%&..

#n~ gw: UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

b

%> P NATIONAL VEHICLE AND FUEL EMISSIONS LABORATORY 4L WOlEG 

2565 PLYMOUTH ROAD 

ANN ARBOR, MICHIGAN 48105-2498 

OFFICEOF 

AIR AND RADIATION 

March 11, 2004 CCD-04-06 (LDVLDT) 

Dear Manufacturer: 

Subject: Updated Analytically Derived Fuel Economy (ADFE)Policy for 2005 MY and Later 

Attached to this letter are revised guidelines applicable to the use of analytically derived fuel 

economy (ADFE). Use of ADFE has been available from the beginning of the fuel economy 

program. The provisions of 40 CFR 600.006-89(e) allow EPA to accept analytical expressions to 

generate fuel economy data. In the early 1980’s, a fuel economy sensitivity equation was 

developed and was used in conjunction with EPA Advisory Circular 83A to implement the 

ADFE program. On May 12, 1995, EPA published guidance letter number CD-95-08 which 

substantially expanded the use of ADFE and provided new sensitivity equations which included a 

statistical confidence factor for the first time. On April 24, 2000, EPA published guidance letter 

CD-00-04 which provided an interim method to use the existing ADFE equations for testing 

conducted on a single roll dynamometer using the three-term road force equation. The guidance 

in CD-00-04 was set to expire after the 2004 model year, by which time EPA anticipated that a 

new equation would be developed using only single roll dynamometer data. This letter provides 

that updated equation. 

The updates presented in this letter are based in large part on discussions with and data 

submitted by the Alliance of Automobile Manufacturers (Alliance). The changes include 

updated ADFE equation coefficients, to allow data from all drivetrain types (2WD, 4WD and 

AWD) to be pooled together under specific conditions, to allow manufacturers to submit their 

ADFE documentation upon EPA request, and to expand the maximum fuel economy allowed 

from 10 percent to 20 percent from the baseline test. 

All other previous provisions for ADFE usage will continue to apply. The full details about the 

sensitivity coefficients, baseline test selection, restrictions, and required documentation are 

contained in the Enclosure 1. 

Enclosure 2 contains the details of the regression analysis used to calculate the ADFE equation 

coefficients which are contained in this letter. This enclosure was provided by the Alliance. 

The analytical equation and procedures enclosed with this letter are effective immediately for 

model year 2005 and later vehicles, and replace the guidance on ADFE presented in EPA 

guidance letters numbers CD-95-08 and CCD-00-04. A manufacturer may optionally use these 

guidelines and equations for model years prior to 2005. EPA retains the right to order actual 

@ Printed on Recycled Paper BQ 

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confirmatory testing if necessary to assure the integrity of the fuel economy program or if there is 

a concern about emissions compliance. 

EPA believes these updated ADFE guidelines represent a reasonable balance between the need for 

accurate fuel economy data and the need to contain the cost of testing for both industry and EPA. 

Please contact Mr. Eldert Bontekoe at (734) 214 4442 or your Certification Team Representative if 

you any questions related to the ADFE process. 

Sincerely, 

Merrylin Zaw-Mon, Director 

Certification and Compliance Division 

Office of Transportation and Air Quality 

Enclosures (2) 
Enclosure 1 to CCD-04-06 

Updated Analytically Derived Fuel Economy (ADFE) Guidelines 

1. Without prior EPA approval, manufacturers may select the baseline test to be used for an 

ADFE, providing the following guidelines are followed: 

a. Vehicles considered for selection for the baseline test must pass all applicable 

emission standards in the model year associated with the ADFE. 

b. All official tests (which pass all applicable standards) of the same or equivalent 

basic engine, transmission class, engine code, transmission code, engine 

horsepower, dynamometer drive wheels, ahd compression ratio as the ADFE 

subconfiguration must be included in the pool of tests which will be considered 

for baseline selection. 

c. In order to minimize the mpg adjustment, the manufacturer may supplement the 

pool with tests associated with worse case engine or transmission codes and 

carryover or carry-across engine families. In these cases, all the data which 

qualifies for inclusion using the elected worse-case substitution (or carryover or 

carry-across) must be included in the pool as supplemental data (i.e., individual 

test vehicles may not be selected for inclusion). Once the manufacturer decides to 

supplement the pool in this manner, the supplemental data must be included in all 

subsequent pools, where applicable. 

d. To limit the effect of an "above average" test, tests previously used during the 

subject model year as baseline tests in five other ADFE subconfigurations must be 

eliminated from the pool. 

e. All remaining tested subconfigurations in the pool must be evaluated against the 

target ADFE subconfiguration by using the new three-parameter composite mpg 

coefficient including the 95% confidence limits. 

f. The tested subconfiguration with the smallest net combined fuel economy 

adjustment (Le., smallest absolute value of (ADFE - test FE))for combined fuel 

economy) will be selected as the baseline test for the target ADFE 

subconfiguration and used for both city and highway adjustments. 

2. Any proposed baseline test not selected according to the provisions of paragraph 1 

(above), must be reviewed and approved by EPA on a case-by-case basis. 

3. The ADFE will be calculated using the three-parameter 95% confidence limits as listed 

on the following table. The result shall be rounded to a tenth of an mpg. The upward 

Page 1of 3 
adjustment of ADFE from the baseline shall be limited to 20% over the baseline fuel 

economy (i.e., baseline fuel economy X 1.2). The downward adjust is not limited. 

City Fuel Economy Highway Fuel Economy 

Parameter parameter parameter parameter parameter 

increases decreases increases decreases 

ETW -0.532 -0.449 -0.308 -0.239 

NIV -0.173 -0.117 -0.274 -0.228 

TRLHP -0.213 -0.149 -0.460 -0.407 

4. Manufacturers may not submit an ADFE if an actual test has been run on the target 

subconfiguration during the certification process or on a development vehicle which is 

eligible be declared as a fuel economy data vehicle. 

5. To maintain the integrity of the fuel economy program, manufacturers may not use ADFE 

under the following circumstances: 

a. For Passenger Automobile labels - manufacturers may not use any ADFE with a 

combined fuel economy of less than 1.0 mpg above the Gas Guzzler Tax $0 

threshold (currently this limit is 23.5 mpg - see 40 CFR 600.513-91). 

b. For Passenger Automobiles and Light Truck labels - manufacturers may not use 

any ADFE with a combined fuel economy greater than or equal to the leader in the 

applicable Vehicle Classification Class based on the previous model year's 

unadjusted general label values rounded to a whole mpg. If manufacturers are 

unaware of these values they must contact their Certification Team Representative 

before using ADFE. 

6. To limit the impact of ADFE on CA&, no more than 20 percent of the subconfigurations 

tested in a manufacturer's final CAF@ may be represented by ADFE. For example, if the 

manufacturer has 100 subconfigurations which are tested (or represented through data 

substitutions and equivalencies), only 20 of the 100 may be based on ADFE calculations 

(or represented through data substitutions or equivalencies from ADFE generated data 

points). 

7. The manufacturers must retain for five years (under the provisions of 40 CFR 600.005- 

81(a)(3)) the pool of tests, the vehicle description and tests chosen as the baseline and the 

basis for its selection, the target ADFE subconfiguration and the calculated city and 

highway adjusted fuel economy. EPA may request this information as part of an audit. 

Page 2 of 3 
8. If EPA determines that it is necessary to assure the integrity of the fuel economy database 

or if EPA has concerns about compliance with emission standards, EPA retains the right 

to order a confirmatory test of the subconfiguration covered by the ADFE. 

If the manufacturer chooses, EPA will accept a temporary Fuel Economy Label based on 

the ADFE while a suitable data vehicle is being procured. However, if the confirmatory 

test value results in a lower rounded fuel economy value (city, highway, or combined) for 

any model type the label must be updated. The updated label value shall be used on all 

vehicles produced more than 15 days following its submission. 

9. EPA is presenting this option as a manufacturer self-approval process. EPA will not be 

responding to routine submissions of ADFE data indicating our acceptance of the 

calculation or waiver of confirmatory testing. 

If EPA later discovers that the procedures for self-approval were not followed, EPA may rescind 

the use of ADFE data and require actual test data be generated and require recalculation of labels 

and CAFE values. 

Page 3 of 3 
Enclosure 2 to CCD-04-06 -- ADFE Equation Development 

General Note: The development of the ADFE coefficients mirrors the process used to develop the 1996 ADFE 

equation and coefficients. 

Dataset 

Single roll tests from the 2000-2004 test car lists through September 2003 were included in the original database. 

Only official data is contained in the EPA test car lists. 

Dataset only includes tests on gasoline test fuel (6272 tests) 

ADFE were deleted (570) 

Duplicate tests were deleted (1724) 

HEV, AFV, 91 Octane tests were deleted (252) 

Suspect tests were deleted (147) 

0 Suspected high or low TRLHP 

0 Frontal area DPA 

0 Three known outliers 

Unique vehicle tests were deleted (1 62) 

12 cylinder engines 

0 Prowler 

0 Rotary engine 

Engine horsepower greater then 380 

Suspect dual rolls tests (127) 

Total tests removed from the dataset: 2624 

Total tests in the dataset: 3642, approximately half city and half highway 

Test parameters on several tests were corrected based on manufacturer information. 

The drive data was corrected and 4WD and AWD information was converted to either FWD or RWD, based on how 

the vehicle was tested. 

Data Analvsis 

An initial regression was performed which considered most of the vehicle parameters in the test car list and a 

number of technologies including valves per cylinder, VVT, turbo or supercharging. The following parameters 

were selected based on this analysis: ETW, TRLHP, NN,CID, HP, drive and car and truck indicators. 

Regressions were run to verify that the In function gave a better correlation than an analysis based on the 

straight values of the parameters (the In function was used in the development of the original coefficients) 

Stepwise regression was used to verify the significant factors. 

Multiple regressions were run to identify whether separate equations should be established based on the 

significant factors and that use of a city TRLHP at 20 mph would not improve correlation of the data. 

Results 

Using the Infunction resulted in a better correlation, and therefore was preferred over the use of the straight 

values. 

0 There was no improvement in the correlation if separate equations were developed for car and truck, or front or 

rear wheel drive or by using the TRLHP at 20 mph for the city tests over a TRLHP at 50 mph. 

0 Car and truck, drive, cid, hp, VVTi were all considered significant in the development of the coefficients and 

therefore remained in the final analysis. 

Parameter City Fuel Economy Highway Fuel Economy 

parameter increases parameter decreases parameter increases parameter decreases 

ETW -.532 -.449 -.308 -.239 

” -.173 -.117 -.274 -.228 

TRLHP -.2 13 -.149 -.460 -.407 

Submitted by The Alliance of Automobile Manufacturers - Peg GutrnandMGutmann Page 1 of 6 Date IE 

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4 

Residuals Versus the FittedValues 10 xi 

Fmed Value (R1JPO"V is I" mpg1 

T I 

25 30 35 

Fitted Value 

3 -

2-

1 -

0 -

1- 4-J 

2-

. /-' 3 -

'y I I I I I 8 ' 5 0 5 10 2 0 

Resdual E -1 

. '/"' = 2 

4 I I I I I 

Test of appropriate analysis method 

straight values In of values 

Regression Analysis: mpg versus etw, clt, drive, cid, hplcid, nlv, trlhp (All data) 

The regression equation is 

mpg = 74.6 - 0.00216 etw - 1.14 c/t + 0.155 drive - 0.0449 cid - 2.87 hp/cid 

Submitted by The Alliance of Automobile Manufacturers - Peg GutmannlMGutmann Page 2 of 6 Date I$ 

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- 0.227 n/v - 0.803 trlhp 

Predictor Coef SE Coef T P 

Constant 74.5554 0.9617 77.52 0.000 

etw 0.0021638 0.0001989 -10.88 0.000 

c/t

drive 

-1.1414 

0.15463 

0.2334 

0.05704 

-4.89 

2.71 

0.000 

0.007 

cid -0.044870 0.002050 -21.89 0.000 

hp/cid -2.8660 0.2995 -9.57 0.000 

trlhp

n/v -0.22660 

-0.80292 

0.01496 

0.04296 

-15.15 

-18.69 

0.000 

0.000 

S = 2.442 R-Sq = 85.4% R-Sq(adj) = 85.4% 

Analysis of Variance 

Source DF ss MS F P 

Regression 

Residual Error 

7 

1806 

63066.4 

10771.8 

9009.5 

6.0 

1510.52 0.000 

Total 1813 73838.2 

The correlation using the In method is better at 92.6% and the normal probability plots and residuals vs. fits 

indicate that the In function better represents the population. 

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Citv Data 

Stepwise Regression: In mpg versus clt, drive, ... Alpha-to-Enter:0.15 Alpha-to-Remove: 0.15 

Response is In mpg on 8 predictors, with N = 1833 

Step 

Const ant 

1 

10.462 

2 

10.137 

3 

9.923 

4 

9.625 

5 

9.450 

6 

9.505 

In n/v 0.028 -0.159 -0.168 -0.163 -0.145 -0.145 

T-Value 1.73 -10.31 -11.37 -11.45 -10.27 -10.27 

P-Value 0.085 0.000 0.000 0.000 0.000 0.000 

In etw -0.863 -0.582 -0.497 -0.460 -0.486 -0.490 

T-Value -38.07 -26.73 -22.78 -21.68 -23.05 -23.21 

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 

In trlhp -0.142 -0.113 -0.170 -0.177 -0.155 -0.181 

T-Value -9.63 -9.03 -13.40 -14.39 -12.61 -11.24 

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 

In cid -0.270 -0.200 -0.196 -0.158 -0.160 

T-Va lue -27.05 -18.29 -18.66 -13.97 -14.09 

P-Value 0.000 0.000 0.000 0.000 0.000 

In hp 0.1277 0.1327 -0.1299 0.1226 

T -Value -13.22 -14.25 -14.19 -12.76 

P -Value 0.000 0.000 0.000 0.000 

WTI 0.0818 0.0818 0.0809 

T-Value 11.92 12.13 11.99 

P-Value 0.000 0.000 0.000 

drive 0.0333 0.0336 

T -Value 8.27 8.33 

P-Value 0.000 0.000 

c/t

T-Value 

0.0150 

2.48 

P-Value 0.013 

S 0.0830 0.0701 0.0670 0.0646 0.0634 0.0633 

R-Sq 81.66 86.90 88.05 88.91 89.31 89.35 

R-Sq(adj) 81.63 86.87 88.01 88.87 89.27 89.30 

C-P 1314.7 419.1 225.4 79.7 13.1 9.0 

Conclusion - all significant except c/t, correlation improves with the addition of the indicators. 

Regression Analysis: In mpg versus In etw, In n/v, ...(all parameters) 

The regression equation is 

In mpg = 9.51 - 0.490 In etw - 0.181 In trlhp - 0.145 In n/v + 0.0150 c/t 

+ 0.0336 drive - 0.160 In cid - 0.123 In hp + 0.0809 WTI 

Predictor Coef SE Coef T P 

Constant 9.5054 0.1566 60.69 0.000 

In etw -0.49040 0.02113 -23.21 0.000 

In trlhp -0.18127 0.01613 -11.24 0.000 

In n/v -0.14482 0.01411 -10.27 0.000 

c/t 0.015014 0.006055 2.48 0.013 

drive 0.033561 0.004027 8.33 0.000 

In cid -0.15956 0.01132 -14.09 0.000 

In hp -0.122576 0.009607 -12.76 0.000 

WTI 0.080855 0.006741 11.99 0.000 

S = 0.06334 R-Sq = 89.3% R-Sq(adj) = 89.3% 

Submitted by The Alliance of Automobile Manufacturers - Peg Gutmann/MGutmann Page4 of 6 Date I: 

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Analysis of Variance 

Source DF ss MS F P 

Regression 8 61.3597 7.6700 1911.91 0.000 

Residual Error 1824 7.3173 0.0040 

Total 1832 68.6770 

Source DF Seq SS 

In etw 1 55.4431 

In trlhp 1 0.6180 

In n/v 1 0.0205 

C/t 1 0.4107 

drive 1 1.8484 

In cid 1 1.8583 

In hp 1 0.5837 

W TI 1 0.5771 

Regression Statistics 

Multiple R 0.94522351 

R Square 0.893447484 

Adjusted R Square 0.8929801 48 

Standard Error 0.063339575 

0bservations 1833 

ANOVA 

df ss MS F Significance F 

Rearession 8 61.35930737 7.6699134 191 1.79 0 

Residual 1824 7.317708865 0.00401 19 

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% 

InterceDt 9.507 0.157 60.672 0.000 9.199 9.814 

cit 0.01 5 0.006 2.468 0.014 0.003 0.027 

drive 0.034 0.004 8.329 0.000 0.026 0.041 

In cid -0.160 0.011 -14.097 0.000 -0.182 -0.137 

In hp -0.123 0,010 -12.768 0.000 -0.141 -0.104 

In nlv -0.145 0.014 -10.272 0.000 -0.173 -0.117 

In etw -0.490 0.021 -23.206 0.000 -0.532 -0.449 

In trlhp -0.181 0.016 -11.227 0.000 -0.213 -0.149 

WTI 0.081 0.007 11.998 0.000 0.068 0.094 

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Highway data 

Stepwise Regression: In mpg versus clt, drive, ... Alpha-to-Enter:0.15 Alpha-to-Remove:0.15 

Response is In rnpg on 8 predictors, with N = 1810 

Step 3 4 5 6 7 

Constant 5.153 7.434 9.423 9.235 9.082 8.999 8.949 

In n/v 0.100 -0.231 -0.272 -0.253 -0.249 -0.252 -0.252 

T-Value 6.90 -17.79 -22.14 -20.97 -20.98 -21.36 -21.40 

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 

In trlhp 0.7635 -0.5620 0.4519 0.4309 -0.4335 -0.4545 -0.4323 

T -Value -80.45 -67.68 -44.99 -43.30 -44.20 -43.80 -32.03 

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 

In cid -0.3145 0.2506 0.2067 -0.2064 -0.1834 -0.1822 

T-Value -41.84 -31.66 -23.64 -23.98 -19.48 -19.35 

P -Value 0.000 0.000 0.000 0.000 0.000 0.000 

In etw -0.298 -0.325 -0.307 -0.276 -0.272 

T -Va lue -17.08 -18.96 -18.05 -15.54 -15.26 

P-Value 0.000 0.000 0.000 0.000 0.000 

drive 0.0363 0.0359 0.0351 0.0349 

T-Value 10.56 10.59 10.44 10.40 

P-Value 0.000 0.000 0.000 0.000 

WTI 0.0440 0.0456 0.0466 

T-Value 7.54 7.87 8.05 

P-Value 0.000 0.000 0.000 

In hp 

T-Value 

-0.0438 

-5.77 

-0.0500 

-6.28 

P-Value 0.000 0.000 

c/t

T -Value 

-0.0129 

-2.56 

P-Value 0.010 

S 0.0840 0.0599 0.0556 0.0540 0.0531 0.0527 0.0526 

R-Sq 82.14 90.93 92.19 92.65 92.87 93.00 93.03 

R-Sq (ad]) 82.12 90.92 92.17 92.63 92.85 92.97 92.99 

C-P 2808.4 540.2 216.3 101.0 44.9 13.6 9.0 

Conclusion, all parameters are significant, correlation improves with addition of 

parameters. 

Regression Analysis: In mpg versus In etw, c/t, ...(all parameters) 

The regression equation is 

In mpg = 8.95 - 0.272 In etw - 0.0129 c/t + 0.0349 drive - 0.182 In cid 

- 0.252 In n/v - 0.432 In trlhp - 0.0500 In hp + 0.0466 WTI 

Predictor Coef SE Coef T P 

Constant 8.9491 0.1321 67.76 0.000 

In etw -0.27157 0.01780 -15.26 0.000 

c/t -0.012949 0.005053 -2.56 0.010 

drive 0.034914 0.003358 10.40 0.000 

In cid -0.182196 0.009417 -19.35 0.000 

In n/v -0.25205 0.01178 -21.40 0.000 

In trlhp -0.43230 0.01350 -32.03 0.000 

In hp -0.050031 0.007963 -6.28 0.000 

WT I 0.046623 0.005792 8.05 0.000 

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S = 0.05259 R-Sq = 93.0% R-Sq(adj) = 93.0% 

Analysis of Variance 

Source DF ss MS F P 

Regression 8 66.4320 8.3040 3002.89 0.000 

Residual Error 1801 4.9804 0.0028 

Total 1809 71.4124 

Source DF Seq SS 

In etw 1 54.6247 

drive

c/t 1 

1 

3.5881 

2.6664 

In cid 1 0.7294 

In n/v 1 1.7560 

In trlhp 

In hp 

1 

1 

2.7965 

0.0917 

W TI 1 0.1792 

SUMMARY OUTPUT 

Regression Statistics 

Multiple R 0.96460 

R Square 0.93045 

Adjusted R Square 0.93014 

Standard Error 0.05247 

Observations 1810 

ANOVA 

df ss MS F SignificanceF 

Regression 8 66.325 8.291 301 1.906 0 

Residual 1801 4.957 0.003 

Total 1809 71.282 

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% 

Intercept 8.948 0.132 68.002 0.000 8.690 9.206 

clt -0.012 0.005 -2.442 0.01 5 -0.022 -0.002 

drive 0.035 0.003 10.454 0.000 0.028 0.042 

In cid -0.180 0.009 -19.151 0.000 -0.1 98 -0.161 

In etw -0.274 0.018 -1 5.427 0.000 -0.308 -0.239 

In nlv -0.251 0.012 -21.369 0.000 -0.274 -0.228 

In trlhp -0.434 0.013 -32.135 0.000 -0.460 -0.407 

In hp -0.049 0.008 -6.1 86 0.000 -0.065 -0.034 

WTI 0.047 0.006 8.181 0.000 0.036 0.059 

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