speed variation limits. The basic approach used to develop PKE-based speed variation
criteria for the IM240 consisted of the following elements:

1. Establishing upper and lower “composite” PKE limits for full 240-second
tests from the Arizona data sample; and

2. Scaling these composite limits based on the cumulative PKE at each second of
the IM240 reference trace to produce second-by-second PKE variation limits.

Second-by-second PKE variation limits were established to ensure that compliance with
the reference trace was maintained throughout the test to minimize emissions bias during
fast-pass and fast-fail determinations.

From analysis of the Arizona data, the PKE variation limits were established.over a range
expected to produce no more than a 3% increase in the test abort rate when applied as a
replacement for the speed variation criteria, in conjunction with the existing speed
excursion criteria.

Development of Positive Power-Based Variation Limits

Under the current study, the randomly collected triplicate Arizona IM147 tests described
in Section 3 were also analyzed to develop IM147 trace variation limits. Similar to the
earlier IM240 study, upper and lower composite limits were first established over the full
duration of the IM147 test. The composite variation limits were then scaled at each
second of the reference trace to produce second-by-second variation limits.

Before discussing the details of how these IM240-developed methodologies were adapted
for the IM147 test, an explanation of why positive power was used as a replacement for
PKE as the variation limits metric is provided.

Use of Positive Power Instead of PKE - During the course of developing the second-by-
second IM147 variation limits, a number of tests were identified for which PKE-based
variation limits were being exceeded under periods of deceleration. This was clearly a
problem. As discussed earlier, statistical metrics used to establish variation criteria were
selected based on their ability to identify high emissions-producing variations that
coincide with acceleration events. Although the composite PKE statistic does this well
on a cumulative basis over the entire test, it is less-suited when applied on a second-by-
second basis.

The reason for this can be seen by first considering how PKE is calculated. Over a

traveled driving cycle of distance x, cumulative PKE per unit distance is defined as
follows:

-56-


Appendix D

Excess Emissions with Fast-pass, Retest, Fast-Fail Enabled

Max CO Cutpoints
Excess | Excess HC Excess | Excess CO Excess | Excess NO
Vehicle |Model Year| IM240 HC | with fast- | Excess HC | 1M240 CO | with fast- | Excess CO |IM240 NOx] with fast- |Excess NOx
Class Range (grams) |pass(grams)} Identified | (grams) {pass (grams)} Identified | (grams) |pass (grams)| Identified

81-82 0 0 N/A 56.74 49.73 87.6% 3.61 3.61 100.0%
83-85 6.13 6.13 100.0% 243.24 237.02 97.4% 3.49 3.49 100.0%
LDGV 86-89 10.72 10.65 99.3% 301.8 294.27 97.5% 9.92 9.92 100.0%
90-95 8.96 8.68 96.9% 185.26 157.96 85.3% 6.59 6.02 91.4%

96+ 0 0 N/A 0 0 N/A 0 0 N/A
ALL 25.81 25.46 98.6% 787.04 738.98 93.9% 23.61 23.04 97.6%
81-85 10.99 8.32 75.1% 605.55 590.71 97.5% 19.02 18.72 98.4%
88-89 5.55 5.13 92.4% 533.37 485.61 91.0% 2.37 2.36 99.6%
LDGT1 90-95 0.42 0 0.0% 177 0 0.0% 1.65 1.65 100.0%
96+ 0 0 N/A 0 0 N/A 0.43 0.43 100.0%
ALL 16.96 13.45 79.3% 1140.69 1076.32 94.4% 23.47 23.16 98.7%
81-85 15.62 15.62 100.0% 441.44 441.44 100.0% 6.69 6.69 100.0%
86-87 10.39 10.39 100.0% 285.54 285.54 100.0% 411 291 70.8%
LDGT2 88-95 0.21 0.21 100.0% 26.15 15.16 58.0% 0.68 0.27 39.7%

96+ 0 0 N/A 0 0 N/A 0 0 N/A
ALL 26.22 26.22 100.0% 753.13 742.14 98.5% 11.48 9.87 86.0%
Total ALL 68.99 65.13 94.4% 2680.86 2557.44 95.4% 58.56 56.07 95.7%



Figure 3-2

Model Year Distribution of
Sample Fleet and AZ Overall Fleet

20%

15%

SNA)

RSS
BS

10%

Percent 1998-1999

5%

0%
81 82 83 84 85 8 87 88 89 90 91 92 93 94 95 96 97 98 99
Model Year

MME 2512 Vehicle Sample 535 Vehicle Sample 300 Vehicle Sample —*— YTD October 1999

according to their T90 response times. The T90 response time is the length of time
necessary for the analyzer to see 90% of a positive step change in the gas concentration
that was introduced at the exhaust collection cone. By correcting for response time,
emission events can be linked to the corresponding drive trace event.

The justification for T90 time-alignment centered on the response curve of the gas bench.
Typically, the response curve for a bench will appear somewhat asymptotic; as the
measured gas value closes in on the actual value, the absolute rate at which the measured
value approaches the target value decreases. As a result, the T90 time, which is relatively
short, becomes a good approximation to time an event.

Unfortunately, the response time measurement of the analyzer is composed of two
elements, gas bench response time and transport time, which is the amount of time
necessary for the CVS (Constant Volume Sample) blower to transport the sample from
the collection cone to the gas analyzer bench. This second response time element,
transport time, obscures the gas bench response curve through gas mixing that occurs
during transport.

-22-


Failure Rate Analyses and Development of
Fast-Pass, Retest, and CPP Algorithms
for IM147 Max CO Cutpoints

prepared for:

USS. Environmental Protection Agency
Regional and State Programs Division
Office of Mobile Sources

Under Contract No. 68-C7-0051
Work Assignment No. 1-08

December 14, 1999

prepared by:

Richard W. Joy
Garrett D. Torgerson
Thomas R. Carlson

Sierra Research, Inc.
1801 J Street
Sacramento, CA 95814
(916) 444-6666


IM147 Composite Regression Coefficients, NOx, 1983 to 1985 Model Year LDGV's

Regression Coefficients
Segment IRMS —|Reg.
iNumber Error Constant {C1 ic2 ic3 ica cS: C6 ic7 ics ico C10 C11 C12 jc13 C14 C15 C16 C17 C18 ic19
| a 0 85 | 0.96463] 1.66521] 16.7416). r
[Pz LOGV__[83-85 | 0.58177] 0.74711] _0.782| 4.5182]. : : z
[Ps LOGV__ {83-85 | 0.57164] 0.69924] 1.7395] 3.8215] 3.9404]. a
[Ps LOGV_[83-85 | 0.56866] 0.69162] 2.2485 3.7307] __ 2.712 i "7
[Ps LOGV__[83-85 | 0.54912] 0.68329] 0.0309] 3.6538] -0.5552] 4.4539]. a a
[Ps LOGV_ [83-85 | 0.54056] 0.65766 -0.2605] 3.6825] -0.6893 3.3123] 6.9261 :
[Pz LOGV__ [83-85 | 0.52478] 0.64771] -1.2424] 3.0981] -0.7131 1.2743] 8.0479] 3.3901].
iPS ILOGV__|83-85 | 0.49039] 0.64355] -0.2025} 2.5593] -0.8873} 0.9327] 0.9917, 2.648] 6.9554].
Po LDGV_ [83-85 | 0.49081) 0.6366] -0.2479] 2.5692] 0.9586 0.7709] 0.8195] 2.6479] 6.804) 0.8250]. s
P10. LDGV__ [83-85 | 0.48198] 0.62502] -0.6852] 2.5126] -1.7481 0.3327] 1.3097] 1.8572] 6.9911] -1.6919] 3.7996].'
P14 ILDGV__|83-85 | 0.43238] 0.38649} 0.6471 1,837] -1.1074] ¥ 4.3594] 1.4974] 0.1122] 6.1798) 0.33] 1.9052] | 1.0346].
PAZ LOGV__ [83-85 | 0.35372] 0.249071 1.0365} 4.599] -1.5529] 1.0484] 1.5953] -0.2612] 1.2403] 3.835] -0.566] 1.7683]40.4528] 3.3405} a
P13 LOGV__[83-85 | 0.22239] 0.11604] 1.4435] 0.8789] 1.1136] 3.1005] 0.6475] 1.7479) 0.451] 1.0221] 0.5773] 0.455} 0.4514] 1.7854] 2.2469]. .
P14 ILDGV__{83-85 | 0.16685] 0.04792] 1.1199] 0.8083} 0.993] 2.3512] 0.3301] 2.1868] 0.9638] 0.6226] 1.2885] 1.0545] .0.5328] 0.7862] 1.2774] 2.0142].
P15: ILOGV__ {83-85 |" 0.09344] 0.00442] 0.6972) 0.724] 1.0561] 1.1547] 0.647] 1.6301] 0.8766] 0.4133] 1.7045] 0.185} 10.7132] 0.6701] 0.6719] 1.0248] 1.5987]. .
P16 LOGV__[83-85 | 0.07618} 0.01155} 1.4799] 0.6974] 0.9307] 0.8525] 0.7603] 0.9115] 1.0175] 0.3596] 1.3353] 0.4874710.7501[ 0.6536] 0.7419[ 0.8969] 1.1713] 1.4857].
P17 LOGV_ {83-85 | 0.07136] 0.00873] 1.0722] 0.7157, 0.74] 1.0551] 0.6004] 0.9688] 0.8579] 0.6022] 0.9517] 0.6109}10.7611] 0.5992] 0.7747[ 0.9329 1.1217] 1.2072] 1.6127] .
P18 ILOGV__ {83-85 | 0.03952} 0.0055] 0.6259] 0.6692] 0.8058] 1.0136] 0.6208] 1.1765] 0.512] 0.9231] 0.7662] 0.5965] 10.7047] 0.7879] 0.6504[ 0.8038] 0.8335] 1.1663] 1.2587] 0.9322].
P19 LOGV_-{83-85 | 0.01224] -0.00128] 0.8422] 0.7007] 0.7898 0.724] 0.7372] 0.8013] 0.7199] 0.7142] 0.6943] 0.7149]:0.7141] 0.7285] 0.7042] 0.7381] 0.7145] 0.6689] 0.7795} 0.691} 0.7508}
IM147 Phase 2 Regression Coefficients, NOx, 1983 to 1985 Model Year LDGV's
Regression Coefficients
Segment RMS Reg. H
Number Error Constant |C1 C2 ic3 C4 cs © icé C7 cs ico C10 C14 }C12 C13 C14 C15 C16 C17 C18 cig,
Bi [TOGV_ Jes-85 | O7ss7a| O.et776l. 724382]. r
B12 ILOGV__ [83-85 | 0.54834] 0.39309}. 1.1577] 5.767]. . -
B13. LOGV__ {83-85 | 0.31629] 0.20822! 0.6375] 2.8068] 3.1803}
B14 ILOGV__|83-85 | 0.24412 0.1176} '(0.8028] 1.3747] 1.9218] 2.6998]. a
B15 LOGV__ [83-85 | 0.13375] 0.03665]. 30.9668] 1.0509] 0.9033] 1.3245] 2.2612]. .
816 ILOGV__}83-85 | 0.10708] 0.03554, 11.0501] 0.8984] 1.0487] 1.1265] 1.6402] 2.0329) |.
B17 ILOGV__ [83-85 | 0.09639] 0.02013]. 1.0496] 0.8104] 1.0655] 1.2345] 1.5252] 1.6719] 2.3989)
B18 ILOGV__]83-85 | 0.05614] 0.0163]. 0.9253] 1.1246] 0.8614) 1,096] 1.1113] 1.8248] 1.4559] 1.1912].
B19 ILOGV_ ]83-85 } 0.01369} 0.0022). 0.9686] 0.9844] 0.9642] 0.9894] 0.962] 0.9116] 1.1138] 0.9386] 1.0241)



Arizona data sample, which contained only full tests. The recommended CPP limits were
found to produce a total effective abort rate of 3.4% based on the Arizona IM147 data.
The average time at which the aborts occurred under these limits was determined to be
101.6 seconds.

Thus, the “with limits” average test time was then calculated as follows:

With Limits Test Time

u

Base Test Time + (Abort Rate x Average Abort Time)

146 + (3.4% x 101.6 seconds) = 149.5 seconds

It should be noted that this approach also assumes that an aborted test is performed
successfully on the subsequent re-test. Under these assumed conditions, the CPP
variation limits will increase average test times by approximately 2% [(149.5-146) +
146].

-65-


Startup Cutpoints

Failure Rate By Model Year Grouping - Second IM147

HC

co

NOx

OVERALL

Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail
24 | 81 | 22.9% | 13 | 92 | 12.4% | 18 | 87 | 17.1% } 38 |} 67 | 36.2%
35 | 193 | 15.4% | 53 } 175 | 23.2% | 43 | 185 | 18.9% | 92 | 136 | 40.4%
36 | 389 | 8.5% | 42 | 383 | 9.9% | 55 | 370 | 12.9% | 101 | 324 | 23.8%
14 | 938 | 1.5% | 25 | 927 | 2.6% | 36 | 916 | 3.8% | 60 | 892 | 6.3%
0 | 70 | 0.0% } O | 70 | 0.0% 0 70 | 0.0% | 0 | 70 | 0.0%
31 | 229 | 11.9% | 23 | 237 | 8.8% | 18 | 242 | 6.9% | 58 | 202 | 22.3%
Lpct1 18 | 204 | 8.1% | 14 | 208 | 6.3% | 18 | 204 | 8.1% | 39 | 183 | 17.6%
90-95 | 2 | 448 | 0.4% | 3 | 447 | 0.7% | 14 | 436 | 3.1% | 17 | 433 | 3.8%
96+ | 0 | 32 | 0.0% 32 | 0.0% 1 31 3.1% 1 31 3.1%
81-85 | 20'| 74 | 21.3% | 23 | 7L | 24.5% | 9 | 85 | 9.6% |.35 | 59 | 37.2%
86-89 56 | 12.5% | 5 | 59 | 7.8% 8 | 56 | 12.5% | 16 | 48 | 25.0%
LDGT2 88-95 423 | 0.9% | 3 | 424 | 0.7% | 17 | 410 | 4.0% | 23 | 404 | 5.4%
96+ 18 | 0.0% | 0 18 | 0.0% | 0 18 | 0.0% | 0 18 | 0.0%
3155} 5.7% | 204 13143 | 6.1% | 23713110] 7.1% | 480 |2867] 14.3%

Failure Rate By Model Year Grouping - Third IM147

Startup Cutpoints
HC co NOx OVERALL

V Type | Year |Fail| Pass | % Fail Fail | Pass % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail
81-82 | 23 | 82 | 21.9% | 13 | 92 | 12.4% | 14] 91 | 13.3% | 35 | 70 | 33.3%
83-85 | 33 | 195 | 14.5% | 56 | 172 | 24.6% | 39 | 189 | 17.1% | 90 | 138 } 39.5%
LDGV 1|86-89| 35 | 390 | 8.2% | 36 | 389 | 8.5% | 56 | 369 | 13.2% | 96 | 329 | 22.6%
90-95} 15 | 937 | 1.6% | 24 | 928 | 2.5% | 32 | 920 | 3.4% | 59 | 893 | 6.2%
96+ | O | 70 | 0.0% 1 69 14% | 0 70 | 0.0% 1 69 1.4%
81-85 | 34 | 226 | 13.1% | 20 | 240 | 7.7% | 17 | 243 | 6.5% | 56 | 204 | 21.5%
LocTi 86-89 | 17 | 205 | 7.7% | 12 | 210 | 5.4% | 16 | 206 | 7.2% | 35 | 187 | 15.8%
90-95] 1 | 449 | 0.2% | 1 | 449 | 0.2% | 14 | 436 | 3.1% | 16 | 434 | 3.6%
96+ | 0 | 32 | 0.0% | O | 32 | 0.0% | 1 | 31 {3.1% | 1 | 31 | 3.1%
81-85] 21 | 73 | 22.3% | 22 | 72 | 23.4% | 7 87 | 7.4% |} 33 | 61 | 35.1%
LwGT2 86-89] 9 55 | 14.1% | 6 58 | 9.4% 57 | 10.9% | 16 | 48 | 25.0%
88-95] 3 | 424 | 0.7% | 2 | 425 | 0.5% | 14 | 413 | 3.3% | 18 | 409 | 4.2%
96+ | 0 18 | 0.0% | 0 18 | 0.0% | O'| 18 0.0% | 0 18 | 0.0%
ALL 1191 13156} 5.7% _|193 13154 | 5.8% 121713130} 6.5% | 456 |2891| 13.6%



Table 1-7
Comparison of IM147 Max CO Cutpoints to IM240 Final Standards
Impact on Excess Emissions Lost*
% Excess Emission Identified % Excess Emissions Identified
Class (Without Fast-Pass, Retest, Fast-Fail) (With Integrated Algorithms)
HC co NOx HC co NOx
—EEEE————————— EEE
LDGV 94.1% 96.5% 85.6% 91.7% 95.6% 86.2%
LDGT1 75.6% 100.0% 79.5% 65.0% 98.1% 81.2%
LDGT2 89.0% 100.0% 53.4% 98.5% 100.0% 59.6%
Weighted | 36.5% | 98.8% 75.1% | 85.6% | 97.9% | 77.5%
erage

* Percent of IM240 (Final Standards) excess emissions identified was determined using the 835 vehicle
sample.

enabled, the identification rates for this scenario are the ones that should be used to adjust
the MOBILE modeling results (based on final IM240 standards) for SIP credit purposes.

As expected, the table shows that HC and NOx identification rates are significantly lower
with the IM147 Max CO cutpoints relative to final IM240 standards. This is due to the
fact that the Max CO cutpoints are designed to maximize the CO benefits of the program
at the expense of HC and NOx benefits, while keeping maximum failure rates in each
cutpoint category to acceptable levels. The CO identification rate of 97.9% (with the
algorithms enabled) shows that the Arizona program will achieve nearly all of the
modeled benefit of the final IM240 standards. Note that this will be substantially more
effective than the current phase-in IM240 standards. The table also shows that the _
addition of the integrated algorithms results in less than a 1% reduction in the excess
emissions identification rate for CO. (As noted above, the addition of the algorithms
reduce dynamometer test time from 217 to 90 seconds.)

Need for Follow-Up Analysis

As discussed above, the analysis results presented in the report are based on a relatively
small sample of IM147 and IM240 data. While the available data are significantly more
robust than the previous sample of 300 vehicles, it is clear that these results should be
revisited with a much larger sample once IM147 testing is initiated in Arizona. We
therefore recommend that as soon as one to two months of IM147 data are collected in
the program, they should be used to verify the validity of the cutpoints and algorithms
developed in this study. This follow-up analysis would allow for any required fine-
tuning of the cutpoints and algorithms.

-12-


IM147 CUMULATIVE POSITIVE POWER (CPP) VARIATION CUTPOINTS

IM147 REFERENCE DATA

TIME
(sec)

92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
419
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137

SPEED
{mph)
53.2
54.1
54.6
54.9
55.0
54.9
54.6
54.6
54.8
55.1
55.5
55.7
56.1
56.3
56.6
56.7
56.7
56.3
56.0
55.0
53.4
51.6
51.8
52.1
52.5
53.0
53.5
54.0
54.9
55.4
55.6
56.0
56.0
55.8
55.2
54.5
53:6
52.5
51.5
50.5
48.0
44.5
41.0
37.5
34.0
30.5

CPP

3715.41
3811.98
3866.33
3899.18
3910.17
3910.17
3910.17
3910.17
3932.05
3965.02
4009.26

" 4031.50

4076.22
4098.70
4132.57
4143.90
4143.90
4143.90
4143.90
4143.90
4143.90
4143.90
4164.58
4195.75
4237.59
4290.34
4343.59
4397.34
4495.35
4550.50
4572.70
4617.34
4617.34
4617.34
4617.34
4617.34
4617.34
4617.34
4617.34
4617.34
4617.34
4617.34
4617.34
4617.34
4617.34
4617.34

POWER VARIATION CUTPOINTS (mph2/sec)
CPP LIMITS

3,220.33
3,314.53
3,372.43
3,411.82
3,432.20
3,432.20
3,432.20
3,432.20
3,462.23
3,502.17
3,552.29
3,583.09
3,634.06
3,665.39
3,707.05
3,728.63
3,728.63
3,728.63
3,728.63
3,728.63
3,728.63
3,728.63
3,758.70
3,798.38
3,847.93
3,907.64
3,968.10
4,029.31
4,131.49
4,194.70
4,227.76
4,281.74
4,281.74
4,281.74
4,281.74
4,281.74
4,281.74
4,281.74
4,281.74
4,281.74
4,281.74
4,281.74
4,281.74
4,281.74
4,281.74

4,210.49
4,309.43
4,360.23
4,386.54
4,388.14
4,388.14
4,388.14
4,388.14
4,401.87
4,427.87
4,466.23
4,479.91
4,518.38
4,532.01
4,558.09
4,559.17
4,559.17
4,559.17
4,559.17
4,559.17
4,559.17
4,559.17
4,570.46
4,593.12
4,627.25
4,673.04
4,719.08
4,765.37
4,859.21
4,906.30
4,917.64
4,952.94
4,952.94
4,952.94
4,952.94
4,952.94
4,952.94
4,952.94
4,952.94
4,952.94
4,952.94
4,952.94.
4,952.94
4,952.94
4,952.94

"BASE" MULT. VARYING
DELTA FACTOR DELTA LOW HIGH

270.0 1.833 495.1
277.1 1.795 497.5
281.0 1.758 493.9
283.4 1.720 487.4
284.2 1.682 478.0
284.2 1.682 478.0
284.2 1.682 478.0
284.2 1.682 478.0
285.8 1.644 469.8
288.2 1.606 462.8
291.4 1.568 457.0
293.0 1.530 448.4
296.3 1.492 442.2
297.9 1.455 433.3
300.4 1.417 425.5
301.2 1.379 415.3
301.2 1.379 415.3
301.2 1.379 415.3
301.2 1.379 415.3
301.2 1.379 415.3
301.2 1.379 415.3
301.2 1.379 415.3
302.7 1.341 405.9
305.0 1.303 397.4
308.0 1.265 389.7
311.8 1.227 382.7
315.7 1.189 375.5
319.6 1.152 368.0
326.7 1.114 363.9
330.7 1.076 355.8
332.4 1.038 344.9
335.6 1.000 335.6
335.6 1.000 335.6
335.6 1.000 335.6
335.6 1.000 335.6
335.6 1.000 335.6
335.6 1.000 335.6
335.6 1.000 335.6
335.6 1.000 335.6
335.6 1.000 335.6
335.6 1.000 335.6
- 335.6 1.000 335.6
335.6 1.000 335.6
335.6 1.000 335.6
335.6 1.000 335.6
335.6 1.000 335.6

E-3

4,281.74

4,952.94


Intermediate IM240 Cutpoints and IM147 Cutpoints Developed in This Study
(Composite/Phase 2 Cutpoints in g/mi, IM240 - IM147) -

| Model Years

HC co NOx
LDGV
1981-82 1.40/0.88 - 1.40/0.90 45.0/36.0 - 44.0/23.0 2.3 - 2.8/1.0
1983-85 1.40/0.88 - 1.40/0.90 23.0/18.0 - 23.0/12.0 . 2.3- 2.8/1.0
1986-90 1.40/0.88 - 1.40/0.70 23.0/18.0 - 23.0/9.0 2.3 - 2.6/1.0
1991-93 1.00/0.63 - 1.10/0.50 18.0/14.0 - 18.0/9.0 2.3 - 2.6/0.9
1994-95 1.00/0.63 - 1.00/0.50 _18.0/14.0 - 18.0/9.0 2.3 - 2.5/0.9
1996+ (Tier 1 0.70/0.45 - 0.80/0.40 13.0/10.0 - 15.0/6.0 1.8 - 2.2/0.8
LDGT1
1981-83 5.50/3.50 - 4.90/3.40 85.0/68.0 - 81.0/43.0 5.8 - 6.3/2.4
1984-85 2.40/1.50 - 2.30/1.50 60.0/48.0 - 60.0/30.0 5.8 - 6.3/2.4
' 1986-87 2.40/1.50 - 2.30/1.20 60.0/48.0 - 60.0/26.0 5.8 - 5.8/2.2
1988-89 2.40/1.50 - 2.30/1.20 60.0/48.0 - 60.0/26.0 3.0 - 3.3/1.2
1990 2.40/1.50 - 2.30/1.20 60.0/48.0 - 59.0/26.0 3.0 - 3.3/1.2
1991-93 2.00/1.25 - 2.10/1.00 50.0/40.0 - 51.0/26.0 2.8 -3.2/L.1
1994-95 2.00/1.25 - 2.00/1.00 50.0/40.0 - 51.0/25.0 2.8- 3.0/1.1

1996+ (Tier 1) 0.90/0.57 - 1.30/0.60

17.0/13.0 - 31.0/8.0 2.2 - 2.7/1.0

LDGT2
1981-83 5.50/3.50 - 4.90/3.40 85.0/68.0 - 81.0/43.0 5.8 - 6.3/2.4
1984-86 2.40/1.50 - 2.30/1.50 60.0/48.0 - 60.0/30.0 5.8 - 6.3/2.4
1987 2.40/1.50 + 2:30/1.20 60.0/48.0 - 60.0/26.0 5.8 - 6.3/2.2
1988-90 2.00/1.50 - 2.30/1.20 60.0/48.0 - 60.0/26.0 4.3 - 4.6/1.6
1991-93 2.00/1.25 - 2.20/1.00 50.0/40.0 - 51.0/26.0 4.0 - 4.6/1.6
1994-95 2.00/1.25 - 2.00/1.00 50.0/40.0 - 51.0/25.0

1996+ (Tier 1

1.60/1.00 - 2.00/0.90

38.0/30.0 - 51.0/18.0

4.0-4.3/1.6
3.0- 4.1/1.3


Table 4-4
Failure Rates, Third IM147

Vehicle co Nox
Type % Fail % Fail

Max CO Cutpoints
LDGV 114 | 1666 | 6.4% | 250 | 1530 |14.0%| 155 | 1625 | 8.7% | 390 | 1390 |21.9%
LDGT1 49 | 915 | 5.1% | 156 } 808 }16.2%] 70 | 894 | 7.3% | 219 | 745 122.7%
LDGT2 25 578 14.1% | 63 540 |10.4%| 30 573 | 5.0% | 93 510 |15.4%
All 188 | 3159 | 5.6% | 469 | 2878 |14.0%| 255 | 3092 | 7.6% | 702 | 2645 |21.0%

Startup Cutpoints
LDGV 106 | 1674 | 6.0% | 130 | 1650 | 7.3% | 141 |.1639 | 7.9% | 281 | 1499 |15.8%
LDGT1 52 | 912 |5.4% | 33 | 931 [3.4% | 48 | 916 | 5.0%} 108 | 856 {11.2%
LDGT2 33 570 | 5.5%} 30 573 | 5.0% | 28 575 | 4.6% | 67 536 }11.1%
193 | 3154 | 5.8% | 217 | 3130 | 6.5% | 456 | 2891 {13.6%

Intermediate Cutpoints

161 | 1619 | 9.0% | 196 | 1584 |11.0%] 367 | 1413 20.6%

53

O11

5.5%

73

891

7.6%

165

799

42

561

7.0%

42

561

7.0%

93

510

256

3091

7.6%

311

3036

9.3%

625

2722

Final Cutpoint:

S

240

1540

13.5%

277

1503

15.6%

507

1273

879

8.8%

127

837

13.2%

239

725

547

9.3%

82

521

13.6%

149

454

2966

11.4%

Figures 4-2 through 4-5 show how the failure rate changes as vehicles progress through
the three IM147 tests. Since most of the increase in failures occurs between the first and
second IM147 tests, it is reasonable to expect that algorithms designed to shorten the test
would end the test prior to the third IM147. As will be shown later in the report, this is
indeed the case. Once the fast-pass, retest, and fast-fail criteria are applied, only 162
vehicles out of 3347 (4.8%) are tested beyond the second IM147.

-29-


IM147 Composite Regression Coefficients, CO, 1988 to 1995 Model Year LDGT2's

Regression Coefficients
Segment RMS — Reg.
Number Error {Constant {C1 c2 c3 Ica cs_—_ce c7 ce Ico cro fer fer2z_— cra. cra_ cts. —cte—fci7_—fcis_— cio
[Pi [tbc Yee-e5 | 720043] 6.02041] 9.0375
LOGT2 [88-95 | 5.72678] 5.74122] _-0.9169] 2.3772]. a
LOGT2 [66-95 | 5.24439] 4.67052] -1.3811| 1.1541] 5.5635 :
LOGT2 [68-95 | 5.14826] 4.26813] -1.2272| 1.0883|_3.6162| 4.9841]. :
LOGT2 [88-95 | 5.09801] 4.25023 -1.2746] 0.9561] 3.0791] 3.914] 1.7530]. :
LOGT2 [38-95 | 5.03227 _4.30487| 1.045] 2.75] 2.5651] 1.1445] 4.6242).
LOGT2 [98-95 | 4.94785] 3.9903] -1.6248] _0,9734] 2.2915] 2.261] 0.0539] 4.0716] _2.4087| :
LOGT2 [88-95 | 4.89866] 3.89264] _-1.7403| 1.0233| 1.631] 1.7630] 0.2408] _2.7207| _1.6017| 2.1028]
LOGT2 [98-95 | 4.85108] 3.81187] -1.2151] 1.0106] 1.7096] 1.9917] -0.0267| 1.4417] 1.4322] 1.6155] 3.0080].
LOGT2 [66-95 [4.62605] 3.68452] -1.0168] _1.0461| 1.7253] 1.7644] -0.2401| 1.2295] 7.1312| 1.6015] 1.909] 2.3455]. ;
LOGT2 [88-95 | 3.00685] 1.74476] _ 1.0382] 0.6435 2.2076] 0.8361] 0.297] 0.8814] 1.2011] _-0.12| 2.4158] 1.0536] 1.0849 i
LOGT2 [98.95 |_2.71202] 1.40261] 1.3560] 0.5437] 1.8136] 1.0668] 0.3264] 0.7761] 1.4578] 0.0633] 1.6352] 0.8125] 0.9029] 1.3047, :
LOGT2 [66-95 | 1.9204] 0.60523] 1.2096 0.6003|_1.3401] 0.909] 0.5644] 1.2411] 0.8508] 0.3162] _ 0.575] 0.1662] 0.8851| 0.7097] 2.7113]. :
LOGT2 |88-95 | 7.26478] 0.42753] _1.2507|_0.6101[ 1.151] 0.906] 0.608] _1.707| 0.8161] _ 0.593] 0.3194] 0.7475] 0.7497| 0.6072) 1.7136] 1.2385]. :
LOGT2 168-95 | 0.8779] 0.28841| 0.9697 0.6415 1.0695] 0.7999] 0.5885] 1.284] 0.8121| 0.5776] 0.5467] 0.957] 0.7335] 0.6178 _ 1.057| 0.6364] 1,226] :
LOGT2 [68-95 | 0.79251] 0.18425] 1.0108] 0.6516) 1.0689] 0.8061] 0.5356] 1.3495] 0.8301| 0.6371] 0.4534] 0.9417] 0.7116] 0.8113] 1.0192] 0.6043] _0.9962| 1.2531]. :
LOGT2 [68-95 | 0.69731 0.07985] _ 0.8509 0.7135] 0.8792] 0.7703] 0.5284] 1.3068] 0.6755 0.6246] 0.4121] 0.958] 0.7118] 0.6066] 0.9167| 0.9121] 0.9794] _ 0.751] 2.050].
LOGT2 |88-95 | 0.52944] 0.00314] 0.7655] 0.7161] 0.8485] 0.6381] 0.64] 1.2114] 0.5735] _ 0.7301] 0.4666] _0.8616| 0.7036] 0.7599] 0.6897| _0.8666| _0.9015| 0.8581] 1.2682] 1.0027|.
LOGT2 [68-95 [ 0.16226 -0.02029{ 1.0068] 0.7103] 0.7155] 0.8232] 0.7148] 0.8349{ 0.6247] 0.8302] 0.6614] 0.7366] 0.7094] 0.724] 0.6921] 0.7083] 0.7232] 0.6986] 1.0875] 0.7309] 0.7511
.___1M147 Phase 2 Regression Coefficients, CO, 1988 to 1995 Model Year LDGT2's
Regression Coefficients
Isegment RMS Reg.
Number Eror {Constant {C1 c2 c3 ca Ics ce c7 ce co cro fcr ferz_ cra cra fcts- cre fciz_—ci_— cro
Bit oGr2 [e6-05 | 5.55431] 4.60126]. 7.4596. r
B12 LOGT2 [88-95 [ 3.61315 2.54349]. 7.2451] 2.0945]. :
B13 LOGT2 [88-95 | 2.63707] 0.63295]. 1.1848] 0.9524] 3.6628
B14 LOGT2 [88-95 | 1.77124] 0.72633} 7.0256 1.1004] 2.4663] 1.6377]. :
B15 LOGT2 [8-95 | 7.22263] _ 0.50627]. 7.0014] 1.1189] 1.5439] 1.0872] 1.7128].
B16. LOGT2 [88-95 |_1.11757| 0.38075]. 0.9754| 1.1094] 1.4934] 1.1553] 1.4156] 1.6429]. :
B17 LOGT2 |88-95 | 0.95396] 0.13503]. 0.9673] 1.1001] 1.2482[ 1.2114] 1.3643] 0.9484] 2.0337]. :
B18 LOGT2 {88-95 | 0.72512) 0.00576] 0.9541] 1.0331] 0.9195] 1.158] 1.2511] 1.1276] 1.7748] 1.3609]
B19 LOGT2 [88-95 | 0.26372] 0.00704]. 0.9656| 0.9834] 0.9474] 0.9412] 1.0187] 0.8827] 1.65] 0.9988] 0.9646]



Table 5-1

Revised IM147 Segments
Initial Second Data Final Second Data
5 29 34
6 35 42
7 , 43 48
8 49 54
9 55 60
10 61 66
1l 67 76
12 77 82
13 83 92
14 93 98
15 99 108
16 109 112
17 113 116
18 117 122
19 123 132
20 133 146

-36-


Table 6-1
Preliminary CPP Cutpoints (mph’/sec) Based on Top-50% Drivers
(Sample Size = 7,962 Tests)

Top-50% Top-50% Top-50% Low-End High-End Interval
Driver Driver Driver CPP CPP Width
Lower Tail Upper Tail Percentile (mph’/sec) (mph?/sec) ‘mph?/sec)
0.25% 99.75% +£0.25% 4,348 5,077 728
0.5% 99.5% +£0.5% ; 4,368 5,039 671
0.75% 99.25% +0.75% 4,391 5,013 622
1.0% 99.0% £1.0% 4,403 4,987 584
2.0% 98.0% +£2.0% 4,437 4,949 512
| 3.0% 97.0% +£3.0% 4,459 4,920 462
4.0% 96.0% +4.0% 4,475 4,903 428
5.0% 95.0% +5.0% 4,483 4,882 398

Note that these preliminary cutpoints are not centered about the IM147 reference CPP
value of 4,617 mph’/sec (as evidenced by the shifted CPP distribution shown earlier in
Figure 6-2). To generate a series of “final” composite limits for evaluation, Sierra
applied the interval widths shown in Table 6-1 to the reference CPP value to produce
“centered” limits about the reference value.

Incremental abort test rates for each set of centered cutpoints were then calculated based
on both the entire 9,306 test Arizona data sample and the top-50% driver subset. The
results are presented in Table 6-2, which lists both “simple” and “effective” abort rates.

Simple abort rates represent the fraction of tests in the sample for which the composite
CPP cutpoints would have been exceeded. Effective abort rates were calculated from
simple rates by subtracting the fractions of emission-pass tests that exceeded the upper
CPP cutpoint and emission-fail tests that exceeded the lower CPP cutpoint. The idea is
that tests on vehicles that had passing emission scores but were driven with high CPP
should not be aborted. Similarly, tests that failed on emissions despite being driven
below the lower CPP cutpoint should also be considered valid tests and not aborted.

Thus, tests should be aborted only when the upper CPP variation limit is exceeded for an
emissions failure or the lower CPP variation limit is exceeded during a passing test. The
emissions pass/fail determinations used to calculate the effective abort rates shown in
Table 6-2 are based upon the “Max CO” cutpoints developed earlier in the study.

In addition to the calculated test abort rates, Table 6-2 also shows the percentage of
drivers who are always within the limits of each set of CPP cutpoints.

-61-


-ZE-

Figure 4-4

Failure Rate by Consecutive IM147

Intermediate Cutpoints
HC Failure Rate by Vehicle Type CO Failure Rate by Vehicle Type
18.0% 16.0%
16.0% 14.0%
2 14.0% 2 12.0%
ae 12.0% Dist im147 = 10.0% mm Oise imi47
£ 10.0% g "
2.0% 2nd IM147 2 8.0% | 2nd 1M147
6.0% (3rd iMi47 & 6.0% (3rd imi47
4.0% o 4.0% Z
2.0% 2.0%
0.0% ul 0.0%
Pc LoTI = LDT2 ite All
Vehicle Type Vehicle Type
NOx Failure Rate by Vehicle Type Overall Failure Rate by Vehicle Type
18.0% 35.0%
16.0% 30.0%
2 14.0% < An
= 120% az 25.0% it
me T Gist M147 2 y il Gist 1m 147
5 10.0% i onainiay 2 20.0% Hy
2 fn a HT
= 5.0% / r © sox il W2ndIM147
S 6.0% i (3rd 1M 147 3 HH (3rd imia7
g 5S 10.0% q
> fi
Z 4.0% i 6 li
2.0% H 5.0% ,
i fi
0.0% 0.0% a

PC LDTI LDT2 All

Vehicle Type

PC

LoT1 LDT2

Vehicle Type

All



To establish second-by-second CPP variation limits that produced expected test aborts
near the 3% target rate, a range of initial CPP multipliers (from 2.0 to 6.0) were
evaluated. These initial multipliers specify the width of the variation limits at the starting
point (t=30 seconds) relative to the composite interval width at the end of the test. For
example, an initial CPP multiplier of 2.0 means that the starting interval width was 14.6%
(2.0 x 7.3% composite CPP interval width) of the reference CPP trace at that point.

Figure 6-4 shows the increase in effective abort rate as a function of varying initial CPP
multipliers. The diagonally striped region represents lower CPP variation limit aborts,
the shaded region above shows upper limit aborts. Since these abort events are mutually
exclusive, their sum represents the total expected effective abort rate from
implementation of the CPP variation limits.

Figure 6-4

Effect of Initial CPP Limits Multiplier on Effective IM147 Abort Rate
(Sample Size = 9,306)

i > wari

20 25 30 35 40 45 5.0
Initial CPP Limits Multiplier

Based on the analysis results, an initial CPP multiplier of 3.5 is recommended for
implementation in Arizona. Second-by-second CPP variation limits based on the use of
this multiplier are shown in Appendix E.

Evaluation of IM147 Variation Limits on Test Time

Using the second-by-second CPP variation limits described in the preceding section, an
analysis was conducted of the impact of these variation limits on average dynamometer
test time. This was a simplistic analysis since it addressed only the singular impact of the
variation limits. (A more exhaustive analysis of the combined test time impact of CPP
variation limits in conjunction with fast-pass, fast-fail and retest decisions is presented in
Section 7.)

In this simple analysis, the “without limits” or base average test time was assumed to be
146 seconds, the length of a full IM147 test. This assumption was necessitated by the

-64-


|M147 Composite Regression Coefficients, CO, 1996 and Newer Model Year LDGT2's

Regression Coefficients
Segment | | IRMS —|Reg.
iNumber 7 Error [Constant {C1 ic2 ic3 ica ics. C6 C7 C8 ice C10 C11 C12 C13 C14 C15 C16 C17 cig © |c19
Pr (bere 96+ 1.3088] 1.42622] 17.1995}
fP2. ILDGT2 [96+ 1.1479] 1.00342] -1,.078] 3.0953}. a
{P3 ILDGT2 [96+ 0.98949] 0.98019] -2.6776] 0.7119] 4.3482]. .
[Pa ILDGT2 [96+ 0.82952] 0.97186] -4.3164] 0.4586] -0.0693] 9.4052].
[Ps ILOGT2 [96+ 0.82272] 0.92541] 6.1685] 0.6234] 0.0838] 7.9052] 1.1172]. .
[Pe ILOGT2 {96+ 0.80854] 0.93687] -2.5537] 0.4345] 0.9927] 5.0074] 0.2744] 5.1496]. .
[Pz LOGT2 [96+ 0.78612] 0.93219] 0.8299] 0.1902] 0.867] 2.2463] -0.6382] 7.4747] 1.8026]. a
[Ps LOGT2 [96+ 0.7922| 0.9145] -0.5421] 0.3098] 1.1613] 0.1172] 0.5476] 8.6914] 1.4725] 1.1768]. =
[Ps ILDGT2 [96+ 0.79439] 0.90764] 0.4323] 0.2804) 157] 0.584] 8.2734! 4.281] 1.4763] 1.8463]. . :
[P10 ILOGT2 [96+ 0.80176}° 0.89441] 0.6291] 0.4422] 1.1925] -2.1872] 0.6255] 7.6689] 1.3637] 1.6415] 0.9728] 1.2038} .
Pu LOGT2 [96+ 0.45829] 0.43587] 2.7438] 0.9615] 1.1761] -4.6902) 0.051] 7.5376] 0.5427] 2.8889] 0.9224] 1.6589] 0.7053]. a
P42 ILOGT2 {96+ 0.43361] 0.27262] 1.6429] 0.8974] 1.1808] -3.2179] 0.1827] 6.7307] 0.6851] 2.4296] 1.0559] 0.404} 0.7027] 1.0882].
[P13 ILOGT2 [96+ 0.35545] 0.13528] 0.1926] 1.1224] -0.5669| 2.979) 0.8935] 3.5882{ 0.4381] 0.8157] -0.0928] 0.4589] 0.7161 0.668] 1.9197]. .
P14 ILOGT2 [96+ 0.30207] 0.07328] -2.5856] 1.3224] -0.4318] 1.9157] 1.1728] 1.7862] 0.501] 0.7493] 0.097] 1.2673] 0.6916) 0.617{ 1.1886] 1.8756].
P15: ILOGT2 |96+ 0.11959} 0.01946] 0.8675] 0.9676] -0.1805] 1.423] 0.6812] 2.4687| 0.9513] 0.6769[ 0.0759] 1.097] 0.7126} 0.6574] 1.1486] 0.3594] 1.6893]. a
[P16 LDGT2 [96+ 0.09136] 0.01277] -0.0995] 0.9293] -0.1463] 2.0145] 0.7771] 2.1495] 0.9056] 0.4482| 0.4595] 0.6343] 0.7138] 0.6364] 1.0287] 0.625] 1.3436] 1.1992]. a
[P17 LDGT2 |96+ 0.08571} 0.00815] 0.3122 0.914] 0.0235} 1.495] 0.8462} 2.28] 0.6668] 0.6935] 0.6686] 0.5081] 0.7115] 0.6609] 0.9459] 0.5995] 1.3506} 0.864] 1.1952]. .
| Ga ILDGT2 [96+ 0.07115] -0.00155] 0.1596] 0.9576] 0.1434] 1.207] 0.8318] 1.7452] 0.5467} 0.8902] 0.2782] 0.9571] 0.7097] 0.6328] 0.8136} 0.7233] 1.1938] 0.7903] 0.6252] 0.8195].
[Pi9 LDGT2 [96+ 0.02689} -0.00249} -0.2617} 0.9243] 0.5171] 0.6083] 0.8825) 1.183} 0.5554} 0.926} 0.5734] 0.7532] 0.715] 0.6571 0.7458] 0.7682] 0.6645] 0.7015] 0.8289] 0.6613] 0.7521]
{M147 Phase 2 Regression Coefficients, CO, 1996 and Newer.Model Year LDGT2's
Regression Coefficients
Segment RMS: Reg.
Number Error Constant }C1 jc2 C3 C4 (ea C6 C7 ic8 ico C10 C11 C12 C13 C14 C15 C16 C17 C18 ic19°
Bi CbGt2 \oer | 1.20406] 1.12220]. 0.7609 r
B12 ILDGT2 [96+ 0.62558] 0.40305]. + 0.9685] 2.1567]
B13 ILOGT2 [96+ 0.54186] 0.23919]. 0.9716} 1.5278] 2.1489].
B14 ILOGT2 [96+ 0.44605] 0.15037]. 0.9303} 1.3986] 1.0695} 2.924]. .
B15 ILOGT2 [96+ 0.24307] 0.03103]. 0.9646] 1.2679] 1.1889] 0.9274) 2.4187]. a
B16 LDGT2 [96+ 0.21899] 0.02112} 0.9694] 1.1748] 0.9741] 1.2313] 1.9455] 4,756}. .
B17 ILOGT2 [96+ 0.19501] 0.00313} 0.9536} 1.2274] 0.7628] 1.1104] 1.9911] 0.8779] 2.8529]. .
B18 ILDGT2 {96+ 0.14188] -0.00797]. 0.9522] 1.0935] 0.6831] 1.2308 1,578] 1.0698] 0.7732| 1.802].
B19 LOGT2 ]96+ 0.06636] 0.01816}. 0.9663] 0.9491] 0.8971] 7.0203] 0.7701] 0.8901[ 1.1191] 1.0268] 1.2815}



IM147 CUMULATIVE POSITIVE POWER (CPP) VARIATION CUTPOINTS

1M147 REFERENCE DATA

TIME SPEED CPP

{sec) (mph) ({mph2/sec)
te) 0.0 0.00
1 0.0 0.00
2 0.0 0.00
3 0.0 0.00
4 0.0 0.00
5 3.3 10.89
6 6.6 43.56
7 9.9 98.01
8 13.2 174.24
9 16.5 272.25
10 19.8 392.04
11 22.2 492.84
12 24.3 590.49
13 25.8 665.64
14 26.4 696.96
15 25.7 696.96
16 25.1 696.96
17 24.7 696.96
18 25.2 721.91
19 25.4 732.03
20 27.2 826.71
21 26.5 826.71
22 24.0 826.71
23 22.7 826.71
24 19.4 826.71
25 17.7 826.71
26 17.2 826.71
27 18.1 858.48
28 18.6 876.83
29 20.0 930.87
30 20.7 959.36
3 21.7 1001.76
32 22.4 1032.63
33 22.5. 1037.12
34 22.1 1037.12
35 21.5 1037.12
36 20.9 1037.12
37 20.4 = 1037.12
38 19.8 1037.12
39 17.0 1037.12
40 17.1 1040.53
4 15.8 1040.53
42 15.8 1040.53
43 17.7 1104.18
44 19.8 1182.93
45 21.6 1257.45

POWER VARIATION CUTPOINTS (mph2/sec)
CPP LIMITS

DELTA FACTOR DELTA LOW HIGH

"BASE"

69.7
72.8
75.1
75.4
75.4
75.4
75.4
75.4
75.4
75.4
75.6
75.6
75.6
80.3
86.0
91.4

E-1

MULT.

3.500
3.424
3.386
3.348
3.348
3.348
3.348
3.348
3.348
3.348
3.311
3.311
3.311
3.273
3.235
3.197

VARYING

244.0
249.3
254.2
252.4
252.4
252.4
252.4
252.4
252.4
252.4
250.4
250.4
250.4
262.7
278.1
292.2

715.31
752.44
778.47
784.71
784.71
784.71
784.71
784.71
784.71
784.71
790.16
790.16
790.16
841.53
904.80
965.27

1,203.41
1,251.08
1,286.79
1,289.53
1,289.53
1,289.53
1,289.53
1,289.53
1,289.53
1,289.53
1,290.90
1,290.90
1,290.90
1,366.83
1,461.06
1,549.63


Figure 5-2

Retest Algorithm During First IM147 - LDGV
Max CO Cutpoints

UM 147 TEST
YES . NO
Vehicle Fast Passes After
Segment 19
y
NO | Mode 7 Predicted HC < STD * 1.2
or
Mode 19 Predicted HC < STD * 1.2
YES
No yes
Mode 7 Predicted HC-STD_ . 05
Mode 19 Predicted HC - STD °
NO | Mode 7 Predicted CO < STD * 1.4
or
Mode 19 Predicted CO < STD * 1.4
YES
NO YES
Mode 7 Predicted CO - STD >0.4 |
Mode 19 Predicted CO - STD .

NO | Mode 7 Predicted NOx < STD * 2
i and
Mode 19 Predicted NOx < STD * 2

YES

NO RETEST RETEST

Note: STD = Composite Standard for the specific pollutant.

Mode 7 Predicted = Predicted emissions after 7 segments using the composite regression
equations without error term included.

Mode 19 Predicted = Predicted emissions after 19 segments using the composite
regression equations without error term included.

-42-


|M147 Composite Regression Coefficients, HC, 1996 and Newer Model Year LDGT1's

Regression. Coefficients

Segment IRMS —|Reg.
Number Error Constant {C1 ic2 ic3 ic4 ics icé iCc7 ics ico C10 C14 C12 c13 C14 C15 C16 C17 C18 ic19
iP1 ILOGT1_ [96+ 0.05316] 0.03402] 18.1995]. .
[P2 ILOGT1 [96+ 0.04307] 0.02411] 0.2121 2417). .
[Ps ILOGT1 [96+ 0.03308] 0.02193] 2.6279] 1.1433] 2.9158]. .
[Pa ILOGT1 [96+ 0.0321] 0.02215] 3.4627] 1.1835] 1.5689] 1.9266]. a
yPs. ILOGT1_ [96+ 0.0267] 0.01928] 1.088] 0.5294] 2.0247] -3.8687] 8.4714]. .
{P6 ILOGT1 [96+ 0.02641] 0.01933] 0.5547] 0.4887] 1.8979] -4.0225] 7.8031] 1.8996]. .

ILDGT1_ [96+ 0.02534] 0.01654! 0.953} 0.3268] 2.2496] -3.2672] 4.7342] 1.2515] 3.3211]. .

ILOGT1 [96+ 0.02524] 0.01597] -1.5963] 0.6417 2.0178] -4.3447] 3.8465} 1.9882] 2.8209] 1.9699]. .

ILOGT1_|96+ 0.02522! 0.0165] -0.9944} 0.5289] 2.1342] -4.3968] 4.6568] 1.7412] 2.8435] 2.4683] -2.6396].
P10 ILOGT1 [96+ 0.02529] 0.01642] -1.4673] 0.5347] 2.3503] -5.1056] 4.7009] 2.0105] 2.7607] 2.6563] -1.6108] -0.7547| .
P44 ILDGT1_|96+ 0.01755] 0.00739} 3.4599] 0.1087 4.652] -1.1947] 3.4359] 0.4622) 0.6672) 0.794) -0,.5304] 0.2246) 1.7144].
P12 ILDGT1 [96+ 0.0151} 0.00659] 3.8459] 0.4104] 0.2567] 1.1211] 0.4848] 0.8471] 1.2853] 0.5367] 1.1122] 0.0179] 1.1163] 1.9287]. .
P13 ILOGT1 [96+ 0.01241] 0.00389| 1.7514] 0.4378] 1.7539] 0.5187] -0.8979] 0.5987] 2.4378] 1.2817] 1.9041] -1.2482] 0.9844] 0.1784] 2.6171]. .
[P14 ILOGT1 [96+ 0.01176] 0.00367} 1.875] 0.4721] 1.4005] -0.0285] -0.4781] 0.4911] 2.4771] 0.9339] 1.5111] -0.5207] 0.7769] 0.1877] 2.2359] 1.1134]. a
P15 ILOGT1 [96+ 0.00769] 0.00241 1.0482] 0.6074] 0.9552] -0.3978] 1.4364] 1.1394] 0.7747] 1.4416] 0.0533] -0.1206] 0.7233] 0.4499] 1.3519] 0.2003| 2.2561]. .
P16 ILOGT1 [96+ 0.00774) 0.0024] 1.0165] 0.6108] 0.9582] -0.4078] 1.4359] 1.1436] 0.7653] 1.4501] 0.0568] -0.1213] 0.7218] 0.4484] 1.3528] 0.1992] 2.2428] 0.0735]. a
PAZ ILOGT1_ [96+ 0.00585] 0.00306] 1.3687] 0.5499] 1.0977] 0.7636] 0.4488] 0.6346] 0.7879] 0.6314] 0.3617] 0.6995] 0.7613] 0.3632] 1.0963] 0.709] 0.7912] 1.2412] 3.4916].
P18 ILOGT1 [96+ 0.00432] 0.00121 1.569} 0.5806] 0.9368] 0.6842] 0.9482] 0.5032] 0.7086] 0.6239] 0.5212] 0.4646] 0.6817] 0.6338] 0.8277] 0.6983] 0.744] 0.4028] 2.2628} 1.7838].
P19 LDGT1 [96+ 0.00416] 0.00114} 1.2715} 0.6218] 0.8437] 1.0272] 0.6835] 0.4345] 0.8099} 0.3982] 0.7078) 0.75} 0.6649] 0.7239] 0.6761] 0.8112] 0.5347] 0.2273] 2.2233] 1.3046] 0.5973}

IM147 Phase 2 Regression Coefficients, HC, 1996 and Newer Model Year LDGT1's
Regression Coefficients

Segment IRMS Reg.
Number Error Constant {C1 ic2 (ex) C4 C5 C6 ic? cB ico C10 cit ic12 C13 C14 C15. C16 C17 C18 C19
B11 Lt 1 [96+ 0.02393] 0.00865]. 2.4226]. a
B12 ILDGT1_ [96+ 0.02095] 0.00863]. 1.6528] 1.9763]. .
EXE] ILDGT1 [96+ 0.01824] 0.00691]. 5.444] 0.667] 2.5512]. .
B14 ILDGT1_ [96+ 0.01653] 0.00607]. 1.2007} 0.3963] 2.3614] 1.9099].
| ILOGT1_ [96+ 0.01077] 0.00256]. 1.0854] 0.6091] 1.8127] 0.4665] 3.018} .
[Bie ILDGT1 [96+ 0.01076] 0.00241 1.0549} 0.6405] 1.7899] 0.4709] 2.8141] 0.9908] a
pe ILOGT1_ [96+ 0.00794} 0.00414]. 0.8756} 0.7439] 1.2805] 1.0073] 1.458] 1.4709} 3.9228]. .
{B18 ILOGT1 [96+ 0.00581] 0.00161]. 0.8235] 0.9534] 1.0124] 1.0609] 1.0031] 0.8433] 3.018] 2.4016].
’ EE) ILOGT1 [96+ 0.00549} 0.00161}. 0.823} 1.0394] 0.8747] 1.0551] 0.9342] 0.2958] 2.5227] 1.8686] 0.7675]



While a fast-pass procedure of this nature has the ability to greatly reduce test time, this
reduction has to be balanced against false passes. False passes occur when vehicles that
would otherwise fail an inspection are fast-passed out because their emissions over the
drive cycle are not appropriately characterized by the regression. For this study, false
passes are quantified by measuring excess emissions, which are defined as emissions
collected during an IM240 test in excess of the applicable standard for a given vehicle.
The IM147 test receives credit for identifying excess emissions if it fails a vehicle that
had excess IM240 emissions when using the same emission cutpoints (e.g. Max CO).

In this study, both the regression of the IM147 segments and the analysis of that
regression were performed using the combined data sets (3347 vehicles) and the Max CO
standards. Table 1-2 details the results of this analysis.

Table 1-2
Modeled Fast-Pass Results
Excess Emissions vs. Average Test Time

No Fast-Pass* Fast-Pass Enabled
Excess HC Identified 94.3% 92.7%
Excess CO Identified 98.2% 94.3%
Excess NOx Identified 92.1% 91.2%

Average Test Time** 217 seconds 125 seconds

* While vehicles cannot terminate in the middle of an IM147 without the fast-pass algorithm, the test may
end prior to completing three IM147s if the emissions measured at the end of any one of the IM147s meet
the applicable standards. :

**Test time refers to the time actually required to operate the vehicle on the dynamometer.

Predictive Retest Algorithms

The workplan for the study called for Sierra to refine algorithms originally developed for
SR99-10-02 and then to apply them to the total vehicle sample (3347 vehicles) to
determine their net effect on test time. In contrast to the fast-pass algorithm, where
misidentification results in false-passing vehicles and a loss in excess emissions
identification, the retest algorithm errors result in false failures, which can lead to
consumer complaints. Like the fast-pass algorithm, decreases in test time need to be
weighed against false failures to determine a reasonable compromise.

While the original retest procedure described in SR99-10-02 used a combination of mass
and concentration emissions measurements to anticipate whether a vehicle would benefit
from additional testing, concerns expressed by Gordon-Darby regarding the complexity


Table of Contents (continued)

page

6. Development of IM147 Variation Limits .......... 0.00. eee cence 53
Existing Tolerance Limits ........ 0.00.0 c ccc cece cece eee eee nee
Previous Analysis of Speed Variation Limits .............0. 600s cece eee

Development of Positive Power-Based Variation Limits .
Evaluation of IM147 Variation Limits on Test Time ....................0055

7. Integration of CPP Variation Limits ........... 0... c cee eee e eee eee 66

8. References 2.0... eee ee tte e teen nee 69

Appendix A - Startup, Intermediate and Final IM240 and IM147 Cutpoints
Appendix B - Max CO, Startup, Intermediate and Final IM147 Failure Rates
Appendix C - IM147 Regression Coefficients

Appendix D - Excess Emissions Identification for Max CO Cutpoints
Appendix E - Second-by-Second CPP Variation Limits

-ii-


Table 5-3
Excess Emissions Identified®
(With and Without Fast-Pass)

No Fast-Pass

Fast-Pass Enabled

Vehicle | Model |p cess HC | Excess CO | Excess NOx | Excess HC | Excess CO [Excess NOx
Class | Year | identified | Identified | Identified | Identified | Identified | Identified
75.1% 99.62% 98.42% N/A 87.6% 100.0%
100.00% | 98.83% 71.31% 100.0% 97.4% 100.0%
pev 100.00% 0.00% 100.00% 99.3% 97.5% 100.0%
90-95 N/A N/A 100.00% 96.9% 85.3% 91.4%
96+ | 84.26% 99.10% 95.82% N/A N/A NIA
ALL | 92.64% 100.00% 85.50% 98.6% 93.9% 97.6%
81-85 | 100.00% | 100.00% 56.20% 75.7% 92.6% 98.4%
88-89 | 100.00% | 100.00% | 55.88% 92.4% 91.0% 71.3%
LDGT1 | 90-95 N/A N/A N/A 0.0% 0.0% 100.0%
96+ | 95.61% 100.00% | 73.26% N/A N/A 0.0%
ALL N/A 87.65% 100.00% 79.3% 91.7% 94.0%
81-85 | 100.00% | 97.44% 100.00% 92.6% 100.0% 85.5%
86-87 | 100.00% | 99.81% 100.00% 100.0% 100.0% 56.2%
LDGT2 | 88-95 | 99.00% 86.37% 91.35% 100.0% 58.0% 39.7%
N/A N/A N/A N/A NIA
99.65% 95.04% 97.59% 98.5% _ 72.3%

* N/A means that no vehicles failed the applicable IM240 cutpoint within this vehicle class/model
year grouping.

Predictive Retest Algorithms

As with the fast-pass standards, the predictive retest algorithms were developed as part of
SR99-10-02. Retest algorithms are intended to predict whether a vehicle would benefit
from additional testing. If the algorithm determines that a vehicle that was failing at the
end of the first or second of three IM147 tests would benefit from additional testing, then
the next IM147 would begin. If, on the other hand, the algorithm determines that the
vehicle would not benefit from additional testing, the test would be terminated at that
point and the vehicle would fail the inspection.

In the current study, the workplan called for Sierra to refine the algorithms using the
3,347-vehicle sample and the Max CO cutpoints. The original algorithms used a

-39-


Appendix A

Startup IM240 Cutpoints and IM147 Cutpoints"
(Composite/Phase 2 Cutpoints in g/mi, IM240 - IM147)

Model Years

HC
LD

co

GV

NOx

1981-82 2.00/1.25 - 2.00/1.20 60.0/48.0 - 58.0/30.0 3.0 - 3.3/1.2
1983-85 2.00/1.25 - 2.00/1.20 30.0/24.0 - 30.0/15.0 3.0 - 3.3/1.2
1986-90 2.00/1.25 - 2.00/1.00 30.0/24.0 - 30.0/10.0 3.0 - 3.0/1.2
1991-93 1.20/0.75 - 1.30/0.60 20.0/16.0 - 21.0/10.0 2.5 - 2.9/1.0
1994-95 1.20/0.75 - 1.20/0.60 20.0/16.0 - 21.0/10.0 2.5 - 2.7/1.0
1996+ (Tier 1 0.80/0.50 - 0.80/0.50 15.0/12.0 - 15.0/ 7.0 2.0 - 2.1/0.9
LDGT1
1981-83 7.50/5.00 - 6.70/4.70 100.0/80.0 - 95.0/50.0 7.0 - 7.6/2.9
1984-85 3.20/2.00 - 2.90/2.00 80.0/64.0 - 76.0/40.0 7.0 - 7.6/2.9
1986-87 3.20/2.00 - 2.90/1.60 80.0/64.0 - 76.0/31.0 7.0 - 7.0/2.7
1988-90 3.20/2.00 - 2.90/1.60 80.0/64.0 - 76.0/31.0 3.5 - 3.6/1.3
1991-93 © 2.40/1.50 - 2.60/1.20 60.0/48.0 - 61.0/31.0  3.0- 3.4/1.1
1994-95 2.40/1.50 - 2.40/1.20 60.0/48.0 - 61.0/29.0 3.0 - 3.2/1.1 |
1996+ (Tier 1) 1.00/0.63 - 1.0/0.60 20.0/16.0 - 21.0/10.0 2.5 - 2.7/1.1
LDGT2
1981-83 7.50/5.00 - 6.70/4.70 100.0/80.0 - 95.0/50.0 7.0 - 7.6/2.9
1984-86 3.20/2.00 - 2.90/2.00 80.0/64.0 - 76.0/40.0 7.0 - 7.6/2.9
1987 3.20/2.00 - 2.90/1.60 80.0/64.0 - 76.0/31.0 7.0 - 7.6/2.7
1988-90 3.20/2.00 - 2.90/1.60 80.0/64.0 - 76.0/31.0 5.0- 5.1/1.9
1991-93 2.40/1.50 - 2.60/1.20 60.0/48.0 - 61.0/31.0 4.5-5.1/1.9
1994-95 2.40/1.50 - 2.40/1.20 60.0/48.0 - 61.0/29.0 4.5 - 4.8/1.9
1996+ (Tier 1) 2.40/1.50 - 2.40/1.20 60.0/48.0 - 61.0/29.0 4.0 - 4.3/1.7

“Developed for SR99-10-02



Failure Rate

Final Cutpoints
HC co NOx OVERALL
VType [Fail | Pass | % Fail | Fail | Pass | % Fail | Fail] Pass | % Fail | Fail | Pass | % Fail
First IM147

LDGV 499 11281 | 28.0% | 346 | 1434] 19.4% | 396 | 1384 | 22.2% | 733 | 1047| 41.2%
LDGT1__[210 | 754 | 21.8% | 109 | 855 | 11.3% | 184 | 780 | 19.1% | 348 | 616 | 36.1%
LDGT2 135°} 468 | 22.4% | 66 | 537 | 10.9% | 120 | 483 | 19.9% | 219 | 384 | 36.3%

All 844 | 2503 | 25.2% | 521 | 2826] 15.6% | 700 | 2647 | 20.9% | 1300 | 2047 | 38.8%
Second IM147
LDGV 309 | 1471 | 17.4% | 247 | 1533 | 13.9% | 296 | 1484 | 16.6% | 539 | 1241] 30.3%
LDGT1 137 | 827 | 14.2% | 91 | 873 | 9.4% | 134 | 830 | 13.9% | 256 | 708 | 26.6%
LDGT2 77 _| 526 | 12.8% | 56 | 547 | 9.3% | 88 | 515 | 14.6% | 157 | 446 | 26.0%

All 523 | 2824 | 15.6% | 394 | 2953 | 11.8% | 518 | 2829} 15.5% | 952 | 2395} 28.4%
Third IM147
LDGV 280 | 1500 | 15.7% | 240 | 1540] 13.5% | 277 | 1503 | 15.6% | 507 | 1273] 28.5%
LDGT1 120 | 844 | 12.4% | 85 | 879 | 8.8% | 127 | 837 | 13.2% | 239 | 725 | 24.8%
LDGT2 73 | 530 | 12.1% | 56 | 547 | 9.3% | 82 | 521 | 13.6% | 149 | 454 | 24.7%

All 473 12874 | 14.1% |381 129661 11.4% | 486 [2861] 14.5% | 895 12452 | 26.7%

Failure Rate By Model Year Grouping - First [M147
Final Cutpoints
HC co NOx OVERALL

Year |Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail
81-82 | 70 | 35 | 66.7% | 30 | 75 | 28.6% | 39 | 66 | 37.1% | 78 | 27 | 74.3%
83-85 | 148 | 80 | 64.9% | 120] 108 | 52.6% | 81 | 147 | 35.5% | 183 | 45 | 80.3%
86-89 | 179 | 246 | 42.1% | 102 | 323 | 24.0% | 144 | 281 | 33.9% | 247 | 178 | 58.1%
90-95 | 100 | 852 | 10.5% | 90 | 862 | 9.5% | 132} 820 | 13.9% | 220 | 732 | 23.1%
96+ 2 68 | 2.9% | 4 | 66 | 5.7% 0 | 70 | 0.0% 5 65 71%
81-85 | 116 | 144 | 44.6% | 69 | 191 | 26.5% | 70 | 190 | 26.9% | 165 | 95 | 63.5%
86-89 | 71. | 151 | 32.0% { 29 | 193 | 13.1% | 53 | 169 | 23.9% | 104 | 118 | 46.8%
90-95 | 23 | 427 | 5.1% | 11 | 439 | 2.4% | 59 | 391 | 13.1% | 77 | 373 | 17.1%
96+ | 0 | 32 | 0.0% | O | 32 | 0.0% 2 30 | 6.3% 2 30 | 6.3%
81-85 | 50 | 44 | 53.2% | 41 | 53 | 43.6% | 32 | 62 | 34.0% | 73 | 21 | 77.7%
86-89 | 29 | 35 | 45.3% | 14 | 50 | 21.9% | 20 | 44 | 31.3% | 42 | 22 | 65.6%
88-95 | 56 | 371 | 13.1% | 11 | 416 | 2.6% | 68 | 359 | 15.9% | 104 | 323 | 24.4%
96+ | 0 18 | 0.0% | 0 18 | 0.0% | 0 18 0.0% 0 18 { 0.0%
ALL 1844 [2503 | 25.2% [521 12826 | 15.6% | 700 12647 | 20.9% 1130012047] 38.8%



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DRAFT FINAL

Failure Rate Analyses and
Development of Fast-Pass,
Retest, and CPP Algorithms for
IM147 Max CO Cutpoints

prepared for:

U.S. Environmental Protection Agency

December 14, 1999

prepared by:

Sierra Research, Inc.

1801 J Street

Sacramento, California 95814
(916) 444-6666


1M147 Composite Regression Coefficients, CO, 1990 to 1995 Model Year LDGV's
Regression Coefficients
Segment RMS — [Reg.
Number Error [Constant |C1 ic2 ic3 C4 C5. C6 C7 cB ico C10 C11 C12 C13 C14 C15. C16 C17 C18 C19
PT TOGV. 190-95, 5.5829] 4.52493] 10.561]. .
iP. ILOGV__ {90-95 | 4.19016] 255934] -2.4176] 3.5176].
iP3 ILOGV_}90-95 | 3.59909] 1.7089] -2.0167] 1.8381] 5.8658}.
iP4 ILOGV_ }90-95 | 3.52751] 1.57279] -2.1878] 1.7555] 4.1493] 4.0887]. .
IPS ILOGV__ [90-95 | 3.35967] 1.52742] -1.8847| 1.3658] 3.0822] 2.7595] 3.0615]. .
iPS. ILOGV__|90-95 | 3.30911] 1.53896] 1.433] 2.5763] 2.1518] 2.309] 3.2307]. .
iP7 ILDGV_ {90-95 | 3.20465] 1.37753] -1.7351} 1.2677] 2.4869] 1.7618] 0.6153] 2.8007] 3.0019}. a
iP8 ILDGV_}90-95 | 3.08075] 1.25584] -0.8703] 1.3157] 1.5808] 1.0639] 0.6544] 1.6621] 1.8801] 3.4334)
iPS LOGV__|90-95 | 3.03308] 1.28742] 0.9409] 1.3472] 1.2629] 1.2814] 0.1146] 0.9574] 2.0383] 2.5679] 2.6687].
P10 ILDGV__ {90-95 | 2.99284] 1.29175] -0.6667] 1.2906] 1.1937] 1.4946] -0.2007{ 0.8633] 1.7797] 2.4995] 0.7394] 2.5847] .
Ga ILDGV__|90-95 | 1.79334] 0.39573] _0.8907/ 0.875] 0.9738] 0.6525] 0.6724] 1.4441] 0.9505] 1.8704] 1.0336] 1.2774] 0.946]. .
P12 ILOGV_]90-95 | 1.48201] 0.27031] 0.8772] 0.7905] 0.9191] 0.8699] 0.6683] 0.8288] 0.9734] 1.6847] 0.9938] 1.2059] 0.7634] 1.2265]. .
P13, LOGV [90-95 | 1.22124] 0.18956] 0.9352] 0.7049] 1.1123] 0.7943] 0.6333] 0.6174! 0.819] 1.3469] 0.8633] 0.9048] 0.7518] 0.8615] 1.6924].
[P14 ILOGV__|90-95 | 0.96232] 0.19909] 1.2249] 0.6231] 0.8871] 0.5243] 0.6879| 1.0339] 0.9491] 1.0285 1.2323] 0.735] 0.7153] 0.7873| 1.2566] 1.1834]. .
fPis LOGV_ [90-95 | 0.67059, 0.09713] 1.1865] 0.6235] 0.8220[ 0.6645] 0.7023[ 0.9149] 0.9983] 0.9575] 1.0487] 0.5318] 0.7254] 0.7636] 0.8921] 0.7821] 1.3034]. .
pis ILOGV_ [90-95 | 0.61862} 0.0845] 1.1157] 0.6363} 0.8291 0.672} 0.6393} 0.9358] 0.9492] 0.8718] 1.0229] 0.6422) 0.7279] 0.7605] 0.8704] 0.8116] 0.9084] 1.6959) a
P17, LOGV_ {90-95 | 0.56789] 0.04406] 1.1034] 0.6685} 0.6934] 0.7551] 0.6966] 0.8027] 0.9189] 0.7923] 0.9078] 0.6476] 0.7299] 0.7393] 0.8716] 0.8032] 0.8822] 1.1592] 1.9586]. .
P18 ILDGV_ [90-95 | 0.38985] 0.01218] 1.0107] 0.6685] 0.7981] 0.7109] 0.6672] 0.9253] 0.6833] 0.8064] 0.9037] 0.6447] 0.7163} 0.726] 0.7487{ 0.7691] 0.7468] 1.0451] 1.2236] 0.9766].
[P19 (LOGV_ [90-95 | 0.13949] 0.01779] 0.7784] 0.7029] 0.7108] 0.7886] 0.6798] 0.8583] 0.733} 0.6745] 0.7337) 0.685} 0.7139] 0.7148] 0.711] 0.7002} 0.7152] 0.6362] 0.9674] 0.7322] 0.7843}
IM147 Phase 2 Regression Coefficients, CO, 1990 to 1995 Model Year LDGV's
Regression Coefficients
Segment RMS Reg.
Number Error [Constant {C1 ic2 ic3 C4 ics C6 C7 ica ico C10 C11 C12 C13, C14 C15 C16 C17 C18 ic19
fe [CoGV_|s0-55 F103] 2.35754]. 71.4957].
B12 LOGV_ {90-95 | 2.32229] 0.91944]. 1.1523] 2.0251]. a
1813 ILOGV__ [90-95 | 1.76794] 0.5229] 1.0723] 1.2322] 2.7642]. .
[B14 ILOGV__|90-95 | 1.40264] 0.44364]. 0.9979] 1.1201] 2.0763] 1.6438] .
B15 LOGV__]90-95 | 0.97281] 0.25022]. 1.0003] 1.0639] 1.4071] 1.0512] 1.8686]. a
B16 ILOGV_ [90-95 | 0.88873] 0.20771]. 1.0023] 1.0561] 1.3449] 1.0929] 1.2656] 2.5533]. .
B17, ILOGV__|90-95 | 0.79003] 0.10796) 0.9981 1.009] 1.2809] 1.0795] 1.1947] 1.6393] 3.0349].
1818 LOGV__[90-95 | 0.54771] 0.04247] 0.9734} 0.9933] 1.0674] 1.0348] 1.0249] 1.4049] 2.0212] 1.3189]
B19 LOGV_ 90-95 | 0.19036] 0.03127]. 0.9671{ 0.9711] 0.9708] 0.9442] 0.9644] 0.8618] 1.3588] 0.9845] 1.0769}



Failure Rate Analyses and Development of
Fast-Pass, Retest, and CPP Algorithms
for IM147 Max CO Cutpoints

Table of Contents

page
Summary 0.20. nen nent e enn eens 1
Scope of Work 2.0.60. eee een e ene etn eee 2
Projected IM147 Failure Rates ..............00. Peden nen ee eee eee eee 3
Modal IM147 Fast-Pass Standards . we 4
Predictive Retest Algorithms ..... a)
Modal Fast-Fail Criteria... 2... cece eee eee ee 6
Integrated Fast-Pass, Retest, and Fast-Fail Algorithm Results .................. 7
Segment 2 Revised Integrated Algorithms Results - ,
Development of IM147 Driver Variation Standards . . an
Integration of CPP Variation Limits ........... 00... c cece cece eee eee ee
SIP.Credit Analysis 00... 0... o occ ccc cent eet ennnenee
Need for Follow-Up Analysis ....... 0.0... cece cece cence eee ene ee
. Introduction .. 0.0... ccc cece ene t eter n entree eee ae 13
SCOPE 6 en eee eee ene nee 16
Organization of the Report... 00.66. e tenn ene 18
» Test Data... 0. eee eee eee eee eee 19
. IM147 Cutpoint Analysis... 0... cen eet e ene ee 24
IM147 Test Length . 2.22.00... 00 cc cceeceee cece eeeeeees Loveeeeeees ..24
Cutpoint Analysis :
Comparison of Failure Rates .. 6.0... ence een eee 28
. Optimized IM147 Test Criteria 0.06... nee ence 34
Modal Fast-Pass Standards ........ 6. e eee eect teens 34
Predictive Retest Algorithms . . ..39
Modal Fast-Fail Criteria... 0.2.6.0... ec cee eee eee eee . 46
Integration of Fast-Pass, Retest, and Fast-Fail Algorithms . . - 48
Segment 2 Revised Integrated Algorithms Results ........

SIP Credit Analysis .........
Need for Follow-Up Analysis


IM147 Composite Regression Coefficients, CO, 1996 and Newer Model Year LDGV's
Regression Coefficients
Segment RMS — Reg.
Number Error [Constant |C1 ic2 ic3 \C4 cS C6 C7 ica ico C10 C11 C12 C13 C14 C15. C16 C17 C18 }C19
PT TOGV [ser “4.2502 181864) 53.0314], C
jP2 ILOGV__|96+ 2.79375] 0.86889} 6.9133 5.3642]
[P3. ILOGV__[96+ 2.31951] 0.49886} -22.4698] 2.9121] 7.8787|
iP4 ILOGV_ [96+ 2.30066] 0.48852] -20.2357] 2.6582] 5.7046] 6.7489]. .
iPS ILOGV__|96+ 2.29856| 0.4874] -19.0504{ 2.2906] 4.9636] 7.0228] 1.4214]. .
iPS ILOGV__|96+ 2.17485} 0.36586} -18.1249] 2.1863] 5.4292] 5.3931] -0.6432] 6.8037]. a
iP7- ILOGV__}96+ 2.17872} 0.34724] -17.6869] 2.1129] 5.4618] 5.4355] 0.8956] 6.7245] 0.3983} .
iP8 ILOGV__ [96+ 2.17729] 0.32839] -15.8387] 1.9759] 5.2235] 5.2769] -1.1683| 6.4131] 0.2201] 1.4309]. .
Pg ILOGV__ [96+ 2.16612) 0.4024] -11.8473] 1.4566] 4.5337] 6.4172] -1.2079] 5.4459] 0.3128] 0.4287] 3.2042| .
P10 ILOGV__[96+ 2.17081} 0.40258] -11.4985] 1.3861] 4.5521) 6.4182] -1.1694) 5.5108] 0.2871] 0.3698] 2.6985] 0.5987]. .
Gil ILDGV_|96+ 1.3457] -0.04872] -1.5411] 1.3212] 1.6098} 6.5928] 0.785] 3.2891] 0.7158] 0.2278] 1.3044] 1.2284] 1.1171]. .
P12 LDGV__ [96+ 0.94601] 0.03677] 0.1569] 1.1325] 0.7478} 7.1762] -2.7331] 2.8294] 0.8581] 0.5253] -0.3667[ 2.4476] 0.8375] 1.6915].
P13, ILOGV__[96+ 0.83127] 0.04161] 6.5433] 0.7725] 0.7897] 2.3429] -1.2999] 2.5769] 0.8026] 1.0385} -0.6814/ 0.7591] 0.8719] 1.3576] 1.4743].
P14 ILOGV__[96+ 0.36237{ 0.07318] -1.7846] 0.7477] 1.0289] 0.323] 0.8116} 2.2639] 0.6876] 0.4145] 1.1104] 0.4144] 0.6973] 0.7747] 0.877] 1.8748) .
P15 ILOGV__|96+ 0.29315} 0.05053] -0.3484] 0.5009] 0.8684] 2.0337/ 0.8189] 2.1406] 0.8321] 0.2796] -0.0113] 0.3684] 0.7271] 0.7433] 1.1116] 1.0359] 0.88171.
[P16 ILOGV__}96+ 0.29205] 0.04924] 0.0274] 0.4967] 0.8209] 2.0574] 0.8019] 2.0106] 0.8654] 0.1479] 0.1851] 0.3987] 0.7276] 0.7477] 1.1209] 1.0535] 0.7862] 0.5132] a
PP ILOGV__ [96+ 0.23867] 0.02554] -0.2222] 0.7177] 0.3166] 1.7918] 0.9891] 1.2672] 0.7007] 0.7511] 0.1642] 0.6983] 0.7129] 0.7751] 0.8196] 1.3698] 0.5666] 0.1481] 2.2487]. a
18 ILOGV__ [96+ 0.211] 0.02076) 1.8186] 0.5321] 0.4849] 0.8707] 1.2541 1.116] 0.6247] 0.9096] -0.3981] 0.9301] 0.7173] 0.7777] 0.8774] 0.7931] 1.0214] -0.0389] 1.7607] 0.6392]
P19 ILOGV_ [96+ 0.05419] 0.00995] 0.7814] 0.7041] 0.7989] 0.4985| 0.7204] 0.9434] 0.6972} 0.728} 0.6052] 0.6829] 0.7065] 0.7486] 0.7235] 0.6863] 0.7334] 0.6843] 0.7333] 0.7391] 0.7203]
IM147 Phase 2 Regression Coefficients, CO, 1996 and Newer Model Year LDGV's
Regression Coefficients,
Segment RMS — |Reg.
Number Error Constant {C1 jc2 ic3. C4 ics icé C7 ic8 (o:) C10 cit ic12 [C13 C14 C15. C16 C17 C18 C19
fei [LOGV_ [ser 2.6132] 0.6365]. 75369]. a
B12 LOGV__ [96+ 1.55476] 0.22139]. 1.2687 2.444]. a
B13 LOGV__ [96+ 1.21697] 0.12297}. 1.1955] 1.6069] 1.8026} .
ILDGV__ [96+ 0.54556} 0.13004}. 0.9864] 1.1698] 1.3748] 2.3969].
LOGV_ [96+ 0.4806] 0.1147]. 0.9893] 1.0617] 1.3564] 1.8394] 0.7573]. .
ILOGV_ [96+ 0.4633] 0.09077]. 0.985] 1.0604] 1.4116] 1.8485] 0.4981] 1.6802]. .
LOGV__ [96+ 0.33866] 0.04766]. 0.968] 1.0626] 1.1629] 1.9325] 0.5734] 0.8152] 3.1735].
ILOGV__ [96+ 0.30627] 0.03898]. 0.9596] 1.0743] 1.0207] 1.4501] 0.9473] 0.6378] 2.7247] 0.6646].
ILOGV_ [96+ 0.07437} 0.01481]. 0.9598] 1.0096] 0.9451 0.966} 0.9371] 1.0884] 1.159] 0.9807} 0.9897]



Phase 2 scores existing in the IM240 test. IM240 Phase 2 cutpoints are more rigorous
than the composite cutpoints, presumably to minimize falsely passing vehicles. After
studying the IM147 data, a multiplier of 0.9 was used with the regression since it
provided additional defense against false failures while maintaining the possibility of a
Phase 2 pass. The following Phase 2 regression equation (4-2) was used to extrapolate
IM147 Phase 2 cutpoints from the IM147 composite cutpoints.

IM147 Phase 2 = 0.9 * (Slope * (IM147 Composite) + Intercept) [4-2]

Table 4-2 details the appropriate regression coefficients to be used with the above
equation. Unlike the composite regression coefficients shown in Table 4-1, IM147
composite to Phase 2 coefficients were held constant across model years.

Table 4-2
1IM147 Composite to Phase 2 Regression Equation Coefficients

Emission Constituent Slope Intercept

0.807408 0.012886
0.881965 -0.569281
0.989412 -0.083696

Table 4-3 shows the revised IM147 Max CO cutpoints developed using both sets of
regression equations. In two places, the revised NOx cutpoints seem anomalous because
they are actually less stringent for newer vehicles (1989 to 1990 LDGV and 1987 to 1988
LDGT2). As it turns out, these anomalies occurred at or near the regression equation
breakpoints.” The slight discontinuity caused by this change created the apparent
anomaly.

“To apply the regression equations to the model year groupings, which vary depending upon the vehicle
type, cutpoints for some model years were determined using the adjacent regression equation.

-27-


IM147 Composite Regression Coefficients, HC, 1986 to 1987 Model Year LDGT2's

Regression Coefficients

Segment RMS — Reg.
Number Error {Constant |c1 cz ica Ica Ics ice Ic7 ice ico cro jer fcr2_ fers fera_—fcts fete fci7_—icre_—ci9
PT toGT2 [e687 | o7erer| O,7o061] 14.62). r
[Pz LOGT2 [6-87 | 0.53753] 0.28237] _1.2627| 6.2363 i
[P3_ OGT2 [86-87 | 0.38485] 0.18393] -0.7945| 3.7354] 5.7608]. a
[Pa LOGT2 [36-87 | 0.34875] 0.16586] -2.6149| 3.7629] 2.6334] 4.1762]. i
[P5_ LoGT2 [86-67 | 0.34617] 0.17953[_-3.6873|__3.212|_2.76i[_3.4285| 2.4078]. a
[Po LOGT2 {86-87 | 0.20325] 0.20542] -3.5626| 3.7025] 1.7413] 0.4872| 1.0679] 4.3236 5
[Pz LOGT2 [86-87 | 0.2934| 0.19684[ -3.5264| 3.6876] 1.7296] 0.6143] 0.2317] 4.2624] 0.8663]. a
Pa LOGT2 [86-87 | 0.28213{ 0.17396] 3.3926] 1.1562] 0.6702| 0.0208] _ 3.449] _0.3557| 2.6062 i
Ps. LOGT2 [86-87 | 0.27183| 0.1983] 3.3764] 1.0296] 0.699] 0.2722] 2.4556] 0.6353] 0.6] 2.1369]. A
P10, LOGT2 [86-87 | 0.26655[ 0.20643] -0.6291| 3.3422] 0.5757| 0.6074] 0.6213] 2.1855] 0.9202] 0.0129] 1.3566] 17368].
P14 LOGT2 [86-87 | 0.23082|_0.0819| 0.4024] 2.3049[0.6701| 0.0523] 1.2179 2.4136{ _0.3361| 0.0779] 0.9104] 1.6901] 1.4014] 5
P12 LOGT2 [86-87 | 0.21585| 0.04628| 0.8765] _1.6118| 0.7539{ 0.3407] 0.9193] 1.8565] 0.8585] 0.4701|_0.4832[ 1.6797] 1.0220] 2.1489] i
P13 LOGT2 {86-87 | 0.17953| 0.04669 1.6471] 0.9949] 0.685] 1.0029] 0.4324[ 1.1786] 0.7455] 0.9956] -0.3607| 1.9373] 1.0646] 0.024] 2.6969]. A
P14 LOGT2 [86-87 | 0.16923[ 0.01715] _2.1776| 0.6591] 1.0222| 0.7528] 0.0545] 1.7036] 0.8256] 0.5799] 0.1446] 1.6426] 1.1863] 0.1661] 1.154] 2.4636].
P15 LOGT2 [86-87 | 0.16212| 0.02841| 2.045] 0.4649] 0.7435 1.0759] 0.3389] 1.3816] 1.0395] 0.9294] 0.0477] 1.5028] 1.0456] 0.1761] 0.9342] 1.5234] 1.2074].
P16. LOGT2 e687 | 0.15721| 0.03244[ 2.1199] 0.4169] 0.672] 0.9335] 0.3563] 1.562] 1.3979] 0.0804] 0.2926] 1.6162] 1.1907] 0.4448] 0.6092] 0.6887] 0.3523] 3.6537].
Paz. LOGT2 [86-87 | 0.08118] -0.01059| 0.6472] 0.7516] 0.3931] 0.9394| 1.5188] 0.862] 0.3132] 0.5162] 0.8422] 0.3101] 0.8207| 0.6645| 0,7572| 1.2216| 0.5723] 1.2293] 3.38]. is
| aE LOGT2 [86-87 | 0.06878] -0.02245[ 0.747| 0.866] 0.3561] 0.7928] 1.5497] 0.9638] -0.0777| 0.7405] 0.8501] 0.4782] 0.7233] 0.7721| 0.8208] 0.6120] 0.6412] 0.1007] 29199] 1.2143].
| GHEI [LOGT2 [6-87 | 0.05744] -0.02634f 0.8277] 0.9324, 0.4772] 0.6834] 1.4838] 0.7072] 0.6021] 0.5295] 0.8642] 0.1132] 0.7642] 0.7717] 0.5765] 0.3706] 0.4851] 0.0561] 2.1533] 0.9384) 1.4329]

IM147 Phase 2 Regression Coefficients, HC, 1986 to 1987 Model Year LDGT2's

Regression Coefficients

Segment RMS — Reg.
Number Error [Constant {C1 c2 c3 ca Ics ice cz ce co cto fest fcr2_— cra. fca_—fcts. [cies fciz’_—sfcts_ cto
[ei7 2 [86-87 | 0.42556] 0.04924] 41247.
B12 LOGT2 [86-87 | 0.34262| 0.01535]. 2.3444] 4.5554]. a
B13 LOGT2 [86-87 | 0.26401] 0.06727I. 4.8518] 0.0098] 4,3001
| EXE LOGT2 [86-87 | 0.26019] 0.04305]. 18614] 0.0618{ 3.3888] 1.611]. .
[ers LOGT2 86-87 | 0.24666] 0.06334]. 7.6473] 0.0316] _2.594[ 0.5597] 1.9276] i
[ere LOGT2 [86-87 | 0.24422[ 0.06216]. 1.731 0.2275] 2.4543] -0.1669| 1.1753] 3.2006].
[er7, LOGT2 [86-87 | 0.11773] -0.01836]. T1424] 0.827| 1.1114] 1.9999] 0.6793] _ 1.223] 43846]. i
[ae LOGT2 [86-87 | 0.10067] -0.03741 1.0229} 1.0633|_1.2567[ 1.1759] 0.8269] 0.1785] 3.6251] 1.6179).
[B19 LOGT2 [86-87 | 0.09638] -0.03963] 1.0707] 1.0296] 1.0107] 1.2016] 0.7069] _-0.201] 3.2092] 1.6700] 0.8058



APPENDIX C

1M147 Composite Regression Coefficients, HC, 1981 to 1985 Model Year LDGT1's

Regression Coefficients
Segment RMS —|Reg.
Number Error {Constant |c1 c2 Ic3 Ica Ics ce ic7 ca co cro fet jciz_—fcia-_— crafts cre. ciz_—fcrs_—_fcto
Pi TOGT?_[e1-85 | 0.75606] 0.95039] 13.511) r
P2 LOGT1 [81-85 | 0.52614] 0.34501] 2.7265] 5.5143}.
P3 LOGT1 [81-85 | 0.40889] 0.23152] 0.5547] 3.2006] 6.2124).
Ps lLoGT: |81-85 | 0.38717] 0.22535] -1.0106| 3.4095| 3.2694] 3.5979]. .
PS LOGT1 [81-85 | 0.56672) 0.23076] 0.6175] 2.5630] 3.1216] 2.0148] 3.4755]. :
PG COGT [81-85 | 0.34978] 0.25562] -2.8842] 2.7609] 1.8633] 1.5965] 2.3446] 3.2626]. :
PT LOGT? [81-85 | 0.33620] 0.24395| -2.5960] 2.4215] 2.0512] 1.644] 0.5818] 2.2896] 2.9190], r
Ps LOGT1 [81-85 | 0.30707] 0.21404] -0.9821] 1.8007 1.4784] _1.745| 0.0989] 0.8955] 2.4381] 4.62).
Po LOGTs [81-85 | 0.30661] 0.21485] -0.9303] 1.7873] 1.3395] 1.7754] 0.0281] 0.6238] 2.4791] 4.5116] 0.6087]. 5
P10 LOGT1 [81-85 | 0.30246] 0.21468] _-1.189] 1.8114] 1.1577] 2.1188] -0.3285| 0.5235] 2.6637] 4.255] -0.5878| 2.1264. :
a LOGT+ 81-85 | 0.25475] 0.05761] 0.2293] 0.7411] 1.0904] 1.5866] 0.0582] 1.3727] 1.9974] 2.4699] 0.822] 1.4541] 1.8008] a
P12 LOGT1 [81-85 | 0.24251] 0.05503] _-0.1605] 0.7753 0.9307] 1.635] 0.0664] 4.336] 2.0956] 2.1337] 0.2754] 2.1379] 1.2063] _1,1106]. i
P13 LOGT1 [81-85 | 0.19345] 0.05879] _ 1.3617] 0.2166] 0.9329 1.6808] -0.1316| 1.2366] 2.3977] 1.6874] 0.0101] 0.9701] 0.9488] 0.4982] 3.2951 5
P14 LOGT1 [81-85 { 0.1631[ 0.04219| 1,279] 0.1384 _ 1.1759] 1.4082] 0.0948] 1.1156] 2.0131] 1.2938] __0.720| 1.1342] 0.4477| 0.3274] 1.562] 4.0745]. 5
PIS. LOGTs [81-85 | 0.13928] 0.03346] 1.8241] 0.0799] 1.2164] 1.2209] 0.2827] 0.8749] 2.2275] 0.6681] 0.8755] 0.7239] 0.5659] 0.1723] 0.7424] 1.1967] 2.7671
P16 LOGTt [81-85 | 0.13703] 0.03021[ 4.8786] 0.023] 1.2623] 1.1806] 0.305] 0.8514] 2.2773] 0.5758] 0.8491] 0.7433] 0.5775] 0.2633] 0.6459| 1.2728] 2.0331] 2.1607]. :
Paz LOGT1 [81-85 | 0.09132] 0.00475] 1.4179] 0.434] 0.6904] 0.7838] 1.0571] _ 0.616 1.5938] 0.7518] 0.2064] 0.8618] 0.5896] 0.4328] 0.7951] 1.3837] 1.066] 0.6620] 2.7633]. i
[pis LOGT1 [81-85 | 0.07314] -0.00235| 1.5086] 0.5696] 0.7494] 0.8068] 0.8363] 0.5804] 1.3972] 0.5907] 0,4057| 0.6221] 0.5658] 0.584] 0.7038] _0.8567|  0.988| 0.3277] 1.9720] 1.7663).
‘aE LOGT1 ]81-85 | 0.06979] -0.00621] 1.375] 0.6281] 0.6996] 0.852] 0.9084] 0.5029] 1.4414] 0.5441] 0.3042] 0.5659] 0.6012] 0,6081| 0.7083] 0.7313] 0.6296] 0.2443] 1.7266] 1.5869] 0.729]

IM147 Phase 2 Regression Coefficients, HC, 1981 to 1985 Model Year LDGT1's

Regression Coefficients

Segment RMS |Reg.

Number Error —_|Constant }C1 ic2 Ica C4 cs ice Ic7 ca jco cro fcws—jcrz2_—sfcra.—s cra. cis. cre fciz_—fcrs_— cto
fan LOGTi [61-85 | 0.5023] 0.12361]. r C r r " r r r S444]. 5

B12 LOGT1 {81-85 | 0.48187| 0.11013]. . 5 i a i A : 3.3244] 1.9633]. :

B13 LOGT1 [81-85 | 0.35264] 0.07757 : : : : 5 : : : 1.7904] _-1.1796| 5.9021].

B14 lLoGT1 |s1-85 | 0.32195 0.06184]. . . . . . . . . . 0.9978| -1.0185| 3.8122| 5.5161 .

B15 COGTI {e185 | 0.2626 0.04064 : : : : : : 7,0926{ 0.0499] 1.7408] 0.7784] 4.7049]. :

[16 LOGT1 |81-85 | 0.27957] 0.0348]. i a si i i 7.0816] 0.2105] 1.5379] 0.9523] 3.4207] 3.6470].

B17 LOGT1 [81-85 | 0.14692] -0.01627 . : : : a : 7.0035{ _0.5208|_1.1766| 2.2402| 1.4703] _0.4909| 4.6919]. :
B18 LOGT1_{81-85 | 0.11692] 0.02022]. i i : : : 5 0.917| 0.8016] 0.9222] 1.5324] 0.8911] 0.0868] _ 3.252] 2.7656]

| EXE LOGT1 [81-85 [ 0.11286] -0.02299) - - a - - 0,9799{ 0.6216] 0.9206] 1.4305] 0.4043] 0.0294] 2.8150] 2.4608] 0.5600]



Table 1-5
Comparison of Integrated Algorithms vs. Standard IM147
Impact on Test Time and Excess Emissions Lost*
Model Mean Test | Mean Test % Excess Emissions
Year Sample Time 7 Time w/ .
Class Group | Size Standard” | Algorithms HC co NOx
1981-82 | 105 286.4 140.1 - 87.6% | 100.0%
1983-85 | 228 311.2 _ 169.4 100.0% | 97.4% | 100.0%
1986-89 | 425 248.4 112.0 99.3% | 97.5% | 100.0%
rDev 1990-95 | 952 184.8 78.3 96.9% | 85.3% 91.4%
1996+ 70 154.3 37.1 - - -
All 1780 221.0 100.0 98.6% | 93.9% 97.6%
1981-85 | 260 306.6 158.7 75.7% | 97.5% 98.4%
1986-89 | 222 230.8 101.6 92.4% | 91.0% 99.6%
LDGTI | 1990-95 | 450 173.3 59.2 0.0% 0.0% 100.0%
1996+ 32 155.1 31.6 - - 100.0%
All 964 221.9 94.9 79.3% | 94.4% 98.7%
1981-85 94 307.5 158.8 100.0% | 100.0% | 100.0%
1986-87 64 253.2 101.4 100.0% } 100.0% | 70.8%
LDGT2 | 1988-95 | 427 166.5 54.2 100.0% | 58.0% 39.71%
1996+ 18 146.0 28.0 - - :
All 603 197.1 74.7 100.0% | 98.5% 86.0%
Weighted Average | 3347 217.0 94.0 94.4% | 95.4% | 95.7%

* Test time results do not include impact of driver variation limits.

** Mean test time standard refers to the average dynamometer test time without the algorithms enabled.
This was determined using the 3347-vehicle sample.

*** Percent of IM240 (Max CO) excess emissions identified with the integrated algorithms enabled. This
was determined using the 835-vehicle sample.

study. Less rigorous test criteria were also evaluated as part of this latest study.
However, their use yielded relatively little improvement in identification rate at the cost
of a large increase in test time. A decision was therefore made not to pursue this latter
option.

-9-


Segment 2 Revised Integrated Algorithms Results

The original integrated algorithm results were determined assuming fast-pass and fast fail
results could not be rendered prior to the fourth segment (i.e., no earlier than Test Time =
28 seconds). This was consistent with the procedure established in SR98-02-01.

Gordon-Darby, wishing to further minimize test time, requested that Sierra explore the
feasibility of rendering fast-pass and third IM147 fast-fail decisions after earlier segments
without degrading excess emission identification. Unfortunately, simply moving the
decision forward with the existing algorithms, while shortening test time, did degrade
excess emission identification. To adjust for this change, the error multiplier used in the
fast-pass decision needed to be increased to 3 during segment 2 and to 2.5 during segment
3. While the fast-fail algorithm used during the third IM147 also uses an error multiplier,
it can be left at 2 during segments 2 and 3 without increasing false-failure incidence.
Using these criteria, the earliest possible fast-passes were moved to the end of segment 2
(.e., Test Time = 16 seconds) and average test time was reduced to 90.5 seconds without
sacrificing any excess emissions identification. Excess emissions identification remained
at 94.4% for HC, 95.4% for CO, and 95.7% for NOx.

SIP Credit Analysis

The comparison of excess emissions identification between the IM240 and IM147 that is
presented above is based on the use of CO Max standards for both test cycles. However,
to develop an estimate of the allowable SIP credit that should be allocated to the revised
IM147 CO Max standards, it is also necessary to compare excess emissions identification
between this scenario and the IM240 with EPA-recommended final cutpoints in place.
This is due to the need to establish a link to using MOBILE for SIP modeling purposes.
Configuring MOBILE with CO Max standards is not feasible; therefore, a better
approach is to run the model with final EPA standards in place and use the excess
emissions identification rates developed in this study to adjust the resulting outputs.

Table 5-7 shows the excess emission identification rates when the IM147 Max CO
cutpoints are compared to the IM240 final standards. Pollutant-specific identification
rates are shown both without and with the fast-pass, retest, or fail-fail algorithms enabled.
(The latter scenario includes fast-passing vehicles as early as at the end of segment 2.)
Since Arizona will be implementing the IM147 test procedure with the algorithms
enabled, the identification rates for this scenario are the ones that should be used to adjust
the MOBILE modeling results (based on final IM240 standards) for SIP credit purposes.

As expected, the table shows that HC and NOx identification rates are significantly lower
with the IM147 Max CO cutpoints relative to final IM240 standards. This is due to the
fact that the Max CO cutpoints are designed to maximize the CO benefits of the program
at the expense of HC and NOx benefits, while keeping maximum failure rates in each
cutpoint category to acceptable levels. The CO identification rate of 97.9% (with the
algorithms enabled) shows that the Arizona program will achieve nearly all of the
modeled benefit of the final IM240 standards. Note that this will be substantially more

-51-


IM147 Composite Regression Coefficients, HC, 1996 and Newer Model Year LDGV's
Regression Coefficients
Segment RMS Reg.
Number ' Error Constant {Ct ic2 ic3 C4 cS icé C7 C8 ico C10 C14 C12 C13 C14 ic15 C16 C17 C18 C19
PT ILOGV_ [96+ 0.10038] 0.04026] 29.2462].
iP2 ILOGV__{96+ 0.07371] 0.01788] 5.9182] 4.0658). a
iPS ILOGV__ [96+ 0.06844] 0.01874] 2.9638] 1.9584] 5.5186].
iP4 LDGV__ [96+ 0.06132] 0.01973] 2.7332] 1.6433] 3.0543] 4.4161].
iPS: ILDGV_j96+ 0.06046] 0.02162] 2.8327] 1.3424] 2.314] 3.7839} 1.694]. .
PS ILDGV__ {96+ 0.05435] 0.02387] 1.2378] 1.2522! 0.457] 3.4931] -1.2143] 7.4758].
PT ILOGV__]96+ 0.05447] 0.0241 1.0091] 1.2788] 0.4429] 3.4792] -1.2783] 7.8471] -0.2285]. a a
PB ILDGV_ }96+ 0.05359] 0.02337] 0.9941] 0.7729} 0.7794) 3.2658] -2.1201] 7.6602] -0.5933] 3.1409]. ‘
Po. LOGV__|96+ 0.05279] 0.02482] 2.6229] 0.6273] 0.6458] 3.3195] -1.1123] 4.6464] -0.1966) 0.788] 4.1995]. a
[P10 ILDGV__ [96+ 0.05143] 0.02476] 1.0359] 0.4556] 1.1796] 3.0156] -1.3707] 3.8218} 0,108} 0.823} 0.5809] 6.8862]_ .
[Pi 1 ILDGV_|96+ 0.03867] 0.00936] 5.4115] -0.1934] 1.9861] 0.9553] -0.8572] 0.8886] 0.8704] -0.1062| 0.029} 6.6498] 1.6503] .
Piz LOGV__ [96+ 0.02114) 0.009] 3.1864} 0.1934] 1.3914] 1.1882! 0,032] -0.1178{ 0.7255{ 1.0187] 0.0701] 4.5106] 0.6495] 2.1154]. a :
{Peis ILOGV__|96+ 0.01901] 0.00675] 3.9129] 0.3208] 1.0647] 0.8952] 0.4296] 0.6088] 0.9736] 0.1471] -0.7025] 3.5491] 0.6163] 1.6571] 1.3153]. .
[Pia ILOGV__ [96+ 0.01746] 0.00454] 4.3463] 0.3067] 1.0782] 1.0439] 0.3355] 1.3018] 1.0886] 0.2129] 0.7694] 2.9793] 0.5207] 1.3494] 1.2261] 1.5726] .
P15 ILOGV__ [96+ 0.01604] 0.00595] 4.6859] 0.2669] 0.8615] 0.9562] 0.5265] 1.8859] 0.9556] -0.0501] -1.2322] 2.431] 0.5884] 1.2496] 1.3707] -0.6015] 1.5225]. a
JPi6 ILDGV_ [96+ 0.01579] 0.00648] 4.6016] 0.2309] 0.8473] 0.9826] 0.3155] 2.3189] 0.8167] 0.4521[ -1.291] 2.0816] 0.5345] 1.4982] 1.0515] -0.1967| 2.1338] -2.9193]. .
yPi7 ILDGV__ [96+ 0.01236] 0.00351] 2.9817] 0.5075] 0.6011] 1.1216] 0.3319] 1.6187] 0.8518] 0.5096] 0.0325] 1.425} 0.5667] 1.2205] 0.6224] 1.0067] 1.5045} -2.3679] 2.3031 a
[P18 ILOGV_|96+ 0.00892] 0.00122] 2.9957] 0.5568] 0.7792 0.788] 0.2152] 1.8089] 0.6335] 0.4301] 0.2697] 0.9495} 0.5389] 1.1609] 0.7993] 0.5202] 1.4602] -1.9165] 1.033] 1.7225].
{P19 LOGV_ [96+ 0.00643} 0.00092} 1.469] 0.8076{ 0.4864] 0.7865) 0.6192] 1.1938) 0.534] 0.2013] 0.8084{ 1.3759} 0.6416} 0.824} 0.4897} 0.7037] 0.7724) 0.0797] 0.3811] 1.5812] 0.8653}
IM147 Phase 2 Regression Coefficients, HC, 1996 and Newer Model Year LDGV's
7 Regression Coefficients
Segment RMS — Reg. .
Number Error Constant {C1 ic2 ic3 \C4 ics iCé C7 ic8 ico C10 C11 C12 C13. C14 C15 C16 C17 C18 C19
peu ILOGV__ 196+ 0.06231] 0.01367]. 2.7246] a
fei2 ILOGV_ [96+ 0.03471] 0.01289]. 1.0956] 3.2189]. .
[B13 ILOGV__|96+ 0.02909] 0.00972). 0.7958] 2.4462] 2.013} .
[ei LDGV_ [96+ 0.02839] 0.00805}. 0.7353] 2.2615] 1.8431} 1.2834]. a
[B15 LDGV__|96+ 0.02692} _ 0.00988} 0.8024] 2.2022} 1.7552] -1.4225] 1.8689]. .
LOGV_ [96+ 0.02668} 0.01042! 0.7459] 2.4894] 1.4031} -0.8725] 2.5744] -3.5714]. a
[e17 LOGV__|96+ 0.0192] 0.00442) 0.8885} 1.711] 0.9711] 0.9804] 1.8655] -2.5604] 3.883].
[Bis ILOGV__[96+ 0.01449} 0.00185} 0.7232] 1.6635] 1.0171} 0.5616] 1.7084] -1.9289| 2.0901] 2.3918].
jei9 LDGV__]96+ 0.01043] 0.00074}. 0.92] 1.1468} 0.7967} 0.6882] 1.2025] -0.0572[ 0.6503] 2.302] 1.1734]



Final IM240 Cutpoints and IM147 Cutpoints Developed in This Study
(Composite/Phase 2 Cutpoints in g/mi, [M240 - IM147)

Model Years HC co NOx

, LDGV

1981-82 0.80/0.50 - 0.80/0.50 30.0/24.0 - 30.0/15.0 2.0 - 2.3/0.8

1983-85 0.80/0.50 - 0.80/0.50 15.0/12.0 - 16.0/8.0 2.0 - 2.3/0.8

1986-89 0.80/0.50 - 0.80/0.40 15.0/12.0 - 16.0/8.0 2.0 - 2.2/0.8

1990-93 0.80/0.50 - 0.80/0.50 15.0/12.0 - 15.0/8.0 2.0 - 2.2/0.7

1994-95 0.80/0.50 - 0.80/0.50 15.0/12.0 - 15.0/7.0 2.0 - 2.2/0.7

1996+ (Tier 1) 0.60/0.40 -0.80/0.30 10.0/8.0 - 15.0/5.0 1.5 - 2.2/0.6
LDGT1

1981-83 3.40/2.00 - 3.10/2.10 70.0/56.0 - 67.0/35.0 4.5 -4.9/1.8

1984-85 1.60/1.00 - 1.70/1.00 40.0/32.0 - 43.0/20.0 4.5-4.9/1.8

1986-87 1.60/1.00 - 1.70/0.80 40.0/32.0 - 43.0/20.0 4.5 - 4.6/1.7

1988-89 1.60/1.00 - 1.70/0.80 40.0/32.0 - 43.0/20.0 2.5-2.9/1.0

+ 1990-93 1.60/1.00 - 1.60/0.80 40.0/32.0 - 41.0/20.0 2.5 - 2.9/1.0

1994-95 1.60/1.00 - 1.60/0.80 40.0/32.0 - 41.0/20.0 2.5 -2.7/1.0

1996+ (Tier 1)

0.80/0.50 - 1.60/0.50

13.0/10.0 - 41.0/ 6.0

LDGT2

1.8 - 2.7/0.8

A

1981-83 3.40/2.00 - 3.10/2.10 70.0/56.0 - 67.0/35.0 4.5 - 4.9/1.8
1984-86 1.60/1.00 - 1.70/1.00 40.0/32.0 - 43.0/20.0 4.5 - 4.9/1.8
1987 1.60/1.00 - 1.70/0.80 40.0/32.0 - 43.0/20.0 4.5 -4.9/1.7
1988-91 1.60/1.00 - 1.70/0.80 40.0/32.0 -43.0/20.0 3.5 - 4.0/1.3
1992-93 1.60/1.00 - 1.70/0.80 40.0/32.0 - 41.0/20.0 3.5 - 4.0/1.3
1994-95 1,60/1.00 - 1.70/0.80 40.0/32.0 - 41.0/20.0 3.5 - 3.8/1.3
1996+ (Tier 1 0.80/0.50 - 1.60/0.50 15.0/12.0 - 41.0/ 7.0 2.0 - 3.8/0.9



-€€-

Figure 4-5

Failure Rate by Consecu

Final Cutpoints

HC Failure Rate by Vehicle Type

CO Failure Rate by Vehicle Type

Vehicle T ype

Vehicle Type

30.0% 25.0%
25.0% 20.0%
= 20.0% 3
2 Dist imi47 2 15.0% Gist imia7
2 15.0% 2nd iMi47 2 2nd imia?
= 10.0% (3rd 1M147 f 10.0% 3rd M147
= Te 8
5.0% 5.0%
0.0% 0.0% +
pc Lott LDT2 All pc LDT! LDT2 All
Vehicle Type Vehicle T ype
NOx Failure Rate by Vehicle Type Overall Failure Rate by Vehicle Type
25.0% 45.0%
40.0%
20.0% 35.0%
2 i 30.0%
e 15.0% i ise 147 Gist 1mi47
2 i tb2nd M147 25.0%
2 oom Hy a 50.0% 2nd iMi47
é i Ghsrd M147 15.0% (3rd M147
2 5.0% Ba 10.0%
i 5.0%
0.0% + a 0.0%
PC LoT! = LDT2 all Lori LoT2 all



Figure 5-5

Retest Algorithm During Second IM147 - LDGT1, LDGT2
Max CO Cutpoints

UM 147 TEST

“YES

NO

Segment 19

Vehicle Fast Passes After

NO

NO

Mode 7 Predicted HC < STD *1.1

Mode 7 Predicted HC - STD

or
Mode 19 Predicted HC < STD * 1.1

YES

NO

Mode 19 Predicted HC - STD

1.2

No

Mode 7 Predicted CO - STO

¥

Mode 7 Predicted CO< STD* 1
or

Mode 19 Predicted CO < STD* 1

YES

Mode 19 Predicted CO-STD 7°"

NO

Mode 7 Predicted NOx < STD * 1.2

y
NO RETEST

Note:

STD = Composite Standard for the specific pollutant.

and

Mode 19 Predicted NOx < STD * 1.2

YES

y
RETEST

Mode 7 Predicted = Predicted emissions after 7 segments using the composite regression
equations without error term included.

Mode 19 Predicted = Predicted emissions after 19 segments using the composite
regression equations without error term included.

-45-



IM147 Composite Regression Coefficients, NOx, 1996 and Newer Model Year LDGT2's
Regression Coefficients

Segment IRMS —|Reg.
Number Error Constant {C1 ic2 ic3 C4 ics icé C7 ic8 ico C10 C11 C12 1c13 C14 ic1s C16 C17 C18 |c19
Pt LOGT2 |96+ D576] 0.71370] 15.4054]. .
iP. ILOGT2 [96+ 0.33821] 0.37061] -21.218| 3.256}. bk
iP3. ILOGT2 |96+ 0.33307] 0.33021] -17.4893] 2.5344] 3.8906
jP4 ILOGT2 |96+ 0.30416] 0.30038] -14.5682] 2.2242] 11.6872] -12.2279|
iPS. ILDGT2 [96+ 0.29072] 0.28842] -17.6088] 1.8185] 9.3908] -10.8536, 4.734].
‘Z ILDGT2 [96+ 0.2883] 0.30337] -11.6965] 1.745] 9.8903] -10.7171] 5.8457] -7.1022]. a
iP7. LOGT2 [96+ 0.24718] 0.25136] -20.0849] 1.7221] 5.3396] -2.4287] -0.5675] -9.9875| 8.4455} .
PB LOGT2 [96+ 0.23492] 0.2519] -22.4763{ 1.5623] 5.2794] -3.3995] -2.4191] -8.9711] 6.8514] 7.9536].
iP ILOGT2 [96+ 0.23608] 0.24316] -22.27{ 1.5055} 5.3167] -3.4781] -3.0309] -9.6937] 6.8514] 8.1317] 3.1882 .
P10 ILOGT2 [96+ 0.2359] 0.23077] -20.9734] 1.5584] 5.6716] -3.4051| -4.2894] -8.7832] 6.8235] 6.3126] 1.8606] 3.9221]. a
Pit ILOGT2 {96+ 0.18371] 0.16444! 0.5675} 3.1636] -1.1457] -3.3023] 0.5825] 3.4287] 3.1947] 4.4137] 2.9738] 1.3085]. .
[P12 ILOGT2 [96+ 0.18578] 0.16354] 0.6169] 2.8048] -0.7267| -3.2312] 0.1935[ 3.5982] 3.0439] 4.2462] 2.9303] 1.2191] 0.2662]. .
[P13 LOGT2 [96+ 0.12127} 0.05817 0.6116] 4.4173] -3.0746] -1.9566] -5.1095] 2.1593] 3.236] 5.8951] 2.3221] 1.0537] 0.1452] 1.6328]. .
P14 ILOGT2_ [96+ 0.06953] 0.0183} 0.7373] 2.7372 0.575] -1.3744) -3.22| 2.844] -0.5548] 1.4212] 3.1253] 0.6404] 1.0543] 0.791] 1.4102! .
[P15 LOGT2 [96+ 0.05854] 0.01128] 0.7837] 1.5804] 1.7698} 0.6221] -2.7325| 2.4484] 0.2815] 0.0086] 2.3246] 0.5592] 1.0307] 0.7388| 1.1608] 0.7986) .
P16 ILOGT2 [96+ 0.05584] 0.00873] 0.7799] 1.6432] 1.5678] 0.4135] -2.4395{ 2.0557] 0.0994] 0.2982] 1.9349] 0.6182] 0.9336] 0.7441] 1.0662] 0.7148] 1.3475] a
P17 ILOGT2 [96+ 0.05251] 0.00847| 0.7816] 1.4739{ 1.5467] -0.3431[ -2.0093| 2.0146] 0.208] 0.4812) 1.4451[ 0.723{ 0.6611] O.7379[ 1.0331] 06536| 1.3684] 21742]. .
P18 ILOGT2 [96+ 0.01918] 0.00064) 0.7759] 1.0399] 1.1145] 0.2474] 0.0511] 0.6696] 0.6814] 0.6723] 1.4583] 0.5956] 0.9049] 0.7647] 0.8482] 0.6884] 1.3503] 0.5973] 0.8893].
[P19 LDGT 2: }96+ 0.00325] 0.00226} 0.7313} 0.8044] 0.6612] 0.6632} 0.833] 0.6804] 0.6611] 0.5647] 0.8016] 0.72] 0.6732] 0.7118] 0.7105] 0.7272] 0.7857] 0.7169] 0.7106] 0.6983]

IM147 Phase 2 Regression Coefficients, NOx, 1996 and Newer Model Year LDGT2‘'

Regression Coefficients

Segment RMS. Reg.
Number Error [Constant {C1 C2 ic3. C4 ics {Ce C7 ica ico C10 C11 C12 C13 C14 ic15 C16 C17 C18 C19
B11 ILOGT2 [96+ 0.25624] 0.28161f. 2.2649]. .
B12 ILDGT2 [96+ 0.25427] 0.26879}. 1.8738] 1.3389} .
Bis ILOGT2 |96+ 0.18429] 0.10134]. 1.6678] 0.8038] 2.0982]."
[B14 ILOGT2 {96+ 0.11213] 0.05754]. 1.2535] 1.3129] 1.0561] 1.9394]. .
jes, LOGT2 [96+ 0.08972] 0.02934! 1.1523] 0.9849] 1.0364] 1.4467] 1.3152].
[eis ILOGT2 [96+ 0.08172] 0.021614]. 1.1412] 0.9687] 1.0064] 1.2969} 1.0936] 2.5063}. .
[Bi7 LOGT2 [96+ 0.07624] 0.01744) 1.1996} 0.6743] 0.9948} 1.295} 1.0153] 2.0425] 3.1694] .
pis ILOGT2 [96+ 0.03345} 0.0095]. 0.929] 1.0616] 1.0355] 1.1034} 0.9077] 1.821] 0.1023] 1.2122]-
{B19 LDGT2 [96+ 0.00415} 0.0038]. 0.9821] 0.9175] 0.9698] 0.9467] 0.9808] 1.0103) 1.1121] 0.9437] 0.9983]



year distribution older when compared to SR99-10-02, which used data unbiased by
model year exemptions.

Figure 5-6 illustrates how the integrated criteria combined to produce emission results
and final test times. Note that only 5 of the 3347 vehicles were tested the full duration of
all three IM147 tests.

Figure 5-5

Integrated Test Results
Maximum CO Cutpoints Without CPP Limits

Fast Pass
2,490 Vehicles @
46.8 Seconds
_ Pass ] Fail.
8 Vehicles @ 189 Vehicles @
146.0 Seconds 132.0 Seconds
No Decision
660 Vehicles
Fast Pass Fast Fail
260 Vehicles @ 92 Vehicles @
199.8 Seconds 194.0 Seconds
Pass Fail
1 Vehicle @ 145 Vehicles @
292.0 Seconds 278.0 Seconds
No Decision
162 Vehicles
Fast Pass Fast Fail
40 Vehicles @ 115 Vehicles @
360.8 Seconds , 382.5 Seconds
Pass Fail
3 Vehicles @ 4 Vehicles @
438.0 Seconds 438.0 Seconds
TOTAL PASS = 2,802 TOTAL FAIL = 545

TOTAL FALSE FAILURES = 3

-50-


Table 5-4
Retest Algorithm Results

LDGV

LDGT1, LDGT2

Total Number of
Complete Tests 1567 1780
# of Failures Without 0 9
Reet Algorithwn 327 (20.9% of 1567) 273 (15.3% of 1780)
# of Correctly Identified 245 (74.9% of 327° 180 (65.9% of 273)
Failures’
# Failing After 1 IM147 94 (38.4% of 245)" 97 (53.9% of 180)
# Failing After 2 IM147s 151 (61.6% of 245)° 83 (46.1% of 180)
# of Passing Vehicles 0
Falsely Failed by Retest 0 0% of 1567) 0 (0% of 1780)

“Correctly identified failures” refers to those vehicles that were still failing at the end of the third
M147.

The number shown in parentheses is the number of failures without the retest algorithm.

‘The number shown in parentheses is the total number of IM147 Cycle 2 and 3 failures.

pass one of the subsequent IM147 tests, is denied a retest. As shown, there were no false
failures. Despite the conservatism evidenced by this result, average test time dropped
from 125 seconds to 96 seconds when the retest algorithm was added to the fast-pass
criteria.

Modal Fast-Fail Criteria

One of the requirements this work assignment was to develop modal fast-fail criteria.
Unlike the retest procedure, which can terminate the test at the ends of the individual
IM147 tests, the fast-fail algorithm can terminate tests during an IM147 test.

As with the retest algorithms, the fast-fail algorithm has the potential to falsely fail
vehicles that would otherwise pass the inspection in the algorithm’s absence. After
looking at the test data, it quickly became apparent that the data from the first IM147
were too unpredictable to fast-fail any significant number of vehicles during the first
IM147 without also significantly increasing false failure levels. While the retest
algorithm utilizes almost all of the first IM147 data before it makes a decision not to
retest a vehicle, the fast-fail algorithm must make essentially the same decision in a
smaller amount of time. For this reason, it was decided that the fast-fail algorithm would
not function before the second IM147.

In order to build on work already completed for the retest procedure, the fast-fail
algorithm for the second IM147 trace evaluates predicted emissions after segment 7 of

-46-



Failure Rate
Startup Cutpoints
HC co NOx OVERALL
Fail | Pass | % F: Fail | Pass | % Fai % Fail | Fail | Pass | % Fail

1600
LDGT1 75 _| 889 | 7.8% | 37 | 927 | 3.8% | 84 | 880 | 8.7% | 166} 798 | 17.2%
LDGT2 56 | 547 | 9.3% | 37 | 566 | 6.1% | 45 | 558 | 7.5% | 103 | 500 | 17.1%

All 311 | 3036} 9.3% | 253 | 3094 | 7.6% | 345 | 3002 | 10.3% | 685 | 2662 | 20.5%
Second IM147
LDGV 109 | 1671 | 6.1% | 133 | 1647 | 7.5% | 152 |1628}{ 8.5% | 291 | 1489 | 16.3%
LDGT1 51 {913 | 5.3% | 40 | 924 | 4.1% | 51 | 913 {| 5.3% | 115] 849 | 11.9%
LDGT2 32_| 571 | 5.3% | 31 | 572 }| 5.1% | 34 | 569 | 5.6% | 74 |} 529 | 12.3%

All 192 }3155} 5.7% | 204 |3143] 6.1% | 237 }3110] 7.1% | 480 | 2867] 14.3%
Third IM147
LDGV 106 {1674 | 6.0% | 13011650] 7.3% | 141 [1639 | 7.9% | 281 | 1499] 15.8%
LDGT1 52 [912 | 5.4% | 33 | 931 | 3.4% | 48 | 916 | 5.0% | 108} 856 | 11.2%

LDGT2 33 _| 570 | 5.5% | 30 | 573 | 5.0% | 28 | 575 | 4.6% | 67 | 536 | 11.1%
All 191 13156 | 5.7Y 193 13154 | 5.8% | 217 13130 SY 456 12891 | 13.6%

Failure Rate By Model Year Grouping - First IM147
Startup Cutpoints

HC co NOx OVERALL
Fail | Pass | % Fail | Fail | Pass | % Fail | Fail| Pass | % Fail | Fail| Pass | % Fail

B-3


1M147 Composite Regression Coefficients, HC, 1981 to 1985 Model Year LDGT2's
_ Regression Coefficients
Segment IRMS Reg.
Number Error Constant {C1 ic2 C3. \C4 ics iCé C7 ics ic |C10 C14 C12 C13 C14 Ic15 C16 C17 C18 C19
7 ToGt2 e185 | 0.92050] 1.08052] 14,1316 r
iP2. ILOGT2 |81-85 | 0.49433] 0.24032] 2.1062] 6.4379].
iPS ILDGT2 [81-85 | 0.47471] 0.22581 0.921] 5.3132] 2.7842]. 5
Pa LDGT2 181-85 | 0.43631] 0.17782] -1.7872] 5.4763] 0.3261] 3.8518]. .
IPS ILDGT2 [81-85 | 0.42908} 0.2114] -1.5166] 4.7372] 0.0105] 2.9709] 2.5245].
|PS- |LDGT2 [81-85 | 0.41687] 0.23473] -3.8569| 4.5338] 0.9009] 2.7375] 1.9498] 3.2209].
PZ |LOGT2 [81-85 | 0.41066] 0.23366) 4.2499] -1.1231] 2.3552] 1.2812} 2.4743] 2.1991 .
PB ILDGT2 }81-85 0.3672} 0.16688} -1.6179] 3.5835] -0.8816] 1.6341] 0.5481] 3.1967] 0.3748] 5.7221].
Po ILDGT2 |81-85 0.3566] 0.18117) 3.5055] 0.9418] 2.6495] -0.9804] 1.9891] 0.0338] 4.0901] 3.7721]. a
P10 LOGT2 [81-85 | 0.34151] 0.16655] -1.8705] 3.3075] -0.9637] 3.3159] -1.7691 1.43] 0.0942] 4.2235] 1.3603] 3.899]. .
Pat ILDGT2 |81-85 0.2931] -0.04548] 1.0746} 1.8029) -1.2122} 2.864] -0.6499} 0.7309] 0.2679} 3.2607] -0.113] 4.0698] 1.9325]. .
P12 ILOGT2 [81-85 0.2758] -0.01823] 0.7863] 1.8538] 0.984] 2.6077! -0.7763] 0.9299) 0.773] 2.1956] -0.3985] 4.4575] 0.873] 2.4503]. a
P43, ILOGT2 {81-85 | 0.21405{ -0.01142| 0.4126] 1.5075] -0.6583] 2.6141] -0.5064] 0.917} 1.449] -0.2757| 1.0903] 2.7846] 0.8509! 0.031 3.3522]. .
[P14 LOGT2 [81-85 | 0.17574] 0.01255] 0.2502] 1.2847] 0.0742! 1.733] 0.4325] 1.3417] 1.2187] 0.2611] 1.1197] 1.7719] 0.3758] 0.7006] 0.8312] 4.2084]. .
Piss. ILOGT2 {81-85 {| 0.15062] -0.02221] 0.8141 4.325] -0.0587] 1.4355] 0.2426] 1.2996] 1.3459} 0.694] 0.5531) 1.836] 0.507} 0.8254] -0.1245} 2.1165] 2.3277]. .
P16 ILDGT2 [81-85 | 0.14738} -0.02755] 1.0121] 1.3194] -0.0133] 1.4206] 0.2735] 1.0742] 1.3679] 0.895} 0.1188] 2.0783] 0.5591] 0.9525] -0.3444] 1.9718] 1.5317] 2.7401].
pP17 ILOGT2 ]81-85 | 0.11644} -0.04735] 1.4605/ 1.18447 0.1561] 0.8181] 0.2509] 0.6122] 1.2664] 1.4097] 0.3806] 2.1461] 0.6223] 0.997] -0.2513] 1.5706] 1.3565] 1.6797] 2.2361]. .
[P18 ILOGT2 [81-85 | 0.10411} -0.05875] 1.9421] 1.2153] 0.2284] 0.6248] 0.1763] 0.9731] 0.9762] 1.0664] 0.3199] 1.6679] 0.7965] 0.7901 0.01] 1.0203] 0.9553] 1.6582] 1.5344] 1.4185].
[Pi ILDGT2 [81-85 | 0.08606} -0.02861 1.9188] 1.1185] 0.3074] 0.5037] 0.6515] 0.9559] 0.5634] 1.1722] 0.4972] 1.0016] 0.7179] 0.584} 0.4013] 1.317} 0.5551] -0.9186} 1.2833] 0.9697} 1.3485]
IM147 Phase 2 Regression Coefficients, HC, 1981 to 1985 Model Year LDGT2's
Regression Coefficients

Segment RMS. Reg.
INumber Error Constant |C1 ic2 ic3 ica ics iCé C7 ics ic C10 C11 C12 ic13 C14 C15 C16 C17 C18 ic19
a ILOGT2 [81-85 | 0.53604] -0.06735]. 4.7635]. a
1812 ILOGT2 [81-85 | 0.50867] -0.03629) 2.9814] 3.9143] a
pis ILOGT2 |81-85 | 0.37516] -0.03573|_ 2.2833) -1.1494] 5.7782].
B14 ILDGT2 [81-85 | 0.31715] -0.05881 1.5613] 0.0866] 1.7691] 6.5887]. .
B15 ILOGT2 |81-85 | 0.29115] -0.05873]_ 1.6798] 0.2156] 0.484] 3.6579} 3.2814].
B16, LOGT2 }81-85 |] 0.28504) -0.05551}. 4.6963] 0.4373] 0.1942] 3.4658] 1.7593] 4.9957 .
B17 ILOGT2 [81-85 [ 0.19206] -0.07062]. 1.2418] 0.7375] 0.4405] 2.7184) 1.6887] 1.1505] 4.1633]. .
B18 ILOGT2 [81-85 | 0.17741] -0.07525]. 4.3222] 0.5566] 0.4852] 2.1651] 0.9953] 1.6325] 3.2854] 1.6783].
B19 ILDGT2 [81-85 [ 0.15121] -0.03936]. 1.1587] 0.4814] 1.1411] 2.1958} 0.3955} -1.7449] 2.7898] 1.0076] 1.9981]



combination of mass and concentration readings during specific modes of the IM147 test
to predict test outcomes.

In conversation with Gordon-Darby staff, however, Sierra learned that the suggested
retest logic would be difficult to implement. While gas concentrations could be
determined during the test, it would be far less work if all of the retest criteria referred
only to mass. Another concern, as previously mentioned, was that the modes did not
align with the fast-Bass segments. With these issues in mind, Gordon-Darby asked if
Sierra could find a more user-friendly retest logic.

While Sierra agreed to explore alternatives to concentration measurement in the retest
procedure, it was not clear that any viable alternatives existed since the justification for
using both concentration and mass made sense technically. While mass emission
measurements are useful to gauge emission performance relative to the actual cutpoint,
the concentration measurements provide a measure of emission performance less affected
by engine load than mass emissions. As a result, emissions concentration measurements
seemed especially relevant for measuring improvements in engine performance during a
transient test.

After some experimentation, Sierra settled on a procedure that utilizes the previously
determined fast-pass regression coefficients to predict whether a vehicle would benefit
from additional testing. In short, if the vehicle has not fast-passed the inspection by the
end of the 19th segment and the predicted emissions fall outside a tolerance level
allowing automatic retest, the emissions result predicted after segment 7 is compared to
the emissions result predicted after segment 19 to determine whether the vehicle
emissions are converging on the applicable cutpoint. Equation (5-2), named the
convergence ratio for this study, illustrates how emission convergence on the applicable
standard is determined.

Segment] - Comp_Std

Segment19- Comp_Std [5-2]

Where: Segment7 = Predicted emissions after segment 7 (Composite
Regression)

Predicted emissions after segment 19 (Composite
Regression)

Comp_Std = Composite Cutpoint for the specific emission

Xx = Empirically determined criteria

Segment19

According to this equation, the convergence ratio would be greater than one if the
emissions are converging on the cutpoint for vehicles where the emissions are above the
cutpoint. A convergence ratio less than 1, on the other hand, suggests that the emissions
are actually diverging from the cutpoint. With this in mind, it makes sense that a
conservative decision threshold for the retest algorithm would utilize a convergence ratio
greater than one for the HC and CO channels. In general, emissions of both of these

-40-


IM147 CUMULATIVE POSITIVE POWER (CPP) VARIATION CUTPOINTS

POWER VARIATION CUTPOINTS (mph2/sec)
CPP LIMITS

4,281.74
4,281.74
4,281.74
4,281.74
4,281.74
4,281.74
4,281.74
4,281.74
4,281.74
4,281.74

4,952.94
4,952.94
4,952.94
4,952.94
4,952.94
4,952.94
4,952.94
4,952.94
4,952.94
4,952.94

IM147 REFERENCE DATA

TIME SPEED CPP "BASE" MULT. VARYING

(sec) {mph) DELTA FACTOR DELTA Low HIGH
138 27.0 4617.34 335.6 1.000 335.6
139 23.5 4617.34 335.6 1.000 335.6
140 20.0 4617.34 335.6 1.000 335.6
141. 16.5 4617.34 335.6 1.000 335.6
142 13.0 4617.34 335.6 1.000 335.6
143 95 4617.34 335.6 1.000 335.6
144 6.0 4617.34 335.6 1.000 335.6
145 2.5 4617.34 335.6 1.000 335.6
146 0.0 4617.34 335.6 1.000 335.6

Cycle Sums. 4617.34 1.000

E-4


IM147 Composite Regression Coefficients, NOx, 1996 and Newer Model Year LDGT1's
Regression Coefficients

Segment RMS — |Reg. }
puree Error [Constant }C1 C2 ic3 C4 cS C6 Cc? ics ic |C10 cat C12 C13 \C14 c15. C16 C17 C18 ic19

7 TDGT1 [oer] 0.76000] 0.40362] 25.6075]. a
fP2 LDGT1_|96+ 0.46673] 0.18401] 12.1233} 3.0933]. >
iPS. ILDGT1 96+ 0.46374[ 0.16288] 12.749] 2.6461] 3.1462]. . i .
iP4 ILOGT1 [96+ 0.46514] 0.16338] 12.1028] 2.534] 2.3048] 4.9422]. a a
iPS ILOGT1_ [96+ 0.3791] 0.14765] 18.1804] 1.5554] -1.8687] 0.8812] 12.6955] :
iPS ILDGT1_ [96+ 0.38121} 0.14804] 18.193] 1.5564] -1.9029] 1.0072] 12.8058] -0.5274) a a
iP7 ILOGT1 |96+ 0.3284] 0.12947] 19.8606] 1.0854] -0.0898] -8.9504] 5.5367] -2.3077[ 9.8773]. a a
PS LDGT1_ {96+ 0.31982] 0.14076] 18.8826] 0.9749] 1.2061] -5.4908] 2.7593] -4.5733] 4.9464] 8.5778]. .
PQ ILDGT1 [96+ 0.32062] 0.15138] 18.5988] 0.8971] 1.8707] -4.2693] 2.4773 -3.9369] 4.9682] 9.0399] -2.7492]. a
P10. ILDGT1_|96+ 0.24391] 0.13494] 9.3292] 0.7629] 2.6156] -11.0979] 1.3803] 2.9882] 1.7874] 11.2329] -6.1993] 8.6929] .
P11 ILDGT1 [96+ 0.18099] 0.10785] 0.7068] 0.5528] 3.4731] 8.9995] 0.2732] 4.7615] 0.0782] 7.2458] -2.3664] 2.4504]; 1.28]. .
P12 ILOGT1 [96+ 0.17159] 0.09436] -0.6219] 0.5365] 2.5353] -3.0512] 0.8472] 1.9087] 0.1596] 6.9805] -2.7139] 3.1761]: 0.9818] 1.8863]. .
P13 ILOGT1 [96+ 0.12899] 0.07087] 1.7268] 0.3591 2536] 1.5628] 0.5764] -2.7771| 1.4626] 2.9783] -1.5186] 1.4469] 0.7662} 1.0775] 1.7081]. a
P14 ILOGT1 [96+ 0.10789] 0.06233] .-1.0403] 0.4537] 1.9505] 0.0036] 0.7498] 1.6955] 0.3857] 3.6594] -0.8929] 0.9085]0.7212] 0.47791 1.0256] 1.5265].
P15: LDGT1_ [96+ 0.06499] 0.02733] 0.039] 0.6102] 1.5778] 0.9818] 0.0528] 0.1089] 1.1186] 2.0576] -0.9366] 1.0631] 0.7396] 0.86] 0.6974] 0.7671] 1.3416].
P16 LDGT1 [96+ 0.05153] _0.02183[ 0.4719] 0.5741] 2.1518] 0.4167] 0.1408) 0.69] 0.7472] 2.1867] 0.0185] 0.9315]10.8149] 0.7897] 0.797] 0.6681] 0.6861] 2.2482]. a
P17 ILDGT1_ [96+ 0.04794] 0.01483) 0.175] 0.6024] 1.5796] 0.5006} 0.714] -1.1578] 0.8449] 2.0981} -0.6903] 1.1098] 10.7149] 0.9709] 0.8288] 0.7074] 0.7195] 1.7387] 1.6876].
P18 ILDGT1 [96+ 0.02782] 0.00211 0.6747] 0.6416] 1.5148] 0.1218] 0.5752] 0.4924] 0.7534] 1.8426] -0.2848] 0.9695]! 0.7007; 0.83] 0.7552] 0.7376] 0.7664] 1.1279] 0.8032 0.758}.
P19, ILDGT1 [96+ 0.00403] 0.00098] 0.7432] 0.7168] 0.7163] 0.6233] 0.7292] 1.0286] 0.6325] -0.9521} 0.6402] 0.7217}"0.7125] 0.7112] 0.7066] 0.7343] 0.6734] 0.7676| 0.7274] 0.7239] 0.7481)

1M147 Phase 2 Regression Coefficients, NOx, 1996 and Newer Model Year LDGT1's
Regression Coefficients .

Segment IRMS Reg. t
Number Error Constant |C1 ic2 C3 ica ics icé C7 ics |co C10 (an jC12 C13 ic14 \c15 C16 C17 C18 Ic19
(B11 ILOGT1_ [96+ 0.28558] 0.12907]. 62.2171]. .
[B12 ILDGT1_}96+ 0.24779] 0.09313]. 11.6406] 3.4138]. .
B13 ILOGT1_ [96+ 0.18123] 0.08816]. 1,252] 1.6471] 2.2975].
B14 ILOGT1 [96+ 0.15739] 0.06574]. 10.9727{ 1.3922] 1.7725] 1.7148]. =
B15, ILDGT1_|96+ 0.09057] 0.02458]. 1,0721] 1.1839] 0.9796} 1.028] 1.925]. .
B16 ILOGT1_}96+ 0.07613] 0.02379]. 11.1061] 1.2115 1.0191] 0.9869[ 1.1519] 2.6649]. .
B17 ILOGT1_ [96+ 0.07139} 0.00843]. 1.0352] 1.3901] 1.1173} 0.956] 1.1159] 2.3796] 2.0104 a
B18 ILOGT1_}96+ 0.04333} -0.00582]. 10.9841] 1.2998] 1.0418] 0.9729] 1.1585] 1.4094] 0.9684] 1.1153]
B19 LDGT1 [96+ 0.00565} 0.00178]. 0.9761] 0.9749[ 0.9793[ 0.9737] 0.9206| 1.0687] 0.9432] 0.9717] 1.0089]



IM147 Composite Regression Coefficients, NOx, 1996 and Newer Model Year LDGV's
Regression Coefficients

segment RMS — Reg.

Number Error Constant {C1 ic2 ic3. ics iCcé ic? ice ico |C10 C11 C12 C13 }C14 Cis C16 C17 ic18 C19
Pr TOGV__e6+ | 0.20065] 0.37011] 11.7730). C
[P2 ILOGV__[96+ 0.22677| 0.24297] -3.0327] 2.4026]. a
{P3 ILOGV__|96+ 0.21591, 0.222] -0.4545] 1.4421 5.448)

Ps ILOGV_ |96+ 0.21456) 0.2173] 0.7302] 1.4606} 4.3233]

iPS LOGV_ [96+ 0.20702] 0.21364] 1.0698] 1.2242] 3.8494! 4.3718]. a

iPS LOGV_ {96+ 0.20746] 0.2141] 0.6213] 1.2154} 3.841 4.2145] 0.9383]. 5

PZ. ILOGV__ [96+ 0.20382] 0.20285] 2.5242} 1.0207] 3.6971 3.1047] 0.3945} 2.683]. .
[Ps ILOGV__[96+ 0.19807] 0.19482] 4.1563} 0.9074] 3.4911 2.3803] -0.0536] 2.1013] 3.3361]. .
{Po ILOGV__ {96+ 0.19619] 0.19096] 1.87397 1.0364] 2.2721 2.141] -0.5391] 2.1297] 2.5845} 3.318]. .
[P10 ILOGV__ [96+ 0.1935] 0.18673] 1.493} 0.998} 2.3525] 2.0734) 0.242] 1.6938} 2.8999] 1.2192] 1.9845]. .
ypu LOGV__|96+ 0.15206} 0.11563] 6.6686] 0.3814 2.506 0.7057] -0.2193] 2.4209} 0.8267] 1.5013] 0.5498] 1.1615] 5
pPi2 ILDGV__|96+ 0.14242] 0.09025] 7.3264] 0.4696] 2.0937 0.3797] 0.6436] 2.1032] 0.8737| 0.8521] 0.6422] 0.984] 1.8962] .
| aE LOGV_ [96+ 0.1417, 0.0621] 4.8271] 0.6696] 2.0895} 0.9164] -0.565] 1.4492] -0.1321[ 0.12747 0.7773] 0.8735] 0.8257] 2.0634]. a
[P14 LOGV__ [96+ 0.0781] 0.04141 3.4134] 0.5916] 1.3165} 1.3675] -0.2089] 1.4165] 0.0989] 0.2565] 0.3993] 0.7549] 0.6841 4.288} 1.631) a

P15 ILDGV__ [96+ 0.05148] 0.02187] 2.4596] 0.6558] 1.17} 0.7565] 0.2435] 0.7215[ 0.5341] 0.5256] 0.6843] 0.7209] 0.6775] 1.1075] 0.9069} 1.122}.

[P16 ILOGV__[96+ 0.04778] 0.01987} 3.1395] 0.6091 1.203} 0.9077] 0.4201] 0.7848] 0.5764] 0.1403] 0.7957] 0.7431] 0.6929] 1.0516] 0.9276] 0.7894] 1.4029]. a

PAT LOGV__|96+ 0.04525] 0.01326} 3.3943] 0.6187] 0.8612) 1.1673] 0.4078] 0.7682] 0.5486] 0.2441] 0.6409] 0.7404] 0.726] 0.935] 1.0264] 0.7278] 1.3847] _ 1.368} .
P18 ILOGV__ {96+ 0.02353] 0.00022} 1.5178] 0.6979} 0.7651, 0.6991} 0.3426] 0.7998] 0.6796] 0.6094] 0.5847] 0.7147] 0.8429] 0.6623} 0.895} 0.6746] 0.9795] 0.7813] 1.0686].
[Pi9 ILOGV_ [96+ 0.004} 0.00038} 0.7846] 0.7189] 0.7176] 0.6851} 0.7511] 0.7279] 0.6879] 0.7681] 0.6817] 0.7174] 0.7317] 0.7291] 0.7203] 0.7125] 0.7336] 0.7584] 0.7113] 0.7181

IM147 Phase 2 Regression Coefficients, NOx, 1996 and Newer Model Year LDGV's
‘Regression Coefficients

Segment RMS Reg.

Number Error \Constant {C1 ic2 ic3 CS icé ic7 iC8 ico C10 C11 C12 C13 C14 C15 C16 C17 ic18 ici9
Bit LOGV_ [96+ 0.20953] 0.18322]. 1.6724]. a

B12 ILOGV__}96+ 0.19448} 0.14434]. 1.3687] 2.7382]. a

Bi ILOGV__}96+ 0.155} 0.09509]. 1.1262| 1.2948] 2.6037]. .

B14 ILOGV__ {96+ 0.10659] 0.06065]. 0.9685] 0.9898] 1.6977] 2.2378]. .

B15 ILOGV__ |96+ 0.06912] 0.03298] 0.9526] 0.8932] 1.4872 1.252] 1.5305}

[B16 ILOGV__}96+ 0.0649] 0.03049]. 0.9772] 0.9069] 1.4267] 1.2614] 1.1325] 1.7334}. a

{ei17 ILOGV_ [96+ 0.06203} 0.02204]. 0.9694] 0.9325] 1.3118] 1.3384] 1.0793] 1.6717} 1.6273]. .
[eis LDGV__ [96+ 0.03172] 0.00214}. 0.9508] 1.1071] 0.9089] 1.1952] 0.9329} 1.2914] 1.0022] 1.4675].
[B19 ILDGV_ [96+ 0.00523} 0.00111]. 0.9672] 0.98747 0.9855] 0.9791} 0.9675] 0.9838} 1.0369] 0.9689] 0.975.



1M147 CUMULATIVE POSITIVE POWER (CPP) VARIATION CUTPOINTS

IM147 REFERENCE DATA

TIME
(sec)

SPEED
(mph)
22.2
24.5
24.7
24.8
24.7
24.6
24.6
25.1
25.6
25.7
25.4
24.9
25.0
25.4
26.0
26.0
25.7
26.1
26.7
27.3
30.5
33.5
36.2
37.3
39.3
40.5
42.1
43.5
45.1
46.0
46.8
47.5
47.5
47.3
47.2
47.2
47.4
47.9
48.5
49.4
49.5
50.0
50.6
51.0
51.5
52.2

CPP

1283.73
1391.14
1400.98
1405.93
1405.93
1405.93
1405.93
1430.78
1456.13
1461.26
1461.26
1461.26
1466.25
1486.41
1517.25
1517.25
1517.25
1537.97
1569.65
1602.05
1787.01
1979.01
2167.20
2248.05
2401.25
2497.01
2629.17
2749.01
2890.77
2972.76
3047.00
3113.01
3113.01
3113.01
3113.01
3113.01
3131.93
3179.58
3237.42
3295.98
3335.42
3385.17
3445.53
3486.17
3537.42
3610.01

POWER VARIATION CUTPOINTS (mph2/sec)
CPP LIMITS

988.97
1,075.55
1,087.02
1,094.73
1,094.73
1,094.73
1,094.73
1,118.02
1,141.83
1,149.88
1,149.88
1,149.88
1,157.84
1,177.85
1,206.47
1,206.47
1,206.47
1,227.18
1,256.78
1,287.13
1,440.65
1,600.89
1,759.09
1,830.90
1,962.29
2,047.41
2,163.02
2,269.18
2,394.15
2,470.24
2,540.32
2,603.92
2,603.92
2,603.92
2,603.92
2,603.92
2,628.37
2,677.11
2,734.73
2,793.27
2,835.88
2,887.49
2,948.47
2,992.84
3,046.58

1,578.49
1,706.73
1,714.94
1,717.13
4,717.13
1,717.13
1,717.13
1,743.54
1,770.43
1,772.64
1,772.64
1,772.64
1,774.66
1,794.97
1,828.03
1,828.03
1,828.03
1,848.76
1,882.52
1,916.97
2,133.37
2,357.13
2,575.31
2,665.20
2,840.21
2,946.61
3,095.32
3,228.84
3,387.39
3,475.28
3,553.68
3,622.10
3,622.10
3,622.10
3,622.10
3,622.10
3,635.49
3,682.05
3,740.11
3,798.69
3,834.96
3,882.85
3,942.59
3,979.50
4,028.26

"BASE" MULT. VARYING
DELTA FACTOR DELTA LOW HIGH

93.3 3.159 294.8
101.1 3.121 315.6
101.8 3.083 314.0
102.2 3.045 311.2
102.2 3.045 311.2
102.2 3.045 311.2
102.2 3.045 311.2
104.0 3.008 312.8
105.8 2.970 314.3
106.2 2.932 311.4
106.2 2.932 311.4
106.2 2.932 311.4
106.6 2.894 308.4
108.0 2.856 308.6
110.3 2.818 310.8
110.3 2.818 310.8
110.3 2.818 310.8
111.8 2.780 310.8
114.1 2.742 312.9
116.4 2.705 314.9
129.9 2.667 346.4
143.8 2.629 378.1
157.5 2.591 408.1
163.4 2.553 417.1
174.5 2.515 439.0
181.5 2.477 449.6
191.4 2.439 466.2
199.8 2.402 479.8
210.1 2.364 496.6
216.1 2.326 502.5
221.5 2.288 506.7
226.3 2.250 509.1
226.3 2.250 509.1
226.3 2.250 509.1
226.3 2.250 509.1
226.3 2.250 509.1
227.6 2.212 503.6
231.1 2.174 502.5
235.3 2.136 502.7
239.6 2.098 502.7
242.4 2.061 499.5
246.0 2.023 497.7
250.4 1.985 497.1
253.4 1.947 493.3
257.1 1.909 490.8
262.4 1.871 491.0

E-2

3,119.03

4,100.99


DISCLAIMER

Although the information described in this report has been funded wholly or in part
by the United States Environmental Protection Agency under Contract No. 68-C7-0051,
it has not been subjected to the Agency’s peer and administrative review and is being
released for information purposes only. It therefore may not necessarily reflect the
views of the Agency and no official endorsement should be inferred.


IM147 Composite Regression Coefficients, CO, 1983 to 1985 Model Year LDGV's
Regression Coefficients.
Segment IRMS ~—{Reg. .
Number Error Constant {C1 ic2 ic3 C4 ics C6 ic7 ic8 ico C10 C11 C12 C13 C14 C15, C16 C17 C18 C19
Pt CGV [65-85 | 505007] 8.71077] 10.0773). r
PZ. LOGV__ [83-85 | 4.39498] 6.05704] 2.7834] 2.4038) .
PS ILOGV__ [83-85 | 3.79704] 5.05452! 2.132] 1.2291] 4.6404].
iP4 LOGV__}83-85 | 3.62371] 4.67806] 0.5952] 1.3042) 2.354] 5.4862]. .
PS ILOGV__ [83-85 | 3.59484] 4.60345] 0.0971] 1.2134) 2.0391] 4.9125] 1.269} .
iPS LOGV__ [83-85 | 3.59268] 4.59476] 0.3543] 1.2106] 2.0275] 4.62] 1.1146] 1.0934].
fez ILOGV__ [83-85 [| 3.44031] 4.32493] -0.5781] 1.2446] 0.8896] 4.3893] 0.6174] 1.6598] 1.5993]
{Ps ILOGV__ [83-85 | 3.36343} 4.2564] 0.0459] 1.093} 0.9849] 3.5699] 0.5789] 0.9952] 1.2147] 1.5455].
7Po. ILOGV__ [83-85 [3.33029] 4.1852] 0.1659] 1.1245] 0.9751] 3.3158] 0.1104] 0.6868] 1.2398] 1.3047] 2.0264] a
[P10 ILOGV__ [83-85 | 3.29987] 4.12599] 0.0405] 1.1476] 0.8196] 3.3153] -0.2824] 0.8442] 1.1246] 1.22] 1.1895} 1.552}
| Ga ILOGV__ {83-85 | 2.20291] 2.25511 0.3918] 0.6788] 0.9189] 1.6168] 0.932} 1.9937] 0.5168] 0.7853} 1.9632] 1.1107] 0.7316]. .
P12 ILOGV__ 183-85 | 1.87972] 1.58471 0.5439] 0.7415] 0.8536] 1.6537] 0.727} 1.925] 0.7033] 0.7588] 1.3654] 1.0997] 0.6415] 1.1026]. a
[P13 LOGV__ [83-85 [1.28242] 0.91688] 0.7111] 0.7088] 0.7627/ 1.8183] 0.2958] 1.6032] 0.6711] 0.6173] 1.5002] 0.6472] 0.6692] 0.8018] 1.9269]. .
P14 LOGV__ 183-85 | 1.03127] 0.58797] 0.6489] 0.7423] 0.3578] 1.9308] 0.4662] 1.5147{ 0.8643] 0.6344] 1.5828] 0.5435] 0.6488] 0.7632] 1.5457] 1.1794]. a
P15: ILOGV__ {83-85 | 0.69673] 0.34058] 0.7164] 0.7324] 0.354) 1.9052] 0.5776] 1.1365) 0.8592] 0.5437] 1.0426] 0.8335] 0.6765] 0.708] 0.9536] 0.8489] 1.4982]. .
Ps ILOGV__ 183-85 0.6836] 0.3288] 0.6397] 0.7348] 0.4045] 1.8616] 0.5991] 1.1119] 0.8675| 0.5275] 0.9881] 0.8608] 0.6754] 0.7075] 0.9317] 0.828) 1.269] 0.931 A
[Piz ILOGV__ [83-85 | 0.61016] 0.1403] 0.7783] 0.7181] 0.5497] 1.5209] 0.4902] 1.1225] 0.8292] 0.592] 0.9313] 0.8148] 0.6905] 0.7307] 0.9393] 0.8363] 1.1503] 0.6464] 1.6992]. .
pPis ILOGV__ [83-85 | 0.35809] 0.01542] 1.0268] 0.6988] 0.5932] 1.2395] 0.5512] 1.1677] 0.6596] 0.7122] 0.8022] 0.6293] 0.6933] 0.7668] 0.6573] 0.6294| 1.0268] 0.6176] 1.4839] 0.9529).
[Pi9 LOGV_ 83-85 | 0.25947[ 0.01001] 1.0127[ 0.7023] 0.6463] 1.1344] 0.5259] 1.0927| 0.704] 0.6265[ 0.7924] 0.6521] 0.7068} 0.755] 0.7028] 0.7409) 0.743} 0.4739} 1.3579} 0.7294] 0.7327]
IM147 Phase 2 Regression Coefficients, CO, 1983 to 1985 Model Year LDGV's
Regression Coefficients
Segment IRMS ~—-|Reg.
Number Error Constant }C1 ic2 ic3 ica ics iCcé C7 ica ico C10 C11 C12 ic13 C14 C15, C16 C17 C18 ic19
| CX ILDGV__ [83-85 | 4.69212] 5.7524]. 4.0243].
fei2 LOGV_ 183-85 | 2.83315] 3.12116]. 0.9251] 1.6564]. .
fe13 ILDGV_ [83-85 | 1.86354] 1.56942]. 0.9295] 1.1338] 2.8182]. a
{B14 LDGV_ [83-85 | 1.55825] 1.15742]. 0.9063] 1.0736] 2.325] 1.5629].
[eis LDGV_ |83-85 | 1.07233] 0.69467]. 0.943] 0.9688] 1.4054] 1.0868] 2.1751]. a
B16 LOGV_ [83-85 | 1.05389] 0.68187]. 0.9437] 0.9647] 1.3864] 1.0625] 1.8313] 1.3471]. .
B17 ILOGV_ [83-85 | 0.89995] 0.27297]. 0.9612] 0.9964] 1.3316] 1.1059] 1.5922] 0.8992] 2.8516].
B18 ILOGV_ [83-85 | 0.54856] 0.0412]. 0.9415] 1.0459] 0.8624) 1.115] 1.3817] 1.1042) 2.1846] 1.3008].
B19 LOGV_ 83-85 0.4078} 0.005]. 0.9594] 1.0223] 0.9206] 0.9952] 0.9616] 0.8242] 1.9266] 0.9809] 1.0636]



2. To further accommodate wider allowable variations (on a percentage basis)
in second-by-second CPP at the beginning of the transient IM147 test, a
“CPP Multiplier” factor was applied that widened the allowed CPP limits
progressively from the end of the test to where limits begin at t=30 seconds.
From its maximum value beginning at t=30 seconds, the CPP multiplier
factor was linearly decreased to a value of unity (i.e.5 1.0) at t=146. In other
words, at the end of the test, the CPP limits were set equal to the composite
CPP limits. Furthermore, this linear narrowing was applied only over the
acceleration sections of the IM147 trace, during which the reference CPP is
increasing. During cruise and deceleration periods, the limit widths were
held constant (as the reference CPP also remains constant).

This latter improvement to the methodology employed in the 1998 IM240 study, in
conjunction with the use of CPP instead of CPKE, enabled second-by-second-IM147 CPP
limits to be specified so variation limit aborts were not falsely triggered during
deceleration and cruise portions of the transient test. Note that second-by-second CPP
variation limits developed in this manner can still be exceeded during cruise and
deceleration events, signaling tests that should be aborted. However, falsely triggered
“anomalous” exceedances that occur from the use of a PKE-based metric are eliminated
under this modified approach.

Figure 6-3 illustrates this modified second-by-second CPP-based variation limit concept.
The thick solid line shows the reference CPP over the IM147 test; the thinner solid lines
represent the lower and upper CPP limits established as described above.

These CPP traces are plotted against the left axis. Speed, indicated by the dashed line, is
plotted against the right axis. Note that the CPP limits can be held constant during cruise
and deceleration periods. Thus, variation limit exceedances in these intervals are real,
rather than anomalous artifacts of the statistical metric.

Figure 6-3
_ Illustration of Second-by-Second IM147 Cumulative Positive Power Variation Limits
6,000 60
[ Raf. CPP. ‘Lower Limit ‘Upper Limit - =
F 5.000 50
a
a
4,000 40
: z
é oe
e 3,000 3 3
ra =
2 ‘
% 2000 20%
8 7
é ;
g
10
6‘ CPP limits constant during "
cruise/deceleration period id
oO 0
150

oO 10 20 30 40 50 60 70 80 90 100 110 120 130 140
. Time (sec)

-63-


Table 5-6
Comparison of Integrated Algorithms (No CPP) vs. Standard IM147
Impact on Test Time and Excess Emissions Lost

Sample Mean Test Mean Test % Excess Emissions Identified”

Size” Standard” Algorithms’ HC co NOx

1981-82 : 87.6% 100.0%

1983-85 | 228 311.2" 169.4 100.0% | 97.4% 100.0%

LpGv 1986-89 | . 425 248.4 112.0 99.3% 97.5% 100.0%

1990-95 | 952 184.8 78.3 96.9% 85.3% 91.4%
1996+ 70 154.3 37.1 : - -

All 1780 221.0 100.0 98.6% 93.9% 97.6%

1981-85 | 260 306.6 158.7 75.71% 97.5% 98.4%

1986-89 | 222 230.8 101.6 92.4% 91.0% 99.6%

LDGT1 | 1990-95 | 450 173.3 59.2 0.0% 0.0% 100.0%

1996+ 32 155.1 31.6 - - 100.0%

All 964 221.9 94.9 79.3% 94.4% 98.7%

1981-85 94 307.5 158.8 100.0% } 100.0% | 100.0%

1986-87 64 253.2 101.4 100.0% | 100.0% 70.8%

LDGT2 | 1988-95 | 427 166.5 54.2 100.0% | 58.0% 39.7%
1996+ 18 146.0 28.0 - . -

All 603 197.1 74.7 100.0% | 98.5% 86.0%

| Weighted Average | 3347 217.0 94.0 94.4% | 95.4% 95.7%

The excess emissions identification shown in the above table is down from the
identification of SR99-10-02', which identified 99.6% of the HC emissions, 98.2% of the
CO, and 99.9% of the NOx with the fast-pass and retest algorithms enabled. However,
direct comparison of these results may not be relevant for several reasons. First, the
previous study measured excess emissions captured against the Final Cutpoints rather
than the Max CO cutpoints developed for this study. Second, because fast-fail was
created for this study, it was not included in the previous study results. Third, since the
retest algorithm has been modified as part of this study, it will have a different effect on
the results. In the previous study, the retest algorithm improved excess emission
identification by 2.7% versus 1.1% for this study. Lastly, most of the vehicle data used in
this study were collected with the model year exemptions in place. This skews the model

“Mean test time standard refers to the average dynamometer test time without the algorithms enabled.
This was determined using the 3347-vehicle sample.

**Percent of IM240 (Max CO) excess emissions identified with the integrated algorithms enabled. This
was determined using 835-vehicle sample.

-49-


IM147 Composite Regression Coefficients, CO, 1981 to,1985 Model Year LDGT2's
Regression Coefficients
Segment IRMS — Reg. .
Number Error Constant |C1 C2 ic3. C4 (os C6 C7 C8 ico C10 Cit C12 C13. C14 CAS. C16 C17 C18 C19
PT OGT2 [e125 | 16.0708] 25.7173] 7.5612] q
iP2 ILOGT2 [81-85 | 12.3795] 12.9457 3.4376] 3.6374]. . a
iPS. ILOGT2 [81-85 | 11.5598] 10.6748] 1.0212] 2.3546] 5.6508]. a i
iP4 ILOGT2 |81-85 | 11.1259] 9.72659] -0.5375] 2.5842] 2.5322] 5.5716] A a
iPS LOGT2 [81-85 | 11.1623] 9.79162! 0.516] 2.5693] 2.415} 5.44] 0.2344) 1
PB ILOGT2 ]81-85 | 10.5815] 10.5149] -5.1729] 2.5377] 2.4642] 3.5854] -2.439] 9.5373]. a
iP7. ILOGT2 [81-85 | 10.5066] 9.63447] -5.1298] 2.3487] 2.1758] 3.1663] -2.7919] 8.8001] 1.9586]. a
PS ILOGT2 [81-85 | 10.4986} 9.46232] -4.5397| 2.2916] 2.2264] 2.9027] -2.935] 8.1749] 1.56| 1.182]. . 1
iPo ILOGT2 81-85. 10.534} 9.45853] 4.6546] 2.2745] 2.3176] 2.9007} -2.96] 8.3683] 1.5537] 1.29027 0.3956]. a *
P10 ILOGT2 {81-85 | 10.5104] 9.14764] -4.0569] 2.3269] 1.7791] 3.0871 7.9403] 1.0377] 1.7694] -1.8655] 2.3687]. |.
P41 LDGT2 [81-85 | 6.70683} 1.00692] -0.9625] 0.8295] 2.9212} 0.599] 6.2535] 1.0658] 1.6592] -0.2765] 1.4949] 1.0962]. a
P12 ILOGT2 {81-85 | 5.84441} 1.64077] -0.9579] 0.8376] 2.0719] 1.2901 6.1373] 1.3423] 1.3993] -0.7286] 1.7599] 0.6956] 1.5499]. 5
[P13 ILOGT2 [81-85 | 3.26892} 0.61651 1.3225] 0.6113] 0.8575] 1.1276] 0.3105} 2.4597] 0.7224] 0.09847 0.8037] 1.0488] 0.8185] 0.7462] 2.8417].
P14 ILOGT2 [81-85 | 2.22826 0.53462] 1.2795] 0.6654] 0.9477] 1.2845] 0.5586] 1.7615] 0.7222] 0.4197 1.0753] 0.8413] 0.6372] 0.838] 1.8815] 1.2908] a
[P15 ILOGT2 [81-85 | 1.43068) 0.16253] 1.4763] 0.6916] 0.8805] 1.0498] 0.7614] 1,593] 0.6161] 0.2931] 0.9481] 0.6408] 0.759] 0.6649] 1.0119] 0.9471] 1.4604]. .
P16 LDGT2 [81-85 | 1.39761] 0.09202] 1.4913] 0.6618] 0.9629] 0.9832] 0.6115] 1.4561] 0.5963] 0.4052] 0.7374] 0.7242| 0.7696] 0.6414] 0.9815] 0.9546] 1.1597] 1.2077]. .
P17. LDGT2 [81-85 | 1.30226] 0.04105] 1.2639] 0.683] 0.8813] 0.8942] 0.7966 1.641] 0.6092] 0.4611] 0.6586] 0.3214] 0.791] 0.5936] 0.982] 0.9715] 0.9473] 1.1888] 1.5709]. a
P18 LDGT2 [81-85 | 0.98169] -0.23459] 1.3573] 0.7081] 0.8658] 0.8401] 0.6551] 1.4524] 0.5751] 0.6074] 0.7206] 0.4241] 0.7757] 0.6063] 0.7425] 0.8257] 0.6673] 1.0036] 1.3323] 0.8645.
P19 LDGT2 [81-85 | 0.50955] -0.1442] 1.2761] 0.7138] 0.6615] 0.8723] 0.6415] 1.4442[ 0.6884] 0.5997] 0.8506] 0.4704] 0.7392] 0.6704] 0.7048] 0.6711| 0.7256] 0.4192] 1.1487] 0.8223 0.7477]
IM147 Phase 2 Regression Coefficients, CO, 1981 to 1985 Model Year LDGT2's
Regression Coefficients

Segment RMS Reg. .
Number Error Constant |C1 ic2 ic3 C4 cS C6 C7 ey ico |C10 C11 C12 C13. C14 C15. C16 C17 C18 Ic19
| [LoGT2 [61-65 | 130662) 10.119 T5327]. 5
[ei2 ILOGT2 [81-85 9.5454 6.3219}. “| 0.9685] 2.6838]. .
1813 LOGT2 [81-85 | 4.74433] 1.25606}. i 1.0757} 0.9968] 4.0692].
B14 ILOGT2 |81-85 | 3.50162] 1.57213) ‘ 0.8395} 1.1681] 2.9369] 1.7248]. .
EXE ILOGT2 181-85 | 2.41832] 0.59238}. i] 1.0109] 0.918} 1.4255] 1.2686] 2.1564]. .
B16 ILOGT2 [81-85 | 2.31489] 0.51543]. ! 1.0284] 0.8761] 1.3639] 1.2777] 1.5125] 2.5385]. a
B17 ILOGT2 [81-85 | 2.02742] -0.13816]. J 1,0611] 0.7865] 1.3133] 1.3648] 1.0331] 2.4287] 2.7411, .
B18 ILOGT2 [81-85 | 1.59498} -0.3696]. 1.0435] 0.8038] 0.976] 1.1376] 0.9439] 2.1064] 2.2992] 1.2758]
B19 LDGT2 ]81-85 | 1.13333) 0.2565]. a 1] 0.8755} 0.9246] 0.9223] 0.7526] 1.3895] 2.0596] 1.205} 1.0168}



Noting this effect, Gordon-Darby staff, after reviewing the raw data, suggested that the
T90 time alignment overcompensated and thus caused short-duration emission events
during a transient cycle to appear to precede the triggering speed event. Given the
interaction of the transport time with the bench response time, they suggested that a more
appropriate alignment measure is T50 response time, which would be more conservative
and alleviate the aforementioned problem.

To accommodate this change, all the existing data, which were previously aligned using
T90 alignment by Gordon Darby, had to be realigned to T50 response times. This
included data collected specifically for this study. Per Gordon-Darby staff, this change
required shifting data for each of the channels (HC, CO, NOx, and CO,) four seconds
later relative to the speed signal. This was accomplished by adding four seconds of data
to the front of each test, in which it was assumed that the modal emissions for the entire
period were identical to those measured during the “previous first second” (i.e., now
second 5) of the test. This assumption is considered reasonable since the vehicle is at idle
during this entire period.

-23-


3. TEST DATA

Data used in this study are divided into three distinct groups consisting of 304, 543, and
2518 vehicles. Vehicles in each of the groups were given three consecutive IM147 tests
regardless of the result.

The 304-vehicle sample was collected by Gordon Darby for Task 1 of Work Assignment
SR99-10-02' at the Gordon Darby I/M lanes in Phoenix, Arizona, during March 1998.
The data included 193 cars and 111 light-duty trucks tested over triplicate IM147 tests
followed by a full IM240 test as illustrated in Figure 3-1.

Figure 3-1
Test Sequence Used to Investigate Triplicate IM147 Tests in Arizona Test Lanes

60

Speed (mph)
8

20 |
10 4
}t- >| -t- >t > jt >|
0
5-15 minutes First Second Third Hot
in queue M147 test IM147 test (M147 test (M240 Test
Time

-19-


IM147 Composite Regression Coefficients, CO, 1981 to 1982 Model Year LDGV's
Regression Coefficients
Segment RMS — |Reg.
iNumber Error |Constant {C1 ic2 ic3 C4 ics C6 C7 C8 ico C10 C11 C12 C13 C14 IC1S C16 C17 C18 C19.
P+ LOGV_ [81-82 | 7.99839] 11.8892] 10.2939]. a 1
iP2 ILOGV__ {81-82 | 6.58035] 8.47137] 3.9709] 2.1482]. a i
iP3 LOGV__|81-82 | 5.95523] 7.35499] 2.3706] 1.1885] 4.3273] . *
iP4 ILOGV__ [81-82 | 5.84107] 6.94384] 0.9352] 1.4] 2.5147] 4.4184]. a a
iPS ILOGV__|81-82 | 5.67429] 6.92733] 0.9051] 1.3052] 1.0082] 3.8467] 3.2208]. _ . 4
PS ILOGV__|81-82 | 5.60045] 7.2234] -1.4742] 1.4463] 0.1894] 3.6997] 2.3343] 2.4304]. .
iP7 ILOGV__[81-82 | 5.42769] 6.90375] -1.2377[ 1.0873] 0.2076] 2.5448] 1.0667] 3.0396] 2.007]. .
iP8 ILOGV__ [81-82 | 5.17703] 6.82475} -1.395] 0.841 0.567] 2.793] 1.0784] 2.1889] 0.6894] 3.583]. a iy
Po ILOGV__ [81-82 | 5.11543] 6.5602] -1.8042/ 0.9128] -0.049] 1.6424] 0.3327] 1.1075] 0.5581 3.277 3.6636]. at.
P10 LOGV_ [81-82 | 5.09166] 6.36664] -1.8837/ 0.9302] -0.4412] 1.7282] 0.1522] 1.3964] 0.1539] 3.943] 1.4796] 2.6588].
P14 LOGV_ [81-82 | 3.36176] 2.42133] -1.7143] 0.9431] 0.38447 -1.067] 0.3333] 1.7489] 0.1534] 1.8436] 3.6102] 25886] 0.995].
P12 ILDGV__ [81-82 | 2.45131] 1.51191] -0.6053} 0.8264] 0.3739] 0.1848] 0.3965} 4.281] 0.4926] 1.5431] 2.6906] 2.8681] 0.6782] 1.3748]. .
P13, ILOGV__ [81-82 | 1.84222] 0.81857] 0.547] 0.7456] 0.5366] 1.486] -0.1283] 1.4627] 0.5971] 1.0752] 1.2221] 2.1211] 0.6873] 0.8834] 2.0965]. 5
P14 ILDGV__ [81-82 | 1.30098] 0.6002} 0.761] 0.7639} 0.4632] 0.8416] -0.3448] 2.0537] 0.7512] 1.0435] 0.8919] 2.1412] 0.6644] 0.6957] 1.6479] 1.2133] a
P15, LOGV_ {81-82 | 0.99586] 0.48274] 1.4122] 0.6691] 0.5819] 1.4446] -0.1117] 1.6713] 0.9146] 0.8677] 0.8203] 0.9725] 0.6913] 0.6885] 1.0384] 0.8235] 1.3336]. a
P16 LOGV__|81-82 | 0.92334] 0.37973] 1.5086] 0.6203] 0.6059] 1.5811] 0.0371] 1.4292) 1.0143] 0.8284] 0.7229] 0.8179] 0.6963] 0.6941] 0.9588] 0.9625] 0.9091| 1.8136].
fPi7 ILOGV_|81-82 | 0.84728] 0.19448] 1.3677] 0.6505] 0.544] 1.2293] 0.3115] 1.4583] 0.9885] 0.8554] 0.4817] 0.4999] 0.7063] 0.6726] 0.9648] 0.9625] 0.9546] 1.1516] 2.0177]. a
fP1s ILDGV__ [81-82 | 0.52289] 0.04744) 41,1672] 0.7297] 0.3484] 1.2653] 0.3087] 1.1707] 0.66437 0.8253] 0.7848]. 0.8903] 0.6988] 0.7311] 0.7096] 0.8592] 0.8362] 1.0156] 1.7867] 0.8424)
pPis ILOGV_ {81-82 | 0.31963] -0.00493] 1.0652] 0.7221] 0.5675] 1.1578] 0.3797] 0.9834] 0.7238] 0.6876 0.994} 0.7578] 0.7005] 0.7001] 0.7374] 0.7792[ 0.7058} 0.244} 1.232] 0.646} 0.9011
IM147 Phase 2 Regression Coefficients, CO, 1981 to 1982 Model Year LDGV's
Regression Coefficients
Segment IRMS — Reg. . . :
Number Error IConstant [C1 ic2 C3 C4 ics C6 C7 ic8 ico ic10 C11 C12 C13 C14 [C15 C16 C17 C18 ic19
Bit ILDGV__ {81-82 | 7.87561] 8.04896; 7 a | 1.2099]. .
B12 ILOGV_ [81-82 | 4.28681] 4.12849]. i] 0.9421) 1.967]. .
{B13 ILOGV_ {81-82 | 2.91132] 1.85708]. 0.9717] 1.0199] 3.5026}. a
B14 ILOGV_ {81-82 | 2.35783] 1.43426]. 0.9278] 0.7999] 2.9182] 1.5911
[B15 ILOGV__|81-82 | 1.81322] 0.97781 s | 0.9942[ 0.81841 1.529} 0.995{ 2.1475]. .
B16. ILOGV__ {81-82 | 1.64673] 0.74808]. 1.0076] 0.8215] 1.3625] 1.3316] 1.1639] 3.4285]. .
B17 ILOGV__ [81-82 | 1.35105] 0.12268]. 71,0045} 0.858] 1.3822| 1.29] 1.2026] 1.7464] 4.1093} .
B18 ILOGV_ [81-82 | 0.83894] -0.09966]. 0.9679] 0.9648] 0.9202] 1.1549] 1.0845] 1.5149] 3.5169] 1.2191].
B19 ILOGV_ [81-82 | 0.57427] -0.10409]- 0.9662] 0.9148] 0.9743] 1.0447] 0.9099] 0.3112) 2.66] 0.9357] 1.318}



Figure 4-3
Failure Rate by Consecutive IM147

-1E-

Startup Cutpoints
HC Failure Rate by Vehicle Type CO Failure Rate by Vehicle Type
12.0% 12.0%
10.0% 10.0% f
‘ 2 Hn
Z 8.0% ist imia7 Zz 8.0% Lf Qist tm147
=F 6.0% 14 i 2nd M147 2 6.0% (2nd im147
= 4.0% a 3rd 1M 147 © om ZG . @a3rd M147
g Hl 8 Aa
2.0% fi 2.0% A
i a: Ge
0.0% ae 0.0% i
PC LDTI LDT2 All PC LDTI LDT2 All
Vehicle Type Vehicle Type
NOx Failure Rate by Vehicle Type Overall Failure Rate by Vehicle Type
14.0% 25.0%
12.0% 2 20.0%
z ‘00% fi] Ist IM147 2
£ 8.0% Hie isms § 15.0% hts 1M147
6.0% [T HH 2nd 1M 147 = Mi2nd M147
AB | 3rd M147 B 10.0% sed imia7
4.0% iGo §
H S 5.0%
2.0% | i
0.0% — Ee 0.0%
PC Lom) LoT2 alt Pc Lot) LoT2 an
Vehicle Type Vehicle Type



IM147 Composite Regression Coefficients, NOx, 1986 to 1987 Model Year LDGT2's
7 Regression Coefficients
Segment . RMS —|Reg.
Number Error Constant [C1 ic2 ic3 C4 ics icé ic7 ic8 ico iCc10 ic11 C12 |c13 C14 ic1s C16 C17 C18 C19
1 LOGT2 [86-87 | 1. 1179) 3.13663] 31.3453]. a

iP2 ILOGT2 [86-87 | 0.97795] 1.10882] -3.0631| 5.8632]. a At
iPS ILOGT2 |86-87 | 0.94502! 0.9839] _-5.0759| 4.548] 8.9192]. a
iP4 ILDGT2 [86-87 | 0.93501] 0.93053] -3.3825] 4.4591] 5.2516] 12.419] .
IPS ILOGT2 |86-87'| 0.85394] 0.96285] 4.4203] 3.6091] -1.7208] 11.4179] 9.1873]. .
iPé. ILOGT2 [86-87 | 0.84757] 0.91259] -4.4095] 3.6676] -1.5513] 6.2687] 8.3678] 9.6507]
|P7 ILOGT2 |86-87 | 0.85029[ 0.91803] -4.0853] 3.616] -2.1286] 6.0176] 7.8929] 10.0878] 0.7099]
iPS ILOGT2 |86-87 | 0.85275] 0.92194] -3.8044] 3.5415] -2.4148] 4.8872] 7.7612] 9.5053] 0.6261] 1.3726]
Po. ILOGT2 [86-87 | 0.83178] 0.86005] -3.778] 4.3986] 6.3555] 9.9437] 4.6162] 8.3808] -0.6741] -0.2851] 9.9702]. |.
[P10 LDGT2 [86-87 | 0.80985] 0.90764] -7.0196] 4.1229] -5.9089|8.2497[ 2.6936] 10.9511] -1.1815] 0.3123] 3.2549] 6.8821].
| a ILOGT2 [86-87 | 0.76148] 0.62189] -8.9566] 3.094] 4.9268] 6.2947) 5.15} 9.9243] -2.4765] 0.213] 3.9969] 6.4604] 0.8842].
P12 ILOGT2 [86-87 | 0.59003] 0.3735] -2.4217| 2.087] -2.6617| 4.0616) 2.224) 1.9821] -1.3263] -2| 4.7639] 5.4191] 0.2795} 5.406]. . *
fP13 ILOGT2 [86-87 | 0.33385] 0.19007] 0.2814] 1.1386] 0.5118] 3.255] 0.1077] 2.4058] 0.5387] -1.7814] 4.2622] -0.1798] :0.5687] 0.4608] 3.2021].
| Ga ILOGT2 |86-87 | 0.20099] 0.13811 1.8759] 0.6935] 0.5775] 3.0589] 0.9302] 1.5194] 0.7032] 0.6974] 2.2407] 0.0121] 0.4793] 0.2025] 1.9049] 2.1646]. a
Ps ILOGT2 [86-87 0.1038 0.0461] 0.3765] 0.6236 1.678] 0.8805] 1.0648) 1.097] 0.2424] 0.0264] 1.7158} 0.0601] 10.6449] 0.9194) 1.0397] 1.1878} 1.3178) .
Pig ILDGT2 [86-87 | 0.09657] 0.03819] 0.6854] 0.5604] 1.7082] 0.6146] 0.6222] 1.3221] 0.5089] -0.0753[ 1.3097] 0.8115] 0.675] 0.9002] 0.9782] 1.1823] 1.0924] 0.8604].
Piz LOGT2 [86-87 | 0.08202] 0.02243] 0.7341] 0.6449] 1.3141] 0.1109] 0.6071] 1.3461] 0.4166] 0.5635] 1.6121] -0.0273] 0.695] 0.8997} 0.9541] 7.1481] 1.0157] 0.8336] 1.7925].
{P18 ILDGT2 |86-87 | 0.05719] 0.02206} 0.9248] 0.6705] 1.2239] -0.0494] 0.6674] 1.7312] 0.3068] 0.8156] 1.3908] 0.0199] 10.6845] 0.9323] 0.8095] 0.9223] 0.7808] 0.9324] 1.241] 0.7403].
[Pi9 ILDGT2 [86-87 | 0.01482] 0.00786] 0.6527] 0.6745] 0.6642] 0.6799] 0.7944] 0.7706] 0.6813] 0.8969] 0.6883] 0.6733} 0.71f 0.7009] 0.7385] 0.7569] 0.6679] 0.7207] 0.8026[ 0.7243] 0.7208]

IM147 Phase 2 Regression Coefficients, NOx, 1986 to 1987 Model Year LDGT2's

Regression Coefficients

Segment RMS: Reg. 7
Number Error \Constant {C1 C2 C3 C4 C5. C6 C7 C8 ico C10 C14 C12 C13 C14 \c15 C16 C17 C18 Ic19
{B11 ]LDGT2 [eoe7 | 144602] 1.0677) 2507]. r
[B12 ILOGT2 [86-87 | 0.92821] 0.61792]. 0.4543] 9.8299]. .
{813 ILDGT2 [86-87 | 0.45405] 0.32299] 0.7862] 0.5919] 4.3588]
jB14 LDGT2 [86-87 | 0.27745] 0.22123}. 0.6178] 0.2412] 2.551] 2.9603].
B15 LDGT2 [86-87 | 0.14699) 0.0967]. -0.8032] 1.2698] 1.2876] 1.6105] 1.8179}.
B16 LOGT2 [86-87 0.1382] 0.07565]. 0.859] 1.1449] 1.2732] 1.5831] 1.6009] 0.9193]. a
B17 ILDGT2 [86-87 0.1172} 0.05283]. 0.8945) 1.2281} 1.135] 1.5637] 1.3545} 1.3493] 2.1364]. a
(B18 LDGT2 [86-87 | 0.08824] 0.05293}. 0.8887} 1.3239] 0.9624] 1.2543] 1.0381] 1.5212] 1.2998] 0.9849].
B19 ILOGT2 [86-87 0.0176] 0.00876]. 0.958} 0.9848} 0.9632] 0.9799] 0.9647} 0.9458] 1.1217] 0.9723] 0.9846]



The speed variation limits, though a bit more difficult to comprehend, were intended to
ensure'that speed differences from the reference trace within the +2 mph excursion limits
“envelope” would not bias the resulting measured emissions.

However, EPA suspended the use of the speed variation limits on November 23, 1993,
pending further evaluation.

Previous Analysis of Speed Variation Limits

Inadequacy of Speed Variation Limits - In 1998, Sierra completed a study for EPA’ that
evaluated the ability of the linear regression-based speed variation limits to identify high
emissions-producing speed variations. It was found that the linear regression criteria
were inadequate in flagging high-emissions speed variations. The reason for this finding
was that the standard error (SE) statistic does not give “appropriate” higher weighting to
speed deviations occurring at the critical high-emission points along the IM240 trace.
Instead, it gives equal weighting to all speed variations and thus (along with the other
regression statistics used in the speed variation criteria) is ill-suited to identifying those
speed deviations that substantially affect IM240 emissions.

Evaluation of Alternative Statistics - During this study, two alternative statistical
measures were evaluated for their ability to better identify IM240 speed variations that
significantly affect measured emissions:

1. DPWRSUM ’ - the sum of absolute changes in specific power; and

2. Positive Kinetic Energy (PKE) - the sum of positive differences in kinetic
energy per unit distance.

It was found that the PKE statistic provided a better measure than DPWRSUM for
identifying those speed variations from the reference trace that produce high emissions.
This finding was supported by analysis of modal (i.e., second-by-second).speed and
emissions data from a random sample of 16,581 full’ IM240 tests from the Arizona I/M
program. It was determined that the high-emission portions of the IM240 test closely
corresponded with periods of acceleration. A examination of both the DPWRSUM and
PKE statistics found that DPWRSUM is increased during both decelerations and
accelerations. PKE, on the other hand, is increased only during acceleration periods.
Thus, the DPWRSUM statistic is “diluted” with speed variations during decelerations
that have little effect on emissions. As a result, the PKE statistic was reasoned to provide
a better measure of significant emissions-producing speed variations. ,

Development of PKE-Based IM240 Variation Limits - From this finding, PKE variation
limits were then developed as potential replacements to the original regression-based

* “Full” tests refer to those run over the entire test duration, regardless of fast-pass or fast-fail status.

-55-


|M147 Composite Regression Coefficients, NOx, 1990 to 1995 Model Year LDGT1's
Regression Coefficients

Segment RMS — Reg.

Number Error Constant jC1 cz C3 C4 cS C6 }C7 ic8 ico C10 C11 C12 C13. C14 C15 C16 C17 C18 C19
PT TOGT1 [90-95 | 1.19960] 1.24126] 32.9634]. a

iP2 ILOGT1 [90-95 | 0.72181] 0.40146] 0.679] 4.5914].

iP3. ILOGT1 [90-95 | 0.69216] 0.32475] 2.1542] 3.4912] 6.7253]. a

iP4 ILDGT1_ [90-95 | 0.68749] 0.3123] 2.1371] 3.4737] 5.023] 5.8563} .

iPS. ILDGT1 {90-95 | 0.62455] 0.33205] 3.4043} 2.3634] 1.7239| 5.0562] 8.7446]. .

iP6 LOGT1 |90-95 | 0.61978] 0.33046] 2.2722{ 2.3005] 1.5599] 2.95847 8.2913] 6.1514]. .

iP7 ILOGT1_ {90-95 | 0.58792] 0.29749] 2.0622] 1.8473] 0.0555] 3.6238] 4.2818] 7.9809] 5.3164!

iP8 ILOGT1 [90-95 | 0.55746] 0.26129] 5.9179] 1.7445] -0.5662| 1.235] 4.3563} 0.782] 3.7446 7.1976]. .

iPS [LOGT1 [90-95 | 0.54008] 0.24653] 4.1085] 1.8313] -1.1591 2.0187] 2.9278] 0.7348] 3.4121] 5.4986] 7.8688. .

P10 ILOGT1_}90-95 | 0.53603} 0.2342 2.929] 1.8441] -1.4993] 2.6412] 2.6552] 0.0076] 2.7177 5.458] 6.0386] 2.7176] a

P11 ILOGT1 ]90-95 | 0.45579] 0.05778] 3.2806] 1.051] -0.2096] 0.7143] 2.8133] 1.4291] 0.9583] 2.9071] 6.0179] 0.9428] 1.4469]. .

P12 ILOGT1 190-95 | 0.36036] 0.0273] 4.3485} 0.9563] -0.635] -0.3902] 2.43} 0.4675] 1.7444) 0.5941] 4.1616] 1.3325] 0.6814] 3.9706]. .
[P13 LOGT1+ [90-95 | 0.17758] 0.04529] 0.6553] 0.7331] 0.4798] 1.1424] 1.2661] 0.9419] 0.3362] 0.8} 1.2658) 0.277] 0.7426] 1.2309] 2.6602]. .

P14 ILDGT1 [90-95 | 0.12741] 0.02345] 0.6061] 0.7033] 0.6424] 1.2708] 1.124] 1.4838] 0.5682] 0.7251] 0.9404] 0.6888] 0.6431] 0.8253] 1.6054] 1.8253]. .

PAS: ILOGT1 [90-95 | 0.07822] 0.01761 1.1782] 0.6831] 0.7996] 1.3552] 0.7806} 0.9482] 0.6863] 0.8429] 1.0081] 0.3774] 0.6949] 0.7428] 0.8865] 1.0954] 1.3651].

P16 ILOGT1 [90-95 | 0.06969] 0.01628) 1.367] 0.7128] 0.824] 1.0691] 0.8406] 0.6506] 0.711] 0.7051 0.914] 0.6359] 0.7085] 0.724] 0.8452] 1.0797] 1.0435] 1.2099]. .

P17 ILDGT1_|90-95 | 0.06569] 0.00712} 1.0171 0.713} 0.7449] 0.9912] 0.8383] 0.5662] 0.7047] 0.7856] 0.8682] 0.5184] 0.7126] 0.7606] 0.8503] 1.0453] 1.0145] 1.12247 1.218] a
P18 ILOGT1_|90-95 | 0.03909] 0.0008] 0.7599] 0.7108} 0.6532} 1.2612] 0.7148} 1.122] 0.5737] 0.837} 0.7408] 0.6827] 0.6897] 0.8094] 0.6826] 0.8525] 0.8302] 1.1087] 0.8134] 0.8578].
P19 ILOGT1 }90-95 | 0.01086] 0.00079) 0.757] 0.7} 0.7412] 0.7991] 0.7237] 0.854) 0.7206] 0.6726} 0.7413{ 0.7075] 0.7135] 0.7264] 0.7129] 0.729] 0.7202] 0.7122] 0.7434] 0.6941 0.73)

IM147 Phase 2 Regression Coefficients, NOx, 1990 to 1995 Model Year LDGT1's
Regression Coefficients

Segment RMS Reg.

Number Error Constant }C1 C2 C3. Ca ICS C6 C7 C8 ico C10 C14 C12 C13 C14 C15. C16 C17 C18 C19
fer 1 ILDGT1 [90-95 | 0.70725] 0.11508]. 3.158]
[B12 ILOGT1 [90-95 | 0.54083] 0.02981]. 1.5474] 5.8806]. .
[eis ILOGT1 {90-95 | 0.24246] 0.06072]. 0.9747] 1.7935] 3.6)
fea ILOGT1_}90-95 0.1753] 0.03555] 0.8927] 1.2292] 2.2351] 2.4522]. a
1 Chey ILOGT1 {90-95 | 0.10866] 0.02903]. 0.9331] 1.1278] 1.1969] 1.4839] 1.8483].

{B16 ILDGT1_[90-95 | 0.09571] 0.02747]. 0.9823] 1.0076] 1.1844] 1.4428] 1.4242] 1.6458]. .

[ei7 ILDGT+ [90-95 | 0.08918] 0.01127]. 0.9705} 1.0641] 1.1573] 1.415} 1.3597] 1.5734} 1.7341]. a
peis ILOGT1_]90-95 | 0.05491] 0.00454]. 0.9276] 1.1839] 0.9094] 1.1571] 1.0953] 1.6597] 1.2012] 1.1201].
[B19 ILDGT1 [90-95 | 0.01259] 0.0022]. 0.9623} 0.9896] 0.9651] 0.9817] 0.9732] 0.9769] 1.063{ 0.9406] 0.9926]



Appendix B

Failure Rate

Max CO Cutpoints
HC co NOx OVERALL
V Type | Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail
First IM147
LDGV | 207 | 1573 | 11.6% | 352 | 1428 | 19.8% | 236 | 1544 | 13.3% | 543 | 1237 | 30.5%
LDGTI1 | 76 | 888 7.9% | 204 | 760 | 21.2% | 83 | 881 8.6% | 281 | 683 | 29.1%
LDGT2 | 31 | 572 5.1% | 87 | 516 | 14.4% | 44 | 559 7.3% | 127 | 476 | 21.1%
All 314 | 3033 | 9.4% | 643 | 2704 | 19.2% | 363 | 2984 | 10.8% | 951 | 2396 | 28.4%
Second IM147
LDGV | 126 | 1654] 7.1% | 256 | 1524] 14.4% | 166 | 1614 | 9.3% | 406 | 1374 | 22.8%
LDGT1 | 59 | 905 6.1% | 167 | 797 | 17.3% | 70 | 894 7.3% | 231 | 733 | 24.0%
LDGT2 | 24 | 579 | 4.0% | 61 | 542 | 10.1% | 31 | 572 5.1% 91 | 512 | 15.1%
All 209 | 3138 | 6.2% | 484 | 2863 | 14.5% | 267 | 3080 | 8.0% | 728 | 2619 | 21.8%
Third IM147
LDGV | 114 | 1666 | 6.4% | 250 | 1530} 14.0% | 155 | 1625 | 8.7% J 390 | 1390 | 21.9%
LDGT1 | 49 | 915 5.1% | 156 | 808 | 16.2% | 70 | 894 7.3% | 219 | 745 | 22.7%
LDGT2 | 25 | 578 | 4.1% | 63 | 540 | 10.4% | 30 | 573 | 5.0% | 93 | 510 | 15.4%
5.6% | 469 | 2878 | 14.0% | 255 | 3092 | 7.6% | 702 | 2645 | 21.0%

Failure Rate By Model Year Grouping - First IM147

B-1

Max CO Cutpoints
HC co NOx OVERALL

Fail | Pass | % Fail | Fail | Pass |% Fail | Fail | Pass |% Fail | Fail | Pass | % Fail

19 | 86 | 18.1% | 32 | 73 | 30.5% | 24 | 81 2.9% | 55 | 50 | 52.4%

48 | 180 | 21.1% | 108 | 120 | 47.4% | 53 | 175 | 23.2% | 144] 84 | 63.2%

85 | 340 | 20.0% | 96 | 329 | 22.6% | 87 | 338 | 20.5% | 169 | 256 | 39.8%

55 | 897 | 5.8% {112 | 840 | 11.8% | 72 | 880 | 7.6% | 171] 781 | 18.0%

96+ | 0 | 70 | 0.0% | 4 | 66 | 5.7% | 0 | 70 | 0.0% | 4 | 66 | 5.7%
81-85 | 38 | 222 | 14.6% | 109 | 151 | 41.9% | 54 | 206 | 20.8% | 152] 108 | 58.5%
86-89 | 22 | 200 | 9.9% | 57 | 165 | 25.7% | 17 | 205 | 7.7% | 73 | 149 | 32.9%]
90-95 | 16 | 434 | 3.6% | 38 | 412 | 8.4% | 11 | 439 | 2.4% | 55 | 395 | 12.2% |

96+ 0 32 | 0.0% | 0 32 | 0.0% | 1 31 | 3.1% { 1 31 3.1%
81-85 | 18 | 76 | 19.1% | 44 | 50 | 46.8% | 16 | 78 {17.0% | 55 | 39 | 58.5%
86-89 | 8 56 | 12.5% | 13 | 51 } 20.3%] 13 | 51 | 20.3% | 27 | 37 | 42.2%
88-95 | 5 | 422 | 1.2% | 30 | 397 | 7.0% | 15 | 412 | 3.5% | 45 | 382 | 10.5%

96+ | 0 | 18 | 0.0% | O | 18 | 0.0% | O | 18 | 0.0% | 0 | 18 | 0.0%
ALL [314 | 3033 | 9.4% 19.2% 10.8% 28.4%



Figure 4-1
IM147 Trace
Phase 1 and Phase 2

60 >
——_ |W 147 PHASE | ————— +

WM 147 PHASE (| ————>

405

30 4

Speed (mph)

20

104

(rere eres

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150
Time (sec)

Cutpoint Analysis

Task 1 of the work assignment required projecting failure rates for the Arizona I/M
program. using the combined (3,347 vehicles) data set collected by Gordon Darby. The
projection includes four sets of emission cutpoints: start-up, midpoint, Max CO, and final
for the IM147 test. Since the original Max CO cutpoints were developed using the 304-
vehicle sample compiled for SR99-10-02, Sierra first revisited these cutpoints and model
year groupings to ensure their accuracy against the larger data set.

Accordingly, the model year groupings were modified to avoid anomalously high or low
failure rates for any individual model year/vehicle type combination. A second objective
in establishing the endpoints of the model year groupings was to ensure that changes in
emissions control technology were properly reflected in the various model year
groupings. (There is an obvious and direct relationship between the control technology
installed on a vehicle and its ability to comply with a given set of cutpoints.)

Once the model year groupings were revised, the IM147 Max CO cutpoints were
developed by regressing the final emissions results from the IM240 test against the final

-25-


IM147 Composite Regression Coefficients, HC, 1986 to 1989 Model Year LDGV's
Regression Coefficients

Segment IRMS —|Reg. .
Number Error [Constant |C1 ic2 ic3 C4 ICS C6 C7 ics ico C10 (ean C12 ic13 ic14 C15 C16 C17 C18 - ic19
PA LOGV. 186-89 | 0.31427] 0.27681] 15.5205].

[P2 ILOGV_ [86-89 | 0.23056] 0.12883] 1.6181] 4.2149) .
[Ps LOGV__]86-89 | 0.19626] 0.11002] 2.0358] 2.3971} 3.9903] a
[Pa ILDGV_ [86-89 | 0.18598] 0.09989] 0.6425) 2.546] 1.7035] 3.3548} a

iPS LDGV__]86-89 | 0.17584} 0.09767] 0.5819] 1.852] 1.5615) 1.555} 3.8429]. a
iPS LDGV__ {86-89 | 0.16772] 0.10204] -0.8376] 1.9082] 1.0601] 1.0965] 1.9459] 3.8412} .

PZ. ILDGV__|86-89 | 0.16444] 0.09594] -0.6742] 1.6725] 0.9995] 1.3082] 0.7861] 3.2631] 2.3581 .
iPS ILOGV__ {86-89 | 0.15806] 0.08909} 0.1422] 1.5556] 0.6844] 0.9331] 1.0208] 2.2001] 1.5379] 2.5805)

[Ps LOGV_ [86-89 0.1519] 0.08696] -0.1948] 1.5757] 0.7441] 1.0414] 0.6635] 1.0744] 1.3289) 1.468} 3.5915]. a
[P10 ILOGV__|86-89 | 0.14467] 0.0797] 0.4894] 1.5393] 0.9657] 0.7863] 0.5873] 1.2938] 1.3345] 1.1773] -0.2863] 4.8853]. .
[Pat ILOGV__|86-89 | 0.11095] 0.02972] 0.2469} 0.721] 1.1728] 0.8987] 0.4133] 1.9423] 0.6973] 0.4156] 0.9936] 2.4194] 1.6263]. .

P12 ILOGV__|86-89 | 0.09318] 0.02564] 0.8246] 0.6275] 0.7263] 0.7966} 0.542] 1.6116] 0.6663) 0.672] 0.4503] 2.6883{ 1.1012] 1.7602]. .

[P13 ILOGV_"}86-89 | 0.06902] 0.02184) 1.3196] 0.4061] 0.8931] 0.8959] 0.5829] 1.0567] 0.7867] 0.7859} 0.5864] 1.9088] 0.9881 0.291] 2.4099] .

P14 ILOGV__ [86-89 | 0.06241] 0.01809] 1.4552] 0.31] 1.0344] 0.9038] 0.6073) 1.022] 0.7521 0.848} 0.6425] 1.8254] 0.813] 0.5647] 1.1453] 2.4731 .

P15. ILOGV__ [86-89 | 0.05136] 0.01585) 1.2694] 0.4095] 0.9607] 0.9108] 0.6871] 0.8398] 0.7304] 0.8585] 0.5526] 1.7815] 0.6924] 0.6284] 0.7316] 0.9325] 2.1051]. a

P16 ILDGV_ [86-89 | 0.04861] 0.01432] 1.1846] 0.4732] 0.8917] 0.9259] 0.7055] 0.8742] 0.7619} 0.7564] 0.5578] 1.5489] 0.7021] 0.6507] 0.7862] 0.9721] 1.2367] 2.5815]. .

P17 ILOGV__ [86-89 | 0.03332] 0.00699] 1.0682] 0.673] 0.7585] 0.7037] 0.8742] 0.6284] 0.7246] 0.6208] 0.7319] 1.1727] 0.7152] 0.6517] 0.7253] 1.0692] 1.1351] 1.0816] 2.2316]. a
fPis ILDGV__[86-89 | 0.02685} 0.0032} 1.1443] 0.6139] 0.8508] 0.7766] 0.7017] 0.7447] 0.6277] 0.623] 0.6745] 1.0469] 0.7238] 0.6588] 0.7335] 0.8575] 0.8976] 0.668] 1.5797] 1.3415]
{P19 LOGV_ 86-89 | 0.02176] 0.00312] 1.0887} 0.6218} 0,766] 0.6703] 0.7888] 0.6802] 0.7991] 0.5738] 0.7502} 0.9406] 0.7447] 0.674] 0.6947) 0.6605] 0.8184] 0.1544] 1.3072] 0.9142} 0.9739}

IM147 Phase 2 Regression Coefficients, HC, 1986 to 1989 Model Year LDGV's
Regression Coefficients

Segment IRMS ~|Reg.

Number Error Constant |C1 ic2 ic3 ica ics C6 \C7 ics ico C10 C11 ~ {C12 C13. C14 Cis C16 C17 C18 C19
B11 LOGV__ [86-89 | 0.17572] 0.05913] 2.9828].

B12 ILOGV__ {86-89 | 0.14079] 0.04187]. 1.8474) 2.837]. a
B13 LDGV_ [86-89 | 0.10283] 0.03308]. 1.4791] 0.4901] 3.5664]. .

B14 LOGV__|86-89 | 0.09666] 0.02888]. 1.2557] 0.8541] 2.0611] 2.9206]. .

B15, ILOGV_ [86-89 | 0.07943] 0.02543} 1.0638] 0.9286] 1.331] 0.6468] 3.2335] .

B16. ILDGV__ {86-89 | 0.07243] 0.02198; 1.0486] 0.9587] 1.3534] 0.8765] 1.5227] 4.8276]. a

B17 LDGV__ [86-89 | 0.04689] 0.01029} 1.0457] 0.8812] 1.0971 1.3615] 1.4856] 1.7603] 3.2261]. a

B18 LOGV__[86-89 | 0.03789] 0.00418]. 0.9974] 0.9025] 1.0724] 1.0277] 1.1846] 1.1391] 2.3711] 1.8028]

B19 ILOGV_ [86-89 | 0.03036] 0.00369]. 1.0225] 0.8952} 0.9808] 0.8231 1.1] 0.3076] 1.8483] 1.2733] 1.3385}



of this algorithm led to a different approach for this analysis. Instead, a variation of the
fast-pass regression calculation was developed and used to predict emissions
improvement over an IM147 test. In short, the emissions result predicted after Segment 7
is compared to the emissions result predicted after Segment 19 to determine whether the
vehicle emissions are converging on the applicable standard.

Using this procedure, test time was reduced from 125 seconds (with fast-pass enabled) to
96 seconds. Table 1-3 details further results of the retest analysis.

Table 1-3
Retest Algorithm Results
LDGV LDGT1, LDGT2
Total Number of Complete 1567 1780
Tests
# of Failures Without > 6, z °,
Retest Algorithm 327 (20.9% of 1567) 273 (15.3% of 1780)
# of Correctly Identified 245 (74.9% of 327)? 180 (65.9% of 273°
Failures’

# Failing After 1 IM147 94 (38.4% of 245)° 97 (53.9% of 180)°
# Failing After 2 IM147s 151 (61.6% of 245)° 83 (46.1% of 180)°

# of Passing Vehicles °
Falsely Failed by Retest 0 (0% of 1567) 9 (0% of 1780)

TM Correctly identified failures” refers to those vehicles that were still failing at the end of the third
IM147.

*The number shown in parentheses is the number of failures without the retest algorithm,

°The number shown in parentheses is the total number of IM147 Cycle 2 and 3 failures.

Modal Fast-Fail Criteria

One of the requirements of this work assignment was to develop modal fast-fail criteria.
Unlike the retest procedure, which can terminate the test at the ends of the individual
IM147s, the fast-fail algorithm can terminate tests during an IM147 test.

Like the retest algorithm, errors committed by the fast-fail criteria result in false failures.
As a result, decreases in test time have to be weighed against false failures. With this in
mind, fast-failures cannot be made during the first of the three IM147s. Analysis showed ~
that the results of the first IM147 were too unpredictable relative to the final result to risk
false-failing vehicles. The final two IM147s, however, would serve reasonably well for
this purpose.

There are two different fast-fail algorithms, one for each of the final two IM147 tests.
The fast-fail algorithm for the second IM147 fails vehicles with excessively high

-6-


Failure Rate By Model Year Grouping - Second IM147
Max CO Cutpoints

HC co NOx OVERALL

Fail | Pass | % Fail Pass | % Fail | Fail } Pass | % Fail | Fail | Pass | % Fail

13 | 92 | 12.4% 76 | 27.6% | 18 | 87 | 17.1% | 49 | 56 | 46.7%

32 | 196 | 14.0% | 81 | 147 | 35.5% | 43 | 185 | 18.9% | 116] 112 | 50.9%
57 | 368 | 13.4% | 82 | 343 | 19.3% | 67 | 358 | 15.8% | 141 | 284 | 33.2%
24 | 928 | 2.5% | 64 | 888 | 6.7% | 38 | 914 | 4.0% | 100] 852 | 10.5%
96+ | O | 70 | 0.0% | 0 | 70 | 0.0% | 0 | 70 | 0.0% | 0 | 70 | 0.0%
81-85 | 33 | 227 | 12.7% | 99 | 161 | 38.1% | 48 | 212 | 18.5% | 139] 121 | 53.5%
86-89 | 19 | 203 | 8.6% | 48 | 174 | 21.6% | 10 | 212 | 4.5% | 59 | 163 | 26.6%
7 {443 | 1.6% | 20 | 430 | 4.4% | 11 | 439 | 2.4% | 32 | 418 | 7.1%
32 | 0.0% | O | 32 | 0.0% 1 | 3L | 3.1% 1 | 31 | 3.1%
16 | 78 | 17.0% | 40 | 54 | 42.6% | 14 | 80 | 14.9% | 53 | 41 | 56.4%
5 | 59 | 7.8% | 11 | 53 | 17.2% | 11 | 53 | 17.2% | 22 | 42 | 34.4%
3. | 424 | 0.7% | 10 | 417 | 2.3% | 6 | 421 | 1.4% | 16 | 411 | 3.7%
O {| 18 | 0.0% | 0 | 18 | 0.0% | 0 | 18 | 0.0% | 0 | 18 | 0.0%

209 {3138} 6.2% | 484 |2863 | 14.5% |267 [3080 | 8.0% 21.8%

Failure Rate By Model Year Grouping - Third IM147
Max CO Cutpoints

HC co NOx OVERALL
Type

LDGT1

Year |Fail| Pass | % Fail | Fail] Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail
81-82 | 13 | 92 | 12.4% | 29 [ 76 | 27.6% | 16 | 89 | 15.2%
83-85 | 29 | 199 | 12.7% | 79 | 149 | 34.6% | 39 | 189 | 17.1%
LDGV {86-89 | 48 | 377 | 11.3% | 77 | 348 | 18.1% | 65 | 360 | 15.3%
90-95 | 24 | 928 | 2.5% | 64 | 888 | 6.7% | 35 | 917 | 3.7%
96+ | 0 | 70 | 0.0% | 1 | 69 | 14% | 0 | 70 | 0.0%
81-85 | 31 | 229 | 11.9% | 95 | 165 | 36.5% | 47 | 213 | 18.1% | 134] 126 | 51.5%
LDGT1 | 86-89 | 15 | 207 | 6.8% | 43 | 179 | 19.4% | 12 | 210 | 5.4% | 56 | 166 | 25.2%
90-95 | 3 | 447 | 0.7% | 18 | 432 | 4.0% | 10 | 440 | 2.2% | 28 | 422 | 6.2%
96+ | 0 | 32 | 0.0% | oO | 32 | 0.0% | 1 | 31 | 3.1% | 1 | 31 | 3.1%
81-85] 16 | 78 | 17.0% | 40 | 54 | 42.6% | 13 | 81 | 13.8% | 53 | 41 | 56.4%
9.4% | 13 | 51 | 20.3% 54 | 15.6%
0.7% | 10 2.3% 1.6%
0.0% | 0 0.0% 0.0%
5.6% 14.0% 1.6%

18 18

B-2


Failure Rate By Model Year Grouping - Second IM147
Final Cutpoints :
HC co NOx OVERALL

Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail
56 | 49 | 53.3% | 26 | 79 | 24.8% | 37 | 68 | 35.2% | 74 | 31 | 70.5%
104 | 124 | 45.6% | 91 | 137 | 39.9% | 79 | 149 | 34.6% | 157} 71 | 68.9%
110 | 315 | 25.9% | 83 | 342 | 19.5% | 101 | 324 | 23.8% | 181 | 244 | 42.6%
39 | 913 | 4.1% | 47 | 905 | 4.9% | 79 | 873 | 8.3% | 127} 825 | 13.3%
0 | 70 | 0.0% O | 70 | 0.0% | 0 {| 70 | 0.0% | 0 70 | 0.0%

90 | 170 | 34.6% | 63 | 197 | 24.2% | 65 | 195 | 25.0% | 145 | 115 | 55.8%
36 | 186 | 16.2% | 22 | 200 | 9.9% | 39 | 183 | 17.6% | 69 | 153 | 31.1%
11 | 439 | 2.4% 6 | 444 | 1.3% | 29 | 421 | 6.4% | 41 | 409 | 9.1%

0 | 32 | 0.0% O'} 32 | 0.0% 1 31 3.1% 1 31 3.1%

46 | 48 | 48.9% | 41 | 53 | 43.6% | 31 | 63 | 33.0% | 72 | 22 | 76.6%
14 | 50 {21.9% | 12 | 52_| 18.8% | 18 | 46 | 28.1% | 33 | 31 | 51.6%
17 | 410 | 4.0% 3 | 424 | 0.7% | 39 | 388 | 9.1% | 52 | 375 | 12.2%

0 18 | 0.0% 0 18 | 0.0% | 0 18 | 0.0% | 0 18 | 0.0%
523 |2824 | 15.6% 1394 12953 | 11.8% 1518 12829 | 15.5% 1952 12395 | 28.4%

Failure Rate By Model Year Grouping - Third IM147
Final Cutpoints
HC co NOx OVERALL
VType| Year| Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass
81-82 | 53 | 52 | 50.5% | 25 | 80 | 23.8% | 32 | 73 | 30.5% | 67 | 38
83-85 | 91 | 137 | 39.9% | 88 | 140 | 38.6% | 81 | 147 | 35.5% | 156] 72
LDGV | 86-89 | 100 | 325 | 23.5% | 76 | 349 | 17.9% | 96 | 329 | 22.6% | 171 | 254
90-95 | 36 | 916 | 3.8% | 50 | 902 | 5.3% | 68 | 884 | 7.1% |112 | 840
96+ | 0 | 70 | 0.0% | 1 | 69 | 1.4% | O | 70 | 0.0% | 1 | 69
81-85 | 83 | 177 | 31.9% | 57 | 203 | 21.9% | 61 | 199 | 23.5% | 137 | 123
86-89 | 31 | 191 | 14.0% | 24 | 198 | 10.8% | 36 | 186 | 16.2% | 65 | 157 | 29.3%
90-95 | 6 | 444 | 13% | 4 | 446 | 0.9% | 29 | 421 | 6.4% | 36 | 414 | 8.0%
96+ | 0 | 32 | 0.0% | O | 32 | 0.0% | 1 | 31 | 3.1% | 1 | 31 | 3.1%
81-85 | 44 | 50 | 46.8% | 41 | 53 | 43.6% | 31 | 63 | 33.0% |.73 | 21 | 77.7%
86-89 | 16 | 48 | 25.0% | 11 | 53_| 17.2% | 17 | 47 | 26.6% | 32 | 32 | 50.0%
88-95 | 13 | 414 | 3.0% | 4 | 423 | 0.9% | 34 | 393 | 8.0% | 44 | 383 | 10.3%
96+ | 0 | 18 | 0.0% | O {| 18 | 0.0% | O | 18 | 0.0% | 0 | 18 | 0.0%
14.1% 2966 | 11.4% 1486

LDGT1

LDGT2



In a follow-up work assignment in 1998,'" EPA asked Sierra to evaluate 304 triplicate
IM147 tests followed by an IM240 test. These data were analyzed to verify the
preliminary excess emission identification rates and average test time.estimates projected
for the Arizona IM program from the Phase 2 and IM240 data sets collected in previous
studies. In addition, improved fast-pass and retest algorithms were developed for the
IM147 test using the same approach used previously in developing similar IM240
algorithms; however, the cutpoints scenarios evaluated in the study did not include the
Max CO cutpoints previously developed for DEQ.

PKE Speed Variation Criteria - In the 1997 IM240-related evaluation for EPA (SR98-02-
01),’ Sierra developed improved speed variation criteria based on the total Positive
Kinetic Energy (PKE) change per mile traveled during the IM240 cycle. These criteria
were designed to minimize the variation in emissions while still being feasible for use by
minimally trained drivers with a reasonable aptitude for dynamometer driving. However,
only IM240 drive cycle criteria were developed in the 1997 study. Therefore, further
analysis was needed to develop similar speed variation criteria for the IM147.

Scope of Work

To aid in the Arizona IM147 implementation effort, EPA issued a work assignment
(#1-08) to Sierra to complete the following tasks:

1. Develop projected IM147 failure rates for the Arizona I/M program;

2. Develop modal IM147 fast-pass standards for the Max CO cutpoints;

3. Develop modal predictive IM147 retest algorithms for the Max CO cutpoints;
4. Develop modal IM147 fast-fail criteria; and

5. Develop fast-pass and full-duration PKE criteria for the IM147 test.

Three distinct data sets were used in this study. The first two sets were the 304 vehicle
study, collected for SR99-10-02, and a 543-vehicle sample collected for this study. For
both of these sets, randomly selected vehicles were given triplicate IM147 tests followed
by an IM240 test. The third data set comprised 2518 vehicles given triplicate IM147 tests
and, if they failed the third test, an IM240 test. After removing invalid tests, the test data
sets used for this study consisted of 300, 535, and 2512 vehicles respectively (i.e., 3347
vehicles total). All of the data were collected by Gordon-Darby I/M lanes in Phoenix,
Arizona.

” Superscripts denote references listed in Section 8.

-2-


1M147 Composite Regression Coefficients, NOx, 1986 to 1989 Model Year LDGT1's

Regression Coefficients:

Segment RMS —-|Reg.
pm Error Constant {C1 ic2 ex} C4 CS C6 C7 C8 ico 1Cc10 C11 C12 [C13 C14 Cis C16 C17 C18 ic19
PT TOGT1 [86-69 | 1.23043] 2.00004] 23.5015 r
a ILOGT1 [86-89 | 0.70187[ 0.74907] 1.2554] 4.6182]. .

iP3. ILOGT1 [86-89 | 0.65788] 0.66459] 1.9511] 3.4741] 6.6849]. a
[Pa ILOGT1 [86-89 | 0.65847| 0.6622] 2.0121} 3.4718] 6.5638] 0.480%]. .
Ps. ILOGT1_|86-89 | 0.60304] 0.65314] 0.8979] 2.5888] 3.7778] 0.693} 7.82]. .
[Ps ILOGT1 [86-89 | 0.60221] 0.64909] 0.8986] 2.5939) 3.729} -0.3487| 7.391] 2.7912)
[Pz LOGT1 [86-89 | 0.58554] 0.63588] -0.2658] 2.2631] 2.7626] -0.2519] 4.6311 4.814] 3.3278].
[Ps ILOGT1 [86-89 | 0.57068] 0.60583] 0.1372] 2.1042] 2.0549] _-1.463} 4.181] 1.0084} 2.9915] 4.4107]. a
[Ps ILOGT1 [86-89 | 0.57122] 0.60597] 0.1342] 2.1057] 2.0299] -1.4503] 4.1619] 0.9698) 2.989] 4.3952] 0.1053]. .
[P10 ILOGT1 |86-89 | 0.56454] 0.59282] -0.9316] 2.1076] 1.5254] -0.8557| 3.4898] 1.6834] 2.3002] 4.575] -1.5419] 3.1754]_

P11 ILOGT1 {86-89 | 0.44918] 0.31619] 0.8774) 1.547] 0.3721] -1.2258] 3.7047] 1.8503] 0.299] 3.4571] 0.7772] 1.6757] 1.4183) a

P12 ILOGT1_ [86-89 | 0.39015] 0.23116] 2.1899] 1.3771] -0.3977] -1.386] 2.7841] 0.6668] 0.7794] 1.8436] -0.1039| 1.861] 0.955] 2.7651].

P13 LOGT1 [86-89 | 0.21107] 0.11066] -0.9618] 0.6673] 1.0909] 0.613] 1.2202] 0.9175] 0.3457] 1.1499] 1.3016] -0.1198] 0.8075] 1.3691] 2.4303}.

P14 ILOGT1 [86-89 | 0.15824] 0.06623] 0.4774] 0.6974] 0.7145] 0.8449] 1.214] 0.9814] 0.6256] 0.7295] 1.5692] 0.6474]. 0.6399] 0.8685) 1.8063] 1.4835}.
fps. ILDGT1_ [86-89 | 0.10582} 0.03533] 0.0217] 0.6721] 0.9237|- 0.9185] 0.462] 1.2051] 0.9519] 0.6397] 1.0663] 0.5564] 0.7033] 0.8047] 0.8872] 1.1532] 1.2727].
[pis ILDGT1_[86-89 | 0.09624] 0.03427] 0.4994] 0.6974] 0.6351] 1.2145] 0.5601] 0.5254! 1,017] 0.3771] 0.9662] 0.7977]~6.7267] 0.8275} 0.9401] 1.0394] 0.9564] 1.2214]. .
EE ILOGT1 [86-89 | 0.09019] 0.02973] 0.3688] 0.7491] 0.5027] 1.1374] 0.7202] 0.7519] 0.9628] 0.4449] 0.9374] 0.4114]: 0.7103} 0.847] 0.8964] 1,087} 0.896} 1.1054] 1.2517] a
[P18 ILOGT1 [86-89 | 0.05194] 0.01155} 0.561] 0.6874] 0.7247] 0.8944] 0.4555] 1.1866] 0.7413] 4.03] 0.6717] 0.4644] 0.7014] 0.8099] 0.637] 0.8736] 0.8444] 0.9947] 0.7536] _ 0.9298].
Ps [LOGT1 [86-89 | 0.01448] 0.0007] 0.522] 0.7051] 0.7802] 0.7612] 0.7043] 0.8674] 0.7473[ 0.7107] 0.6734] 0.7097] 0.7081] 0.7257] 0.7131{ 0.7176] 0.7067| 0.7115] 0.6881] 0.7158] 0.7428]

|M147 Phase 2 Regression Coefficients, NOx, 1986 to 1989 Model Year LDGT1's
Regression Coefficients:

Segment RMS. Reg.

Number Error [Constant {C1 C2, {ox} C4 ics: iCé ic7 ic8 ico C10 C11 C12 C13 C14 Ic15 Ic16 C17 ic18 1c19
(B11 ILOGT1 {86-89 | 0.71447] 0.56214]. 2.9488) .

B12 ILOGT1 [86-89 | 0.58562] 0.33666] 1,7796|-_ 4.5068} .

B13 ILOGT1 [86-89 | 0.29253] 0.15935]. 1.0449] 2.1599] 3.1651 .

B14 ILDGT1 [86-89 | 0.22004] 0.08924) 0.9001] 1.3657] 2.486] 1.9841]. .

i815 ILDGT1_ [86-89 | 0.14504) 0.05]. 0.9776) 1.164] 1.1562] 1.5604] 1.7468].

B16 ILOGT1 {86-89 | 0.13142] 0.04594) 1.0201] 1.0708] 1.2975] 1.3785] 1.3303] 1.5302]. .

B17 JLOGT1_}86-89 | 0.12255] 0.04125] 0.977] 1.1131 1.218] 1.4703] 1.2356] 1.4639] 1.602]. .
B18 ILOGT1 [86-89 0.0715] 0.02293]. 0.937] 1.1335] 0.8403] 1.2196] 1.0929] 1.5766] 0.9306] 1.2015]
B19 ILDGT1 [86-89 | 0.01559] 0.00168, 0.9648} 0.9858] 0.9551] 0.9715] 0.9562] 1.0147] 0.9809] 0.9807] 0.9956}



Failure Rate By Model Year Grouping - Second IM147
Intermediate Cutpoints
HC co ‘NOx OVERALL
V Type | Year [Fail] Pass | % Fail |Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail
81-82 | 36 | 69 | 34.3% | 15 | 90 | 14.3% | 23 | 82 | 21.9% | 49 | 56 | 46.7%
83-85 | 51 | 177 | 22.4% | 66 | 162 | 28.9% | 60 | 168 | 26.3% | 118] 110 | 51.8%
LDGV | 86-89 | 61 | 364 | 14.4% | 50 | 375 | 11.8% | 76 | 349 | 17.9% | 134 | 291 | 31.5%
90-95 | 22 | 930 | 2.3% | 30 | 922 | 3.2% | 55 | 897 | 5.8% | 85 | 867 | 8.9%
96+ | 0 | 70 | 0.0% | 0 | 70 | 0.0% | 0 | 70 | 0.0% | 0 | 70 | 0.0%
81-85 | 57 | 203 | 21.9% | 34 | 226 | 13.1% | 31 | 229 | 11.9% | 95 | 165 | 36.5%
LDCT1 86-89 | 24 | 198 | 10.8% | 18 | 204 | 8.1% | 25 | 197 | 11.3% | 52 | 170 | 23.4%
90-95 | 6 | 444 | 1.3% | 5 | 445 | 1.1% | 21 | 429 | 4.7% | 28 | 422 | 6.2%
96+ O | 32 | 0.0% | 0 | 32 | 0.0% 1 31 3.1% 1 31 3.1%
81-85 | 29 | 65 | 30.9% | 32 | 62 | 34.0% | 15 | 79 | 16.0% | 50 | 44
LDGT2 86-89 | 10 | 54 | 15.6% | 5 59 | 7.8% | 11 | 53 | 17.2% | 19 | 45
88-95 | 7 | 420} 1.6% | 3 | 424 | 0.7% | 22 | 405 | 5.2% | 31 | 396
96+ 0 18 | 0.0% | 0 18 | 0.0% 0 18 | 0.0% | 0 18
ALL | 303 13044} 9.190 1258 13089 |_ 7.796 1340 13007 | 10.250 | 662 12685

Failure Rate By Model Year Grouping - Third 1M147

Intermediate Cutpoints
HC co NOx OVERALL
V Type | Year [Fail] Pass | % Fail [Fail] Pass | % Fail [Fail | Pass | % Fail [Fail | Pass | % Faill]
81-82 | 34 | 71 [32.4% | 16 | 89 | 15.2% | 20 | 85 [19.0% | 48 [ 57 | 45.7%]
83-85 | 48 | 180 | 21.1% | 66 | 162 | 28.9% | 58 | 170 | 25.4% | 113] 115 | 49.6%
LDGV | 86-89 | 56 | 369 | 13.2% | 49 | 376 | 11.5% | 72 | 353 | 16.9% |128 | 297 | 30.1%
933 | 2.0% | 29 | 923 | 3.0% | 46 | 906 | 4.8% | 77 | 875 | 8.1%
70 | 0.0% 1 | 69 14% | 0 | 70 | 0.0% 1 | 69 14%
204 | 21.5% | 34 | 226 | 13.1% | 29 | 231 | 11.2% | 92 | 168 | 35.4%
201 | 9.5% | 16 | 206 | 7.2% | 25 | 197 | 11.3% |-50 | 172 | 22.5%
447 | 0.7% | 3 | 447 | 0.7% | 18 | 432 | 4.0% | 22 | 428 | 4.9%
32 | 0.0% | 0 | 32 | 0.0% 1 31 3.1% 1 | 31 | 3.1%
69 | 26.6% | 31 | 63 | 33.0% | 14 | 80 | 14.9% | 47 | 47 | 50.0%
54 | 15.6% | 8 | 56 | 12.5% | 8 | 56 | 12.5% | 19 | 45 | 29.7%
422 | 1.2% | 3 | 424 | 0.7% | 20 | 407 | 4.7% | 27 | 400 | 6.3%
18 | 0.0% | 0 | 18 | 0.0% | 0 18 | 0.0% | 0 18 | 0.0%
70 | 8.3% 1256 13091 | 7.6% |311 }3036) 9.3% | 625 12722 | 18.7%
B-6


1M147 Composite Regression Coefficients, CO, 1986 to.1989 Model Year LDGV's

Regression Coefficients
Segment IRMS —|Reg.
Number [Error Constant }C1 C2 C3 }C4 C5. ey C7 ica ico C10 C11 C12 C13 C14 C15 C16 C17 C18 Ic19
1 TOGV. 86-89 | 4.67926] 5.71778] 10.6405} a
[P2 ILOGV__|86-89 | 3.51709] 3.81788] 0.1364) 2.695]. .
{Ps ILOGV_ [86-89 | 3.15842] 3.11069] 1.0124] 1.6189} 3.9459] .
{Pa ILOGV__|86-89 | 3.14894] 3.04972] 0.8613) 1,649] 3.2671] 1.3569]. |.
ILDGV__|86-89 | 3.10204] 3.03267] 0.7486] 1.4903] 2.3894] 0.8299] 1.8322]. a
LOGV__|86-89 | 3.09685] 3.08465] 0.6252] 1.4634] 2.3373] 0.5195] 1.5731] 0.9056]. .
[Pz LOGV__|86-89 | 3.02573] 2.86107] 1.1098] 1.2652] 1.9531] 0.4921] 0.7359] 0.8227] 2.1087]. a
[Ps ILOGV__|86-89 | 2.94297] 2.68329] 0.7801] 1.2178] 1.8801] 0.8138] 0.7593] -0.8902] 1.5027] 2.0203]. .
iPS LOGV_ |86-89 | 2.87652] 2.54621] -0.0543] 1.2924! 4,523] 1.0828] 0.2369] -1.106] 1.5137] 1.3545] 3.0417] a
P10 LOGV__|86-89 | 2.74734] 2.37808] -0.6515] 1.4027] 1.4393] 1.1934] -0.1403] 0.5027] 1.2073] 0.9038] -0.333] 4.4623]. a
P41 LDGV_ [86-89 | 1.80566} 1.04515] 0.0874] 0.8282] 1.4645] 1.1894] 0.8248] 0.2838] 0.6291] 1.0042] 1.4625] 2.0759] 0.8639].
P12 ILDGV_ [86-89 | 1.49356] 0.71931] 0.2804] 0.7493] 0.9793} 1.513] 0.8296] -0.0469} 0.689] 1.0787| 0.769] 1.9598] 0.7434] 1.2423 a
P13, ILOGV__ {86-89 | 1.06067} 0.36461] 0.8207/ 0.6529] 0.9226} 1.272] 0.8321] 0.1223 0.6425] 0.8893] 0.7679] 1.6076] 0.7379] 0.7345] 2.1093]. a
P14 LOGV_ [86-89 | 0.82231] 0.26307] 0.8376] 0.5953, 0.933] 1.0658] 0.8038] 0.4366 0.672] 0.8768] 0.8378] 1.4217] 0.7134) 0.7172] 1.4718] 1.4518].
JP15- ILOGV_ {86-89 | 0.57486] 0.17901] 0.8519[ 0.6644] 0.7016] 1.0893] 0.6865] 0.5537| 0.8363] 0.7844] 0.7334] 1.1584] 0.7057| 0.7004] 0.9553] 0.9039] 1.5623].
P16 ILOGV__|86-89 | 0.54701] 0.18097 0.946] 0.6657] 0.6984/ 0.9951] 0.7165] 0.6267] 0.8323] 0.7529] 0.6652] 1.0979] 0.7051] 0.7127] 0.9675] 0.8974] 1.1743] 1.3482] .
fP17 ILOGV__ [86-89 | 0.49266] 0.13124] 0.9156] 0.6933] 0.6436} 0.974} 0.7016} 0.686{ 0.7814] 0.7572] 0.5724] 0.9245] 0.7053] 0.7153] 0.9399] 0.8866] 1.0881] 0.9489] 1.6484].
ILOGV__ [86-89 | 0.29132] 0.05317] 0.9002 0.685] 0.7018] 0.9872] 0.6206] 0.7756] 0.6789] 0.7453] 0.7249] 0.7506] 0.7065] 0.7322] 0.7884) 0.697] 0.8566] 1.1084] 1.3762] 0.8805].
yPi9 LOGV_ [86-89 0.1491} 0.02273] 0.8668} 0.6976] 0.726] 0.8685] 0.6806] 0.7684] 0.7148] 0.6669] 0.6743] 0.7484] 0.7087] 0.7177] 0.7096] 0.7249] 0.7202[ 0.6852] 0.9434] 0.7022] 0.8271)
IM147 Phase 2 Regression Coefficients, CO, 1986 to 1989 Model Year LDGV's
Regression Coefficients a
Segment IRMS Reg.
Number Error Constant [C1 ic2 ic3 ic4 ics C6 C7 ics (e:) ic10 Cit }C12 \C13. C14 C18 C16 C17 C18 ic19
Bit ILOGV_ [86-89 [4.18172] 3.9913} 1.1684]. .
B12 ILOGV__|86-89 | 2.21032] 1.63478]. 1.0475) 1.964].
B13, ILOGV__|86-89 1.5326] 0.82575]. 1.0148} 1.0859] 3.0948; |.
B14, LDGV__[86-89 [1.21271 0.6268}. 0.9705) 1.0461 2.17] 2.0218]. .
LDGV_ [86-89 | 0.82448] 0.40601]. 0.9653} 0.9766] 1.3403] 1.2022] 2.2818 .
LOGV__ [86-89 | 0.77323] 0.3766]. 0.9636] 0.9888] 1.3495] 1.1956] 1.6328] 2.1301].
ILDGV__ [86-89 | 0.67252] 0.23667] 0.9624] 0.9661] 1.2888] 1.1945] 1.4722] 1.3414] 2.4799}. a
LOGV__[86-89 | 0.40383] 0.10653]. 0.9566) 0.984} 1.0723] 0.9316} 1.1674] 1.5464] 1.9865] 1.1802].
ILDGV_ |86-89 | 0.20724] 0.05209}. 0.9611 0.965] 0.9597] 0.9776] 0.9801) 0.9182] 1.3409] 0.9424] 1.1303]



IM147 Composite Regression Coefficients, HC, 1981 to 1982 Model Year LDGV's

Regression Coefficients
Segment |, RMS — Reg. .
Number | Error__|Constant ice ica Ica Ics Ice c7 ica ico cio’ fot fciz_ cia. fcra.— cts —cie_—fci7_— icra cto
PT DGV__ 81-82 | 0.49216] 0.57705 r
P2 LOGV_ [81-82 | 0.33532] _0.27587| 43107]
PS, LOGV_ [81-82 | 0.28385] 0.2387] 18573] 5.3824). 5
Pa LDGV" [81-82 | 0.26651] 0.22287) 20142[ 3.3342] 3,052 5
PS LOGV_ [81-82 | 0.25039] 0.21729} 7.7343] 3.0584] -0.4156] 5.9309}.
PG LOGV—_|a1-82 | 0.23492] 0.22613} 1.7931] 2.0663] -0.0624] 2.4987] 4.6232]. r
P7 LOGV__a1-82 | 0.22127] 0.21033} 7.5589] 1.4953| 0.1839] 1.2039] 3.6754] 3.5496] :
PS LOGV__Ja1-82 | 0.21552] 0.19445 7.4891] 1.3035] -0.0672| 1.0624] __2.967| 2.9559] 2.5679) a
PS LOGv__|e1-82 | 0.2067] 0.18043) 1.5796| _1.3012| -0.0519| 1.564] 0.0517 1.2979] 2.0321] 6.4454] i
P10 LOGv_ [81-82 | 0.18913] 0.16356 4.5978] 1.0833[ 0.3527] 0.1824} 0.9708] 1.1991] 21788] 0.884] 7.9922]. i
Pat COGV__[a1-82 | 0.15452] _ 0.0563 0.5393{_ 1.7119] 0.1012] 0.6423| 0.9748] 1.0961| 0.177] 1.9283] 6.7153] 1.4557]. i
P12 LOGV_ [81-82 | 0.13848] 0.03382} 0.4616| 1.2496] 0.2523] 0.8742| 0.2381] 0.6512] 1.0074] 1.8257 7Al 0.8496] 1.7116]. :
| GED LOGV_ a1-82 | 0.11063] 0.03458] 0.2871[ 1.8035] 0.9765] -0.5314| 0.4907] 1.1534] 0.6794] 1.3477] 4.1068] 0.6837] 0.0626] 3.0307].
P14 LOGV_ (81-82 | 0.10379] 0.02583] 0.3096[ 1.715 1.0249] 0.5161] 0.4784[_ 1.2933] 0.6542] 1.7892] 3.4911] 0.4746] 0.3833] 2.0844] 1.6075). 5
Pas LOGV_ [81-82 | 0.09549] _ 0.02733] 0.2571{ 1.7659] 0.7304] 0.2147| 0.1823] 1.1956] 0.9924] 1.0719] 2.8131] 0.4955] 0.6194] 1.2124] 0.4430] 2.0080]. a
P16 LOGV_ {a1-62 | 0.07932[ _0.0312| 0.4766| 1.3033 0.8794] 0.0472|_0.402[ 1.6192] 1.449] 0.2368] 2.1792 0.4806] 0.6063] 1.0279] 0.7352 0.5135] 5.3144]. i
Pa LOGV__|81-82 | 0.05521] 0.00814 0.6849[ 0.6167] 0.5737] 0.616] 0.8743] 0.9448] 1.724] 0.1427] 0.6903] 0.6243] 0.6678] 0.6972] 1.4033] 0.6874] 2.3373] 2.2660] i
P16 LOGV_ [81-82 | 0.04359] 0.00235] 0.7063] 0.5683| 0.9993] 0.1434 1.1276] 0.8748] 1.3533] -0.3586] 1.3373] 0.6347| 0.6998] 0.796] _0.716| 0.6921] _1.617| 1.4146] 1.4076].
P19 LoGv—[s1-82 | 0.03591 0.00148 07134] 0.6447] 0.8407] 0.2792] 0.9872] 0.8646] 0.9518] 0.1384] 1.1208] 0.6929] 0.6275] 0.6021] 0.5738] 0.7875] -0.2237| 1.2967] 0.9834] 1.1302]

IM147 Phase 2 Regression Coefficients, HC, 1981 to 1982 Model Year LDGV's

Regression Coeficients

ISegment RMS — Reg.

Number Error |Constant |C1 ic2 ic3 C4 ics iCé C7 cB ico C10 C11 C12 C13 C14 C15. C16 }C17 C18 C19
B11 TDGV. 81-82 | 0.32813] 0.13912]. . a a . . |. . 3.2894! .

B12 LOGV__ [81-82 | 0.29163] 0.08753]. . . a . a . a a 1.8437] 3.4587]. .

B13 LOGV__ [81-82 | 0.18796] 0.06684]. a . . a . . 0.9136] -0.314] 6.0599]. .

B14 LOGV__ [81-82 0.1799] 0.05326]. . a |. . . a . 0.6172] 0.1991] 4.6274] 2.6027]. .

B15 ILDGV_ [81-82 | 0.15358] 0.05277]. . . a a a . a . 0.6903] 0.7618] 2.1672] -0.1274] 4.2479]. .

B16 LOGV_ [81-82 | 0.12174] 0.04997]. . |. . . a a a 0.8196] 0.7862] 1.6353] 0.667] 1.0053] 8.5142]. qi

B17 LOGV__ [81-82 | 0.08162] 0.00913]. a . A . a 1.0095} 0.7175] 1.2863] 1.8724] 0.9087| 3.3981] 3.148]. .
B18 LOGV_ {81-82 | 0.06421] 0.00073}. i : : A : : : 0.9515] 0.8623] 1.1518] 0.996] 0.9777 2.5859] 1.9343] 1.9181

B19 LOGV_ [81-82 | 0.05142] 0.00017]. . . . . . . 0.9854] 0.8032] 1.1865] 0.7374) 1.1] 0.3037] 1.7543) 7.3113] 1.6987]



T
> PP.
CPKE(x) = =2— (6-1]
Jac
0

The term PP, is referred to as the positive specific power at time ¢ and is given by the
following equation:

fe -V-y? when, >V,_,

PP (0 when, SV, [6-2]

By definition, positive (specific) power’ is non-zero during acceleration and zero during
cruise and deceleration events. Cumulative positive power (CPP) is defined as the sum of
positive power at each second ¢ over a transient driving cycle of T seconds, or

CPP(T) = y PP, [6-3]

120

Thus, CPP increases during accelerations over a transient driving cycle and remains
constant during cruise and deceleration.

Conversely, cumulative PKE decreases during cruise and deceleration because the
denominator in Equation [6-1], which represents the summed distance driven, still
increases while a vehicle is cruising or decelerating. Cumulative PKE becomes constant
only during periods of idle (i.e., zero speed).

Figure 6-1 illustrates the different behavior of each metric over the IM147 test. It shows
second-by-second speed, CPP, and cumulative PKE over the reference trace. Speed (in
mph) is plotted against the left axis. Cumulative positive power and cumulative PKE are
plotted against the right axis (in mph’/sec and miles/hr’, respectively).

As seen in Figure 6-1, CPP either increases or remains constant over the entire duration
of the transient IM147 test. On the other hand, cumulative PKE decreases during both
deceleration and cruise events, by definition. Furthermore, cumulative PKE can actually
decrease during modest accelerations after the initial portion of the IM147 test. For

* Power is literally defined as the rate of change in kinetic energy (or work). Strictly speaking, specific
power (i.e., power per unit mass) would be calculated by the velocity times the acceleration at time t.
When the change in kinetic energy is evaluated on a second-by-second basis as defined in Equation 6-2,
instead of as a net change from the beginning to the end of the cycle, PP, as defined in that equation
approaches the strict definition of positive power.

-57-


6. DEVELOPMENT OF IM147 VARIATION LIMITS

In addition to fast-pass/fail cutpoints and retest criteria, trace variation limits were also
developed for the IM147 test. Under Task 6 of the Work Assignment, an analysis
methodology that was used to develop IM240 variation limits under an earlier EPA
study’ was applied to the pilot IM147 data to develop similar limits for the IM147 test.

During the course of the effort, an alternate statistical metric, Cumulative Positive
Specific Power (CPP), was identified that resulted in better second-by-second variation
limits than the Positive Kinetic Energy (PKE) metric employed in the previous EPA
study. This section of the report describes why the new statistic. was selected, and how
IM147 CPP variation limits were developed and evaluated to ensure they do not produce
excessive test abort rates. It also assesses their individual impact on average
dynamometer test time. (The combined effect of fast-pass/fail cutpoints, retest criteria
and IM147 trace variation limits is discussed in Section 7.)

Before describing the effort performed under the current Work Assignment, a review of
the existing IM240 tolerance limits and a summary of the previous evaluation of those
limits are presented.

Existing Tolerance Limits

The prescribed driving cycles for the transient IM240 and IM147 tests consist of varying

. second-by-second speeds ranging from zero (i.e., idle) to 56.7 mph, with maximum speed
changes of +3.3 mph/sec. During actual I/M testing, the driver watches a graphical
display of the prescribed or “reference” speed/time trace overlayed with the actual
second-by-second trace as it is being driven as an aid to following the reference trace and
anticipating upcoming speed changes. (The visual display also indicates prescribed shift
points for manual transmission vehicles along the trace.)

Since each vehicle has different performance characteristics, it is impossible, even for
highly skilled drivers, to precisely follow the second-by-second reference trace speeds
during actual “one-time-only” testing. As a result, EPA originally developed a set of
speed-based tolerance limits for the IM240 test that defined the leeway allowed to the
driver in trying to follow the reference trace for the test to be considered valid. Those
tolerance limits consisted of two components: (1) speed excursion limits; and (2) speed
variation limits. Each of these criteria is described below.°

-53-


To analyze the IM147-only preconditioning option, Sierra used a combination of data
from the 2% random test sample (consisting entirely of full-duration tests) that is
routinely collected in the Arizona IM240 program and a limited number (101 tests) of
triplicate (back-to-back-to-back) IM147 tests that were conducted as part of the 1997
EPA evaluation. A key element of the analysis methodology involved the development
of “Phase 2b” cutpoints to complement the full IM147-only cutpoints. (The Phase 2b
cutpoints were applied in a manner similar to the current IM240 procedure in which
vehicles passing in Phase 2 are considered passing for the entire test.) Fast-pass cutpoints
for both the entire IM147 and Phase 2b were also developed.

As noted above, while the 1997 EPA study involved the analysis of a considerable
amount of IM240 data, only a small subset (101 vehicles) was of use in projecting credit
levels and test times for an IM147 test program. An additional concern is that the 101-
vehicle study was not specifically designed to determine excess emission identification
rates and SIP credit levels. As a result, EPA issued a follow-up work assignment to
Sierra in 1998 that involved the collection of test data from triplicate IM147 tests
followed immediately by a full-duration IM240. Data were collected from 304 randomly
selected light-duty cars and trucks arriving at the test lane during normal queuing
conditions. These data were then analyzed to verify the preliminary excess emission
identification rates and average test time estimates projected for the Arizona IM program
from the Phase 2 and IM240 data sets collected in previous studies.

As part of the 1998 study for EPA, improved fast-pass and retest algorithms for the
IM147 were developed using the same approach used previously in developing similar
IM240 algorithms; however, the cutpoint scenarios evaluated in the study did not include
the Max CO cutpoints previously developed for DEQ. Given Arizona’s need for the
maximum feasible CO reductions from its IM program, DEQ has decided to implement
this set of cutpoints. A follow-up study is therefore needed to develop improved fast-pass
and retest algorithms for the Max CO IM147 cutpoints. Gordon-Darby collected
additional test data from roughly 3,000 vehicles in the Phoenix area that can be used in
this analysis. Of these vehicles, approximately 2,500 received triplicate IM147s only; the
remaining 500 vehicles will receive triplicate IM147s followed by a single full-duration
IM240.

PKE Speed Variation Criteria - An additional IM147 implementation issue was the lack
of allowable speed variation criteria for the shortened drive trace. In addition to the false
failures caused by inadequate preconditioning, inadequate control over vehicle operation
during the IM240 test procedure can contribute to inaccurate results. The ability ofa
driver to follow the IM240 speed-time trace has a significant effect on the emissions
recorded during the test. To limit this variation in test results, tolerances are applied to
driver performance.

In the 1997 IM240-related evaluation for EPA (SR98-02-01),’ Sierra developed improved
speed variation criteria based on the total Positive Kinetic Energy (PKE) change per mile
traveled during the IM240 cycle. These criteria were designed to minimize the variation
in emissions while still being feasible for use by minimally trained drivers with a
reasonable aptitude for dynamometer driving. However, only IM240 drive cycle criteria

-15-


Figure 5-4

Retest Algorithm During First IM147 - LDGT1, LDGT2

NO RETEST

Note:

STD = Composite Standard for the specific pollutant.

Max CO Cutpoints
UM 147 TEST
YES NO
Vehicle Fast Passes After
Segment 19
NO | Mode 7 Predicted HC < STD * 1.2
or
Mode 19 Predicted HC < STD * 1.2
YES
No Yes
Mode 7 Predicted HC - STD >09

Mode 19 Predicted HC - STD °

NO | Mode 7-Predicted CO <.STD* 1.3
or
Mode 19 Predicted CO < STD * 1.3
YES
No Yes

Mode 7 Predicted CO - STD >05

Mode 19 Predicted CO - STD .
NO

Mode 7 Predicted NOx < STD * 1.6
and
Mode 19 Predicted NOx < STD * 1.6

YES

y
RETEST

Mode 7 Predicted = Predicted emissions after 7 segments using the composite regression
equations without error term included.

Mode 19 Predicted = Predicted emissions after 19 segments using the composite
regression equations without error term included.

-44-



E, = Error in regression equation n

n = Equation number (corresponds to the number of modal
segments completed)
m = Segment number

Table 5-5 details the results of the fast-fail algorithm for both the second and third IM147
tests. As the number of false failures indicates, the fast-fail criteria was developed with
the intention of minimizing false failures. The results shown in the table are also
deceptive, since they are based on an analysis of the impact of the fast-fail algorithm in
the absence of the other test criteria. While over 600 fast-failures are shown in the table,
many of these are also subject to the retest criteria, resulting in a much smaller fast-fail
impact when the criteria are combined. In this latter case, the number of fast-fails falls to
roughly 200 vehicles. This effect, combined with the fact that such fails occur relatively
late in the 3-IM147 test cycle, leads to a fairly small impact on average test me. Once the
fast-fail algorithm was enabled with the fast-pass and retest algorithm, average test time
was reduced from 96 seconds to 94 seconds.

Table 5-5
Fast-Fail Algorithm

Second IM147 Third IM147 False
Vehicle Class Fast-Failures Fast-Failures Failures

Integration of Fast-Pass, Retest, and Fast-Fail Algorithms

The next step in the analysis was to integrate the Fast-Pass, Retest, and Fast-Fail criteria
to determine their net effect. Table 5-6 shows how average test times and excess
emissions identified vary by model year range and vehicle class using the integrated
criteria. :

As you can see, once the retest and fast-fail algorithms were added to the fast-pass
algorithm, excess emissions identification improved. There are two reasons for this: (1)
vehicles that would be falsely passed later in the test are now failed prior to that decision
being made; and (2) some vehicles that passed according to the IM147 criteria yet would
have failed the IM140 are now falsely failed on the IM147, thus increasing IM240 excess
emissions identification.

-48-


Failure Rate

Intermediate Cutpoints

HC co NOx OVERALL
Fail | Pass | % Fail [Fait ] Pass | % Fail
First M147
LDGV__| 286 | 1494] 16.1% | 238 | 1542 | 13.4% | 281 |1499| 15.8% | 533 | 1247| 29.9%
Loci {121 | 843 | 12.6% | 62 | 902 | 6.4% |122| 842 | 12.7% |247| 717 | 25.6%
LoGT2_| 84 | 519 | 13.9% | 47 | 556 | 7.8% | 71 | 532 | 11.8% | 142 | 461 | 23.5%
all 491 [2856 | 14.7% |347 [3000 | 10.4% |474 [2873 | 14.2% | 922 [2425 | 27.5%
Second IM147
LoGv [170] 1610] 9.6% |161]1619| 9.0% | 214 ]1566| 12.0% | 386 [1394] 21.7%
Loct1_| 87 | 877 | 9.0% | 57 | 907.| 5.9% | 78 | 886 | 8.1% |176| 788 | 18.3%
LpGT2 | 46 | 557 | 7.6% | 40 | 563 | 6.6% | 48 | 555 | 8.0% |100| 503 | 16.6%
All 303 |3044| 9.1% |258|3089| 7.7% | 340 |3007| 10.2% | 662 | 2685 | 19.8%
Third 1M147
LpGV [157 [1623] 8.8% |161]1619| 9.0% | 196 ]1584] 11.0% | 367 | 1413 | 20.6%
LpGT1_| 80 | 884 | 8.3% | 53 | 911 | 5.5% | 73 | 891 | 7.6% [165 | 799 | 17.1%
Loct2 | 40 | 563 | 6.6% | 42 | sei | 7.0% | 42 | 561 | 7.0% | 93 | si0 | 15.4%
3091 | 7.6% [311 [3036] 9.3% |625 | 2722 | 18.7%
Failure Rate By Model Year Grouping - First IM147
Intermediate Cutpoints
co NOx OVERALL
Fail | Pass | % Fail [Fait | Pass | % Fail [Fail | Pass | % Fail [Fail | Pass | % Fail
81-82 | 52 | 53 | 49.5% | 20 | 85 | 19.0% | 27 | 78 | 25.7% | 64 | 41 | 61.0%
83-85 | 88 | 140 | 38.6% | 91 | 137 | 39.9% | 64 | 164 | 28.1% [146] 82 | 64.0%
LDGV | 86-89 | 94 | 331 | 22.1% | 65 | 360 | 15.3% | 100 | 325 | 23.5% | 169 | 256 | 39.8%
90-95 | 51 | 901 | 5.4% | 58 | 894 | 6.1% | 90 | 862 | 9.5% |150| 802 | 15.8%
96+ | 1 | 69 | 14% | 4 | 66 | 5.7% | 0 | 70 | 0.0% | 4 | 66 | 5.7%
81-85 | 71 | 189 | 27.3% | 42 | 218 | 16.2% | 37 | 223 | 14.2% [117] 143 | 45.0%
oor: |2689-| 36 | 186 | 16.2% | 14 | 208 | 6.3% | 39 | 183 | 17.6% | 71 | 151 | 32.0%
90-95 | 14 | 436 | 3.1% | 6 | 444 | 1.3% | 44 | 406 | 9.8% | 57 | 393 | 12.7%
96+ | 0 | 32 | 0.0% | o | 32 | 0.0% | 2 | 30 | 6.3% | 2 | 30 | 6.3%
81-85 | 37 | 57 | 39.4% | 32 | 62 | 34.0% | 17 | 77 | 18.1% | 53 | 41 | 56.4%
54 | 15.6% 40
10.3%
18 | 0.0% 18
14.2%



Segment 2 Revised Integrated Algorithms Results

The original integrated algorithm results were initially determined assuming fast-pass and
fast-fail results could not be rendered prior to the fourth segment (i.e., no earlier than Test
Time = 28 seconds). This was consistent with the procedure established in SR98-02-01.

Gordon-Darby, wishing to further minimize test time, requested that Sierra explore the
feasibility of rendering fast-pass and third IM147 fast-fail decisions after earlier segments
without degrading excess emission identification. Further investigation found that
decisions could be made as early as the end of segment 2 (i.e., at Test Time = 16 seconds)
if the error multiplier used in the fast-pass decision was increased during segments 2 and
3. For segment 2, the error multiplier was 3, while it was 2.5 for segment 3. Using these
criteria, average test time was reduced to 91 seconds without sacrificing any excess
emissions identification.

Development of IM147 Driver Variation Standards

As will be shown in the body of this report, previous work performed on driver variation
limits utilized Positive Kinetic Energy (PKE) limits to evaluate driver performance.
Analysis conducted for this study, however, revealed limitations with this metric, which
can result in inappropriate driver errors. As a result, a new statistic, Cumulative Positive
Power (CPP), was developed to remedy this problem. Designed to be used in
conjunction with EPA-specified absolute speed variation limits, CPP produces an
improved, more predictable, driver evaluation criteria than PKE.

When applied to the total vehicle sample for this study (minus vehicles with absolute
speed violations), the new CPP criteria produced a total abort rate of 3.4%. Since the
IM147 test, even without fast-pass, fast-fail, or retest, allows for vehicles to pass the test
after a single passing IM147, many of the driver errors in the total sample would not be
experienced since they occurred after the vehicle had already passed. Taking this into
account, the effective abort rate was 2%.

Integration of CPP Variation Limits

The CPP analysis was conducted on a subset of the 3347-vehicle population, with
absolute speed excursion violations (as defined in EPA’s IM240 guidance) removed.
This resulted in an overall data set of 3006 vehicles. Using the 3006-vehicle sample, the
average test time, with the fast-pass, retest, and fast-fail criteria enabled but without the
CPP criteria applied, was 89 seconds. Once the CPP criteria were enabled, 87 tests (of
the 3006 vehicles) were extended, increasing the average test time by 1 second to 90
seconds. This resulting increase of 1.1% is less than the 2% increase projected at the end
of Section 6, which makes sense given that the 2% projection was made without the fast-
pass/fail and retest algorithms in place. The overall test time reductions caused by the
fast-pass/fail and retest algorithms would mean that fewer errors would be committed.

-10-


Table 5-7
Comparison of IM147 Max CO Cutpoints to IM240 Final Standards
Impact on Excess Emissions Lost*

% Excess Emission Identified % Excess Emissions Identified
(Without Fast-Pass, Retest, Fast-Fail) (With Integrated Algorithms)

Class HC co NOx HC co NOx
LDGV 94.1% 96.5% 85.6% 95.6%
LDGT1 75.6% 100.0% 79.5% 98.1%
LDGT2 89.0% 100.0% 53.4% 100.0%

Weighted
Average

86.5% 98.8% 51% 97.9%

* Percent of IM240 (Final Standards) excess emissions identified was determined using the 835 vehicle
sample.

effective than the current phase-in IM240 standards. The table also shows that the
addition of the integrated algorithms results in less than a 1% reduction in the excess
emissions identification rate for CO.

Need for Follow-Up Analysis

As discussed above, the analysis results presented in the report are based on a relatively
small sample of IM147 and IM240 data. While the available data are significantly more
robust than the previous sample of 300 vehicles, it is clear that these results should be
revisited with a much larger sample once IM147 testing is initiated in Arizona. We
therefore recommend that as soon as one to two months of IM147 data are collected in
the program, they should be used to verify the validity of the cutpoints and algorithms
developed in this study. This follow-up analysis would allow for any required fine-
tuning of the cutpoints and algorithms.

-52-


4-2

43

4-4

5-1

5-2

5-3

5-4

5-5

5-6

List of Tables

page
Failure Rates, Third IM147.........00 0.0.00 e eee een eee eee eens 3
Modeled Fast-Pass Results Excess Emissions vs. Average Test Time .......... 5
Retest Algorithm Results ......... 66.6. cece cece eee eens 6
Fast-Fail Algorithm ... 0.0.66. c ccc e eee n nena 7
Comparison of Integrated Algorithms vs. Standard IM147 Impact on
Test Time and Excess Emissions Lost ........... 0000 e eee e eee eee eens 9
Average Test Time by Test Time Reduction Methodology and CPP .......... 11
Comparison of IM147 Max CO Cutpoints to IM240 Final Standards
Impact on Excess Emissions Lost .......... 6.02 cece cece eee eens 12
IM240 to IM147 Composite Regression Equation Coefficients ............... 25
IM147 Composite to Phase 2 Regression Equation Coefficients .............. 27
Revised IM147 Max CO Cutpoints (Composite/Phase 2) .............-.005. 28
Failure Rates, Third IM147........ 000s cece cee cee tenet eet tee e eee 29
Revised IM147 Segments ......... 0.66 c ccc cece cece teen een ene et nee 36
Modeled Fast-Pass Results Excess Emissions vs. Average Test Time ......... 38
Excess Emissions Indentified (With and Without Fast-Pass) .............-... 39
Retest Algorithm Results... 0.0... 60sec cece eee cence ences 46
Fast-Fail Algorithm .............. 00002000 see So occeeevesveceveseveress 48
Comparison of Integrated Algorithms (No CPP) vs. Standard IM147 .......... 49
Comparison of IM147 Max CO Cutpoints to IM240 Final Standards .......... 52
Preliminary CPP Cutpoints Based on Top-50% Drivers ..............-0005- 61
Centered CPP Cutpoints and Resulting Test Abort Rates .............-..--. 62

End Test Decisions Affected by CPP Errors . vec eveeveeeeeeses eee e eee 66

-ii-


8. REFERENCES

. “Determination of Emissions Credit and Average Test Times for IM147 Testing,”
Prepared by Sierra Research for the U.S. Environmental Protection Agency, Report
No. SR99-10-02, October 10, 1999.

. “Additional Study of Preconditioning Effects and Other IM240 Testing Issues,”
Prepared by Sierra Research for the U.S. environmental Protection Agency, Report
No. SR98-02-01, February 2, 1998.

. Heirigs, P.L. and J. Gordon, “Preconditioning Effects on I/M Test Results Using
IM240 and ASM Procedures,” SAE Paper No. 962091, 1996.

. Whitby, R., C. Shih, W. Webster, and R. Card, “Enhanced Inspection and
Maintenance Program: An Investigation of Real Time Data Utility and Dynamic
Quality Assurance Instrumentation - Task 1: A Predictive Modal Regression
Methodology for IM240 Fast-Pass/Fail Decisions,” New York State Department of
Environmental Conservation, EPA Cooperative Agreement #X992008-01-0,
November 29, 1996.

. “Analysis of Alternate IM240 Cutpoints, Phase 2 Testing, and Exempting New
Vehicle Models on Test Duration and Projected I/M Benefits,” Prepared by Sierra
Research for the Arizona Department of Environmental Quality, Report No. SR98-
05-01, May 12, 1998.

. “High-Tech I/M Test Procedures, Emission Standards, Quality Control Requirements,
and Equipment Specifications: IM240 and Functional Evaporative System Tests,”
(Revised Draft), U.S. Environmental Protection Agency, EPA-AA-RSPD-IM-96-1,
June 1996.

. W.J. Webster and C. Shih, “A Statistically Derived Metric to Monitor Time-Speed
Variability in Practical Emissions Testing,” New York State Department of
Environmental Conservation, presented at the 6th CRC On-Road Vehicle Emissions
Workshop, March 18-20, 1996.

HHH

-69-


4. IM147 CUTPOINT ANALYSIS

This section of the report discusses the application of the IM147 Max CO cutpoints to the
3347-vehicle data set. Divided into three parts, the section first discusses changes in
overall IM147 test time. The second part of the section addresses revising the Max CO
cutpoints originally developed in SR99-10-02' based on the large data set now available.
The third and final part of the section details the failure rate when these revised cutpoints
are applied to the 3347-vehicle data set.

IM147 Test Length

Sierra originally developed the IM147 test cycle based on the last 147 seconds of data
from the IM240 drive trace. Following the precedence EPA established with the IM240
drive trace, this meant that the modal results of the IM147 test would have 147 seconds of
data. In short, there were no constraints regarding an odd versus even number of seconds
in the overall test cycle, nor in Phase 2 of the test cycle.

To simplify implementation of the IM147 test in Arizona, Gordon Darby, and DEQ
agreed that modal data would be recorded once every two seconds instead of second-by-
second as is done with the IM240. This allows the IM147 cycle data to fit into the same
size record format as is currently used for full duration IM240 tests. As a result, having
an odd number of seconds in the drive trace creates a problem of what to do with the odd
second.

To alleviate the problem, Gordon Darby suggested that the speed/time trace define the
boundaries for the test time instead of the actual number of data points reported. In other
words, assuming the first speed/time point is labeled zero seconds, the first modal data
result would be recorded for second 1 and the last for second 146. Phase 2 of the test
would also be revised to start at second 66 (first data reported for second 67) and extend
to the end of the test. The net effect of both of these changes is that both the composite
results and the Phase 2 results will contain an even number of seconds. Since this
addresses the issue of Gordon-Darby’s two-second average data collection with no
apparent negative consequences, Sierra revised the test length accordingly. Additional
information on the drive trace is presented in Section 5.

-24-


would necessarily invalidate failing results, nor would all low-end CPP errors invalidate
passing results. If an applicable power error occurred while data required to make a
particular decision were being collected, then the decision would be invalidated and the
test would continue. Specifically, this would apply to fast-pass and fast-fail decisions.
For example, if the vehicle’s emission data were clean enough to permit a fast-pass at
second 40, but there was a low power violation at second 34, then the fast-pass decision
would be invalidated and the test would continue. On the other hand, if the first power
violation occurred after second 40 (e.g., at second 41), then the vehicle could be fast-
passed at second 40.

In a related issue, the CPP error is reset at the conclusion of each IM147. As a result,
low-end CPP errors occurring during the first IM147 do not prohibit pass-oriented
decisions in subsequent IM147 tests. The same is true for high-end CPP errors and fail-
oriented decisions.

Using this logical framework, the CPP variation limits were applied to the 3347-vehicle
sample to determine how this algorithm would affect test time. Since the CPP variation
limits are designed to be imposed in concert with the speed violation limits detailed in the
IM240 guidance, vehicles already failing the speed violation limits were eliminated from
the sample since they would be aborted regardless of the CPP outcome. Of the original
3347 vehicles, 3006 remained after eliminating vehicles with speed violations from the
sample.

The flow chart detailed in Figure 7-1 shows how the CPP decision integrates with the
fast-pass/fail and retest algorithms previously discussed in this report. Note that in cases
where a CPP violation prevented a decision during the third IM147, the vehicle would,
after completing the third IM147, restart the third IM147 again. For the average test time
computation, it was assumed that CPP errors during the third IM147 extended the test
time 146 seconds.

Given the 3006 vehicle sample, the average test time, with the fast-pass, retest, and fast-
fail criteria enabled but without the CPP criteria applied, was 89.07 seconds. Once the
CPP criteria were enabled, 87 vehicles’ tests (of the 3006 vehicles) were extended,
increasing the average test time by 0.98 seconds (or 1.1%) to 90.05 seconds. This is less
than the 2% increase projected at the end of Section 6, which makes sense given that the
2% projection was made without the fast-pass/fail and retest algorithms in place. The
overall test time reductions caused by the fast-pass/fail and retest algorithms would mean
that fewer errors would be committed.

-67-


Figure 6-2

Distribution of Arizona IM147 Positive Power Differences
% Difference Between Reference and Actual Positive Power
(Sample Size = 9,306)

Frequency (% of observations)

“10% -9% -B% -7% -6% -5% 8% -3% -2% -1% 0% 1% 2% 3% 4% 5% 6% 7% B% 9% 10% 11% 12% 13% 14% 15%
Percent Difference from Reference Value

more consistently than others when conducting IM147 tests for a range of vehicles. This
subset of competent drivers and tests was used to establish composite CPP limits.

From the Arizona data sample of over 10,000 valid triplicate IM147 tests, records in
which speed excursions (over +2 mph from the reference trace) occurred were discarded,
leaving a remaining sample of 9,306 tests. For the purpose of establishing composite
CPP variation limits based on the capabilities of “good” drivers, speed excursion tests
were removed from this portion of the analysis.

The remaining data sample was then sorted into groups by individual driver. A total of
231 drivers were found in the sample. Driver groups containing fewer than 25 tests were
then discarded; this left a total of 112 driver groups, which encompassed 86% of the tests
in the total sample (i.e., before discarding small-sample driver groups). Composite CPP
was then calculated for each test in the remaining driver groups. The mean and standard
deviation of CPP from the tests within each driver group were also computed. The driver
groups were then ranked by increasing CPP standard deviation and the top 50% of the
drivers (based on lowest CPP standard deviation) were used to compute possible
composite CPP cutpoints.

Table 6-1 lists a series of possible CPP cutpoints computed from the percentile CPP
variance among the top 50% drivers. For example, the CPP cutpoints shown for the 2%
row under the “Top 50% Percentile” column (the third column in Table 6-1) indicates
that 96% (100% - 2 x 2%) of the tests from the top 50% drivers had composite CPP
within 4,437 and 4,949 mph’/sec.

* Identified by the four-digit “InspectorID” field in the database.

-60-


any problems or questions (e.g., regarding test protocols, data record format, etc.) that
developed during the data collection process in Arizona.

Task 2, Failure Rates - After completion of the vehicle testing described in Task 1, Sierra
analyzed the resulting data. Using the same approach as utilized in the previous EPA and
DEQ studies, projected IM147 failure rates were developed for both passenger cars and
light-duty trucks. Separate projections were generated for the start-up, midpoint, Max
CO, and final IM147 cutpoints developed under the previous analyses.

Task 3, Fast-Pass Cutpoints - Data obtained in Task 1 as well as the 304-vehicle IM147
data set collected in 1998 were analyzed to develop fast-pass cutpoints and algorithms
associated with the Max CO cutpoints. The same approach previously used to develop
fast-pass cutpoints for the other cutpoint scenarios (i.e., start-up, midpoint, and final) was
followed in this analysis.

Sierra developed fast-pass cutpoints and algorithms for the Max CO cutpoints, with
minor adjustments to the model year groups when the data indicated that such a change
improved accurate emission identification. Separate sets of fast-pass cutpoints were
developed for these new model year groups and vehicle classes as contained in EPA’s
IM240 test guidance.

The impact of the resulting fast-pass cutpoints on average dynamometer test time and
excess emissions identified was evaluated using the same techniques as in the previous
analyses. Excess emissions identified will be expressed as the percent of excess
emissions that are identified relative to those identified on the IM240 test.

Task 4, Retest Algorithms - The same data used in Task 3 were analyzed to develop retest
algorithms associated with the Max CO cutpoints. While this task originally charged
Sierra with utilizing the same approach previously used to develop retest algorithms for
the other cutpoint scenarios (i.e., start-up, midpoint, and final), subsequent comments
from Gordon-Darby resulted in an alternate retest algorithms. The impact of the resulting
retest algorithms on average dynamometer test time was evaluated using the same
technique as in the previous analyses.

Task 5, Fast-Fail Criteria - The same data used in Task 3 were analyzed to develop
criteria for evaluating mid-test emissions during IM147s in order to determine whether
emissions performance is improving during the retest. The resulting criteria were
structured to “fast fail” vehicles that are not benefitting from such retesting. The impact
of the resulting fast-fail criteria on average dynamometer test time was evaluated using
the same technique as in the previous analyses.

Task 6, Driver Variation Criteria - The same data as used in Task 3 were analyzed to
develop fast-pass and full duration driver variation limits for the IM147 start-up,

-17-


5. OPTIMIZED IM147 TEST CRITERIA

This section presents the analysis methodology and results used to develop optimized
IM147 test criteria. Criteria developed in the study include fast-pass standards, retest
algorithms, and fast-fail criteria. As decribed below, each set criteria was evaluated in
tu to determine its overall effect on test time and excess emissions identification.

Modal Fast-Pass Standards

IM147 fast-pass standards were originally developed for startup, midpoint, and final
standards as part of SR99-10-02.' The current study furthered that work by developing
fast-pass standards for the Max CO cutpoints. The fast-pass regression coefficients
determined for both SR99-10-02 and the current study were developed using the
methodology described in Sierra Report No. SR98-02-01,” “Additional Study of
Preconditioning Effects and Other IM140 Testing Issues.” As detailed in that report, the
selected drive trace is divided into segments over which mass emissions for HC, CO, and
NOx are summed. By performing a multivariate linear regression of the modal mass
emissions against the composite emissions result, we can determine the coefficients
needed to predict final emissions at mode ends throughout the test. Equation 5-1
illustrates how these coefficients are used to predict emissions.

n
P240n = Cat X {Som x Xnm} + (Mex En) [5-1]

Where: P240, = Predicted emissions after completing n segments

Cc, = Regression intercept for equation n

Sim = Regression coefficient for segment m in equation n

Xim = Total emissions over a given segment m in equation n

M = Error multiplier (usually 2 unless otherwise specified)

E, = Error in regression equation n

n = Equation number (corresponds to the number of modal

segments completed)
m = Segment number

After reviewing the proposed fast-pass and retest procedures described in SR99-10-02,
Gordon-Darby staff expressed several concerns regarding actual in-use implementation of
that procedure. In response, the workplan developed for the current project indicated that

-34-


Speed Excursion Limits [85.2221 (e) (4)] - Speed excursion limits shall apply as
follows:

(). The upper limit is 2 mph higher than the highest point on the trace within
1 second of the given time.

(ii). The lower limit is 2 mph lower than the lowest point on the trace within |
second of the given time.

(iii). Speed variations greater than the tolerances (e.g., during gear changes)
are acceptable provided they occur for no more than 2 seconds on any

occasion.

(iv). Speeds lower than those prescribed during accelerations are acceptable
provided they occur for no more than 2 seconds on any occasion.

Speed Variation Limits [85.2221 (e) (5)]

(i). Alinear regression of feedback value on reference value shall be
performed on each transient driving cycle for each speed using the method
of least squares, with the best fit equation having the form: y = mx + b,
where:

(A). y = the feedback (actual) value of speed;
(B). m= the slope of the regression line;

(C). x= the reference value; and

(D). _b = the y-intercept of the regression line.

(ii). The standard error of estimate (SE) of y on x shall be calculated for each
regression line. A transient driving cycle lasting the full 240 seconds that
exceeds the following criteria shall be void and the test shall be repeated:
(A). SE = 2.0 mph maximum.

(B). =m=0.96- 1.01.
(C). 7° = 0.97 minimum.
(D). b=+2.0 mph.

Simply stated, the speed excursion limits require that vehicles be driven within +2 mph of
the reference trace, accommodating for gear changes and other momentary excursions.

-54-


For this set, the study inspector would select vehicles by scanning the queue for the
closest white vehicle waiting in the lanes. If there were no white vehicles in the queue,
the inspector would look for the palest, closest vehicle waiting in the lanes. The inspector
approached the first 1981 or newer vehicle following that vehicle, checked to make sure
the vehicle had at least half a tank of gas, and asked the vehicle owner if he or she was
interested in participating in a study that would take approximately 30 minutes, for a
payment of $50. The selection process resulted in vehicles waiting in a queue for
approximately 5 to 15 minutes prior to testing. Most of the vehicles participating in the
program were receiving their initial test; however, 12 vehicles in the database were being
re-tested after an initial failing score.

As discussed in SR99-10-02, four vehicles were pulled out of the original 304 vehicle
data set due to anomalous results. For this study, while Sierra did find some vehicles with
anomalous results in the newer data samples (e.g., passing the initial IM147 yet grossly
failing the final one), these vehicles were not removed from the sample, with the data set
instead being viewed as representative of the in-use fleet. To remain consistent regarding
the treatment of data from the older sample, however, the same four vehicles were
removed for this analysis. The four vehicles are described below.

. Record 14, a 1988 Pontiac Bonneville, had relatively low CO emissions
during the first and second IM147 test (1.35 and 3.42 g/mi, respectively).
However, CO emissions during the third IM147 increased substantially (to
55.72 g/mi) and were higher still during the IM240 following the IM147
testing. It is interesting to note that CO was emitted in measurable
quantities throughout the test, and the large increases are not attributable
to a specific section of the trace. It thus appears that the gradual emissions
increase could be attributable to excessive purge as the vehicle warmed up
or to some kind of catalyst protection scheme.

° Record 15, a 1989 Dodge Dynasty, had moderate CO emissions during the
first three IM147 tests (14 to 18 g/mi), but emissions during the IM240
test were excessive, particularly during the end portion of that test
(106 g/mi). Reviewing the modal CO emissions in Figure 3-5, one
observes that the vehicle appears to go into open-loop operation at the start
of the large hill of the end portion of the test (i.e., beginning at about
second 160 of the IM240). Although CO emissions accrue throughout this
test, the period from 160 to 230 comprises the bulk of the emissions.

° Record 23, a 1993 Ford Ranger, shows a very similar emissions response
throughout the three IM147 tests. As seen in Figure 3-6, most of the CO
emissions occur during seconds 62 to 75 of the IM147. During the end
portion of the IM240, a similar pattern is observed. In that test, however,
substantial CO is also emitted during the high-speed portion of the trace.
It is not entirely clear what has caused this, but it appears that the vehicle
did not follow the speed-time trace as smoothly during the end portion of
the IM240 as it did during the first three IM147 tests.

-20-


1M147 Composite Regression Coefficients, HC, 1988 to 1995 Model Year LDGT2's
Regression Coefficients
Segment. RMS Reg.
Number Error Constant ic2 ic3- iC4 ics C6 C7 ica ica C10 cat C12 C13 1014 C15. C16 C17 C18 C19
1 [DGT2 [68-95 | 0.48084] 0.5205) r
jP2 ILOGT2 {88-95 | 0.33713] 0.21906} 4.9422]. a
iP3 ILOGT2 |88-95 | 0.29651] 0.19834] 2.8353] 4.9489]
iP4 ILOGT2 [88-95 | 0.28747] 0.18089} 2.7951] 2.9733] 3.2647].
iPS ILOGT2 |88-95 | 0.27016] 0.17705] 1.8312] 2.2325} 1.406] 5.8661
PG ILDGT2 [88-95 | 0.25421] 0.19481 2.2441] 0.5267] 0.4177] 3.0039] 5.8248] .
iP? ILDGT2 |88-95 | 0.24477] 0.17308] 1,867] 0.6968] 0.5785] 0.8635} 4.352) 4.2662| .
PB ILDGT2 [88-95 | 0.22604] 0.14292| 1.5845] 0.5456] 0.1762] 0.5776] 1.9469] 3.7472] 4.4401 .
fPs LDGT2_ [88-95 | 0.22226] 0.14551) 1.703] 0.284] 0.1479] 0.9179] 0.6334] 3.6501 3.448] 2.3522]. a
{P10 ILDGT2 [88-95 | 0.21442] 0.13617] 1.5866] 0.3214] 0.3077] 0.4365] -0.1063] 3.9046] 2.8806] -0.9464) 5.4516]. a
P11 ILDGT2 188-95 0.1654} 0.04125] 0.773} 0.5132] 0.6898] 0.6807| 0.4255] 2.0621] 1.1813] 0.4048] 2.5588] 2.1807]. .
P12 ILDGT2 |88-95 | 0.14608] 0.03783} 0.5966} 0.307] 0.8399] 0.3903] 0.6138] 1.7604] 1.3774) 0.14] 2.8081] 1.4104] 2.3284) |. .
[P13 ILOGT2 [88-95 | 0.08638] 0.03352! 0.5004] 0.8055} 0.8896} 0.2765} 1.4656] 0.5695] 0.5182) 0.3106} 1.2433] 0.9247] 0.2803] 4.1519].
P14 LDGT2 188-95 | 0.07556] 0.02491 0.524] 0.7653] 0.8982] 0.3475] 1.5181] 0.5104] 0.7567] 0.2981} 1.4094] 0.6811] 0.0851] 2.7444] 1.9985] .
P15: ILOGT2 {88-95 | 0.06718] 0.02356] 0.5183} 0.8188] 0.8118] 0.379] 1.4304] 0.7007] 0.8512] 0.4013] 1.2797] 0.6663] 0.2996] 1.6768] 0.8504{ 1.7815].
pie ILOGT2 [88-95 | 0.06237] 0.02258} 0.5458] 0.8938] 0.7295} 0.4333] 1.4798] 0.6019] 0.7965] 0.4454] 1.2725] 0.6721] 0.4389] 1.2102] 0.7846] 1.1152] 3.1105]. .
[Piz ILOGT2 [88-95 | 0.04421] 0.01056] 0.7137} 0.6003} 0.6133] 0.8089] 0.9512] 0.7459] 0.7414] 0.5887] 0.9827] 0.6061] 0.6414] 0.972] 0.8458] 1.1687] 1.7368] 1.9774) |.
Pris ILOGT2 {88-95 | 0.03718] 0.00462| 0.6818} 0.6872} 0.6822] 0.7561] 0.9525} 0.6907] 0.7852] 0.6052] 1.0177] 0.6169} 0.7309] 0.6146] 0.7739] 1.0312] 0.9695] 1.4043} 1,372].
pPis ILDGT2 {88-95 | 0.03217] 0.00297] 0.7085} 0.7059} 0.6951] 0.7723] 0.8271] 0.7424] 0.7667] 0.703} 0.8462] 0.6346] 0.6886] 0.6361] 0.6291] 0.7448] 0.7476] 1.2353] 1.0716] 0.9076]
|M147 Phase 2 Regression Coefficients, HC, 1988 to 1995 Model Year LDGT2's
Regression Coefficients

Segment IRMS Reg.
Number Error Constant {C1 ic2 ic3 ica C5. icé C7 ic8& ico C10 abl |C12 ic13 C14 ic15 C16 C17 C18 ic19
ber [OGT2 188-95 | 0.23/51] 0.05760]. 3.6759). i
[B12 ILOGT2 [88-95 0.2067] 0.04911}. 2.4274) 3.3621 .
/ EXEY ILDGT2 188-95 0.1199] 0.03897) 1.2412] -0.4078] 5.5892]. .
[E14 ILOGT2 [88-95 | 0.10757] 0.02785}. 1.0283] 0.0365] 3.9664] 2.4645]. .
Bis. LDGT2 [88-95 [0.09668] 0.02657]. 1.0392] 0.3282] 2.5887] 0.8805] 2.4135]. .
[Bis ILOGT2 [88-95 | 0.09084] 0.02471 1.0542] 0.5363] 1.9743] 0.7835] 1.5325] 4.1018].
B17 ILOGT2 |88-95 | 0.06106] 0.01248]. 0.8883] 0.8408] 1.4659] 1.0499] 1.5851] 2.3095] 2.7427].
[B18 ILOGT2 [88-95 | 0.05212] 0.00425]. 0.9142] 0.9654] 1.0256] 0.9252] 1.4081] 1.2094] 2.0957] 1.7973].
iB19 iLDGT2 [88-95 0.0449] 0.00374]. 0.9299] 0.9243] 1.0294) 0.748] 0.9948} 0.9323[ 1.7813] 1.4026[ 1.2774}



pollutants reduce as a vehicle warms up. NOx, on the other hand, may actually increase
the longer a vehicle operates, so the appropriate convergence ratio threshold may be
greater than one.

Iterative solutions to this equation using actual vehicle data suggest, however, that both of
these assumptions may result in overly stringent application of the retest algorithm, thus
creating unacceptable false failure rates. In short, some vehicles failing for either HC or
CO during one of the early IM147 tests may go on to pass a later IM147 in spite of the
fact that their emissions appeared to be diverging from the cutpoint during the earlier
IM147. Therefore, the convergence threshold for HC and CO may have to be greater
than one to safely predict convergence. Regarding NOx emissions, modal emissions are
even more difficult to predict. As a result, the convergence ratio was not applied to the
NOx channel. Instead, the retest algorithm predicts NOx failures by simply comparing
segment 7 and 19 NOx readings to the previously mentioned tolerance level. If the
readings are greater than the prescribed multiple of the cutpoint, then the vehicle does not
receive a retest.

Separate algorithms were developed for LDGVs and LDGTs based upon whether one or
two IM147 tests had been completed. In all cases, a predicted emissions score of less
than the standard (but that does not trigger a fast-pass) is treated as cause for a retest.
This approach avoids the need to deal with negative convergence ratios, while also
providing maximum potential for vehicles to pass the test. The flow charts shown in
Figures 5-2 through 5-5 show the specifics regarding how these algorithms work.

Unlike the fast-pass algorithms, the error term in the fast-pass regression equations is not
included when determining predicted emissions for the Segment 7 to Segment 19
comparison. If the error term (which varies between regression models) were included,
the retest procedure would need to be refined for specific regression models in addition to
vehicle types. Without including the error term, LDGVs can simply be separate from
LDGTs while still maintaining acceptable accuracy.

Table 5-4 presents the results of the retest algorithm independent of the fast-pass
algorithm. As shown in the table, the retest criteria eliminate more vehicles after the
second test than after the first test. This is in contrast to the fast-pass criteria, which pass
a disproportionate number of vehicles after the first IM147 when compared to the second
IM147. Given the difference between the two criteria, this makes sense. While vehicles
can pass the test after early IM147 tests without the fast-pass enabled, the only way a
failing vehicle can end the test without the retest algorithm enabled is to run the full
duration of the test. As a result, the retest criteria on the first IM147 are judged by their
ability to predict emissions on the third IM147 whereas the fast-pass criteria is judged by
its ability to predict emissions on the current IM147, an easier task. In turn, the retest
criteria must be correspondingly more conservative on the first IM147 than the fast-pass
criteria.

In addition to listing the point at which vehicles were denied a retest, Table 5-4 lists the

number of vehicles falsely failed as a result of the retest procedure. The criterion for false
failing is quite simple; a false failure occurs any time a vehicle, which would go on to

-41-


|M147 Composite Regression Coefficients, HC, 1986 to 1989 Model Year LDGT1's
Regression Coefficients
Segment IRMS Reg. !
Number Error [Constant |C1 C2 ic3 ic4 ics C6 ic7 ics i] 1c10 C14 ic12 C13 C14 C15 C16 C17 C18 ic19
[Pi [L0GT1 Jeo-09 | 0.40420] 0.5423] 17.2623]. r
P2 ILOGT1_ [86-89 | 0.37436] 0.2461} 2.2738] 4.5192].
iP3 ILOGT1 {86-89 | 0.33313] 0.22224] 0.5252] 2.5885] 4.6599]. a
iP4 ILOGT1 [86-89 | 0.29595} 0.20426} 0.5943] 2.3293] 2.3375] 3.7634].
iPS. ILOGT1 186-89 0.2938] 0.19895] 0.6763] 2.0377] 2.3025] 2.9749] 1.8038]. .
[Ps ILOGT1 |86-89 | 0.28058] 0.20644] 0.6191] 2.2969] 1.2069] 2.4549] -0.1084] 4.2386]
iP7. ILOGT1_}86-89 | 0.27458] 0.19491} -0.0117] 2.0167] 1.3562] 2.7382] -1.5023] 2.6456] 2.8231]. a t *
iPS ILDGT1 [86-89 | 0.26273] 0.17858] 0.9768] 1.9562] 1.0027] 2.2073] -1.5778] 1.5977] 2.003] 3.2606} .
iPS. ILDGT1_|86-89 | 0.25683] 0.17484] 0.544] 2.0132] 0.9204] 2.1628] -2.2132] 0.7045] 2.0543] 2.3838] 3.4294]. . .
P40 ILOGT1 [86-89 | 0.25153] 0.17214] 0.4457] 2.0253] 0.8623] 1.9864] -2.2523] 0.4119] 2.2232] 1.6726] 1.6078] 3.6006].
i ILDGT1 ]86-89 { 0.19659] 0.07478] 2.1606] 0.6298] 1.0463] 1.2006] -1.6683] 1.7941] 1.4349] 0.7533] 1.4174] 2.3957] 1.957]
ae ILOGT1 [86-89 | 0.17271] 0.05609] 2.4645] 0.4889] 0.5378] 1.0905] -0.6636] 1.2017[ 1.3698] 1.1634] 0.8772! 3.332] 1.2131] 1.5195] .
P13, LDGT1 [86-89 [ 0.13816] 0.05847] 2.1195] 0.4529] 0.7286] 1.2692] -0.7285] 1.427] 1.284] 0.6442] 0.4274] 2.4657] 0.9117] 0.2322] 2.6432]. -
[Pia ILDGT1 [86-89 | 0.12661] 0.04856] 2.2389] 0.3811] 0.6514] 1.4033] 0.4687] 1.3427[ 1.1456] 0.9582] 0.0864] 2.2192] 0.7164] 0.4248] 1.479] 2.6071 .
fP15 ILOGT1 [86-89 | 0.11362] 0.03727] 2.1223] 0.3411] 0.6537] 1.4601 0.202] 1.1529] 1.232] 0.6887] 0.2172] 2.1843] 0.7658] 0.6133] 0.9282[ 0.7457] 2.1404]. a
P16 LDGT1_]86-89 | 0.10769] 0.03525] 1.9852] 0.3076] 0.7867| 1.4374] -0.022] 1.1859] 1.1329) 0.515} -0.1541] 2.0989] 0.7572] 0.7681] 0.784) 0.499] 0.7808] 4.7323]-
P17, ILDGT1 |86-89 | 0.05356] 0.01318] 1.0985] 0.6236] 0.7621] 0.7653] 0.5658] 1.0327] 0.7036] 0.4129] 0.7975] 1.2149] 0.7601] 0.6019] 0.8454] 0.937] 0.7894] 0.9132] 3.009) a
P18 ILDGT1 {86-89 | 0.04127] 0.00269} 1.2483] 0.5985} 0.678] 0.9508] 0.3871 4.095] 0.7732] 0.6052] 0.8039) 0.761] 0.6892] 0.7308] 0.7205) 0.82] 0.9126] 0.0726] 2.0163] 1.4844].
P19, ILOGT1 [86-89 [ 0.03395] 0.00397[ 1.0933] 0.6168] 0.695} 0.9028] 0.4507] 1.0319] 0.9511] 0.5369] 0.7983] 0.6339] 0.6758] 0.6909] 0.7183] 0.7503] 0.7572] -0.3355[ 1.6677] 1.2005] 0.8458]
IM147 Phase 2 Regression Coefficients, HC, 1986 to 1989 Model Year LDGT1's
‘Regression Coefficients

Segment RMS Reg.
Number Error Constant }C1 ic2 ic3 C4 ics C6 ic? C8 ico C10 ici1 }C12 C13 C14 C15 C16 }C17 C18 C19
| (LOGI? [66-60 | 0.20634) 0.1150). 7] 3.3407) r
fei2 ILOGT+_ [86-89 | 0.26979] 0.09053]. 2.3987] 1.9564 .
+ ChEY ILOGT1 {86-89 | 0.20617] 0.08073} 1.4658} 0.0492] 4.1671). .
yB14 ILOGT1_ }86-89 | 0.19052] 0.06515}. 1.1799} 0.3012] 2.4794] 3.6519] a
[B15 ILOGT1 {86-89 | 0.16882] 0.04542]. 1.1829] 0.6531] 1.4949] 0.6859] 3.2881] | .
feis ILOGT1_ [86-89 | 0.16083] 0.04255) 1.1483] 0.9014] 1.2632] 0.3783] 1.3378) 6.662) a
B17, ILOGT1 [86-89 | 0.07626] 0.01566]. 1.0548] 0.7801] 1.1751] 1.2709] 1.2392] 0.9683] 4.2486) .
B18 ILOGT1 86-89 | 0.05999] 0.00012]. 0.9466] 0.9733] 1.0114] 1.038] 1.3674] -0.1144] 2.9584) 1,987].
B19 ILOGT1 [86-89 [ 0.04869] 0.00402]. 0.9313] 0.9308] 0.9901] 0.9768] 1.1033] -0.6703] 2.4005] ~ 1.5697} 1.23}



Although conceptually appealing since variation limits are established independently for
each individual second, the latter approach would require a rigorous modal analysis just
to develop initial variation limits for each second. A complex iterative process would
then be required to evaluate these limits over the entire trace and “tune” them in a manner
that yields an acceptable overall abort rate. This tuning step would also consider the
relative impact of trace variations at each second on measured emissions. For example, it
would be desirable to apply tighter trace variation limits during acceleration segments
that produce high emissions than during less emissions-significant idling segments if the
resulting overall abort rates (across all segments) can be kept at acceptable levels.

In addition, it is believed that imposing tighter, independently established second-by-
second trace variation limits would result in a much greater degree of “re-learning” by the
lane inspectors as they adjust to the impact of these limits. This re-learning process and
any necessary re-tuning of the variation limits could best be evaluated through an initial
pilot study before being implemented on a program-wide basis. During this pilot study, it
is also envisioned that different approaches to providing visual feedback to drivers as
they attempt to follow the trace could be evaluated. For example, this could involve
providing dynamically updated forward-looking “trace envelopes” or speeds that guide
the driver back toward the reference trace when a nominal excursion begins in a manner
that complies with the scond-by-second variation limits.

As a result of the scope of the latter approach, the first approach was selected because it
was believed to substantially overcome the shortcoming of the PKE-based metric while
being less resource-intensive to apply and test than limits developed from a full modal

analysis. It should also be noted that using CPP as a replacement for PKE assumes that

the existing speed excursion criteria developed by EPA will be applied in conjunction
with CPP-based variation limits. This assumption is dictated by the use of a cumulative

metric in specifying second-by-second variation limits.

Development of Composite Variation Limits - Composite IM147 variation limits were
generated using a similar methodology to that employed in the 1998 IM240 study, except
cumulative positive power (CPP), rather than cumulative PKE, was used as the statistical
metric. .

Figure 6-2 shows the distribution of composite (i.e., 147-second) CPP calculated from the
second-by-second actual speeds in the Arizona data sample, expressed as the percent
difference between actual and reference IM147 CPP.

As the figure shows, actual CPP appears normally distributed, although the median CPP
is approximately 1% higher than the reference value. To determine how far to go along
the “tails” of the CPP differences distribution to set composite limits, CPP differences
among the test lane drivers in the Arizona data were examined. The basic concept
applied in setting the CPP limits was to identify a significant fraction of drivers who,
historically, could always (or nearly always) run IM147 tests within the selected CPP
limits. Given a mixture of ability among individual drivers to follow the reference trace,
Sierra sought to identify the fraction of “competent” drivers who could follow the trace

-59-


IM147 Composite Regression Coefficients, HC, 1990 to 1995 Model Year LDGT1's

Regression Coefficients

Segment RMS Reg.

Number Error Constant |C1 ic2 C3 C4 ICS C6 C7 ic8 ico C10 C11 Ic12 C13 ic14 C15. C16 C17 C18 C19
P41 [oct 90-95 | 0.37406] 0.22818] 17.1854 .

iP2 ILDGT1_ [90-95 | 0.23507] 0.05902} -2.145] 5.1262!
E LOGT1 [90-95 | 0.21189] 0.05784] -1.1729] 3.3619] 3.4597]
[Ps LOGT1_ {90-95 | 0.20276] 0.05212] -2.7245] 3.23747 1.6019] 3.7987]. .
[Ps ILOGT1_[90-95 | 0.19505] 0.05057] -3.0392] 2.7597] 1.1351 2.24] 3.8993].
[Ps ILOGT1 {90-95 | 0.18478] 0.05162] -3.3501] 2.6388] 0.6397] 1.4629] 1.9249] 4.9984].
Pz. ILOGT1_ {90-95 | 0.17282] 0.04206] -2.9083] 2.3805] 0.6434] 1.9124] -0.3621] 3.0178] 4.1577]
[Ps ILOGT1 [90-95 | 0.16217] 0.03882] -1.4065] 1.7566] 1.0327] 1.3221] -0.6046] 1.7837[ 29574] 4.5516]. .

iP9 ILOGT1_}90-95 | 0.15997] 0.03954] -1.5284] 1.8223] 0.7356] 1.6157] -0.8447] 0.9682] 2.5193] 3.7565] 3.0158]. a

P10 ILOGT1_}90-95 | 0.15899] 0.03962] -1.3222] 1.7605] 0.8841 1.526] -0.7219| 0.6441] 2.5385] 3.3964] 1.3921] 2.2962}. a

Pat ILOGT1_}90-95 0.1242] 0.00888] 0.4575} 0.8942) 1.2216} 4.078] -0.2219] 0.7626] 1.9585] 1.2513] 1.7074] 1.5915} 1.5461].

P12 ILDGT1_|90-95 0.108] 0.00853] 0.5588] 0.7543} 0.7122] 1.489] -0.1897] 0.6244] 1.9355] 0.7067] 0.8076] 2.4795] 0.9978] 1.8755]. .

P13 ILOGT1_ {90-95 | 0.08846] 0.01744] 0.8836] 0.5089] 0.8095] 1.7266] 0.0115} 0.5537] 1.4386] 0.4008] 0.5776} 1.904] 0.7549] 0.8219] 2.3703) a

P14 |LOGT1 [90-95 | 0.07835] 0.01772] 0.858} 0.474} 0.6951] 1.5244{ 0.3476} 0.75421 1.4394] 0.4383] 0.7477] 2.2305] 0.5943] 0.9555] 0.9132] 2.1616].

[P15 |LOGT1_]90-95 | 0.06853] 0.01326] 0.7115] 0.5394] 0.7616] 1.4283] 0.4159] 0.4769] 1.3363] 0.4071] 0.6623] 1.647] 0.6826] 0.8356] 0.5762] 0.7447] 2.2358].

P16 ILDGT1_[90-95 0.0593} 0.01319] 0.6956] 0.6235) 0.517] 4.2912] 0.6027] 0.5332] 1.4561] 0.3365] 0.4783] 1.6692] 0.6904] 0.8659] 0.7576] 0.7049] 0.1973] 5.5813] .

[P17 ILDGT1_ {90-95 | 0.04054} 0.0066} 0.721] 0.7143} 0.508] 0.9341} 0.7735] 0.3397| 1.3289] 0.8072] 0.4656] 0.9685] 0.6893] 0.7801] 0.6168] 0.8507) 0.941] 2.2139] 2.3062]. .
P18 ILOGT1 [90-95 | 0.03216] 0.00497] 0.6809] 0.6934] 0.5834] 0.9437] 0.759} 0.6708] 0.9442] 0.9559] 0.356] 0.8599] 0.6671] 0.8103] 0.5884] 1.0269} 0.3629] 1.3443] 1.5874] 1.5359].
[Pi ILOGT1 |90-95 | 0.02955] 0.00468] 0.7103] 0.6964] 0.6123] 0.9353} 0.834] 0.5442] 0.9229] 0.8944] 0.4112] 0.8891] 0.6898] 0.7038] 0.6909] 0.7838] 0.3123] 0.6669[ 1.5046] 1.3036] 0.7468]

IM147 Phase 2 Regression Coefficients, HC, 1990 to 1995 Model Year LDGT1's
Regression Coefficients

Segment RMS Reg.

Number Error Constant {C1 ic2 ic3 ica ICS C6 \c7 ics ico C10 ic11 C12 C13. C14 jC1S C16 C17 C18 iCig
B11 TDGTT ]90-85 | 0.19826] 0.0335]. 3.1539} :

B12 ILOGT? |90-95 | 0.16235) 0.0226}. 1.7885] 3.1368}. .

| EXE ILDGT1_ [90-95 | 0.12683] 0.02868]. 1.1305] 1.1678] 3.5627]. .

{814 ILOGT1_|90-95 | 0.11708] 0.02986]. 0.9821| 1.3215] 2.042] 2.4823]. .

{B15 ILOGT1_|90-95 | 0.09769} 0.0205]. 1.024] 1.124] 1.0503] 0.5065] 3.5529]. a

fais ILOGT1 }90-95 | 0.08453} 0.02072]. 1.0421] 1.0995] 1.3341] 0.4695] 0.6556] 7.7639]. .

B17. ILOGT1_}90-95 | 0.05719] 0.01154]. 1.0027] 0.9977] 1.0045] 0.9826] 1.4994] 2.7725] 3.1834]. 5
B18. ILDGT1_[90-95 [ 0.04461 0.008} 0.9558] 1.0711] 0.8811] 1.2953] 0.5862] 1.7403] 2.1672] 2.144].
B19 ILDGT1 [90-95 | 0.04108] 0.00785]. 0.9823] 0.9356] 1.0185] 0.9731 0.499] 0.8303] 2.0505] 1.8394] 1.0152}



IM147 Composite Regression Coefficients, NOx, 1990 to 1995 Model Year LDGV's
a Regression Coefficients
Segment RMS —|Reg.
Number Error Constant [C1 ic2 C3 ica cS. C6 ic7 -|ce ica }C10 C14 jc12 C13 C14 ic15 C16 C17 cis C19
qi TOGV_ 90-95 | 065265] 0.96234] 27.2002.
jP2 ILOGV__|90-95 | 0.55676} 0.37911] 3.4685} 3.9198]. . :
iP3 ILOGV__|90-95 0.5187] 0.31019] 2.4287] 2.702] 6.9157]. . a
iP4 ILOGV__ {90-95 | 0.51364] 0.29256] 2.566} 2.669] 5.4346] 4.5289] a
IPS ILOGV__ [90-95 | 0.47026] 0.27475] 0.7261} 2.0325] 3.4764[ 2.5823] 7.1639]. .
iP6 LDGV__}90-95 | 0.46835] 0.27776] 0.0795] 2.0153] 3.3673] 1.5718] 6.6904] 3.6255]. \. A
iP7, ILOGV__|90-95 | 0.43598] 0.24653] -0.2787| 1.6476] 1.7967} 2.607] 2.986] 4.5264] 4.7362]. . !
[Ps ILOGV__]90-95 | 0.42397] 0.22613] 0.8668] 4.576] 1.4199] 1.0315] 2.7698] 1.4176] 3.8634] 4.4221]. A
[Ps ILOGV_ [90-95 | 0.41414] 0.21779] 0.4251] 1.6155] 0.9945] 0.7619] 2.1999] 0.108] 3.4947] 3.5743] 5.7316]. 1
P10 ILOGV__}90-95 | 0.40712| 0.2139] -0.4251] 1.6685] 0.371] 1.1081] 1.7587] 0.4377] 2.6567] 3.7398] 4.0116] 3.1506]: a
[Pit ILOGV__ [90-95 | 0.32541] 0.07608] 0.4951 1.01] 0.8419] 0.3247] 2.3515] 0.3887] 1.0667[ 2.0267] 3.9523] 1.1292]1 1.4038].
P12 ILOGV__|90-95 { 0.27382} 0.05866] 1.1507] 0.9457] 0.7041] -0.1603] 1.9823] 0.7329} 1.4374) 0.757] 2.5555] 1.2857] 0.8276] 3.2666].
[P13 ILOGV__|90-95 0.1552| 0.0465} 1.245] 0.7538] 1.2793] 0.8753] 0.6342] 0.2174] 0.6015] 0.6589] 2.2733] 0.1902]: 0.7277[ 1.3434] 2.4018].
{P14 LOGV__[90-95 | 0.11701] 0.02975} 1.1371] 0.7436 0.97] 0.8665] 0.7991] 0.5198] 0.7343] 0.5517] 1.7367] 0.4654] 0.6168] 1.0241] 1.4687] 1.7969]. a
{PIs LOGV__[90-95 [0.072197 0.00927] 0.8725] 0.7153] 0.8392] 0.9635[ 0.7945] 0.6765] 0.7708] 0.6798] 1.0226] 0.5631]' 0.7013] 0.8864] 0.8237| 1.0439] 1.3295]. .
P16 ILOGV__ {90-95 | 0.06398] 0.00808] 1.0798] 0.7045] 0.8792] 0.8433] 0.7229] 0.5834] 0.8931] 0.5466] 0.9541] _0.7035]i 0.7254] ~0.6265] 0.8134] 0.9715] 1.0589} 1.191]. .
PAT ILOGV__|90-95 | 0.05809] 0.00266] 0.7561] 0.7292] 0.6974] 0.8682] 0.8458] 0.6179] 0.8355] 0.681] 0.8312] 0.5502]: 0.7239] 0.822] 0.8206] 0.9771} 0.9638] 1.1024] 1.5163].
P18 * LOGV_ [90-95 | 0.03251] 0.00233} 0.6937] 0.7144] 0.6856] 0.9367] 0.7529] 0.9301 0.619] 0.8318] 0.7798] 0.5858]1 0.6898} 0.8309] 0.7014] 0.849} 0.7786} 1.1002 0.83] 0.8865}
P19, LOGV_ 90-95 | 0.00903] 0.00112] 0.8195} 0.7065] 0.733} 0.7558] 0.70727 0.8124] 0.7212] 0.7331] 0.7165] 0.7193]/ 0.7121] 0.7215] 0.7187] 0.7075] 0.7149] 0.7316] 0.7374} 0.7123] 0.7172}
|M147 Phase 2 Regression Coefficients, NOx, 1990 to 1995 Model Year LDGV's
‘Regression Coefficients
Segment IRMS —{Reg.
Number Error [Constant {C1 ic2 ic3 ica ics C6 ic7 ics ico C10 cit C12 C13 C14 C15 C16 C17 C18 cig
Bit TDGV. 190-95 0.4955] 0.16783}. 12.7648] .
B12 LDGV~ {90-95 | 0.39806] 0.10265] 11.5279] 4.9874]. ~
B13 LDGV_[90-95 | - 0.21598} 0.0764]. 10.9775] 2,036] 3.2274].
[ei ILOGV__ [90-95 | 0.16191] 0.04867 10.8655] 1.4947] 2.0303] 2.4727]. a
peis. ILDGV__}90-95 | 0.09916} 0.01709}. 0.9685} 4,262] 1.1221] 1.4177] 1.8321]. .
B16 ILDGV__}90-95 | 0.08828] 0.01425] 11.0137] 4.1226] 1.1413] 1.3001] 1.4762! 1.565]. .
Bi? LOGV__]90-95 | 0.07957] 0.00619} {0.9986] 1.1256) 1,132] 1,322] 1.3198] 1.5162] 2.0499} .
B18 ILOGV__|90-95 | 0.04511] 0.00552]. 0.9273] 1.1868}-0.9219] 1.1679] 1.0538] 1.5928] 1.0608] 1.1805].
(B19 ILDGV_ [90-95 | 0.01076] 0.00217]. {0.9663} 0.9854] 0.9735] 0.9568} -0.9719] 1.0002] 1.022] 0.9682] 0.9807}



Excess Emissions with Fast-pass Enabled

Max CO Cutpoints
Excess | Excess HC Excess | Excess CO Excess | Excess NO
Vehicle |Model Year| IM240 HC | with fast- | Excess HC | IM240 CO | with fast- | Excess CO |IM240 Nox | with fast- [Excess NOx
Class Range (grams) {pass (grams)} Identified | (grams) |pass (grams)} Identified | (grams) [pass (grams)} Identified
81-82 0 0 N/A 56.74 49.73 87.6% 3.61 3.61 100.0%
83-85 6.13 6.13 100.0% 243.24 237.02 97.4% 3.49 3.49 100.0%
LDGV 86-89 10.72 10.65 99.3% 301.8 294.27 97.5% 9.92 9.92 100.0%
90-95 8.96 8.68 96.9% 185.26 157.96 85.3% 6.59 6.02 91.4%
96+ 0 i} N/A 0 0 N/A 0 0 N/A
ALL 25.81 25.46 98.6% 787.04 738.98 93.9% 23.61 23.04 97.6%
81-85 10.99 8.32 15.7% 605.55 560.92 92.6% 19.02 18.72 98.4%
88-89 5.55 5.13 92.4% 533.37 485.61 91.0% 2.37 1.69 71.3%
LDGT1 90-95 0.42 0 0.0% 177 0 0.0% 1.65 1.65 100.0%
96+ 0 0 N/A 0 0 N/A 0.43 0 0.0%
ALL 16.96 13.45 79.3% 1140.69 1046.53 91.7% 23.47 22.06 94.0%
81-85 15.62 14.47 92.6% 441.44 441.44 100.0% 6.69 5.72 85.5%
86-87 10.39 10.39 100.0% 285.54 285.54 100.0% 411 2.31 56.2%
LDGT2 88-95 0.21 0.21 100.0% 26.15 15.16 58.0% 0.68 0.27 39.7% |}
96+ 0 0 N/A 0 0 N/A 0 0 N/A
ALL 26.22 25.07 95.6% 753.13 742.14 98.5% 11.48 8.3 72.3%
ALL 68.99 63.98 92.7% 2680.86 2527.65 94.3% 58.56 53.4 91.2%



Table 4-3
Revised IM147 Max CO Cutpoints
(Composite/Phase 2)
Vehicle Class Model Years HC (ee) NOx
1981-82 2.80/2.05 26.37/20.42 3.28/2.85
1983-85 2.08/1.53 17.19/13.13 3.28/2.85
LDGV - 1986-89 1.46/1.07 15.77/12.00 2.75/2.38
1990-95 0.99/0.73 12.85/9.68 . 2.81/2.42
1996+ 0.80/0.59 12.85/9.68 2.25/1.93
1981-85 3.70/2.70 31.47/24.47 5.41/4.74
1986-89 2.86/2.09 25.16/19.46 4.91/4.30
LDGT1
1990-95 1,95/1.43 21.15/16.28 4.46/3.90
1996+ 1.S7/1.15 21.15/16.28 3.36/2.91
1981-85 4.06/2.96 51.88/40.67 6.48/5.69
1986-87 3.79/2.77 39.24/30.64 5.99/5.26
LDGT2
1988-95 2.92/2.13 26.34/20.39 6.11/5.37
1996+ 2.34/1.71 26.34/20.39 4.46/3.90

Comparison of Failure Rates

After revising the Max CO cutpoints using the 835-vehicle sample, the failure rates were
evaluated using each of the four sets of IM147 cutpoints (Startup, Intermediate, Final, and
Max CO) and the combined vehicle data set (3347 vehicles). While the IM147 Max CO
cutpoints were revised for this study, the IM147 Startup, Intermediate, and Final
cutpoints were developed as part of SR99-10-02 and are shown in Appendix A.

Table 4-4 shows how the failure rate changed with the different cutpoints for the third
and final IM147. The overall failure rate will be slightly less when actually implemented
since some vehicles will pass and therefore complete the test after an earlier IM147 even
though they would go on to the fail the third one if the test was continued. More detailed
information on the failure rates is provided in Appendix B.

This table helps to clarify the relationship between the four sets of cutpoints. The
Startup, Intermediate, and Final cutpoints result in nearly equal HC, CO, and NOx failure
rates, which increase with increasing cutpoint stringency. The Max CO standards, when
compared to the final standards, reduce the HC and NOx failure rates, and instead
increase the CO failures, especially for light-duty trucks.

-28-


This table helps clarify the relationship between the four sets of cutpoints. The Startup,
Intermediate, and Final cutpoints result in nearly equal HC, CO, and NOx failure rates, by
vehicle category, which increase as the stringency of the cutpoints increase. The Max CO
standards, when compared to the final standards, result in lower HC and NOx failure
rates, and higher CO failure rates, especially for light-duty trucks.

Figure 1-1 shows the failure rates for each of the three IM147s, based on the Max CO
standards. As the figure shows, most of the decrease in failure rates due to the use of
multiple test cycles (i.e., to address the lack of adequate preconditioning) occurs between
the first and second IM147s. As a result, it is reasonable to expect that algorithms
designed to shorten the test would often end the test prior to the third IM147. This is, in
fact, the case, as is shown later in this report.

Figure 1-1
‘Overall Failure Rate by Vehicle Type
Max CO Standards
35.0%
2 30.0%
Ss Tr FF]
& fF] F
@ 0% ie Hf Bist (M147
2 20.0% i Ta Hl
E 15.0% .' ! Ce 2nd 1M147
0% ++}
% 10.0% ti . i 3rd M147
5 : th
$ F
6 5.0% / vA i
0.0% : u
PC LDT1 LDT2 All
Vehicle Type

Modal IM147 Fast-Pass Standards

Using the same methodology employed in SR99-10-02, this study developed fast-pass
regression standards for the Max CO cutpoints. This methodology requires dividing the
IM147 test into a series of short segments over which emission mass is accumulated. By
performing multivariate linear regressions of these incremental segments, modal fast-pass

‘ coefficients were developed to predict when vehicles would pass without having to
complete the entire test. Unlike the previous study, which divided the IM147 test into 14 .
segments, this study divided the test into 20 segments, thus increasing the frequency of
opportunities for fast-pass.

4.


Figure 5-3

Retest Algorithm During Second IM147 - LDGV
Max CO Cutpoints

UM 147 TEST

yes

Segment 19

Vehicle Fast Passes After

NO

NO

Mode 7 Predicted HC < STD *1

or

Mode 19 Predicted HC < STD * 1

YES
NO . YES
Mode 7 Predicted HC -STD_ 0.9
Mode 19 Predicted HC - STD .
¥
NO | Mode 7 Predicted CO <STD* 1.1
or
Mode 19 Predicted CO < STD * 1.1
YES
NO YES
Mode 7 Predicted CO - STD >08
Mode 19 Predicted CO - STD °
ul
NO | Mode 7 Predicted NOx < STD * 1.2
and
Mode 19 Predicted NOx < STD * 1.2

NO RETEST

Note:

STD = Composite Standard for the specific pollutant.

YES

RETEST

Mode 7 Predicted = Predicted emissions after 7 segments using the composite regression
equations without error term included.

Mode’ 19 Predicted = Predicted emissions after 19 segments using the composite
regression equations without error term included.

-43-



Projected IM147 Failure Rates

Prior to projecting failure rates for the IM147 test using each of the four set of emissions
standards (Startup, Intermediate, Final, Max CO), Max CO cutpoints developed as part of
SR99-10-02 were revised using the combined 300- and 535-vehicle data sets. To this
end, IM147 scores were regressed against IM240 scores. The resulting regression
equations were then used to derive IM147 cutpoints from the IM240 cutpoints. IM147
phase 2 cutpoints were developed similarly by regressing IM147 composite scores
against IM147 phase 2 scores. A scaling factor of 0.9 was multiplied against the
predicted phase 2 cutpoint to make the phase 2 cutpoint slightly more stringent than the
composite cutpoint, as it is with the IM240 test. Table 4-4 in Section 4 shows the revised
Max CO cutpoints.

After revising the Max CO cutpoints, failure rates for the IM147 test using the Startup,
Intermediate, final, and Max CO cutpoints were determined using the 3347-vehicle data
set. These failure rates, which are shown in Table 1-1, were based upon the results of the
third IM147 test.

Table 1-1
Failure Rates, Third IM147

HC co NOx OVERALL
Vehicle + ran | pass |%Fait| Fait | pass |% Fail] Fait | Pass |o% Fan] Fait | Pass .| % Fai
Type
Max CO Cutpoints

LDGV 114 | 1666 | 6.4% | 250 | 1530 |14.0%| 155 | 1625 | 8.7% | 390 | 1390 |21.9%
LDGT1 49 915 | 5.1% | 156 | 808 |16.2%| 70 894 | 7.3% | 219 | 745 |22.7%
LDGT2 25 578 | 4.1% | 63 540 |10.4%| 30 573 | 5.0% | 93 510 }15.4%

All 188 | 3159 | 5.6% | 469 | 2878 |14.0%| 255 | 3092 | 7.6% | 702 | 2645 |21.0%
Startup Cutpoints

LDGV 106 | 1674 | 6.0% | 130 | 1650 | 7.3% | 141 | 1639 | 7.9% | 281 | 1499 j15. =|

LDGT1 52 912 15.4% | 33 931 | 3.4% | 48 916 | 5.0% | 108 | 856 11.2%

LDGT2 33 570 | 5.5% | 30 573 | 5.0% | 28 575 | 4.6% | 67 536 {11.1%

All 191 | 3156 | 5.7% | 193 | 3154 | 5.8% | 217 | 3130 | 6.5% | 456 | 2891 | 13.6%

Intermediate Cutpoints
LDGV 157 | 1623 | 8.8% | 161 | 1619 | 9.0% | 196 | 1584 }11.0%| 367 | 1413 |20.6%
LDGT1 80 884 | 8.3% | 53 911 | 5.5% | 73 891 | 7.6% | 165 | 799 }17.1%
LDGT2 40 563 | 6.6% | 42 561 | 7.0% | 42 561 | 7.0% | 93 510 |15.4%

All 277 | 3070 | 8.3% | 256 | 3091 | 7.6% | 311 | 3036 | 9.3% | 625 | 2722 |18.7%
Final Cutpoints
LDGV 280 | 1500 |15.7%| 240 | 1540 |13.5%| 277 | 1503 |15.6%| S07 | 1273 }28.5%
LDGT1 120 | 844 |12.4%| 85 879 | 8.8% | 127 | 837 {13.2%} 239 | 725 [24.8%
LDGT2 73 530 }12.1%| 56 547 | 9.3% | 82 521 |13.6%} 149 | 454 }24.7%

All 473_| 2874 114.1%] 381 | 2966 |11.4%| 486 | 2861 114.5%] 895 | 2452 126.7%

3-


Figure 5-1
IM147 Test Segments Used for Fast-Pass Cutpoint Development

Speed (mph)

reaper

veraptereereepeererrepeerererrep rere

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150
Time (sec)

Using these revised test modes, Sierra applied the aforementioned regression method to
the 3,347-vehicle sample supplied by Gordon-Darby. Thirty-nine distinct regression
models, shown in Appendix C, were developed from these data. Models were created for
each vehicle type (LDGV, LDGT1, and LDGT2), emission constituent (HC, CO, and
NOx), and phase (composite and Phase 2 only). There are 20 equations for the composite
models and 10 equations for the Phase 2 models.

After revising the regression models, they were applied to the 835-vehicle sample
(unbiased IM240 sample) to determine how the models would affect excess emissions
identified by the IM147 test. Excess emissions are defined as emissions collected during
an IM240 test in excess of the applicable standard for a given vehicle. The IM147 test
receives credit for identifying excess emissions if it fails a vehicle that had excess IM240
emissions. Table 5-2 shows the excess emissions versus test time results when the
Fast-Pass algorithm was enabled. Max CO standards were used for both the IM147 and
the IM240.

As Table 5-2 shows, the IM147 test with the fast-pass criteria enabled still identifies over
91% of the excess emissions for each of the three exhaust constituents and still

-37-


IM147 Composite Regression Coefficients, NOx, 1986 to 1989 Model Year LDGV's
Regression Coefficients

Segment IRMS —[Reg.

Number Error Constant |C1 ic2 C3. ica cS. C6 C7 ic8 ico }C10 C14 C12 C13 C14 jc15 C16 C17 C18 ic19
{PT LOGV_ 66-69 | 0.76505, 1.40646) 22,9063].
[Pz ILOGV__ [86-89 | 0.57844] 0.77237, 9.21] 3.0925]. .
[P3 LOGV__|86-89 | 0.53576} 0.63136] 9.1703] 2.0307] 7.0173]. a

iP4 LOGV__|86-89 | 0.52985] 0.61276] 9.5621] 2.0154] 5.1612} 5.5354]. .

iPS. ILOGV__ {86-89 | 0.48733] 0.58411) 7.6422] 1.5437] 3.1981] 4.1123] 5.8762]. .

[P6- ILDGV__|86-89 0.469} 0.58] 4.0676} 1.525] 2.9935] 0.4973] 4.8986] 10.1283]. .

iP7. ILOGV__|86-89 | 0.44452] 0.51948] 2.5722] 1.2222] 2.2925) 1.1125] 2.3363} 11.7286) 3.4574]. a

PS lLOGV__ [86-89 | 0.426647 0.46953] 3.5526] 1.2073] 1.7483] 0.3064] 2.0313] 6.7259] 2.6637] 5.3318].

Po ILOGV_ [86-89 | 0.42322] 0.45426] 3.3018] 1.301) 1.139] 0.1369] 1.5033} 5.9904] 2.5273] 4.8613] 3.2834) a

P10. ILOGV_ |86-89 | 0.42048] 0.44434] —_2.7682| 1.279} 0.8669] 0.8265} 1.1624] 5.9839] 1.9554] 4.9824] 2.1016] 2.3949}. .

P14 ILOGV__|86-89 0.3185} 0.16273] 1.6969] 0.9234] 0.2926] 1.5843] 1.5047] 5.7857] 0.6453) 3.2517] 2.4719] 1.4771} 1.3713} .

P12 LOGV__ [86-89 | 0.28211] 0.13132] 0.4472] 0.9805] 0.3268] 1.5058] 1.3641] 3.9271] 1.2167] 1.5887] 1.8636] 1.9637] 0.9702] 2.2297].

P13 LOGV_ [86-89 [0.17181] 0.05351 1.5839] 0.7587] 0.7909] 1.2166] 0.7145] 1.5691] 0.9527] 1.0643] 1.0876] 0.6012] 0.7526] 1.15] 2.3208]. .

{P14 ILDGV__ [86-89 | 0.13065] 0.04173] 0.3316] 0.7154] 1.0709] 0.6518] 0.7486] 1.2646] 0.8718] 0.8749} 0.8522] 0.9529] 0.6065] 0.9816] 1.5163] 1.6439]. .

P15, ILOGV__ [86-89 | 0.08069] 0.01822] 0.1209] 0.6879} 0.733} 1.0408] 0.7212) 1.5437] 0.8545] 0.957] 0.2801] 0.7471] 0.6975] 0.8112} 0.8905] 1.0278] 1.3107]. .

P16 LDGV_ [86-89 | 0.06908] 0.01317] 0.5535] 0.7013] 0.7623} 0.918} 0.6392! 1.068] 0.9735] 0.7864] 0.2568] 0.9626] 0.7416] 0.7472] 0.8532] 0.8939) 1.013} 1.487]. .

[P17 ILDGV__|86-89 | 0.06367] 0.01125] 0.3901] 0.7232] 0.6166] 0.8153] 0.6598] 1.0147] 0.8687] 0.8487] 0.2077] 0.8136] 0.7444] 0.7667] 0.847] 0.9405] 0.9462] 1.3241] 1.4006].

[P18 ILOGV__|86-89 | 0.03823] 0.00394] 0.7351] 0.6866] 0.7439] 1.0847] 0.6838] 1.2044] 0.6292] 0.8002] 0.5291 0.672] 0.6852] 0.8818] 0.7191 0.82) 0.7757] 1.2183] 0.9059} 0.8386].
[P19 LOGV_ 86-89 | 0.01005{ 0.00003] 0.7593] 0.7055] 0.7238] 0.7901] 0.6814] 0.9355] 0.7038] 0.7211] 0.6966] 0.7005{ 0.7134] 0.7342] 0.7231} 0.7021] 0.7072] 0.7254] 0.7597] 0.7156] 0.7326]

IM147 Phase 2 Regression Coefficients, NOx, 1986 to 1989 Model Year LDGV's
Regression Coefficients

Segment IRMS — Reg.

Number Error [Constant {C1 ic2 ic3 C4 ICS C6 C7 ic8 ico |C10 cit C12 }C13. C14 jc1s C16 \C17 C18 C19
B11 ILDGV__ [86-89 | 0.52925] 0.42098]. 2.5542}

[B12 ILOGV__|86-89 | 0.43224] 0.27635}. 4.6545] 3.9022]

B13 ILOGV _}86-89 | 0.23852] 0.0957]. 1.0646] 1.7542] 3.3487]

B14 LOGV__ [86-89 | 0.18118] 0.07257]. 0.867{ 1.4108] 2.1959) 2.261}. .

B15 ILOGV_ [86-89 | 0.11203] 0.02849]. 0.9505] 1.1788] 1.2539] 1.4295} 1.7812]. a

B16 ILOGV__|86-89 0.0963} 0.02351]. 1.0428] 0.9898] 1.2047] 1.2246] 1.4141] 1.9452! .

[e17 ILOGV__ [86-89 | 0.08734] 0.01558]. 41,0275] 1.0114 1,167] 1.2958] 1.2756] 1.7751} 1.9705]. .
[B18 ILDGV__]86-89 | 0.05354) 0.0073]. 0.9133] 1.2514] 0.967) 1.1377] 1.0194] 1.8001] 1.2499] 1.0893].
jpi9 LDGV_ 86-89 | 0.01153] 0.00104}. 0.964] 0.9967] 0.973] 0.9592] 0.9531] 0.9973} 1.0544] 0.9572] 1.0152}



were developed in the 1997 study. Therefore, further analysis was needed to develop
similar speed variation criteria for the IM147.

Scope

To address the issue of inadequate preconditioning without compromising test throughput
excessively, Arizona decided to change its test procedure to the IM147 beginning
January 1, 2000. This program upgrade will also include implementation of the Max CO
cutpoints previously developed by Sierra, which are designed to maximize the CO
benefits of the program.

To aid in this implementation effort, EPA issued a work assignment (#1-08) to Sierra to
develop the necessary test criteria. Under Work Assignment 1-08, Sierra is to complete
the following tasks:

1. Develop projected IM147 failure rates for the Arizona I/M program using
the 3,000-vehicle data set currently being collected by Gordon-Darby.
This evaluation is to include start-up, midpoint, Max CO, and final IM147
cutpoint scenarios.

2. Develop modal IM147 fast-pass standards for the Max CO cutpoints using
(a) the modal regression technique used in the 1998 EPA study to develop
IM147 fast-pass standards, and (b) the 3,000-vehicle data set and the 304-
vehicle data set collected in 1998.

3. Develop modal predictive IM147 retest algorithms for the Max CO
cutpoints using (a) the same technique used in the 1998 EPA study to
develop IM147 retest algorithms, and (b) the 3,000-vehicle data set and
the 304-vehicle data set collected in 1998.

4. Develop modal IM147 fast-fail criteria that can be used to terminate
retests if emissions performance is not improving during the retest.

5. Develop fast-pass and full-duration PKE criteria for the IM147 start-up,
midpoint, Max CO, and final cutpoints using the 3,000-vehicle and 304-
vehicle data sets, as well as the 16,581-vehicle data set from the 1997
study of IM240 PKE limits for EPA.

Seven different tasks were proposed to accomplish these objectives.

Task 1, Test Plan Development and Data Collection Assistance - This task covered

working with Gordon-Darby in its efforts to collect the test data needed to complete the
remaining tasks. Data collection, driver participation incentives, and other program-
related details were performed under the guidance of DEQ and Gordon-Darby and were
not Sierra’s responsibility. Sierra provided assistance on an as-needed basis to resolve

-16-


Table 1-6 summarizes the change in dynamometer test time with each succeeding set of
enabled criteria. The overall impact of all the criteria is to reduce the test time by 58%,
from 217 to 90 seconds.

Table 1-6
Average Test Time (seconds)
by Test Time Reduction Methodology and CPP

Dyno Test Test Time

Scenario Time Reduction (%)
Cutpoint only, two possible
retests 217 -
Added fast-pass 125 42
Added retest 96 56
Added fast-fail 94 57
Allowed fast-pass at end of
Segment 2 : 91 58
Removed speed excursion
violations 89 59
Added CPP limits 90

SIP Credit Analysis

The comparison of excess emissions identification between the IM240 and IM147 that is
presented above is based on the use of CO Max standards for both test cycles. However,
to develop an estimate of the allowable SIP credit that should be allocated to the revised
IM147 CO Max standards, it is also necessary to compare excess emissions identification
between this scenario and the IM240 with EPA-recommended final cutpoints in place.
This is due to the need to establish a link to using MOBILE for SIP modeling purposes.
Configuring MOBILE with CO Max standards is not feasible; therefore, a better
approach is to run the model with final EPA standards in place and use the excess
emissions identification rates developed in this study to adjust the resulting model
outputs.

Table 1-7 shows the excess emission identification rates when the IM147 Max CO
cutpoints are compared to the IM240 final standards. Pollutant-specific identification
rates are shown both without and with the fast-pass, retest, or fail-fail algorithms enabled.
(The latter scenario includes fast-passing vehicles as early as at the end of segment 2.)
Since Arizona will be implementing the IM147 test procedure with the algorithms

-ll-


5-3

5-4

5-5

List of Figures

page
Modal IM147 Fast-Pass Standards .......... 0... c cece cece ence eee ee 4
Integrated Test Results Maximum CO Cutpoints Without CPP Limits .......... 8
Test Sequence Used to Investigate Triplicate
IM147 Tests in Arizona Test Lanes ....... 0... cece eee cee eee 19
Model Year Distribution of Sample Fleet and AZ Overall Fleet .............. 22
IM147 Trace Phase 1 and Phase2 ........... 0. ccc eee ee 25
Failure Rate by Consecutive IM147 Max co Cutpoints .................00. 30
Failure Rate by Consecutive IM147 Startup Cutpoints ..................... 31
Failure Rate by Consecutive IM147 Intermediate Cutpoints ................. 32
Failure Rate by Consecutive IM147 Final Cutpoints ....................008 33
IM147 Test Segments Used for Fast-Pass Cutpoint Development ............. 37
Retest Algorithm During First IM147-LDGV .................006 beeen eee 42
Retest Algorithm During Second IM147-LDGV ...................0000005 43
Retest Algorithm During First IM147-LDGT1, LDGT2 .................... 44
Retest Algorithm During Second IM147-LDGT1, LDGT2 .................. 45
Integrated Test Results Max CO Cutpoints Without CPP Limits ............. 50
Comparison of IM147 Variation Limit Metrics ...............0...000.000 58
Distribution of Arizona IM147 Positive Power Differences ................. 60
Illustration of Second-by-Second IM147 Cumulative Positive
Power Variation Limits 20.00. 63
Effect of Initial CPP Limits Multiplier on Effective IM147 Abort Rate ........ 64
No Title 00. e nett eens 68

-iv-


|M147 Composite Regression Coefficients, NOx, 1981 to 1985 Model Year LDGT1's

Regression Coefficients.

Segment RMS Reg.

iNumber Error Constant |C1 iC2 ic3 C4 cS icé C7 ics ico C10 C11 C12 Ic13 C14 C15 C16 C17 C18 |C19
Gi MDGT1 [e185 | toz5zo| 2.96054) 22.6751). C

jP2. ILOGT1 |81-85 | 0.82038} 0.84089] -0.5552] 5.7627, .

[Ps |LDGT1 [81-85 | 0.76521} 0.73117] -1.5829] 4.5821] 7.1986]. .

[Pa LDGT1 [81-85 | 0.75286] 0.70493] -1.1236] 4.4969] 4.9474] 8.0174]. .

Ips ILOGT1_ {81-85 | 0.74049} 0.73769] 3.3308] 4.0049] 3.8497} 6.663} 3.702).

[P6 ILOGT1 [81-85 | 0.73674] 0.71486] -3.9127] 4.0869] 3.8046] 3.6966] 3.0011] 4.8137]. .

PP? ILOGT1_}81-85 | 0.71739] 0.69385} -3.0212] 3.5234] 3.5081] 3.9937] 0.1792] 6.3496] 3.9431] a

[Ps ILDGT1 [81-85 | 0.70652{ 0.67353] -2.6651] 3.5435] 2.1822] 2.6706] 0.0115] 3.2904] 2.8238] 3.9683]. a

[Ps ILOGT1 |81-85 | 0.70505] 0.67708] -1.8358[ 3.4819] 2.4475] 3.1809] 0.6337] 3.7784) 2.952] 4.1428] -2.2115].

P10 ILDGT1 [81-85 | 0.70227] 0.66689] -2.4177, 3.494[ 2.3056] 3.6942] 0.2591] 3.8357) 2.4151} 4.2563] -4.019] 2.4819]. .

Pu ILDGT1_ [81-85 | 0.61346] 0.34129] 0.06] 2.6963] 1.5535] 2.4383] 1.5201 5.655] 0.7401] 2.8173] -2.4441] 1.0401] 1.3101]. .

| ILDGT1 [81-85 | 0.59022] 0.30932] -0.6809} 2.658] 1.3697] 1.2656] 1.4801] 3.8951 1.356] 2.1592] -2.7018] 1.3911] 0.8585] 1.6633].

P13 ILOGT1_ [81-85 | 0.27799] 0.12998} -0.1294| 0.9449] 1.4979] 2.9854] 0.7785] 0.1823] -0.313] 0.9976] 0.5738] 0.4624] 0.694] 1.076] 2.7355].

P14 ILOGT1 [81-85 | 0.19309] 0.05416] 0.6065] 0.856] 0.9251] 1.6344] 0.8251 0.094] 0.4201] 1.4703] 0.998] 0.7742] 0.5699] 0.79] 1.6886] 1.7582].

P15: ILOGT1 [81-85 | 0.10887] 0.02502] 0.5161] 0.6742] 1.1768] 1.4558] 0.2902] 1.3526] 0.7996] 0.8754) 0.732] 0.6248] 0.6617| 0.8183] 0.8141] 1.2315] 1.3505].

P16. LOGT1 [81-85 | 0.09725] 0.01363| 0.4935] 0.6784] 1.1208] 1.1307] 0.3923] 0.9511] 0.8969] 0.5086] 0.7311] 0.6773] 0.699] 0.7813] 0.8355] 1.1375] 1.1283] 1.0972]. .

P17 ILOGT1 [81-85 | 0.09344] 0.01174] 0.5252] 0.7346] 0.9639] 1.1015] 0.6838] 0.7005] 0.7356] 0.6406] 0.6359] 0.4841] 0.6953] 0.7762] 0.8511] 1.1597] 1.0383] 1.0222] 1.0523].

[Pis ILDGT1 [81-85 | 0.05721} 0.00436] 0.6442{ 0.6817] 1.0499] 1.0725] 0.6056] 4.15] 0.5722] 0.7474] 0.8465] 0.5131] 0.6842] 0.8341] 0.6429) 0.922] 0.8319} 1.1396] 0.7657] 0.8484].
P19 ILDGT1 {81-85 0.0165} 0.00076] 0.7099] 0.6986] 0.7653] 0.7747] 0.7168] 0.9007] 0.7285] 0.7007] 0.7342] 0.6359] 0.7093] 0.736{ 0.7337] 0.6964] 0.7033] 0.7238] 0.7471] 0.7212[ 0.7207

IM147 Phase 2 Regression Coefficients, NOx, 1981 to 1985 Model Year LDGT1's
Regression Coefficients

[Segment RMS |Reg.

Number . Error Constant }C1 ic2 iC3 C4 C5 C6 C7 C8 ico C10 C14 C12 \C13 }C14 C15 C16 C17 C18 }C19
fen LOGTT 81-85, 1. O76] 0.85959]. 3.4954]. a

fe12 ILDGT1 [81-85 | 1.00048] 0.67351]. 2.4254] 3.4463]. a

[e13 ILOGT1_}81-85 | 0.38989] 0.20314]. 0.8889] 1.8075] 3.7573]. .

[e14 ILOGT1_}81-85 | 0.27037, 0.0891) 0.8213] 1.2937] 2.454] 2.3704]. .
pes. ILOGT1 [81-85 | 0.15297] 0.04337] 0.8745} 1.292} 1.0958] 1.7178] 1.8359]. .
fei LDGT1 [81-85 | 0.13123] 0.01886]. 0.9513] 1.0861] 1.1153] 1.5763] 1.4991] 1.5837]. a
[B17 ILOGT1 [81-85 | 0.12525] 0.01412] 0.9423] 1.0698] 1.128] 1.6173] 1.4024] 1.4526] 1.3599]. .
1 EE ILOGT1 {81-85 | 0.08127] 0.0094]. 0.8966] 1.2134] 0.8469] 1.3163] 1.0875] 1.7522] 1.1639] 1.0764].
{819 ILOGT? [81-85 | 0.01759} 0.00132]. 0.9552] 1.0089} 0.9919] 0.9568] 0.9493] 1.0003] 1.0401] 0.9479} 1



7. INTEGRATION OF CPP VARIATION LIMITS

The final phase of this analysis involved integrating the CPP criteria with the other
algorithms included in this study to determine net test time.

As shown in Section 6, there are both high-end CPP errors and low-end CPP errors.
High-end CPP errors occur when the vehicle is driven too aggressively, whereas low-end
CPP errors occur when the vehicle is driven too smoothly, essentially minimizing the
peaks and valleys of the trace. Because high-end CPP errors will create additional
emissions and.in tum make it more difficult for a vehicle to pass the test, a test where a
vehicle passed in spite of high-end CPP errors is considered a valid test. If the same
vehicle failed because of high emissions, the cause is assumed to be the high-end CPP
error; therefore, the test would need to be extended to ensure fairess. Low-end CPP
errors, however, would result in lower mass emissions and make it easier for vehicles to
pass falsely. In those cases where the vehicle passes with a low-end CPP error, the result
would be invalid and the test would need to be extended. Ifa vehicle fails with a low-end
CPP error, the result is valid and the test would terminate. Table 7-1 details which CPP
violations affect which decisions.

Table 7-1

End Test Decisions Affected by CPP Errors

Decision Type Prohibited by:
Fast-Pass Low-end CPP error
Retest High-end CPP error

(initiate another IM147 cycle)

Fast-Fail High-end CPP error
End-of-Test Pass Low-end CPP error
End-of-Test Fail High-end CPP error

While the above decision types can be prohibited by the corresponding CPP errors, the
error must occur while data for that decision were being produced in order to affect the
decision. In other words, not all high-end CPP errors occurring during an IM147 test

-66-


. Record 24, a 1995 Toyota 4Runner, had decreasing emissions throughout
the first three IM147 tests, emitting only 0.72 g/mi CO during the third
IM147.

After these vehicles were removed, the remaining sample from the original data set
consists of 191 cars and 109 trucks for a total of 300 vehicles.

The 2518- and 543-vehicle data sets were collected for this work assignment by Gordon
Darby at the ten I/M lanes in Phoenix, Arizona during June, July, and August 1999. Like
the original data set, these tests were conducted at the I/M lanes in Phoenix, Arizona.
Unlike the original data set, however, all motorists were asked to participate in the study,
rather than simply those following a white-colored vehicle in the queue. The exception to
this occurred toward the end of the testing when Gordon Darby staff, based on direction
from Sierra, targeted certain vehicle model years and vehicle types to ensure that these
groups were adequately represented in the test data.

The main difference between the 2518- and 543-vehicle samples was administration of
the IM240 test at the conclusion of the IM147 test. For the 2518-vehicle sample, only
failing vehicles were given the IM240 test. In the 543-vehicle sample, all vehicles were
given the IM240 test regardless of their IM147 result.

Motorists were not required to participate in this testing. To encourage motorists to
participate, inspection fees were waived for these tests. Inspection fees amount to $25
per inspection. Statistics detailing the number of refusals were not kept.

As previously mentioned, anomalous vehicle test data were not thrown out of the latter
two samples. There was, however, one vehicle identification number (VIN), “123456,”
that appeared multiple times with different vehicles. Gordon-Darby staff confirmed that
this was a test VIN and should be excluded from analysis, which was done. Once this
VIN was removed, the larger sample comprised 2512 vehicles (1360 cars and 1152
trucks) while the smaller sample comprised 535 vehicles (229 cars and 306 trucks).

The model year distribution for each of the samples is shown in Figure 3-2. The YTD
(year-to-date) October 1999 line represents the initial test for 2% random sample vehicles
tested in 1999 through October. Anomalous vehicles have been removed.

For the most part, the model year distribution of the data samples mirrors the random
sample distribution reasonably well. One notable exception can be seen with newer
model year vehicles for the 300-vehicle sample. When that data set was collected, model
year exemptions for the five newest model years were not in place. As a result, this
sample has greater representation throughout these years. The newer data sets were
collected with the model year exemptions in place; therefore, they follow the 1999 YTD
random sample more closely.

One important difference in this study versus previous studies was how time-alignment
was handled. For the previous studies, individual channels (HC, CO, etc.) were aligned

-21-


predicted emissions after segment 7 of the second IM147. The fast-fail algorithm for the
third IM147 test uses a variation of the fast-pass algorithm to predict failing vehicles
throughout the test. Both of these algorithms are described more completely in the body
of this report.

Table 1-4 shows the results of the fast-fail algorithms when applied to the 3347 vehicle
sample. The fast-fail algorithm reduces average test time an additional 2 seconds, from
96 seconds (with fast-pass and retest enabled) to 94 (with fast-pass, retest, and fast-fail
enabled).

Table 1-4
Fast-Fail Algorithm

Vehicle Second IM147 Fast- Third IM147 Fast- False Failures
Class Failures Failures

Integrated Fast-Pass, Retest, and Fast-Fail Algorithm Results

This portion of the study combined all of the optimized algorithms to determine their net
effect on test time, false failures, and excess emissions while using the Max CO standards
with the 3347 vehicle sample. The flow chart contained in Figure 1-2 shows the point at
which vehicles concluded the test and the reason they passed or failed. It also indicates
the average dynamometer test time for each category of vehicles. Following the flow
chart, Table 1-5 shows the net effect of the procedures on test time and excess emissions.

The table shows that excess emission identification using the Max COQ cutpoints with the
fast-pass, retest, or fail-fail algorithms enabled is 94.4%, 95.4%, and 95.7% for HC, CO,
and NOx, respectively. (For comparison, the respective identification rates are 94.3%,
98.2%, and 92.1% without the various test criteria enabled.) This is down from the
identification rates developed in SR99-10-02,' which identified 99.6% of the HC, 98.2%
of the CO, and 99.9% of the NOx with the fast-pass and retest,algorithms enabled (fast-
fail was not considered). However, direct comparison of the two sets of results may not
be relevant for several reasons. F irst, the previous study measured excess emissions
captured against the Final Cutpoints rather than the Max CO cutpoints use for this study.
Second, because fast-fail was created for this study, it was not included in the previous
study results. Third, the retest algorithm has been modified as part of this study and will
therefore have a different effect on the results. Finally, the majority of the data used in
this study were collected with the newer model year exemptions in place. As a result, the
vehicle distribution was skewed toward older vehicles relative to that in the previous

7.


emission results from the third IM147 test for each of the three exhaust constituents. For
this regression, Sierra used only vehicles from the two of the three data sets where
vehicles were automatically given an IM240 test regardless of their IM147 test results
(the 300- and 535-vehicle samples). Because the third data set contained IM240 data
only for vehicles failing the IM147, this data set would have created a regression bias and
was therefore excluded from this part of the analysis. The resulting regression equations
were then used to extrapolate IM147 composite cutpoints from the IM240 composite
cutpoints developed previously in SR99-10-02. Three linear regression equations were
developed for each of three model year groupings. Equation 4-1 illustrates the
regression equation.

IM147 Cutpoint = slope * (IM240 Cutpoint) + Intercept [4-1]

Table 4-1 details the appropriate model year/emission constituent regression coefficients
to be used in the above equation. The model year groupings identified in this table are
not the same as the model year groupings used in the emission cutpoint tables, which
vary depending upon vehicle type.

Table 4-1
IM240 to IM147 Composite Regression Equation Coefficients

Emission
Model Year Group Constituent Slope Intercept
HC 0.896629 0.110694
1981-1985 co 1.020463 0.858255
NOx 1.065128 0.085613
HC 0.933646 0.056509
1986-1989 co - 0.939067 1.679632
NOx 1.077932 0.058971
HC 0.963839 0.026672
1990+ co 1.037836 0.392486
1,102698 0.048771

Phase 2 cutpoints were developed by regressing the IM147 composite test scores against
the IM147 Phase 2 scores. Unlike the composite score regressions, however, the
equations had to be adjusted to preserve the relationship between composite versus

-26-


|M147 Composite Regression Coefficients, CO, 1986 to 1989 Model Year LDGT1's
Regression Coefficients
Segment IRMS — Reg.
Number : Error Constant }C1 ic2 ic3 ica ics: C6 C7 ics ico C10 C11 C12 C13 C14 C15 C16 C17 C18 Ic19
PT TDGT1 [66-69 | 7.16532] 10.8066] 9.9102]. r
iP ILOGT1 86-89 | 5.68789} 7.0459] 1.8741] 2.6779]. .
iPS ILOGT1 86-89 | 5.04319] 5.5558] _ 1.7455] 1.371] 5.3393]. .
iP4 ILOGT1 [86-89 | 4.88921] 5.13459] 1.4696] 1.5629] 2.9026] 5.3333}
iPS ILDGT1 [86-89 | 4.78915] 5.08892] 1.6656] 1.4035] 2.0371] 4.1723] 2.0022)" .
iPS JLOGT1 [86-89 | 4.74332] 5.11226] 1.0229] 1.4755] 1.9866] 3.8059] 1.0898] 2.4375]. a
iP? ILOGT1 [86-89 | 4.65225] 4.91955] 1.7462] 1.3554] 1.7097] 3.8418] -0.0053] 1.9673] 1.6899]. .
[Ps ILOGT1 [86-89 | 4.48557] 4.61047] 2.0142] 1.3439] 1.7523] 2.7365} ~0.09} 0.1646] 0.9526] 2.9249]. .
[Ps ILOGT1 [86-89 {4.44811 4.5639] 2.1382| 1.346] 1.6099] 2.7676] -0.2399} -0.7903] 0.9655] 2.4766] 2.079]. .
P10 ILOGT1 {86-89 | 4.41872] 4.48084] 2.0022] 1.3046] 1.5957] 2.4537] -0.4021] 0.7631] 0.7899] 2.5819] 0.8104] 2.0187].
Pu ILOGT1 {86-89 | 3.05161] 2.26609] 2.8544] 0.9359] 0.8115] 1.4536] 0.41] 0.3277] 0.4047] 2.1497] -0.2782] 2.0562] 0.9911 |.
P12 ILOGT1 |86-89 | 2.51613] 1.51088] 2.8342] 0.7464] 0.9245] 1.9441] 0.5707] 0.2141] 0.4114] 2.0168] 0.2073] 1.9253] 0.7438] 1.2083] a
P13 ILOGT1 [86-89 | 1.68355] 0.8064} 2.1795] 0.6233] 0.8001} 1.3103] 0.4715] 0.7177} 0.6871] 0.7545] 0.7656] 1.1501] 0.7503] 0.8943] 2.2692]. .
P14 LDGT1 [86-89 | 1.31741] 0.68081 2.1784] 0.5849] 0.6912] 1.3598] 0.4357] 0.9373] 0.6956] 0.8278] 0.6331] 1.2087] 0.7057] 0.7686] 1.824] 1.1207]. .
P15: JLOGT1_|86-89 | 0.93911 0.47] 1.8779] 0.6014) 0.6301] 1.3592] 0.4395] 0.8121 0.876} 0.6638] 0.7005} 0.9787] 0.7117] 0.7437] 1.0888] 0.8731] 1.3809]. .
P16 LDGT1 [86-89 | 0.88497] 0.41787] 1.6764] 0.5971] 0.6595] 1.3638] 0.5037[ 0.8323] 0.8426] 0.6198] 0.6814] 1.0802] 0.7147] 0.7671 4.022] 0.8818] 0.9654] 1.3933]. a
Piz ILOGT1 [86-89 | 0.76094) 0.28167] 1.1884) 0.633} 0.6369] 1.0655] 0.6729] 0.7423] 0.7527] 0.7112] 0.7965] 0.8042] 0.7055] 0.7447] 0.9245| 0.8862} 1.048] 0.6815] 1.7386]. .
[Pia ILDGT1 {86-89 | 0.51513] 0.11517] 1.107} 0.6616] 0.6251] 1.1599} 0.7126] 0.7927] 0.5228] 0.8379] 0.7639] 0.7464] 0.7046] 0.7204] 0.7302] 0.7811| 1.0809] 0.4756} 4.39] 0.889].
pPi9 LOGT1 [86-89 | 0.22775] 0.06824)" 0.9804 0.6792] 0.6446] 1.0212] 0.6645] 0.7468] 0.6847] 0.7006] 0.7495[ 0.7734] 0.7063. 0.7111] 0.7207] 0.7067| 0.7928] 0.5475] 0.9726] 0.7062] 0.7346}
IM147 Phase 2 Regression Coefficients, CO, 1986 to 1989 Model Year LDGT1's
Regression Coefficients
Segment IRMS —|Reg.
iNumber Error Constant {C1 ic2 ic3 C4 CS C6 C7 C8 Co. C10 ic11 C12 C13 C14 C15. C16 C17 C18 C19
Bit TOGTI e650 | 573419] 6.44505). 7.4669]. 5
[ei2 LOGT1 [86-89 [ 3.83261] 3.46613} 1.1555] 1.6694) .
| CED ILOGT1 [86-89 | 2.36328] 1.33398]. 1.0419] 1.1807] 3.2911 .
[B14 LDGT1 ]86-89 { 1.90917] 1.13186]. 0.9737| 1.0107] 2.6834] 1.4946].
| EXE LDGT1 |86-89 | 1.36531] 0.75413} 0.976] 0.9839] 1.5355] 1.1464] 1.973]. .
{p16 ILDGT1 [86-89 | 1.28845] 0.69885} 0.9841] 1.0114] 1.4453] 1.1588] 1.3867] 1.9667]. .
fe17 LDGT1_]86-89 | 1.04812] 0.39116] 0.9599] 0.9873] 1.256] 1.1763] 1.4736] 0.8337] 2.6609]. .
| EX) ILOGT1 {86-89 0.7463] 0.14799] 0.9544] 0.9576] 1.0029] 1.0365] 1.5243] 0.6229] 2.1332| 1.14471
[B19 ILDGT1 {86-89 | 0.33544] 0.07788]. 0.9581] 0.9522] 0.9677] 0.9398] 1.0857] 0.7154] 1.4816] 0.9344] 1.0357]



IM147 Composite Regression Coefficients, HC, 1996 and Newer Model Year LDGT2's
Regression Coefficients
Segment IRMS = {Reg.
Number Error Constant [C1 C2 ica ic4 ics icé ic7 ics ice |c10 cit C12 [C13 C14 Cis C16 C17 C18 jc19
PT ToGt2 |9er | 0.07243] 0.06002 | 70.2]. a
iP2. ILOGT2 [96+ 0.04564] 0.0237] -7.2812] 4.6718}. a
iP. ILOGT2 [96+ 0.04313] 0.03223] -2.4032| 1.3094] 6.1404]. .
iPS ILOGT2 [96+ 0.03729] 0.03292] -1.4437] 2.1679} -2.2327] 9.7739) .
iPS ILOGT2 [96+ 0.02887] 0.03311] -7.1815] 1.5592] 3.8084] 6.997] 8.5612]. .
PS ILOGT2 [96+ 0.02468] 0.03246] -5.2125] 2.8958] 0.7992] -3.162] 2.3757] 7.8985] .
iP? ILOGT2 |96+ 0.02471} 0.03212] -5.6903] 2.6269] 0.3486] -3.879] 1.8571 6.769] 2.1598]. .
PB ILOGT2 [96+ 0.02458] 0.03221] 5.7203] 2.2642] 1.1436] -7.0745] 3.56547 5.3631] 0.5718] 2.5776]. a
Po ILOGT2 [96+ 0.02153] 0.02755] -5.6174] 3.2239] 0.1186] 6.6505] 2.7679] 1.8663] -1.0747[ 0.5327] 8.5733]. .
P10 ILDGT2 [96+ 0.02175) 0.0276] -5.5557[ 3.1793] -0.113] 6.663] 2.9877] 1.5294] -1.4181] 0.9223] 7.6307] 0.9647]. .
P11 ILOGT2 [96+ 0.01561} 0.01512] 0.1593] 1.3225] 0.8979] -3.5644] 1.4176] 3.2321] -2.661] 2.4201] 3.7658] 1.2056] 1.069].
[P12 ILOGT2 [96+ 0.01535} 0.01155] 0.1184] 1.3108} 0.2317] -1.9686] 1.0103] 3.2791] -1.9369] 2.0526] 2.5266] 1.346] 0.9439] 0.9442]. a
[P13 ILOGT2 [96+ 0.0118] 0.00591] 0.5316] 0.8014] 1.3453] -1.5835] 1.0716] 4.5702] 0.6276] 1.6068] 1.2052] -0.739] 0.9742] -0.8137] 3.1462]. .
[P14 ILOGT2 |96+ 0.00894] 0.00333] 0.9294] 0.6687] 0.8295] 0.2036] 0.5645] 4.0094] 0.2628] 1.1846] -0.1226] 0.2024] 0.7931] -0.6864] 2.3879] 2.3089]. a
P15, ILOGT2 }96+ 0.00519] 0.00298] 0.5728] 0.3247] 2.2863] -2.5268] 2.2831] 1.4541 0.658] 2.6037] -0.4889] 1.5224] 0.8251] -0.0309] 1.4354] 0.4248] 1.7293]. .
P16 ILOGT2 [96+ 0.00341] 0.00091] 0.6938} 0.6433] 0.9494] -0.2435] 1.2108] 1.9848] 0.1134] 1.3832] 0.4148} 0.7129] 0.7604] 0.2037] 1.1446] 0.6148] 0.7145] 4.0429]. a
P17 ILOGT2_|96+ 0.003} 0.0006] 0.5483] 0.8035] 0.6446] 0.0208] 1.0382] 2.0883] 0.1055] 1.0991] 0.7076] 0.6796] 0.7787] 0.2756] 1.0048] 0.7528] 0.7994] 3.2034] 1.1992]. a
P18 ILOGT2 [96+ 0.00229} 0.00034] 0.8423] 0.7701] 0.5405] 0.6178] 0.7621 1.54] 0.2461] 0.9463} 0.785] 0.7135] 0.7393] 0.5553} 0.7347] 0.5376] 0.9234] 2.5586] 0.3528] 1.0986].
P19 LDGT2 [96+ 0.00148] -0.00015} 0.5489} 0.7737] 0.6253] 0.5369] 0.7319} 1.0865] 0.7396] 0.777} 0.8586] 0.6472] 0.714] 0.5043] 0.8109] 0.8951 0.552} 1.6026] 0.688} 0.6722] 0.8733}
IM147 Phase 2 Regression Coefficients, HC, 1996 and Newer Model Year LDGT2's
Regression Coefficients
Segment RMS: Reg.
iNumber Error Constant }C1 ic2 iCc3 C4 ics C6 C7 ic8 ica C10 C11 C12 C13 C14 C1 C16 C17 C18 C19
| Cx LDGT2 96+ 0.02667] 0.01375]. 1.9495]. a
[ei2 ILDGT2 [96+ 0.02266] 0.00456]. 1.3011 3.138). .
B13 ILDGT2 [96+ 0.01837] -0.00086}. 1.3623] 0.2518] 4.0937] a
B14 ILOGT2 [96+ 0.015] -0.00298]. 1.1501] 0.0589] 2.9165] 3.3772]. a
[B15 ILOGT2 [96+ 0.01018] -0.00336}. 4.1443] 0.9415} 1.7337] 0.5943] 2.5841]. .
B16 ILOGT2 [96+ 0.00753] -0.00463]. 1.0828} 1.0417] 1.2129} 0.903] 1.0508] 6.8124].
[B17 ILOGT2 [96+ 0.00686] -0.00476]. 41.1334] 1.0977] 1.0456] 0.7683] 1.3074] 4.7654] 2.3941]. .
B18 LOGT2 [96+ 0.00371] -0.00109]. 0.9732] 1.1893 0.7137| 0.4116] 1.4972] 2.806aT 0.0005] 2.4412].
B19 LOGT2 [96+ 0.00243} -0.00121}. 0.9785] 0.9384] 0.9374] 1.0534] 0.8374] 1.4572] 0.7147 1.4339[ 1.3705]



Fast Pass
2,490 Vehicles @
46.8 Seconds

Figure 1-2

Integrated Test Results
Maximum CO Cutpoints Without CPP Limits

Pass
8 Vehicles @
146.0 Seconds

Fast Pass
260 Vehicles @
199.8 Seconds

Fail
189 Vehicles @
132.0 Seconds

No Decision
660 Vehicles

Pass
1 Vehicle @
292.0 Seconds

Fast Pass
40 Vehicles @
360.8 Seconds

!

Fail

Fast Fail

92 Vehicles @
194.0 Seconds

145 Vehicles @
278.0 Seconds

No Decision
162 Vehicles

Pass
3 Vehicles @
438.0 Seconds

Fail

Fast Fail
115 Vehicles @
382.5 Seconds

4 Vehicles @
438.0 Seconds

TOTAL PASS = 2,802

-8-

TOTAL FAIL = 545

TOTAL FALSE FAILURES = 3



IM147 Composite Regression Coefficients, CO, 1981 to 1985 Model Year LDGT1's

Regression Coefficients,
RMS Reg.
Error {Constant |c1 cz {ca Ica cs__{ce ic? ce Ico cto fet erz_—sfcia_—sfcta. cis fcr. fcr7_— [crs cia
CoGT1 [e185 | 10.1754] 14.5276] 12.5442
LOGT1 [81-85 | 7.53286] 6.22078| 5.3420] 3.5563].
LOGT1 [81-85 | 6.63268] 6.60978] 2.7905] 2.1525] 5.3312].
LOGT1 [81-85 | 6.52317] 6.63083 1.5125] 2.2786] 3.2782] 3.8618]. :
LOGT1 [81-85 | 6.508] 6.69361] 1.2349] 2.1748] 2.865] 3.4811 1.0492]. i
LOGT1 [81-85 | 6.43562] 6.71221] 0.1813] 2.3646] 2.9243] 1.5307] 0.1722] 2.7427]. a
LDGT1 {81-85 | 6.19445] 6.37324] 0.5635] 1.9693] 2.7172] 0.6361] -1.661] 3.2843] 2.6055 :
LOGT1 [81-85 | 5.94027] 5.98064] 1.2119] 1.6393] 2.7167] 0.5356] -1.4442| 1.3750] 1.7876] 3.6004].
LOGT1 (81-85 | 5.9353] 5.97383] 1.0059] _1.6737| 2.5314] 0.8508] 1.7967] 3.4358] 1.1042).
LOGT1 81-85 | 5.85738] 6.02848] _1.177| 1.7285] 2.0533] 0.7067] -2.0802| 1.0202] 1.2427] 3.6063) 1.2444] 3.1472].
LOGT1 [21-85 | 4.17486] 2.67355] 0.0251] 1.2531] 0.5349] 0.8614] -0.4647| 2.1824] 0.0334] 2.27] 1.2267| _ 2.243] 1.0307]. i
LOGT1 [e185 | 3.65635] 1.97698] -0.5472| 1.2512] 0.2635] 2.0162] -0.5276| 2.225] 0.3841] 1.8143] 0.5359] 2.6593] 0.7692] 1.1045]. :
LOGT1 [81-85 | 2.30258] 0.92158 0.7798] 0.5868] 0.3445] 2.2218] 0.3028] 1.3536] _ 0.523[ 1.1633] 0.7632] 1.2664] 0.7979] 0.6038] 2.4441]. 5
LOGTs_|e1-85 { 1.68769] 0.51912[ 1.7468] 0.429 0.6515] 1.5616 0.7182] 0.8416] 0,48] 1.2506] _ 1.093| 1.1172] 0.6993] 0.5869] 1.7776] 1.5319]. ;
LOGT [81-85 | 1.13559] 0.36037| 17,4986] 0.461 0.5203] 1.5061] 0.696] _ 0.646] 0.8751] 0.9462] 0.7302| _ 1.215] 0.6963| 0.7448] 0.985] 0.9478] 1.4391]. :
LOGT1 {81-85 | 1.03437] 0.37532| 1.4149] 0.5205] _0.4715| 1.4256] 0.7753] 0.6646] 0.8775] 0.7646] 0.5543] 1.4051] 0.6989] 0.7734] 0.9487] 0.9680] 0.8955] 1.6125]. 5
LOGT1 [81-85 | 0.96568| 0.20115| 1.2423] 0.5923] 0.2634] 1.3236] 0.9825] 0.7034] 0.7693] 0.7682] 0.1979] 1.3763] 0.7136] _0.7407| 0.9773] 0.9693] 0.6011] 1.2002] 1.2053]. :
LOGT1 [81-85 { 0.75949| 0.06880{ 1.1529] 0.5685] 0.4365] 1.1423] 0.7942] 0.9943] 0.7711] 0.6846] 0.5815| __ 1.031] 0.7017] 0.7499] 0.8098] 0.8097] __0.847| _ 0.846] 1.2494] 0.9820].
LOGI 81-85 | 0.34973 -0.06051{ 1.1508] 0.6559] 0.4115] 1.1472] 0.7599} 0.9619| 0.799] 0.6744 0.5327] _0.985| 0.7106] 0.7204] 0.7378] 0.6801] 0.6385] 0.7063] 1.0232] 0.6907] 0.6306}
IM147 Phase 2 Regression Coefficients, CO, 1981 to 1985 Model Year LDGT1's
Regression Coefficients
Segment RMS: Reg.
Number Error {Constant {C1 ic2 ica ica cs ce c7 ca Ico cio ers ferz_—fera_—era_ crs cts. —fci7_—fcrs_—cis
om TOGTs e185 | 9.2120] 9.11062). T5105
B12 LOGT1 [81-85 | 6.96613] 4.72673]. 1.1535] 1.7914
B13 LOGT1 81-85 | 3.55673] 1.89825]. 1.0491 _ 0,955] 3.6598].
B14 LOGT1 [81-85 | 292633] 1.4436). 0.9077] _0.9194[ 2.7661] 1.929]
B15 LOGT1 [21-85 | 1.95626] 0.66159]. 0.9453] 1.0943] 1.32] 1.0292| 2.3406]. :
B16 LOGT1 [81-85 | 1.76086 0.83361 0.9517] _1.1151{ 14,2896] _1.1023{ 1.3595 2.6647]. a
B17 LOGT1 [81-85 | 1.44658) _ 0.21699]. 0.9768] 0.9803] 1.3491] 1.2314] 1.27] 1.5265] 2.526]. :
B18 LOGT1 [81-85 | 1.172] 0.01644]. 0.9407] 1.0108] 1.0591] 1.0119] 1.2102] 1.0895] 2.58] 1.3347]
[819 LOGT1 [81-85 | 0.69787] 0.12536). 0.9656] 0.9797[ 0.9936] 0.8576] 0.9082] 0.8798] 2.1293] 0.9645] 1.1513]



IM147 Composite Regression Coefficients, CO, 1986 to 1987 Model Year LDGT2's
Regression Coefficients
[Segment RMS Reg.
Number Error [Constant {C1 ic2 ic3 C4 ics C6 C7 ic (e:) C10 ic14 C12 C13 \C14 IC1S C16 |C17 C18 Ic19
jP1 ILDGT2 {86-87 | 10.9444] 12.9835] 9.9921].
iP2. ILOGT2 [86-87 | 7.55854] 7.97406] 5.9552] 2.7507]. a
PS ILOGT2 |86-87 | 6.99501] 6.13276] 4.0152] 1.8868] 4.4945].
PS ILOGT2 [86-87 | 6.97238] 5.95324] 3.4929] 1.8483] 3.4182] 2.3575] a
PS. ILOGT2 [86-87 | 6.96833] 6.05169] 3.0277] 1.6681} 2.9579] 1.9105] 1.5754] .
PS LDGT2 [86-87 | 6.92215] 5.98054) 2.189] 1.8432] 2.2804] 1.5316] 0.9365] 3.0663] .
iP7. |LDGT2 [86-87 | 6.94695] 5.96146] 2.2344] 1.7997] 2.1735] 1.5149] 0.7182] 3.2075] 0.349] a
iPS ILDGT2 [86-87 | 6.86254] 5.92929] 3.5275] 1.4609] 2.7997] 0.5921] 0.3135] 2.0879] -0.2127] 2.0942]. .
Po LOGT2 [86-87 | 6.87158] 5.95327] 3.8626] 1.5007] 2.5734] 0.597] 0.1327] 1.6367] -0.0355] 1.5977] 1.3024]. .
P10 ILOGT2 {86-87 6.8465] 5.84494) 3.819] 1.5958] 2.1327] 0.6932] 0.2752] 1.0075] _-0.5524] 4.789} 0.4389} 2.5615]. a
fPit ILOGT2 [86-87 | 4.00509] 1.79665] 1.9731 0.449] 2.1242] -1.7461] 2.0304] 1.3946] -0.7103| 1.246] 1.7513} 2.4635] 1.0775]. a
P12 LOGT2 |86-87 | 3.20981] 1.45021) 1.8521] 0.2977] 1.3666) 4.06] 2.0745] -0.8491] 0.2216] 1.3232] 1.1353] 1.6136] 0.6802] 1.8785]. .
P13 LOGT2 |86-87 | 2.18993] 0.93828] 1.6364] 0.6089] 1.2938] 1.9515] 0.3685] 0.5328] 0.0587] 1.2006] 0.4256] 1.2367} 0.6922] 1.2207] 2.0554]. a
P14 ILOGT2 [86-87 | 1.37534] 0.49488] 1.2757] 0.6254] 1.0665] 0.9774] . 0.576] 0.938} 0.198} 1.1141] 0.5629] 1.3587] 0.6742] 0.7466] 1.5811] 1.3798)
P1S: LOGT2 [86-87 | 0.82014] 0.15633] 1.0946] 0.6111] 0.9745] 0.7593] 0.7576] 1.1728] 0.4226] 1.0261] 0.3048] 1.0888] 0.7145] 0.6425] 0.9896] 1.02] 1.5309) .
P16 ILOGT2 {86-87 | 0.69514] -0.03757] 1.0745] 0.6025] 1.0009] 0.7342| 0.6475] 1.2043] 0.645} 0.9395] 0,3972] 0.8668] 0.7284} 0.6071] 0.9474] 1.1426] 0.9553] 2.2245]. .
[Piz ILDGT2 [86-87 | 0.60214] -0.0821) 1.1799] 0.6279} 0.8864] 0.6465{ 0.8573] 0.9847] 0.4956] 1.0034] 0.4603] 0.6907] 0.7232] 0.6711] 0.8613] 1.1323] 0.9966] 1.4224] 1.7537]. .
{P18 LDGT2_ {86-87 | 0.44333] 0.03364] 1.1598] 0.6606] 0.7253] 0.5353] 0.9864] 1.1754] 0.4065] 0.9965] 0.5489] 0.8032] 0.7002] 0.7326] 0.6569] 1.0226] 0.9838] 0.6221] 1.7755] 0.6944].
P19 LOGT2 {86-87 | 0.18065] -0.04526[ 1.0637] 0.7123] 0.7409] 0.8109] 0.662] 0.9665} 0.652] 0.7498] 0.6309] 0.6545] 0.715] 0.7245] 0.7123] 0.6921] 0.7437] 0.6817[ 1.153] 0.6878] 0.7348]
IM147 Phase 2 Regression Coefficients, CO, 1986 to 1987 Model Year LDGT2's
Regression Coefficients
ISegment RMS: Reg.
Number Error Constant {C1 ic2 ic3 ic4 ICS C6 C7 ica ico C10 C11 |C12 C13 C14 C15 C16 C17 C18 C19
Bit TOGTS 86-87 | 7.78582] 5.80047]. 1.4386 a
B12 ILOGT2 |86-87 | 4.68466] 3.16394]. 0.9308] 2.6872].
B13 ILOGT2 186-87 | 3.05474] 1.67487] 0.9606] 1.5982] 2.7862]. .
B14 ILOGT2 |86-87 | 2.02059} 1.1291]. 0.8908] 1.1326] 2.3405] 1.8259]
(B15 ILOGT2 [86-87 | 1.24999] 0.47151]. 0.9544] 0.9442] 1.4229] 1.3063] 2.1977]. .
[16 ILOGT2 {86-87 | 1.06317] 0.16747]. 0.9833] 0.8605] 1.3578} 1,508} 1.2803} 3.3341] .
| (Ee ILOGT2 186-87 | 0.90457} -0.06369] 0.9757] 0.9354] 1.2019] 1.5292] 1.2917] 2.1953] 2.4754]. .
{eis ILOGT2 [86-87 | 0.76129] -0.02736]. 0.9433] 1.0434] 0.9962} 1.3721] 1.2582] 1.4319] 2.4588] 0.8181.
{B19 ILOGT2 [86-87 | 0.34719] —-0.0285} 0.9787] 0.9885] 0.9663} 0.8916] 0.9566] 0.9606] 1.5579] 0.947 1.0918]



IM147 Composite Regression Coefficients, NOx, 1981 to 1982 Model Year LDGV's
Regression Coefficients.
Segment RMS: Reg.
Number Error Constant [C1 C2 ic3 C4 cS C6 ic7 C8 (ei) C10 C11 C12 C13 C14 \C15 C16 C17 C18 ic19
ie LOGV_ [81-82 | 0. 51799) 1.65041] 42.7692] .
iP2 LOGV__ [81-82 | 0.63601] 0.72708] 17.5251] 4.1787]. .
iP3 ILOGV__ [81-82 | 0.52331] 0.58513] -4.6789] 2.4424] 11.7817]. a
iP4 ILOGV__ [81-82 | 0.52237] 0.58386} 6.215] 2.4752) 10.4671] 3.1134] .
PS. LOGV__ [81-82 | 0.50775] 0.50405] 4.2723] 2.2111] 9.2772] 2.5761] 3.9991]. a
iP. LOGV__ [81-82 | 0.50352] 0.48347] -3.9075] 2.1456] 8.9952] 1.1125] 3.4211] 5.3989]. a
iP7 ILOGV__ {81-82 | 0.49839] 0.48687] -4.3121] 2.0136] 8.6708] 0.9449] 1.0442] 5.8614] 2.4035]. .
iPS. ILOGV__ [81-82 | 0.46669] 0.46683] 1.6928] 1.6175] 7.9174] -2.5315] 1.6902] 0.2702| 0.6553] 7.5772. a
iPS ILOGV_ [81-82 | 0.46311 0.452] 0.8119] 1.7211] 7.5725) -2.1824] 0.4138] -2.0068] 0.7582] 7.4243] 3.9487]. .
P10 ILOGV__ {81-82 | 0.46376] 0.44782] -1.0048] 1.7433] 7.4758] -2.0726] 0.0943] -1.7963] 0.4584] 7.4711] 3.3527] 1.0886]. a
P11 ILOGV__{81-82 | 0.35194] 0.23815] -8.5826] 0.7688] 4.8431 0.79] 0.1979} 2.4979) 0.684] 4.4696] 3.7564] 0.8973] 1.4384].
P12 LOGV__ [81-82 0.2927] 0.17049] -9.8733] 0.9681 2.776) -2.0862] 0.4024] 1.4025] 2.1207] 3.7386] 2.3379] 1.5715] 0.6911] 2.5825] .
P13 LDGV__|81-82 | 0.18109] 0.07569] ~4.5202] 0.7632] 1.5888] 1.3953] 0.0498] 2.8038] 0.6444] 2.0452] 0.4039] 1.7917] 0.6671] 1.3367] 2.4626]. a
[Pia ILDGV__ [81-82 | 0.14099] 0.02431] 4.1674] 0.7506] 1.6956] 1.9641] 0.1583] 1.4761] -0.6375] 1.7248] 0.4851] 2.7419] 0.5236] 0.9627] 1.8418[ 1.5633]. a
Pas. ILOGV__}81-82 | 0.08737] 0.01811 1,382] 0.8401] 1.3947] 1.6138] 0.2041] 1.0019] 0.7778] 0.8836} 1.2699] 0.6037] 0.6393] 0.8763] 0.6065[ 0.8832] 1.7403]. .
pis ILOGV__ [81-82 | 0.07312] 0.0041] -1.1445] 0.8151) 4,058] 1.5224] 0.0582] 0.8216} 1.2094] 0.5747] 1.1231] 0.8514] 0.7173] 0.8313] 0.6082] 0.7402] 1.2902] 1.5334] a
[Piz ILOGV__|81-82 | 0.07264] 0.00367] -0.9811] 0.8032] 1.0413] 1.4118] 0.1444] 0.848} 1.1681] 0.6401} 1.1443] 0.7002] 0.7202| 0.816] 0.6414) 0.7463] 1.2653] 1.4284] 0.5328].
[P18 ILOGV__ [81-82 | 0.04243] 0.00563] 0.017} 0.7339] 0.8526 1.377] 0.2716] 1.0442] 0.6919] 0.8178] 0.9258] 0.5464] 0.7223] 0.7683] 0.6426] 0.8313] 0.8107] 1.4947| 0.4033] 0.8387].
[P19 ILOGV__ {81-82 | 0.01342] 0.00395] 0.7869} 0.7185] 0.788] 0.8447} 0.6524] 0.7441] 0.6472] 0.857] 0.6045] 0.7562] 0.6997] 0.7449] 0.6929] 0.7279] 0.7185] 0.7737] 0.6197| 0.7765] 0.6907}
IM147 Phase 2 Regression Coefficients, NOx, 1981 to 1982 Model Year LDGV's
Regression Coefficients

Segment RMS Reg.
iNumber Error \Constant {C1 ic2 ic3 \C4 ics: C6 C7 C8 ico C10 C14 C12 [C13 C14 C15. C16 C17 C18 c19
fer 1 ILOGV__ {81-82 | 0.59924] 0.60079} 2.8438] a
[B12 ILOGV__|81-82 | 0.50466] 0.48014]. 1.6] 3.6643]. a
| EXE LOGV__ 81-82 0.2646] 0.11652]. 0.8595] 2.316} 3.4315) .
[era ILDGV__ [81-82 | 0.21288] 0.04456}. 0.7104} 1.676] 2.6006] 2.1072]. .
B15 ILOGV__ [81-82 | 0.13034] 0.02244]. 0.8971] 1.4034] 0.8839] 1.2572) 2.3849] .
B16 ILOGV__ [81-82 0.1083] 0.01418]. 1.0311} 1.1362] 1.0575] 0.9885] 1.7362} 2.0954]. A
B17, ILOGV_ [81-82 | 0.10478) 0.0082] 1.0248} 1.1015] 1.1113] 1.0374] 1.6264] 1.8908] 1.4012].
B18 ILOGV__{81-82 | 0.06108] -0.00004) 0.9845] 1.139} 0.8671 1.145] 0.9874] 2.2913] 0.654] 1.1584].
B19 LOGV_ {81-82 | 0.01536] 0.00328]. 0.9626] 1.0202} 0.9556] 0.9706} 0.9459} 1.0635] 0.9025] 0.9818] 0.9686]



Table 6-2
Centered CPP Cutpoints and Resulting Test Abort Rates

Centered CPP Limits Abort Rates (%) Abort Rates (%) from Percentage of All
(mph’/sec) from All Tests Top-50% Driver Tests | Drivers Within Limits

Top-50% | Lower | Upper | Interval | Simple | Effective | Simple | Effective
Driver CPP CPP Half- Abort Abort Abort Abort > Lower < Upper
Percentile | Limit Limit_| Width Rate Rate Rate Rate Limit Limit

£0.25% | 4,253 4,982 364.2 5.8% 1.6% 1.2% 0.2% 95.7% 53.9%

“0.5% -|.4,282- | 4,953. | 335.6 | > 72%] > 21%] ~1.9%| 04%] 93.5%.| * 47.4%

£0.75% | 4,307 4,928 310.8 8.7% 2.6% 2.7% 0.6% 91.8% 40.5%!

+1.0% |4325 |4,909 | 2921 | 105%] 3.1%] 3.7%] 0.9%] 88.4%] 37.1%
42.0% [4361 ]4873 | 2559 | 141%| 42%] 63%] 1.6%] sis%| 28.9%
33.0% [4386 }4g4s | 2309 | 173%| s3%| 89%] 26%] 776%| 26.3%
24.0% [4,403 [4831 | 2142 | 199%] 62%] 109%] 3.1% | 74.1% | 24.6%
45.0% [4418 ]4si6 | 1991 | 226%] 72%] 13.2%] 3.9% | 672% | 22.4%
Sample - - : 9,306 Tests 7,962 Tests 231 Drivers

Based on the results given in Table 6-2, Sierra proposes the use of composite lower and
upper CPP variation limits of 4,282 and 4,953 mph’/sec, respectively (shown in the
shaded row in Table 6-2). As indicated in the table, these composite CPP limits are
expected to increase the (effective) abort test rate by 2.1% relative to that resulting from
EPA’s recommended speed excursion limits based on available test data. Since the goal
of the analysis was to specify variation limits that kept the abort rate due to these
variation limit violations to 3%, composite CPP limits resulting in only a 2.1%
incremental abort rate were selected. This left some “room” below the 3% target for the
impact of also imposing second-by-second CPP variation limits.

If drivers are selected based on their ability to perform as well as the best 50% of the
current drivers, then the abort rate would drop to just 0.4%. In practice, it is expected that
the abort rate will increase by less than this amount as drivers “adjust” to the new limits.

Development of Second-by-Second Variation Limits - Using the recommended
composite CPP limits of 4,282 and 4,953 mph’/sec, second-by second CPP limits were
generated by scaling the percentage difference of these limits from the composite
reference value (7.3%) to the CPP calculated at each second from the reference trace.
This approach was further modified as described below.

1. To provide drivers with a short period to “learn” to drive each test vehicle,
second-by-second CPP limits were not imposed until t=30 seconds; and

-62-


Figure 7-1

FAST-PASS
Partial Test CPP
<Lower Limits? PES ____,|
CONTINUE TEST
NO
Valid Fast-Pass #1
No | Full Test CPP Full Test CPP [NO os octe LNO
< Lower Limit? > Upper Limit?
YES YES YES
Valid Pass #1 “>| Invalid Test Valid Fail #1
- FAST-PASS FAST-FAIL ,
Partial Test CPP. Partial Test CPP.
<Lower Limits? ~ YES - YES!» > Upper Limits? ~
CONTINUE TEST CONTINUE TEST
NO
Valid Fast-Pass #2 PASS Fall
No | Full Test CPP Full Test CPP | NO NO

<Lower Limit? |__| > Upper Limit? Retest?

YES YES YES
Valid Pass #2 —TM) Invalid Test Valid Fail #2

FAST-PASS| FAST-FAIL
Partial Test CPP Partial Test CPP
<Lower Limits? PES “ES| < Lower Limits?

CONTINUE TEST ‘CONTINUE TEST
NO
PASS FAIL Valid Fast-Fail #3
NO | Full Test CPP Full Test CPP |. NO Retest? NO
< Lower Limit? > Upper Limit? ‘eles!

Yes YES Yes
Valid Pass #3 —TM) Invalid Test Valid Fail #3 :

Repeat IM147 #3 Test

Hi

-68-


2. INTRODUCTION

Under the Clean Air Act Amendments of 1990, metropolitan areas with the most serious
air quality problems are required to implement so-called “enhanced” I/M programs. One
element of an enhanced program is a more effective test procedure than the simple idle
tests used in “basic” I/M programs. Two different test procedures for exhaust emissions
testing in enhanced programs have been approved by EPA: the “IM240” test, and the
“Acceleration Simulation Mode” (ASM) test. Both of these procedures have been shown
to be capable of separating vehicles with excessive exhaust emissions from other
vehicles; however, the accuracy of the test depends on whether tested vehicles have been
adequately preconditioned and whether the speed-time profile associated with each test
procedure is closely followed. With either procedure, the efficiency of the testing process
depends on how quickly accurate decisions can be made as to whether a vehicle should
pass or fail.

Inadequate preconditioning of vehicles prior to testing is a potential cause of inaccurate
or inconsistent test results because exhaust emission levels depend on how thoroughly a
vehicle has been warmed up. Before the vehicle is thoroughly wartned up, high
emissions can be caused by air-fuel ratio enrichment or an inactive catalytic converter. In
addition, increased emissions due to purging of loaded canisters may also be an issue
associated with inadequate preconditioning prior to I/M testing.

Inadequate vehicle preconditioning has previously been identified as a cause of false
failures in I/M programs. Under current EPA guidance, IM240 preconditioning
procedures are woven into the “two-ways-to-pass” standards. Vehicles that exceed the
emissions standards established for the entire 239-second test are passed or failed based
on emissions occurring during the last 147 seconds of the test (also called Phase 2 or the
IM147). The separate set of standards that applies to Phase 2 is slightly more stringent.
For vehicles that initially demonstrate high emissions, the first 93 seconds (Phase 1) of
the test are used to precondition the vehicle for the second phase of the test. In addition,
EPA calls for a “second-chance” test whenever a vehicle fails the initial test by less than
50% of the standard and was in a queue for more than 20 minutes before being tested.

Previous EPA and DEQ Analyses - Considerable data have already been collected
regarding the preconditioning requirements for IM240 testing. During 1996 and 1997,
Sierra conducted evaluations of this issue using data obtained from samples of vehicles
recruited from IM240 lanes in Phoenix, Arizona, and a laboratory test program at Sierra’s
facilities in Sacramento. The results of the 1996 analysis were reported in SAE Paper
No. 962091.? The 1997 evaluation also included an analysis of the effect on test duration

“-13-


IM147 Composite Regression Coefficients, CO, 1990 to:1995 Model Year LDGT1's
Regression Coefficients

Segment RMS — |Reg. a
Number Error Constant [C1 ic2 ic3 C4 ics C6 C7 cB ica ic10—, [C14 ic12 ic13 ic14 C15. C16 C17 C18 ic19
Pt COGT1 [90-95 | 7.96708] 6.47310] 8.6737]. i a
jP2. ILOGT1 [90-95 | 5.93561] 3.60059] 0.3029] 2.9243] . a
iP3 ILOGT1_ [90-95 | 5.49867] 2.58569] 0.1967] 1.6676 5.73]. . "

ILOGT1 [90-95 | 5.47555] 2.41839} 0.0472] 1.683] 4.5333] 2.9942]. .

ILDGT1 [90-95 | 5.34097] 2.4681} 0.3008] 1.3842] 2.8744] 1.4949] 3.7142} '!

ILDGT1_ {90-95 | 5.23567] 2.50459] -0.4457] 1.3784] 2.6883] 0.8341) 2.2673] 4.9505]. a 4

ILOGT1 {90-95 | 5.12065] 2.31146] -0.3312] 1.2837] 1.873} -0.1146] 0.7803] 4.7671} 3.2802].

ILOGT1 [90-95 | 4.93002] 2.15394] -0.1308] 1.2614] 1.6587] 0.7666] 0.4112] 1.9844[ 1.3783] 5.3488]. a =

ILOGT1 |90-95 | 4.92436] 2.15209] -0.2521] 1.2934[ 1.4663] -0.6767] 0.1855] 1.6357] 1.4433] 4.9503] 1.3627]. a
P10 ILOGT1 |90-95 | 4.83877] 2.07448] -0.0883] 1.3418] 1.2572] -0.6746] 0.0356] 1.44321 1.0559] 4.4422] 1.7507] 4.57]. a
P11 LDGT1 [90-95 | 3.05447] 0.67888] 0.7448] 0.9163] 0.7304] -1.1973] 0.6625] 1.9991] 1.1363] 2.2194] -0.3005{ 2.3958] 1.1499]. .
P12 ILDGT1 ]90-95 | 2.59434] 0.35922] 0.8716) 0.834] 0.2278] -0.1746| 0.685} 1.603} 1.4913] 1.7526] -0.5786] 2.4993] 0.9124] 1.5835].
[P13 ILOGT1 [90-95 | 1.69051 0.3538] 0.7846] 0.7978] 0.2453] 0.7291} 0.7109] 0.6836] 0.9317] 0.9732] 0.7569] 0.3609] 0.8116] 0.8909] 2.4633]. .
[P14 ILOGT1 [90-95 | 1.25118] 0.29289] 0.7084[ 0.7156] 0.4878] 0.5609] 0.6826] 0.8354] 0.9735] 1.1145} 0.55] 0.9761] 0.7373] 0.7493] 1.6233] 1.1532]. a
[P15 ILOGT1_|90-95 | 0.88666] 0.17653] 0.6832] 0.7443] 0.6247{ 0.7585} 0.551] 0.8411] 0.8994] 0.6594} 0.516] 0.6847] 0.7688] 0.6805] 1.0955] 0.8841) 1.4675} : a
P16 ILDGT1 [90-95 | 0.83688] 0.14342] 0.6546] 0.7325] 0.6626] 0.7804] 0.6 0.754] 0.9659] 0.5296] 0.6743] 0.5828] 0.7741] 0.6855] 0.9886] 0.964] 1.0266) 1.788} a
P17, LDGT1 [90-95 | 0.75939] 0.03084] 0.6751] 0.7626] 0.4984] 0.7727] 0.6819] 0.6512) 0.8746] 0.5588] 0.7025] 0.4405] 0.774] 0.6594] 0.9682] 0.97247 1.0041] 1.3254] 2.054].
P18 ILDGT1 [90-95 | 0.48016] 0.02847] 0.6665] 0.7378] 0.5678] 0.7356] 0.81] 0.7878} 0.6105} 0.7172] 0.7969] 0.5663] 0.7101 0.721} 0.8057] 0.915} 0.6348] 1.2367] 1.3997] 1.0199].
P19 ILDGT1 [90-95 | 0.16891] -0.00569] 0.7401] 0.7243] 0.6471] 0.7729] 0.6775] 0.7704] 0.7643] 0.6818] 0.7573] 0.6839| 0.7157] 0.7187] 0.7375] 0.6868) 0.717] 0.6511] 1.1384] 0.7248] 0.7498)

IM147 Phase 2 Regression Coefficients, CO, 1990 to 1995 Model Year LDGT1's
. Regression Coefficients

Segment RMS — | Reg. | i I
Number . Error Constant }C1 C2 C3 C4 ics C6 C7 ics ico C10} C11 ic12 C13 C14 IC1s C16 C17 C18 C19
B11 tt 1 [90-95 | 5.48884] 2.92303]. 4.7403} .
B12 |LOGT1 |90-95 | 3.74619] 0.83254) 1.3383] 2.3792]. .
B13’ _|LDGT1 [90-95 | 2.29939] 0.47273] | 1.1019] 1.1903] 3.3532]. a
[e14 ILDGT1 [90-95 1.7181] 0.48708}. "1 1.0162] 1.02] 2.3871] 1.5186} a
B15 ILOGT1_|90-95 | 1.20254] 0.23609]. 14,0388] 0.9035] 1.4703} 1.2205} 1.9615]. a
B16 LDGT1 [90-95 [1.13572] 0.20105} i} 1.0447] 0.9125] 1.3168] 1.3287] 1.3674] 2.3837]. a
(B17 ILOGT1_ [90-95 | 1.04495} 0.01286]. 1.0374] 0.8678] 1.2457} 1.3709] 1.2968] 1.8468] 2.5009} .
B18 ILOGT1 [90-95 | 0.65189] 0.0106] 0.9551} 0.9717] 1.0606] 1.2615] 0.8351] 1.7471] 1.7371] 1.3845].
(B19 ILDGT1 [90-95 0.219} 0.00146]. «| -0.968] 0.9695] 0.9969] 0.9364) 0.9681{ 0.9047] 1.5264) 0.9898] 1.012)



midpoint, Max CO and final cutpoints.’ The analytical approach used in the 1997
analysis was initially followed; however, subsequent results led to the development of an
improved variation metric, Cumulative Positive Power (CPP). Consistent with the
approach used in the previous analysis, the evaluation was structured to develop CPP
limits designed to keep the effective abort rate due to drive trace violations to less than
3%. The impact of the resulting criteria on average dynamometer test time was also
evaluated.

Organization of the Report

Following this introduction, Section 3 describes data collection and the data sets used
throughout this study. Section 4 explains the cutpoint analysis including the revision of
the Max CO cutpoints using the larger data sample; it also details failure rates for the
Startup, Intermediate, Final, and Max CO standards. Section 5 describes the optimized
fast-pass and retest criteria. In addition, it details the new fast-fail algorithm and criteria
as well as the net results of optimized criteria when run simultaneously. Section 6
describes the new CPP driver variation limits and Section 7 integrates the CPP limits with
the optimized IM147 to show the net effect on test time. Section 8 lists the references
cited in the report.

HH

* The original workplan called for the 16,581-vehicle data set from Sierra’s 1997 PKE study for EPA
(SR98-02-01) to also be used in this latest analysis. However, as discussed in more detail in subsequent
sections, a “time realignment” (of emissions versus vehicle speed) was incorporated into the analysis. This
made use of the previous data more problematic. It was determined that the effort required to adjust these
data could not be justified in terms of a significant increase in the accuracy of the results; therefore, a
decision was made not to use these previous data.

-18-


the drive trace. To maintain uniformity for lane software programmers, the error term
was not included in the prediction or the mode 7 emissions since it was not in the retest
algorithm.
The vehicle fast-fails the second IM147 if any of the following are true:
For LDVs:

Predicted HC after 7 segments > (1.5 x Composite HC standard)

Predicted CO after 7 segments > (2.2 x Composite CO standard)

Predicted NOx after 7 segments > (1.4 x Composite NOx standard),
For LDTs:

Predicted HC after 7 segments > (1.1 x Composite HC standard)

Predicted CO after 7 segments > (1.5 x Composite CO standard)

Predicted NOx after 7 segments > (1.5 x Composite NOx standard)
Using this criteria, 99 trucks and 124 cars are fast-failed after segment 7 of the second
IM147, with no false failures when the retest algorithm is disabled.
During the third IM147, the potential for falsely failing vehicles in subsequent IM147
tests is eliminated. As a result, the modal fast-pass algorithm, with a minor modification,
can be implemented to predict failing vehicles. Instead of adding the error term to the
predicted score as is done with the fast-pass algorithm, the error term is subtracted. This
adjustment ensures conservative emission estimates, which will help to minimize false

failures when using the algorithm. Equation 5-3 illustrates the third IM147 fast-fail
algorithm.

P240n = Cn+ ¥) {Snmx Xnm} - (Mex En)
mel [5-3]
Where: P240, = Predicted emissions after completing n segments
Cc, = Regression intercept for equation n
Sam = Regression coefficient for segment m in equation n
Xam = Total emissions over a given segment m in equation n
M, = Error multiplier (usually 2 unless otherwise specified)

“It is theoretically possible to identify additional fast-fails by using the same or different predictive
algorithms at the end of subsequent segments. While Gordon-Darby has expressed interest in this
enhancement in order to further reduce average test time, the timing of the study did not allow for the
evaluation of this issue.

-47-


IM147 Composite Regression Coefficients, HC, 1990 to 1995 Model Year LDGV's

Regression Coefficients
Segment RMS — |Reg. :
Number Error {Constant |C1 cz fea. ca cs ce c7 ce ice cio: fox fer2_—_jcra_ cra. ica creer?’ crs_— cia
PT TDGV— [90-85 [0.2971] 0.16705] 15.7946],
{Pz LOGV_ [90-95 | 0.19663] 0.05505] -1.9351 4.6837].
1P3 LOGV_ [90-95 | 0.16628] 0.03974] 1.5889] __2.5646] 5.6414]. :
[Pa LOGV_ [90-95 | 0.1584] 0.03534 _-1.9531| 2.662] 3.0556] 3.5632]. i
[Ps LOGV_ [90-95 | 0.14861] _ 0.0377 1.7659] 2.1399] 2.3054] 1.5953] 3.670). :
[Pe LOGV_ [90-95 | 0.1428] 0.04157|_-2.1976] 2.3528] 1.3397] 1.1611] 1.6243] 4.1799]. :
[Pz LOGV_ [90-95 | 0.13017] 0.03906] _-1.447| 1.6086] 1.3743] 1.2125] -0.2620| 2.1347] 4.0626). 7
[Ps LOGV [90-95 | 0.12213] 0.03722| -0,5405] 1.6099] 1.2031] 0.3856] -0.1326] 1.4892] 3.3678] 3.4765]. 1
[es LOGV_|90-95 | 0.11733] 0.03754 -0.7341| 1.7237] 1.0051] 0.4924] -0.9573| 0.6324| 3.2462] 2.3004] 3.9101] >
[P10 LOGV_ [90-95 | 0.11526] 0.0368 -0.8148{ 1.6847] 1.071] 0.425] _-1.176| __0.876| _ 2.645] 2.502] 1.4335] 2.0572]. :
P11 LOGV_ [90-95 | 0.08741{ 0.00996] 0.6446] 0.8311] 1.0111] 0.5067] -0.4113| 1.2288| 2.2261| 1.2696| 1.6991| 1.3219] 1.5051|. :
P12 LOGV__|90-95 | 0.07472] 0.0105] 0.6979] 0.7423] 0.706] 0.5223] -0.0564| 1.3101] 1.9432] 1.288] 1.2908] _1.3897| 0.6636] 1.6454]. :
P13 LOGV_ [90-95 | 0.05916] 0.0139] 1.3045] 0.4375] 0.9242] 0.6499] 0.0689|_1.2742| 1.493] 1.04] 1.3582] 0.4913] 0.8544] 0.3112 25560), _____|.
P14 LOGV_ [90-95 | 0.05362] 0.01549] 1.386] 0.3479] 1.0903] 0.5466] _0.252| 1.3522| 1.424] 0.6349] 1.5658] 0.679] 0.6613] 0.3943] 1.6017] 2.0376]. :
PAS LOGV_ [90-95 | 0.04337| 0.01025] 1.2116] 0.4485] 0.9328] 0.7036] 0.5036] 1.3664] 1.1003] _0.7903[ 1.3103] 0.2473] 0.6803] 0.7135] 0.7193] 0.5976] 2.3130 :
P16 LOGV_ [90-95 | 0.04152] 0.00959] 1.1338] 0.4558] 0.9892] 0.8217] 0.5423] _1.2542| 0.9416] 0.7779] 1.3289] 0.1879] 0.6808] _0.7232| 0.692 0.6981] 1.451] 2.8503]. :
PA? LOGV [30-95 | 0.0313] 0.00573] 0.9123] 0.6301] 0.5621] 0.8487] 0.6701 1.1772] 0.8709] 0.7111] 1.0362] 0.425] 0.686] 0.7574] 0.6663] 0.8447] 1.2224] 1.3549] 2.4330]. :
P18 LOGV_ [30-95 | 0.02304{ 0.004] 1.0116] 0.6089] 0.6873] 0.6706] 0.6772] 1.1436] _0.7472| 0.6904] 0.8920] 0.5431] 0.6856] _0.7093| 0.6726] 0.682] 1.042] 0.6819] 1.3735] 7.501].
P19 LOGV_ [90-95 | 0.01929] 0,00299| _ 0.874] 0.6682] 0.6242] 0.6993] 0.6005] 1.2226] 0.7716] 0.6058] 0.6359] _ 0.606] 0.6915] _ 0.724] 0.6677] 0.6623] 0.8334] 0.1215] 1.1662] 1.238] 0.9207
M147 Phase 2 Regression Coefficients, HC, 1990 to 1995 Model Year LDGV's
= Regression Coefficients
Segment RMS — Reg. '
Number Error {Constant {C1 cz c3 Ica cs {ce c7 ce Ico cio; for fer2_— erasers. cts. cre iciz?_— cisco
Bit ILDGV__ [90-95 | 0.13011] 0.0203]. 29776]. r
B12 LOGV__|90-95 | 0.11712] 0.01783]. 1.7491] 2.9654].
B13 LOGV__|90-95 | 0.08624) 0.01945). 7.2881) 0.3333| 3.9332. :
B14 LDGV__|90-95 | 0.08064] 0.02126]. 7.0669] 0.4262 2.6321| 2.3928]. :
B15 LOGV__ [90-95 | 0.06433] 0.0142]. 7.0083) 0.9588] 1.2508] 0.4227] 3.4379].
[p16 LOGV_ |90-95 | 0.06126] 0.01375] 0.9905] 0.9989] 1.1536] 0.6257| 2.0703] 4.3157]. :
{ar7 LOGV {90-95 { 0.04442] 0.00749}. 6.9692] 0.997] 1.0495] 1.0206 1.6198] 2.0719] 3.5604]. :
fore LOGV__{80-95 | 0.03245] 0.00457]. 0.9306] 0.9602} 0.9645] 0.8007] 1.3774] 1.0699] 1.9911] 2.2266].
| LOGV_ [80-95 | 0.02708] 0.00366]. 0.9309] 0.9723] 0.9484] 0.8169] 1.0622 _0.3307| 1.6177| 1.7198] 1.2796



-0€-

Figure 4-2

Failure Rate by Consecutive IM147

Vehicle Type

Vehicle Type

Max CO Cutpoints
HC Failure Rate by Vehicle Type CO Failure Rate by Vehicle Type
14.0% 25.0%
12.0%
20.0%
= 10.0% 3
“ 3.0% Gist M147 “ 15.0% Gist M147
2 @2ndimis7 2 2nd imia7
“a 6.0% ‘3
« [3rd M147 © 10.0% G (3rd M147
YZ 4.0% 8
5.0% y
2.0%
0.0% aD 0.0% am
PC LOT! ~—-LDT2 All PC LoTI LbT2 all
Vehicle Type Vehicle Type
NOx Failure Rate by Vehicle Type Overall Failure Rate by Vehicle Type
35.0%
4, 30.0%
3 2 25.0%
dst IM147 2 Ist 7
g Gis £ 20.0% Gist mia
2 2nd M147 = M2ndimi47
= £ 15.0% Y
% (3rd M147 2 (3rd 1M147
i} EB 10.0%
Z 3
°
5.0%
0.0% +1
PC LpT! LoT2 an



of adopting EPA-recommended “final” IM240 cutpoints. Preliminary conclusions from
the two evaluations are summarized below.

1. Using the current IM240 test procedures, it is estimated that 25% of the
vehicles failing the final IM240 standards would pass with further
preconditioning.

2. Vehicles that would. benefit from further preconditioning can be identified

through modal analysis of the emissions recorded during the IM240 test.

3. Two possible approaches to modifying the current preconditioning
procedures would be to:

a. retain existing IM240 test procedure and two-ways-to-pass
standards, with the entire IM240 to be repeated if the Phase 2
emissions failure is marginal, emissions near the end of Phase 2 are
relatively low, or emissions during Phase 2 are significantly lower
than during Phase 1; or

b. eliminate Phase 1 and make the initial pass/fail decision based on
running only the IM147, with a second-chance test (another
IM147) for all vehicles that initially fail, and a third-chance IM147
test if emissions during the second-chance test are significantly
lower than emissions during the initial test.

4. Adoption of final cutpoints and more effective preconditioning procedures
involving a second full-IM240 (Option 3.a. above) will increase the
portion of the test involving dynamometer operation by more than 100%.

The 1997 evaluation also involved the development of improved IM240 fast-pass
cutpoints using a modal regression approach originally pioneered by the New York
Department of Environmental Conservation (NYDEC).* This study also involved the
development of modal predictive IM240 retest algorithms designed to minimize the
fraction of vehicles either (1) identified as needing a retest when they would still fail, or
(2) not identified as needing a retest when they would have passed if retested.

As a follow-up to the 1997 evaluation for EPA, Sierra subsequently conducted an
analysis (SR98-05-01)° for the Arizona Department of Environmental Quality (DEQ) of
the effect on failure rates, I/M program benefits, and test duration of the following
changes to the current IM240 procedure: (1) implementation of the Option 3.b
preconditioning procedures summarized above; (2) adoption of interim (“Max CO”)
cutpoints designed to maximize the carbon monoxide emission reduction benefits being
achieved by the program; and (3) the exemption of either the first four or first five model
years from program requirements.

-14-


Figure 6-1

Comparison of IM147 Variation Limit Metrics

° 30,000
Speed ——___, 7
° 25,000 &
Cumulative PKE (CPKE) \ Fi
3° 20.000
3 * 15,000 §
& rh aad Cumulative Positive Power (cr, \ 3
“2 YW 10,000 g
[ NA toe?
° SS 5000 &
T &

° 0

°
3
8

30 40 «450 60 70 866 90 100 110 120 130 140 150
Time (seconds)

example, this phenomenon can be seen at Seconds 11-14 in Figure 6-1, where the
reference trace exhibits acceleration from Second 5 through Second 14 but cumulative
PKE begins dropping beyond Second 11.

Because of this behavior, it was difficult to establish reasonable second-by-second
variation limits based upon cumulative PKE differences between the reference and driven
traces using a scaling approach similar to that developed under the 1998 IM240 study.
That approach basically consisted of scaling the composite PKE interval limits
established at the end of the test by the percentage difference of these limits from the
composite reference value.

Since the magnitude of critical emissions-affecting deviations during accelerations is
difficult to distinguish from the magnitude of deviations that do not substantially affect
emissions during deceleration and cruise, this scaling approach fails when based upon
cumulative PKE. The result is that cumulative PKE-based second-by-second variation
limits of a specific scaled interval width will either falsely flag less important deviations
during decelerations or not identify deviations during accelerations that do affect
measured emissions.

Thus, two alternate approaches were considered for establishing reasonable second-by-
second IM147 variation limits:

1. Use of a better-behaved statistic, such as positive power, in conjunction with
the basic scaling approach; and

2. Development of separate variation limits for each second using deviation

distributions (reference vs. actual) at each second compiled froma full
modal analysis of the test data sample.

-58-


IM147 Composite Regression Coefficients, NOx, 1981 to 1985 Model Year LDGT2's

Regression Coefficients
Segment RMS Reg.
Number Error IConstant |C1 ic2 ic3 C4 (oy icé C7 ics ico C10 cit ic12 |c13 C14 C15 C16 C17 C18 ic19
LoGT2 ]81-85 | 1.90604] 3.69101] 10.2660].
LOGT2 [81-85 | 0.87409] 0.91821] -7.9377] 6.5305]. .
LDGT2 [81-85 | 0.84122] 0.83444) 8.613] 5.4145] 7.2247] .
ILOGT2 |81-85 | 0.82892] 0.89342] -10.2897| 5.4633] 2.5096] 8.8657]. a
LOGT2 [81-85 | 0.80333] 0.89081{ -9.4068] 4.7319] 0.276] 9.1558] 5.3418]. a
ILOGT2 [81-85 0.7983] 0.87735] -13.437] 4.8675] -0.2244] 7.2171] 4.8373] 4.2644].
ILOGT2 [81-85 | 0.79972] 0.89425] -13.7206] 4.6923] -0.887] 7.62] 4.3577] 4.6628] 1.0456)
LOGT2 |81-85 | 0.79473] 0.84415] -11.4499| 4.5789] -1.4261] 5.7988] 3.4916] 2.71747 1.1653] 3.2995]
LOGT2 [81-85 | 0.79705] 0.83069] -11.6615] 4.6051] -1.4956] 5.8933] 3.2922] 2.8161] 1.1255) 3.028] 0.9261
LOGT2 |81-85 | 0.79648] 0.81114] -17.8131 4.561] -1.2986] 5.9935] 2.4713} 3.4666] 0.516} 3.1827] 0.4726] 2.7254]. a
; (a LOGT2 [81-85 0.6659] 0.51142] -7.4827| 3.0898] -1.5249] 6.9352] 3.5295] 2.9385] 1.4316] 3.7346] 1.3238] -2.5498] 1.2169]. a
[P12 ILOGT2 |81-85 | 0.54957) 0.33422] 8.1307} 2.4318] -0.7549] 0.1755] 1.141] 5.7388] 3.1487] 2.2295] 0.7669] 0.0157] 0.6425] 3.1201]. .
[P13 ILOGT2 [81-85 | 0.26368] 0.1926] -3.0729] 0.495} 1.2524] 2.9861] -0.2928] 2.9259] 1.4715] 0.5835] 2.0376] 0.4876] 0.712] 1.7639] 2.5466].
P14 LOGT2 [81-85 | 0.17079] 0.09831] -1.9366] 0.7267] 0.5707] 1.48147 0.6512] 2.8236] 1.4133] 1.5048] 1.8051] 0.7881] 0.6265] 1.0991] 1.5732] 1.6465]. a
P15 ILOGT2 }81-85 | 0.13538] 0.05816] -1.2593] 0.6843] 0.9631 1.699] -1.1762] 2.6604) 1.5116] 1.2555] 1.0619] 1.1308] 0.6644] 0.9993] 0.9615] 1.3864] 0.9139]. .
P16 ILOGT2 [81-85 | 0.12927] 0.04855] -0.6587] 0.6411] 0.9878] 1.2005] -0.6445] 2.468] 1.4956} 0.99] 1.0154] 1.2544] 0.6922] 0.8896] 1.0162] 1.349} 0.717] 0.6881]. a
PAZ. ILOGT2 [81-85 | 0.12798] 0.04485] 0.3609] 0.6627] 0.916} 14.0272] 0.4442] 2.2519] 1.3213] 1.1276] 1.0486] 1.1459] 0.6823] 0.8485] 0.9897] 1.3779] 0.7091) 0.682] 0.6099}. .
P18 ILOGT2 [81-85 | 0.08606] 0.02155} 0.3328) 0.587] 1.0724] 1.0072] 0.5742] 1.9792] 0.4674] 1.4066] 1.3569] -0.0211] 0.6682] 0.8649] 0.7604] 0.9339] 0.6473] 0.8806] 1.1799] 0.9731].
[pis [LDGT2 |81-85 | 0.02488} 0.0013f" 1.1429] 0.6401] 0.9555] 0.8446} 0.5989] 0.8719] 0.8311] 0.6923] 0.5888] 0.7318] 0.7118] 0.6787] 0.7213] 0.7279| 0.7365] 0.6649] 0.7072} 0.679[ 0.7799]
IM147 Phase 2 Regression Coefficients, NOx, 1981 to 1985 Model Year LDGT2's
Regression Coefficients
Segment IRMS —|Reg.
Number Error Constant |C1 ic2 ic3. \C4 C5 iCé C7 ic8 ico C10 C114 }C12 C13 C14 C15. C16 C17 C18 }C19
| Ca 81-85 | 1.42447] 1.55851] 3.2137}.
[B12 ILOGT2 [81-85 | 1.03629} 0.77984]. 1.5673] 6.4951]. .
| EXE ILDGT2 |81-85 | 0.38433] 0.27378]. 0.8325) 2.718} 3.5162]. .
[B14 (LOGT2 [81-85 | 0.26921] 0.18815]. 0.8123] 1.6716] 2.4085] 2.1998]. .
{B15 ILOGT2 [81-85 | 0.22469] 0.11816]. 0.8718] 1.4697] 1.4685] 1.8766] 1.2618) .
[eis LDGT2 {81-85 | 0.19939] 0.09578]. 0.9679] 1.128] 1.6012] 1.7461] 0.8574] 1.4532]. a
B17 JLOGT2 [81-85 | 0.19039] 0.09229}. 0.9221] 1.0819} 1.4847] 1.7994] 0.8974) 1.351] 1.5644].
| Gee ILOGT2 [81-85 | 0.15301} 0.07095]. 0.8347 1.2468] 1.0994] 1.3645] 0.8676] 1.6334] 1.4952] 1.0081
{19 ILDGT2 [81-85 | 0.03621] 0.00936) 0.943] 0.9237] 0.9993] 0.9988] 0.978] 0.8541] 1.0342) 0.8947] 1.1014]



IM147 Composite Regression Coefficients, NOx, 1988 to 1995 Model Year LDGT2's

Regression Coefficients

|Segment RMS — Reg.
Number Error {Constant |C1 c2 ica ica Ics Ice cz ica ico cio. fort fcr2_fcia_—fcta. fers fcre.—jciz_—fcrs_—_cio
TOGT2 [66-95 | 1.24876] 2.35108] 23,5646). r
LOGT2 [88-95 | 0.82176] 0.87411] 0.1479] 4.6355]. i
LOGT2 [88-95 | 0.77845] 0.71657] _-1.7162] _ 3.425] 6.1364].
LOGT2 {88-95 | 0.7742 0.70003] -1.8478| 3.4367 6.2476] 5.1308]
LOGT2 [28-95 | 0.73556] 0.71294] -1.4757| 2.6692] 3.7613] 3.2699] 6,663],
LOGT2 [86-95 | 0.72738] 0.72398] 2.5889] 2.6139] 3.8661] -0.5835] 5.6306 6.8957]. i
LOGT2 [88-95 | 0.70212] 0.63963] _-1.6939] 2.0359] 3.1437] 0.5064] 1.7183] 8.4446] _ 5.0667]. 5
LOGT2 [88-95 | 0.68022] 0.60531] 0.4848) 1.8702] 2.347|_-1.174] 1.1716] 2.9882] 4.0341] 6.3227]. i
LOGT2 [86-95 | 0.67704] 0.59719] 1.6152] 1.9533] __1.922| 0.993] 0.5073] 2.3082] 3.7294] 5.9607| 3.1202]

P10 LOGT2 [88-95 | 0.66525] 0.59249] -1.2143]1.9942| 1.1620] 0.3777] -0.1791] 2.4339] 2.4689] 6.3272] -0.1222] 4.5356!

jeu COGT2 [86-95 [0.49566] 0.16456] -1.995]" 0.9652] 1.6003 1.6143] 0.2176] 0.8508] -0.2399[ 3.1105] 1.8001] 2.7668] 1.9044].

P12, LOGT2 [88-95 [0.39017] 0.13915| 0.5048] 0.6942] 0.6646] 0.7011] 0.5649 0.3036] 0.9763] 0.3149] 0.4631] 3.3575] 0.9895] 3.9361).

P13 LOGT2 [88-95 [0.22071] 0.13586] "0. 1205] 0.5892[ 1.2326] 1.6722] 0.4435[0.7176| 0.4615] 0.651] 71-5772] 0.5428 0.7152] 1.6198] 2.5117]

P14 LOGT2 [88-95 [0.16814] 0.08085] 0.4177] 0.6242[ 1.2076] 1.54] 0.4306] 1.0374] 0.8469 0.7753[_ 1.5915] —0.5229| 0.5567 1.0483] _1.735| 1.6545] a

Ge LOGT2 [88-95 { 0.09792{ 0.04274] 1.2914] 0.6299[ 0.932) 15301] 0.7496{ 0.6925] 0.7213| 0.5676] 1.3142] 0.4284] 0.6686] 0.9379| 0.6221] 1.0317] 1.4681).

P16. LOGT2 [88-95 [0.08515] 0.04] 1.3615] 0.6274] 0.8992[ 1.2443 O.777| 0.5902 0.9112] 0.3659] 1.1635] 0.6339] 0.6996] 0.8562] 0.6431] 0.9655] 1.1161] 7.2384]. i

P17. LOGT2 [88-95 | 0.0793} 0.03{ 1.1604] 0.6609] 0.7332|_ 1.166] 0.8211 0.5607| 0.8664] 0.4784| 1.1288] 0.4527] 0.7155] 0.8591] 0.8406] 0.9975| 1.0422] 1.1105] 1.2087].

P18 COGT2 [88-95 [0.04462 0.00528] 1.0205] 0.6891] 0.8472] 0.9762] 0.6666] 1.0589] 0.5961] 0.8411] 0.7658] 0.5158] 0.703[ 0.8891] 0.6603] 0.6286] 0.8216] 1.0348] 0.9346] 0.8792

P19 [tOGT2 [86-95 [0.0136] 0.00252] O7213[ 0.711] 0.747 0.6258] 0.7089[ 0.7055] 0.696] 0.744] 0.7329] 0.7233] 0.715] 0.7362) 0.7136] 0.716] 0.7117| 0.7363] 0.7333| 0.6995] 0.7243]

IM147 Phase 2 Regression Coefficients, NOx, 1988 to 1995 Model Year LDGT2's

Regression Coefficients

Segment RMS — Reg.

Number Error __ {Constant }C1 ic2 ics ca Ics ce ic7 ica ico cio fers er2_—fcra_—fera_—fcis_—cie_—sfci7_—crs__—_fcta
Bit CoGT2 [88-95 | 0.71642] 0.34014] " r r r r 3.1856).

B12 LOGT2 88-95 | 0.55127] 0.22451 i - - a a i i 7.6945] 5.1988]. 5

B13 LOGT2 [88-95 | 0.30245] 0.20374]. i - " a i a 0,8854[ 2.4317] 3.3283]. 5

B14 LDGT2 [88-95 | 0.23377] 0.14045]. r i i a oT. i 0.7528{_ 1.6901] 2.356] 2.1936]. r

B15 LOGT2 [88-95 | 0.13683] 0.07379]. i a i i i 5 i 5 o.88i{ 1.4099] 1.0647} 1.3754] 2.0168]. A

[ere LOGT2 [88-95 | 0.11912] 0.06757]. a i A i a i a 0.9498[ 1.1559] 1.1515] 1.31] _1.5401| 17,6409]. i

fev LDGT2 [88-95 | 0.10918] 0.04812]. a i i i 5 i - 0.9645[ 11668] 1.1296] 1.3343|_1.4075| 1.494] 1.6722] i
| EXE LDGT2 [88-95 | 0.0635] 0.01509]. 5 5 i 5 5 5 a - 0.9291] 1.3099] 0.8518] 1.1259[ 1.093] 1.5612] 1.2713] 1.1432].

fei9 LOGT2 [88-95 | 0.01423} 0.0016]. . . . . a . . . 0.9672] 1.0052] 0.9647} 0.96] 0.9599] 1.0009] 1.0227] 0.9483[ 1.0006}



1. SUMMARY

Under the Clean Air Act Amendments of 1990, metropolitan areas with the most serious
air quality problems are required to implement so-called “enhanced” I/M programs. Two
different test procedures for exhaust emissions testing in enhanced programs have been
approved by EPA: the “IM240” test, and the “Acceleration Simulation Mode” (ASM)
test. With either procedure, the efficiency of the testing process depends on how quickly
accurate decisions can be made as to whether a vehicle should pass or fail.

Inadequate vehicle preconditioning has previously been identified as a cause of false
failures in I/M programs. In fact, previous EPA and DEQ analyses estimate that 25% of
the vehicles failing the final IM240 standards would pass with further preconditioning,
and that these vehicles can be identified through modal analysis. To address this
problem, Sierra suggested, in another study, that Phase 1 of the IM240 test be eliminated,
instead using only the second hill of the IM240 (i.e., the “IM147”) up to three times in
succession to ensure adequate preconditioning. Based on this recommendation, and to
address the issue of inadequate preconditioning without compromising test throughput
excessively, Arizona therefore decided to change its test procedure to the IM147
beginning January 1, 2000. This program upgrade will also include implementation of
“Max CO” cutpoints previously developed by Sierra, which are designed to maximize the
CO benefits of the program.

Regarding this proposed alternative to the existing IM240 test, several concerns needed to
be addressed prior to implementation in Arizona. First of all, how would emissions
identification and credit change with the new procedure. Secondly, because the length of
the proposed test may be equivalent to that of three IM147 tests, it can use considerably
more dynamometer time than the other aforementioned tests, thus increasing the cost of
the program. Prior to this study, two other studies have already been conducted to start
addressing these issues.

The first study addressed, among other things, reducing test time and projected emission
credit levels for the IM147 test. Part of the data analyzed in this study consisted of 101
tests where vehicles were given three back-to-back IM147 tests. These data were used to
create “Phase 2b” cutpoints, which are analogous to phase 2 cutpoints for the IM240 test,
thus giving vehicles two ways to pass. In addition, fast-pass cutpoints for both the entire
IM147 as well as Phase 2b were also developed. The other data used in this study
consisted of 2% random sample IM240 test data collected in the Arizona IM240 program.
This, in combination with the 101 vehicle data, was used to project emission credits.
Unfortunately, since this study did not include back-to-back IM147 to IM240 testing,
excess emissions identification rates and SIP credit could not be conclusively established.

-l-


IM147 Composite Regression Coefficients, HC, 1983 to 1985 Model Year LDGV's
Regression Coefficients
Segment RMS Reg.
Number Error Constant {C1 ic2 ic3 C4 ics icé C7 ic8 ico ic10 C11 ic12 ic13 C14 jc15 C16 C17 C18 ic19
Pt OGV__[es-65 | 0.40712] 0.46122] 11.2061). r
iP. ILOGV__ [83-85 | 0.28584] 0.23584] 0.3793] 4.0312] a “
iP3 ILOGV__ [83-85 0.2251] 0.18194] 0.1504] 1.6625] 5.8839].
iP4 ILOGV__ [83-85 | 0.20949] 0.1636] -0.6566| 1.89] 3.4063} 3.0941]
iPS LOGV__ [83-85 | 0.19447] 0.16094] -1.0863] 1.2885} 2.6869] 2.1291] 3.7766]. .
iPS LOGV__ {83-85 | 0.19153] 0.16531] -1.4624] 1.3044] 2.1907] 1.8106] 3.1299] 2.0652]. .
[P7 ILOGV__{83-85 | 0.18355] 0.1542] -1.1191} 1.2382] 2.1956] 1.3759] 2.2879} 1.3053] 2.1756] .
PB LOGV_ [83-85 0.1742] 0.14505] -1.1029] 1.1144] 1.7862] 0.8889] 2.4267} 1.2368] 1.6895] 1.9949] a
iPS ILOGV_ {83-85 | 0.16499] 0.14815] -0.7229} 1.073] 1.8176] -0.7769] 1.4795] 0.6194] 1.4189] 0.9084] 4175]. .
P10 ILOGV_ {83-85 | 0.16024] 0.14347] 0.7243] 1.1373] 1.5682] 0.8427] 0.8657] 0.8685] 1.1993] 0.834) 2.645] 2.9155]. .
P11 LOGV__ [83-85 [0.12238] _0.06297| 0.38{ 0.2293] 1.7054] 0.1938] 1.2879] 1.6893] 0.5856] 0.5369] 2.6104] 1.7875{ 1.5441].
[P12 ILOGV_ [83-85 [ 0.10785] 0.05022] 0.4878] 0.4094] 0.8846] 0.5066] 1.0774] 1.8031] 0.7369] 0.5491] 1.8603] 2.2819] 1.0196] 1.6327]. .
[P13 ILOGV_ [83-85 | 0.07961 0.0331] 0.7767] 0.4332] 0.8179] 0.9806] 1.0015} 1.2108, 0.561] 0.4915] 1.5593] 1.6272] 0.8246] 0.3997] 2.5228] | .
[Pia ILOGV__ [83-85 0.068] 0.02445} 0.861] 0.3782] 0.9405] 0.9692] 0.9471] 1.0556] 0.6664] 0.4702] 1.5692] 1.559] 0.6216] 0.5139] 1.483] 2.5758]. .
P15 -JUDGV_ [83-85 [ 0.05617] 0.01633] 0.6641] 0.3847] 0.9494] 0.9339] 0.8878] 1.1718] 0.6879] 0.3691] 1.2553] 1.3016] 0.7499] 0.5579] 0.6795| 1.1332] 2.2871]. a
P16 LOGV_ [83-85 [ 0.05545] 0.01675] 0.7233) 0.393] 0.9564] 0.9028] 0.9463} 1.1024) 0.7794] 0.3887] 1.2701] 1.1977] 0.7436] 0.5865] 0.627] 1.1212] 1.8334] 1.417]. .
P17 LOGV_ {83-85 | 0.03879] 0.00841 0.893} 0.5126] 0.7936] 0.6136} 1.0495] 0.8218} 0.8047] 0.6079) 0.984] 0.9734] 0.7209] 0.7276] 0.5005} 1.2506] 1.4269] 0.4297] 2.417] a
P18 ILOGV__ 183-85 | 0.02939] -0.0015] 0.9063] 0.6276] 0.7336} 0.799] 0.8401] 0.7909] 0.6547} 0.6256] 1.0592] 0.5782] 0.7013} 0.749] 0.5944] 0.8615] 1.0441] 0.6308] 1.7952] 1.3705}
P19 ILOGV_ {83-85 0.0284} -0.00036] 0.8248] 0.6354] 0.7403] 0.7849] 0.8885} 0.8016] 0.6772] 0.6005] 1.0662} 0.4753] 0.7239] 0.7376] 0.6183} 0.7249] 0.9182] 0.3388] 1.7224] 1.1415] 0.5047]
{M147 Phase 2 Regression Coefficients, HC, 1983 to 1985 Model Year LDGV's
Regression Coefficients
Segment IRMS Reg. {
Number Error IConstant {C1 ic2 ic3 ica ics icé C7 icé ico ic10 ! C14 }C12 C13 C14 C15. C16 C17 C18 C19
11 LOGV__ [83-85 0.2057] 0.10209]. 2.848) a
[B12 LOGV__ [83-85 | 0.17708} 0.0755}. 1.8316] 2.7713].
1813 LOGV__ [83-85 | 0.12398] 0.04472] L 1.2782} 0.5298] 4.0757].
B14 ILOGV_ [83-85 | 0.10965] 0.03254]. 0.9666] 0.713] 2.5673] 3.6248]. .
B15 ILOGV__ {83-85 | 0.08809] 0.01872]. i 1.0596] 0.7937] 1.0694] 1.2338] 3.6814].
B16 ILOGV__ [83-85 | 0.08613] 0.02003}. 1.0528] 0.8555] 0.9553] 1.2083] 2.7852} 2.7887].
, (B17, ILDGV_ [83-85 | 0.05674] 0.00853]. 0.9868] 0.9946] 0.7356] 1.5594] 2.1184] 0.6029] 3.6309} a

B18 ILDGV_ {83-85 | 0.04106] _-0.0038! 1 0.9461} 1.0589] 0.7763] 1.1008) 1.4725) 0.8392] 2.602} 1.9484)
B19 LDGV_ ]83-85 | 0.039647 -0.00206) 0.9706} 1.0465] 0.8189] 0.9395] 1.277] 0.41] 2.4945] 1.6256] 0.6907}



Sierra would initiate further discussions with Gordon-Darby and evaluate possible
changes to the previously developed procedures to address these concerns. Specific
concerns that were voiced by Gordon-Darby staff include the following:

1. Insufficient number of fast-pass segments - SR99-10-02 divides
the IM147 drive trace into 14 segments. While the segments in the
first half of the test usually comprise 8 to 10 seconds, several
segments in Phase 2 of the drive trace are considerably longer (up
to 19 seconds in duration). Gordon-Darby expressed concern that
extended segments may force vehicles to be tested longer than
necessary prior to a fast-pass.

2. Fast-pass segments containing an odd number of seconds - To allow the
resulting modal test data up to three possible IM147 cycles to fit into the
same size record format as currently used for full duration IM240 tests,
Gordon-Darby plans to record two-second averages rather than once every
second as is presently done. To simplify its lane software, Gordon-Darby
requested that Sierra realign the segments to agree with the planned
frequency of data storage.

3. Nonalignment of fast-pass segments with proposed retest modes - To
further simplify its programming process, Gordon-Darby asked that Sierra
try to coincide retest mode breaks with segment breaks.

In addition to simply developing updated fast-pass regression coefficients and retest
algorithms based on an expanded data set, this study addresses the above concerns. In
response to the first two concerns as well as the test length issue (odd vs. even number or
seconds), the fast-pass segments have been revised as shown in Table 5-1.

As shown in the table, there are now 20 segments ranging from 4 to 10 seconds in
duration. Each segment contains an even number of seconds. Phase 2 of the IM147 now
begins at segment 11 and extends through the end of the test. The test includes 146
seconds of data (collected starting at second 0 and ending at second 146).

When the IM240 segments were originally created as part of SR98-02-01, they were
divided in such a way that segments characterized different modes of vehicle operation.
Some segments were composed of hard accelerations, while others characterize cruises,
and everything between, including decelerations. While this study re-aligned the
segments with deference to ensuring 4- to 10-second segment lengths divided along even
increments, care was taken to preserve the original intent of the segmentation. Figure 5-1
shows the re-aligned segments positioned against the drive trace.

-35-


Excess Emissions with Fast-pass Disabled

Max CO Cutpoints
Excess Excess HC Excess Excess CO . Excess Excess NO
Vehicle |Model Year| 1M240 HC | w/o fast- | Excess HC | IM240.CO | wio fast- | Excess CO |1M240 Nox | w/o fast- |Excess NOx|
Class Range (grams) pass (grams)} Identified (grams) [pass (grams)} Identified (grams) [pass (grams)| Identified
81-82 10.99 8.32 75.7% 605.55 603.26 99.6% 19.02 18.72 98.4%
83-85 5.55 5.55 100.0% 533.37 527.14 98.8% 2.37 1.69 11.3%
LDGV 86-89 0.42 0.42 100.0% 1.77 0 0.0% 1.65 1.65 100.0%
90-95 0 0: N/A 0 0 N/A 0.43 0.43 100.0%
96+ 16.96 14.29 84.3% 1140.69 1130.4 99.1% 23.47 22.49 95.8%
ALL 15.62 “14.47 92.6% 441.44 441.44 100.0% 6.69 5.72 85.5%
81-85 10.39 - 10.39 100.0% 285.54 285.54 100.0% 4.1 2.31 56.2%
88-89 0.21 0.21 100.0% 26.15 26.15 100.0% 0.68 0.38 55.9%
LDGTI 90-95 0 0 N/A 0 0 N/A 0 0 N/A
96+ 26.22 25.07 95.6% 753.13 753.13 100.0% 11.48 8.41 -73.3%
ALL 0 0 N/A 56.74 49.73 87.6% 3.61 3.61 100.0%
81-85 6.13 6.13 100.0% 243.24 237.02 97.4% 3.49 3.49 100.0%
86-87 10.72 10.72 100.0% 301.8 301.22 99.8% 9.92 9.92 100.0%
LDGT2 88-95 8.96 8.87 99.0% 185.26 160 86.4% 6.59 6.02 91.4%
96+ 0 0 N/A 0 0 N/A 0 0 N/A
ALL 25.81 25.72 99.7% 787.04 747.97 95.0% 23.61 23.04 97.6%
Total ALL 65.08 94.3% 2680.86 2631.5 98.2% 58.56 53.94 92.1%

D-3



IM147 Composite Regression Coefficients, CO, 1996 and Newer Model Year LDGT1's
+ Regression Coefficients
Segment RMS —|Reg. .
iNumber Error Constant |C1 C2 jc3 C4 C5 icé C7 ica ico 1c10 C14 ic12. «(C13 C14 C15, C16 C17, C18 Ici9
| Ga TOG: 1 [96+ 1.82409] 1.39419} 5.531.
[P2 ILOGT1 [96+ 1.63165] 0.95561] -5.9413] 3.0869] _ te
iP3 ILOGT1 [96+ 1.57733] 0.85422] -1.5783{ 1.5971] 3.8856].
iP4 LDGT1 [96+ 1.57696} 0.84862] -1.5432| 1.758] 1.4232] 4.1058].
iPS ILOGT1 [96+ 1.55655] 0.77387] -1.8497] 1.5252] 1.4375] 1.5669] 3.3072]. a
iPS ILOGT1 [96+ 1.48358] 0.83228] -5.5406] 1.6396] -0.2011] -2.3153] 2.1554] 10.6111 .
iP7 ILOGT1_ [96+ 1.47504) 0.7448] -5.2472] 1.8872] -0.9914] -0.8568] 0.4965] 7.7306] 2.5842].
iPS ILOGT1 [96+ 1.48314] 0.71859] -5.1507} 1.8553] -0.9277] -0.9266| 0.671] 8.0227] 2.621] -0.2793].
iPS ILOGT1 [96+ 1.49155] 0.72084] -5.0954} 1.8331] -1.0008] -0.6946] 0.7565] 7.9469) 263] -0.1935] -0.3333} a
P10. ILOGT1_}96+ 1.48528] 0.70223] -3.9278} 1.5364] 0.9574] -0.075] 1.1515] 8.3687] 2.439] 0.1927] -2.1271] 1.4542]. -
P11 ILOGT1_ [96+ 0.71796] 0.22242] -1.2984] 1.4739] 0.5359] -1.1903] 0.4023] 2.2155] 2.7505{ 0.2649] 0.6008] 1.0433] 0.8928]. a
P12 ILOGT1 [96+ 0.54429] 0.43226 1.524} 1.022] 0.7868] -1.432] 0.1372] 2.482} 1.6269] -0.3682] 2.2202] 0.4979] 0.8781] 1.6265} .
peas ILOGT1 [96+ 0.43766] 0.08336] 1.4971] 0.7999] 1.0816] -1.4102] 0.8701] 2.4778] 1.5114] 0.0111] 0.8915] 0.5019] 0.8812} 0.3603 3.2131]. a
[Pia LOGT1 [96+ 0.27254 0.0707] 1.3293] 0.9437] 0.7684] 0.6826] 0.5074] 1.8189] 1.5858] 0.2076] 0.9221] 0.5798] 0.729] 0.6284] 2.4591] 0.7018] .
P15 ILOGT1 [96+ 0.19651} 0.03007] 1.3733] 0.8799] 0.3876] 0.2025} 0.6389] 2.4824] 0.9733] 0.4929] 0.3408] 0.8102] 0.7261] 0.7297] 1.4475] 0.6703] 1.466]. .
p16 LOGT1 [96+ 0.17615] 0.02979] 1.0484] 0.8101] 0.7323] 0.205] 0.5366] 2.4872] 0.7994] 0.4735] 0.5618] 0.7865] 0.7275] 0.7712{ 1.4017] 0.6818] 0.9833] 2.2195]. .
[Piz ILDGT1_ |96+ 0.13011] 0.05988] 1,062] 0.6811] 0.3816} 0.7616] 0.4795] 2.2042] 0.9584} 0.5832] 0.1538] 0.8447/ 0.7233] 0.7198] 1.1616] 0.6987] 1.0372] 0.2683] 1.8782]. a
Pia ILDGT1 [96+ 0.08591] __0,02837| 1.3696] 0.6559] 0.7295] 0.2618] 0.8191] 1.2599] 0.5828] 0.7744] 0.6755] 0.6858] 0.712] 0.7269] 0.7236] 0.7111] 0.9386] 0.4881] 1.7121| 1.0654]
[P19 LDGT1 [96+ 0.03308] 0.00956] 0.6181} 0.7187} 0.7481 0.61] 0.6377] 0.8223] 0.7051] 0.6935] 0.8481] 0.6648[ 0.7101] 0.7232] 0.6944] 0.7189] 0.8125] 0.226} 1.1367] 0.7471] 0.7019}
IM147 Phase 2 Regression Coefficients, CO, 1996 and Newer Model Year LDGT1's
Regression Coefficients
Segment RMS Reg.
iNumber Error Constant }C1 jc2 C3 C4 C5. C6 C7 C8 ico C10 C44 C12 C13. C14 cis C16 C17 C18 C19
Es TOGTT [eer | 1.20604] 0.90023 09526]. r
[B12 ILOGT1 [96+ 0.74363] 0.24386]. 1.1933] 2.1843} a
{813 ILOGT1 }96+ 0.60473} 0.1527]. 1.2056] 0.5562] 4.3384]. .
[e14 ILOGT1_|96+ 0.40405} 0.15643}. 0.9921] 0.9282] 3.3758] 0.9673].
| [Xe ILOGT1_ [96+ 0.30462] 0.06538} 0.9969} 1.0801] 1.9431 0.914} 2.0105} .
[B16 ILOGT1 [96+ 0.26097] 0.03614, 0.9954 1.08] 1.8842] 0.9365] 1.1866] 3.7438].
{B17 ILDGT1_[96+ 0.2048) 0.0653]. 0.9914] 1.0331] 1.5352] 0.9586] 1.2475] 4.026] 2.448). .
[Bis ILOGT1 {96+ 0.12661] 0.03391 0.9631] 0.9575] 0.9469} 0.9698] 1.2044] 0.8083] 2.4102] 1.4239].
[B19 ILOGT1 [96+ 0.04352] 0.01126]. 0.9604] 0.9595] 0.9395] 0.9708] 1.1345] 0.2607] 1.4873] 1.0202] 0.9453}



Table 5-2
Modeled Fast-Pass Results
Excess Emissions vs. Average Test Time

No Fast-Pass Fast-Pass Enabled
Excess HC Identified 94.3% 92.7%
Excess CO Identified 98.2% 94.3%
Excess NOx Identified 92.1% 91.2%

Average Test Time 217 seconds : 125 seconds

significantly reduces average test time.’ Without fast-pass enabled, the pass/fail
evaluation occurs only after each IM147 is complete. If the vehicle passes the IM147, the
test would be complete at that point. If the vehicle fails, another IM147 would be run,
with up to three IM147s conducted on any one vehicle. If the vehicle is still failing at the
end of the third IM147, it fails the overall test. Since each IM147 lasts 146 seconds, the
maximum time a vehicle could be tested is 438 seconds.

Without fast-pass, the average test time for the 3,347-vehicle sample was 217 seconds.
Once the fast-pass algorithm was applied, the average test time dropped to 125 seconds,
for a reduction of 92 seconds (42%). This is similar to the test time estimate of 121
seconds shown in SR99-10-02' for the final standards with fast-pass.

Table 5-3 details excess emissions identification by vehicle type and model year groups.
Appendix D provides additional excess emission analysis results. As will be shown later
in the report, excess emissions identified increase with the addition of retest and fast-fail
algorithms.

“Average test time refers to the estimated time for that portion of the test in which the vehicle is driven on
the dynamometer.

-38-