%). Clearly, the Honda hybrids turned off their engines less frequently than the Toyota Prius. This m a y be due to differences in hybrid technology utili zed by the two m a nufac turers.
We evaluated whether this difference in e ngine of f tim e affected the com p arisons sho w n in Table A-5. For both the Prius and Civic m ode ls, the dynamom eter m e asured fuel economy over Bag 3 is 21% higher than those values estim at ed from onroad fuel rate m easurements, after adjusting for am bient temperature. For Bag 2, the Prius m o dels show a 63% difference, while that for the Civic hybrid m odels is 38%. Thus, the lower percentage of indicated engine off operation for the Civics is not li kely the cause of the greater difference in hybrid fuel economy over Bags 2 and 3 of the FTP shown in Tabl e A-5 for hybrids compared to conventional vehicles. T he difference could be due to the e fficiency of regenerative braking onroad versus on the dynam o m e ter, as the severity of deceleratio nis not considered in the VSP m e thodology. Further study of the data obtained in Kansas City and additional data collected elsewh ere in the f u ture will b e needed to bette r iden tif y the cau se of the dif f

erence. Overa ll, however, f o r hybrids, as well as conventional vehicles, th e dynam o meter m easured fuel economy over US06 appears to b e mu ch more directly representative of onr oad fuel consum ption than those m easured over the current fuel eco nomy cycles

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### Appendix A References
1. Eastern Research Group. Kansas City PM Characterization S tudy: Round 1 Testing R e port. 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 Vehicl e Em issions and Fuel Econ omy Characterization Study: Addendum to the Kansas City Exhaust Characterization Study-Draft Report. ERG No. 0133.18.004.001, September 26, 2005.
4. Sierra Res e arch, Inc., “Task Order N o. 2 SC F I mprovem e nt – Field Da ta Collection,” Sierra Report No. SR02-07-04, July, 2002.
5. Sierra Res e arch, Inc., “Task Order N o. 2 SC F I mprovem e nt – Field Da ta Collection,” Sierra Report No. SR02-07-04, July, 2002.
6. Brzezinski , D., E. Na m , J. Koupal, G. Hoffm an. Changes in Real W o rld Driving Beh a vior: Analysis of Recent Driv ing Activ ity Data. Pr oceedings of the 15th Coordinating Research Council On Road Vehicle Em issions Workshop, 2005.
7. R.A. Rykowski, Nam , E.K., Hoffm an, G., “O n-Road testing and Characterization of Fuel Economy of Light-duty Ve hicles,” SAE 2005-01-0677.

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