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

as follows:

(i). 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 I
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). A linear 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.

-56-


seo

1M147 Composite Regression Coefficients, NOx, 1981 to 1982 Model Year LDGV's

Regression Cootheionts
Segment RMS |Rep. ;
[Number |Error \Constant [C1 ic2 C3 C4 ics ice ic7 ice ico ic10 Su jc12 jc13 C14 Ic15 ic16 C17 [cis C19
Py ILDGV_ [61-82 0.91799] 1.65041] 42.7692]. .
| (a [LOGV_ [61-82 [0.63601] 0.72708] 17.5251] 4.1767]. a
iPS \LOGV__ [81-82 | 0.52331] 0.58513] 4.6789] 2.4424] 11.7817].
74 fGv— far-02 | 0.52297] o.seaee] 0.218] 2.4752] 10-4071] 3.1794)
I5————ItoGv—|av-a2 [0.50775] —0.s0a0s| —-a.2723| 2.2111] 0.2772] 2.5761] 359i
PG LOGV_ [81-82 | 0.50352] 0.48347] -3.9075[ 2.1456] 8.9952] 1.1125] 3.4217,
iP? LOGV__ [81-82 J 0.49839] 0.48687[" 4.3121] 2.0136] 6.6708| 0.9449] 1.0442} . .
ipa |LoGv—Jar-a2 | 0.48668] 0.46683] 1.6028] 1.6175] 7.0174] 2.5315] _ 1.6902 TaH.
[PS LOGV_ [81-62 | 0.46317) 0.452] 0.8119] 1.7213] 7.5725] -2.1824] 0.4138) 7.4243] 3.9487]. a
pi0__|LoGv—[a1-a2| 0.40376] 0.44762] 1.0046] 1 7439] 7.4758] 2.0726] 0.0949 Tarii| 3.3527] 1.0886 F
Pit |loGv—fa1-82 | 0.35104] 0.73015] —e.seze| —0-7es0] —4.e<a1| 0.79] 0.1070] 4.4696] 3.7564] 0.6973] 4384].

IP12____|LoGv__|a1-82 | 0.2927] 0.17049] 0.8733 0.9681] 2.776] -2.0862] 0.4024
[P13 |LOGV_ [1-82 | 0.16109] 0.07569] 4.5202] 0.7632] 1.5866] 1.3953] 0.0499]
a LOGV—|ar-82 | 0.14099] 0.02431| 4.1674] 0.7506] 1.6956] 1.9641] 0.1583
P15 |LOGV__Je1-82 | 0.08737 0.01811 7.3947] 1.6138] 0.2041]
| a LOGV_ [81-62 | 0.07312] 0.0041) 1.058] 1.5224] 0.0582]
Je1z___[t0Gv Jerez |-0.07264[_ 0.00367] 0.9813] 0.6032] 7.0413] 1.4118] 0.1444]
[Pie LOGV—Jer-a2 | 0.04243{ 0.00563] 0.017] 0.7399] 0.8526] 1.377] 0.2716]
[Pi |tocv— Jar-s2 [0.01342] 0.00395] 0. 7ec0] 0.7105] 0.768] 0.6447] 0.6524]

3.7366| 2.3379] 1.5715] 0.6911] 2.5825]. "
2.0452] 0.4039] 1.7917] 0.6671] 1.3367] 2.4626] a
4.7248] 0.4851] 2.7419] 0.5236] 0.9827] 1.8418] 1.5633]
10.8836] 1.2695] 0.6037] 0.6393] 0.6763] 0.6065] 0.8832) 1.7403]. 5
0.5747] 1.1231] 0.6514] 0.7173] 0.8313] 0.6082] 0.7402] 1.2002] 1.5334]. 5
0.6401] 1.1443] 0.7002] 0.7202[ 0.616] 0.6414] 0.7463) 1.2653] 1.4284] 0.5928].
0.8178] 0.0258] 0.5464] 0.7223 0.7683| 0.6426] 0.8313] 0.8107] 1.4947] 0.4033] 0.6387].
0.857} 0.6045] 0.7562] 0.6097 0.7449] 0.6929] 0.7279] 0.7165] 0.7737] 0.6197] 0.7165] 0,6507|

M147 Phase 2 Regression Coefficients, NOx, 1981 to 1982 Model Year LDGV's

Regression Coefficients
Segment IRMS = {Reg.
Number lEmor {Constant |c1 cz jca_—fca_—fcs_—fce_—fc7_—fca—co—fero fers ferz erasers cis creer? erate
er [e182 | 0.05eza] 0.60070). " r r r r r r " 2.0436). r r
| EXE LOGV_"}1-62 [0.50466] 0.48014]. i . " - 1.6] 3.6643]. -
[833 LOGV__ [81-82 0.2646] 0.11652] . a 5 5 a 5 0.8595} 2.316] 3.4315]. .
{B14 [Lov er-a2 [0.21266] 0.04456) i " C i r 0.7104] 1.676] 2.6006] 2.1072]. i"
{815 LOGV_ {81-82 | 0.13034] 0.02244]. . a . . 0.8971] 1.4034] 0.8839] 1.2572] 2.3849]. .
{sie [toGv—Jar-e2 | 0.1083] o.0rata]. i i 4 5 a a i ‘o3ri] 1.1362] 1.0575] 0.9885] 1.7362 2.0054]. i
[e17_tocv~Jar-82 [0.10476] o.0082]- i 5 r a i a 7.0248{ 1.1015] 1.1113] 1.0374] 1.6264] 1.8008] 1.4012).
B18 LOGV_ [81-82 | 0.06108] -0.00004]. a . a . . . 0.9845} 1,139] 0.8871 1.145] 0.9874) 2.2913] 0.6547 1.1584]

sia [toGv__ ar-az | 0.01536] 0.00328]. F is E i E 0.9626] 1.0202} 0,9566] 0.8706 0.9450] 1.0695] 0.9025] 0.9610] 0.0688)



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.

-22-


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

prepared for:

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

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

March 2000

prepared by:

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

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


1g-9

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

“ Regression Coefficients .
Segment IRMS — | Reg.
Number Error iConstant }C1 ic2 ica C4 ics ice \C7 ics ico C10 [cit C12 C13 C14 Cis C16 C17 C18 C19
fer OGY? [er-85 | 1.90604] 3.60101] 10.2500].
[Pz ILOGT2 [81-85 | 0.87408] 0.91821] -7.9377] 6.5305]. .
fP3. [LOGT2 [61-85 | 0.64122] 0.63444] 8.613] 5.4145] 7.2247]. .
1 CO LOGT2 [81-85 | 0.82892] 0.89342] -10.2897] 5.4633] 2.5096] 8.8657]. a
[Ps [LOGT2 [81-85 | 0.80333] 0.89081] -9.4068] 4.7319] 0.276] 9.1558] 5.3418] a
[Ps ILDGT2 [81-85 0.7883] 0.87735| -13.437| 4.8675] 0.2244) 7.2171| 4.8373] 4.2644]. a
Pz ILOGT2 [81-85 | 0.79972} 0.89425] -13.7206] 4.6923] — -0.887| 7.62) 4.3577] 4.6628] 1.0456} a
[Ps [LOGT2 [61-85 | 0.79473] 0.84415] -11.4499] 4.5789] -1.4261| 5.7988] 3.4916] 27174] 1.1653] 3.2995]. .
PS. [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].
[P10 (COGT2 [81-85 | 0.79646] 0.61111) -11.8131 4.561] -1.2086] 5.9935} 2.4713] 3.4666] 0.516] 3.1827] -0.4726] 2.7254]
ILOGT2 [81-85 0.6659] 0.51142] -7.4827| 3.0898] -1.5249[ 6.9352] 3.5205] 2.9385] 1.4316] 3.7346] 1.3238] -2.5498] 1.2169]. a
(LOGT2 |81-85 | 0.54957] 0.33422] 8.1307! 24318] 0.7549] 0.1755) 1.144} 5.7388] 3.1487] 2.2295] 0.7669] 0.0157] 0.6425] 3.1201] a
[LOGT2 [81-85 | 0.26368] 0.1926] 1.2524] 2.9861] 0.2928] 2.9259] 1.47145] 0.5835] 2.0376] -0.4876] 0.712] 1.7639] 2.5466].
|LOGT2 [81-85 | 0.17079] 0.09831 0.5707] 1.4814] 0.6512] 2.8236] 1.4133] 1.5048] 1.8051] 0.7881) 0.6265] 1.0991] 1.5732] 1.6465]. a
[LOGT2 [81-85 | 0.13538] 0.05816] 0.9631] 1.699] -1.1762] 2.6604] 1.5116] 4.2555] 1.0619] 4.1308] 0.6644] 0.9993] 0.9615] 1.3864] 0.9139]. a
ILOGT2 [81-85 | 0.12927] 0.04855] 0.9878] 1.2005] -0.6445} 2.468] 1.4956} 0.99] 1.0154] 4.2544] 0.6922] 0.8896] 1.0162} 1,349) 0.717] 0.6881]. a
LDGT2 [81-85 | 0.12798] 0.04485] 0.916] 1.0272] 0.4442] 2.2519] 1.3213] 1.1276] 1.0486] 1.1459] 0.6823] 0.8485] 0.9897} 1.3770] 0.7091] 0.682] 0.8090]. i.
[LOGT2 [81-85 [0.08606] 0.02155] 4.0724] 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].
[LOGT2 [81-85 | 0.02488) 0.0013] 0.9555] 0.6446] 0.5989] O.8719[ 0.8311 0.6923 0.5888] 0.7316] 0.7118] 0.6767] 0.7213] 0.7278] 0.7365] 0.6649] 0.7072] 0.679] 0.7709]
1M147 Phase 2 Regression Coefficients, NOx, 1981 to 1985 Model Year LDGT2's
Regression Coefficients
Segment IRMS —_|Reg. :
Number [Error Constant [C1 C2 C3 C4 ics C6 ic7 CB ico C10 C11 C12 C13 C14 \c1s IC16 C17 C18 C19
11 [LDGT2 [81-85 | 1.42447] 1.55851]. 3.2137]. a
4812 ILOGT2 [81-85 | 1.03629] 0.77984]. 1.5673] 6.4951]. a
[B13 LOGT2 [81-85 | 0.38433] 0.27378]. 0.8325] 2.718) 3.5162]. a
[B14 ILOGT2 181-85 | 0.26921} 0.18815] 0.8123] 1.6716} 2.4085] 2.1998] a
B15. JLOGT2 [81-85 | 0.22469] 0.11816). 0.8718} 1.4697] 1.4685] 1.8766] 1.2616]. a
B16 LOGT2 [81-85 | 0.19939] 0.09578]. 0.9679] 1.528] 1.6012] 1.7461] 0.8574] 1.4532]. a
| ea LOGT2 [81-85 | 0.19039] 0.09229]. 0.9221] 1.0819} 1.4847] 1.7994] 0.8974] 1.351] 1.5644].
| EXE ILDGT2 [81-85 | 0.15301{ 0.07095]. 0.8347] 1.2468] 1.0994] 1.3645] 0.8676} 1.6334] 1.4952] 1.0081] _
xB19 LOGT2 [81-85 | 0.03621] 0.00936]. 0.943, 0.9237] 0.9993] 0.9988, 0.976} 0.6541] 1.0342] 0.8947] 1.1014]



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., 1.0) at =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
Ulustration of Second-by-Second !M147 Cumulative Positive Power Variation Limits
6,000 60
E Ref. CPP. Lower Limit Upper Limit -
3 5.000 50
3
3 “
— 4.000 : 40
= , zg
5
: E
é 3,000 20 >
$ ~ WY 3
2 “ a
& 2,000 ~ 20
é we —y
g
10
3 en CPP limits constant during
cruise/deceleration period
o °

Fy

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

-65-


Appendix B

Max CO, Startup, Intermediate, and Final IM147 Failure Rates


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 greater than + 2 mph), 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 3,347-vehicle population, with
absolute speed excursion violations (as defined in EPA’s IM240 guidance) removed.
This resulted in an overall data set of 3,006 vehicles. Using the 3,006-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 3,006 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.
Table 1-7 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.

SIP Credit Analysis -

The above comparison of excess emissions identification between the IM240 and IM147
is based on the use of CO Max standards for both test cycles. 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.

-12-


Appendix D

Excess Emissions Identification for Max CO Cutpoints


1M147 CUMULATIVE POSITIVE POWER (CPP) VARIATION CUTPOINTS

1M147 REFERENCE DATA
TIME SPEED CPP
(sec) (mph)
46 22.2 1283.73
47 24.5 1391.14
48 24.7 1400.98
49 24.8 1405.93
50 24.7 1405.93
51 24.6 1405.93
52 24.6 1405.93
53 25.1 1430.78
54 25.6 1456.13
55 25.7 1461.26
56 25.4 1461.26
57 24.9 1461.26
58 25.0 1466.25
59 25.4 1486.41
60 26.0 1517.25
61 26.0 1517.25
62 25.7 1517.25
63 26.1 1537.97
64 26.7 1569.65
65 27.3 1602.05
66 30.5 1787.01
67 33.5 1979.01
68 36.2 2167.20
69 37.3 2248.05
70 39.3 2401.25
71 40.5 2497.01
72 42.1 2629.17
73 43.5 2749.01
74 45.1 2890.77
75 46.0 2972.76
76 46.8 3047.00
77 47.5 3113.01
78 47.5 3113.01
79 47.3 3113.01
~ 80 47.2 3113.01
81 47.2 3113.01
82 474 = 3131.93
83 47.9 3179.58
84 48.5 3237.42
85 49.1 3295.98
86 49.5 3335.42
87 50.0 3385.17
88 50.6 3445.53
89 51.0 3486.17
90 51.5 3537.42
91 52.2 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
4,600.89
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
1,717.13
1,717.13

“4,717.13

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 2.932 314.4
106.2 2.932 341.4
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 2.553 447.1
174.5 2.515 439.0
181.5 2.477 449.6
191.1 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


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

-71-


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
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 detailsthe appropriate regression coefficients to be used with the above
equation, as well as.the resulting r values. The r’ values contained in the table
demonstrate excellent correlation between the composite and Phase 2 cutpoints. 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
IM147 Composite to Phase 2 Regression Equation Coefficients

Emission Constituent Slope Intercept Correlation Coefficient

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.

-29-


SI-D

|M147 Composite Regression Coefficients, CO, 1986 to 1989 Model Year LDGT1's

Regression Coefficients

Segment RMS .
Number fError__|Constant_|C1 c2__ica__|ca__fes__ce__|er_fca__fcs_|ero _foms_|cr2_Icia_|cra__|ers__|cre_fcr7_|crs_|c19
ie LDGT1 [86-89 | 7.18532] 10.8086] 9.9102] .

LDGT1 166-89 | 5.68769] 7.0459] 1.8741] 2.6779)

cor [6-29 | 5.04319] 5.5558] 1.7455] 1.371] 5.3903).

COGTr |ae-e9 | 4.a8ezi| 5 13459[ 1.4606] 1.5620] _2.0026| 5.3393].

IcoGtt |ae-09 | 4.78915] 5.08892) 1.6656] 1.4035] 2.0371] 4.1723] 2.0023].

ILOGT1_ [86-89 | 4.74332] 5.11226] 1.0229] 1.4755] 1.9866] 3.8059] 1.0898] 2.4375]

LOGT1 [86-89 | 4.65225] 4.91955] 1.7462] 1.3554] 1.7097 3.8418) _-0.0053] 1.9673] 1.6899} a

LOGT1 [86-89 | 4.48557] 4.61047] 2.0142] 1.3439] 1.7523} 2.7365] 0.09} 0.1646] 0.9526] 2.9249]. 5

LDGT1 [86-89 | 4.44811) 4.5639] 2.1382} 4.316] 1.6099] 2.7676] -0.2399]_-0.7903] 0.9655] _ 2.4766] 2.078]. a
|Pic_—_[toGrr [es-a3 [4.41672 4. ae0e4] 2.0022] 1.3046] 1.5957] 2.4537] 0.4021] 0.7631] 0.7690] 2.561] 0.8104] 2.0167]. r
{P11 ——locr[e6-a9 [3.05161] 2. 26609] 2.6544] 0.0350] o.ett5] 1.4536] 0.41] 0.3277] 0.4047] 2.1497] 0.2762] 2.0562] OBIT. P
|Piz___|tbGri_Jes-s9 | 2516t3| 1.51088] 2.6342] 0.7464] 0.9245 1.9441] 0.5707] 0.2141] 0.4114] 2.0168] 0.2073] 1.0253] 0.7436| 1.2083] a
P13, UDGTi [86-89 | 7.68355|" 0.6064[ 2.1795] 0.6233] 0.8001] 1.3103] _0.4715[_O.7177{ 0.6871] 0.7545] 0.7656] 1.1501] 0.7503] 0.8943] 2.2692]. a 5
Pid JLOGTs _as-89 [1.31741] 0.68081] 2.1784] 0.5849] 0.6912] 1.3508] 0.4357] 0.8373] 0.6956] 0.8278] 0.6331] 1.2087] 0.7057] 0.7686] 1.824) 1.1207] a
P15 __|toGr1 Jes-89 | osaori| _ 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] 1.3808].
P16 TLOGT1 [86-89 [0.88497] 0.41767] 1.6764] 0.5971] 0.6595] 1.3638] 0.5037] 0.8323] 0.8426] 0.6198] 0.6814] 1.0802] 0.7147] 0.7671] 1.022] 0.8818] 0.9654] 1.3933] .
Pa7. 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 |LOGT1_|e6-89 | 0.51513] 0.11517] 1.107] 0.6616] 0.6251] 1.1599] 0.7128] 0.7927] 0.5228] 0.8376] 0.7630] 0.7464] 0.7046] 0.7204 0.73021 0.7611] 1.0809] _0.4756| 1.39] 0.889].
[Pio LOGT1 [86-89 | 0.22775] 0.06824] 0.9804] 0.6792) 0.6446] 1.0212] 0.664! 0.7468] 0.6847] 0.7006] 0.7495] 0.7734] 0.7063] 0.7111] 0.7; 0.7067] 0.7928] 0.5475] 0.8726] 0.7062] 0.7346]

1M147 Phase 2 Regression Coefficients, CO, 1986 to 1989 Model Year LDGT1's
Regression Coefficients

Segment RMS Reg.
[Number lEmor__|Constant {C1 lc2__fcs__—fca_ cs —_—fce_—fer_fca_ fos __|cro _fors_|cr2_fcrs_fcra_|crs_|cre_|er7_fcrs__|cro
en [COGTT [eo-es | 5.73419] 6.44560). 74669 C
|ei2z_—__|tocrt Jes-s0 [3.03261] 3.46613] 7.1555] 1.6604
{e13 ——toGr1 [6-08 [2.36328] — 1.33398]. 7.0419} 1.1807 3.2011 i

LoGrt {26-89 | 1.0917] 1.13186]. 7.9737| 1.0107] 2.6834] 1.4546].

LOGT1 [86-89 [1.36531] 0.75413] 0.976] 0.9839] 1.5355] 1.1464} 41.973}.
[B16 ILOGT1 [86-89 | 1.28845] 0.69885]. 0.9841] 1.0114] 1.4453] 1.1588] 1.3867] 1.9667]. .
817 ILOGT1 [86-89 | 1.04812] 0.39116]. 0.9599] 0.9873] 1.256] 1.1763] 1.4736] 0.6337] 2.6609]

[Lori [26-89 | 0.7463] 0.14709]. 0.9544] 0.9576] 1.0029] 1.0385] 1.5243] 0.6220] 2.1332] 1.1447].
[ers Jerr Jas-09 | 0.33544] 0.07760]. 0.9581] 0.522] 0.9677] 0.9398] 1.0857] 0.7154] 1.4816] 0.9344] _1.0367|



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 described below, each set of criteria was evaluated in
turn 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 IM240 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 = Cn+ X {Som x Xn} + (Mex En) [5-1]
me
Where: 240, = Predicted emissions after completing n segments
Cc, = Regression intercept for equation n
Sam = Regression coefficient for segment m in equationn
Xam = Total emissions over a given segment m in equation n
M, = Enror 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 concems regarding actual in-use implementation of
that procedure. In response, the workplan developed for the current project indicated that

-36-


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 bé 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 second-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 +2 mph 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

-61-


Fleet Distribution Data
Vehicle Distribution Data - 9 months 2% random sample (initial test)
Data Collected between 7/1/97 and 3/31/98

LDGV LDT1 LDT2

Model # Tests % of Fleet # Tests % of Fleet # Tests % of Fleet
Year

1981 102 1.02% 31 0.31% 13 0.13%
1982 138 1.38% 33 0.33% 18 0.18%
1983 153 1.52% 52 0.52% 17 0.17%
1984 318 3.17% 80 0.80% 33 0.33%
1985 372 3.71% 129 1.29% 29 0.29%
1986 414 4.13% 172 1.71% 48 0.48%
1987 450 4.49% 167 1.66% 40 0.40%
1988 540 5.38% 205 2.04% 54 0.54%
1989 540 5.38% 235 2.34% 62 0.62%
1990 512 5.10% 183 1.82% 44 0.44%
1991 542 5.40% 229 2.28% 40 0.40%
1992 533 5.31% 201 2.00% 74 0.74%
1993 559 5.57% 270 2.69% 62 0.62%
1994 654 6.52% 306 3.05% 110 1.10%
1995 673 6.71% 310 3.09% 131 1.31%
1996 100 1.00% 45 0.45% 10 0.10%
1997 () 0.00% ) 0.00% 0 0.00%
1998 () 0.00% () 0.00% i‘) 0.00%
1999 ) 0.00% 0 0.00% (") 0.00%
2000 (‘) 0.00% 0 0.00% 0 0.00%

Total 6600 2648 785

Distribution Between Vehicle Types

#Tests _% of Fleet

LDGV 6600 65.8%
LoT1 2648 26.4%
LDOT2 785 7.8%
Total 10033

G-l


Figure 4-2
Failure Rate by Consecutive IM147

-tE-

Max CO Cutpoints
HC Failure Rate by Vehicle Type CO Failure Rate by Vehicle Type
14.0% 25.0%
12.0% m7!
20.0% - i
2 10.0% 2 tt i
Ist IM147 B Ist IM147
© 8.0% qu Gis © 15.0% ti eis
2 ‘| BE Mi2ndIMi47 2 ii } M@2ndIMi4a7
‘a 6.0% Hil 3 i i:
& i 3rd 1m147 é 10.0% i i pa3rd M147
2 4.0% i i 8 i
| 5.0% +4!
2.0% HH | i— fi Hl
0.0% i ue 0.0% tHE
Pc LoT1 LDT2 aul PC LoTI LDT2 An
Vehicle T ype . Vehicle Type
NOx Failure Rate by Vehicle Type Overall Failure Rate by Vehicle Type
14.0% - 35.0%
7
12.0% | 4 30.0%
3 10.0% i i = 25.0%
i Ist IM147 2
2 80% i i Eaist £ 0.0% O} ist M147
3 | i 2nd 1M147 = 2nd 1M147
E s.0% Hil | = 15.0% .
Pr i fa3rd M147 ra 3rd imi47
2 4.0% +44) +} S 10.0%
i ; i 6}
2.0% Hi 5.0%
0.0% uu 0.0% ul
Pc Lot! LpT2 alt PC Lot LDT2 all
Vehicle Type Vehicle Type



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%

4
| 205% |42e0 [4953 | : 19% _ oe
20.75% |4307 | 4,928 3m| 26%] 2.7%| 06%] si.e%| 40.5%
21.0% |4325 | 4,909 105%] 3.1%] 3.7%] 09%] s8.a%| — 37.1%
22.0% |4361 [4873 [2559 | 14im] 42%] 63%] 10%] ais%| 289%
33.0% [4386 |4s4s | 2309 | 173%] s3%| 89%] 26%] 776%] 26.3%
24.0% [4403 ]4e31 [2142 | 19.9%] 6.2%| 109%] 3.1%] 74.1%] 24.6%
35.0% [aais [4ei6 [i991 | 226%] 72%| 13.2%] 3.9%| 672%] 22.4%
Sample | - . 9,306 Tests 7,962 Tests 231 Drivers

Size

Note: Shading indicates the CPP limits proposed for use by Sierra, and corresponding data.

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 + 2 mph 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

-64-


91-0

1M147 Composite Regression Coefficients, CO, 1990 to 1995 Model Year LDGT1's
Regression Coefficients
Segment IRMS — Reg.
Number Error [Constant jC1 ic2 ica ica ics icé ic7 cB ico C10 cwti C12 C13 Cia Cis. C16 [C17 C18 C19
a OGT1 [90-05 | 7.00708] G.47ai0] 6.6737] r
Pz ILOGT1 [90-95 | 5.93561] 3.60059] 0.3029] 2.9243].
Ps ILOGT1 [90-95 | 5.49867] 2.58569] 0.1967] 1.6676]
@ ILOGT1 [90-95 | 5.4755S[ 2.41839] 0.0472] 1.683] 2.9942] ni
[PS ILOGT1 [80-95 | 5.34097] 2.4681] 0.3008] 1.3842] 4.4949] 3.7142]
wi ILOGT1 [90-95 | 5.23567] 2.50459] 0.4457] 1.3784] 0.8341] 2.2673] 4.9505} .
[PT LOGT1 [90-95 | 5.12065] 2.31146] -0.3312{ 1.2837] 0.1146] 0.7803] 4.7671] 3.2802] a
PB ILOGT1 {80-95 | 4.93002] 2.15394] -0.1308] 1.2614] 0.7666] 0.4112] 1.9844] 1.3783] 5.3488].
iPS ILDGT1 [90-95 | 4.92436] 2.15209] 0.2521] 1.2934] 0.6767] 0.1855] 1.6357] 1.4433] 4.9503] 1.3627] a
IP 10. ILOGT1 [90-95 | 4.83877| 2.07448] 0.0883] 1.3418) 0.6746] 0.0356] 1.4432] 1.0559] 4.4422] -1.7507] 457].
PAT COGTi [90-05 | 3.0S447| 0.67888] 0.7448] 0.9163} -1,1973] 0.6625] 1.9991] 1.1363] 2.2194} 0.3005] 2.3958] 1.1499]
[P12 (LOGI [90-95 [2.59434] 0.35922] 0.8716} 0.834! 0.1746} 0.685] 3.603] 1.4913] 1.7526] 0.5786] 2.4993] 0.9124] 1.5835] a
P13 LOGT1 [90-95 | 1.69051 0.3538] 0.7846] 0.7978) 0.7201] 0.7108} 0.6836] 0.8317] 0.9732] 0.7569] 0.3609] 0.8116] 0.8909] 2.4633].
[P14 ILOGT1_ [90-95 | 1.25118] 0.29289] 0.7084] 0.7156] 0.5609} 0.6826] 0.8354] 0.9735] 1.1145] 0.55} 0.9761] 0.7373] 0.7493] 1.6233] 1.1532]. .
[P15 lLOGT1 [80-95 | 0.88666] 0.17653] 0.6832] 0.7443] 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
P36. [LOGT1 [90-95 | 0.83688] 0.14342] 0.6546] 0.7325} 0.7804} 06 0.754] 0.9659] 0.5296] 0.6743] 0.5828] 0.7741] 0.6855] 0.9886} 0.964] 1.0266} 1.788}.
Piz, ILDGT1 [90-95 | 0.75939] 0.03084] 0.6751 0.7626) 0.7727] 0.6819] 0.6512] 0.8746] 0.5588] 0.7025] 0.4405] 0.774] 0.6594] 0.9682] 0.9724] 1.0041] 1.3254] 2.054]. a
| LOGT1 [90-95 | 0.48016] 0.02847| 0.6665] 0.7378} 0.7356) 0.81] 0.7878] 0.6405] 0.7172] 0.7969] 0.5663] 0.7101) 0.721] 0.8057] 0.915] 0.6348] " 1.2367] 1.3997] 1.0199].
[pis ]LOGT1 {90-95 | 0.16891] -0.00569] 0.7401] 0.7243) 0.7720] 0.6775] 0.7704] 0.7643] 0.6818] 0.7573] 0.6639] 0.7157] 0.7187] 0.7375] 0.6868) O717{ 0.6511] 1.1384) 0.7248] 0.7496]
M147 Phase 2 Regression Coefficients, CO, 1990 to 1995 Model Year LDGT1's
| Regression Coefficients,
Segment IRMS — |Reg.
Number Error [Constant [C1 ic2 ica ica ics C6 ic? cB ico ‘C10 ta C12 IC13 C14 Ic1s C16 C17 \c18 C19
(B17 ut ‘1_[90-95 | 5.48884] 2.92303] 4.7403}
[B12 ILOGT1 [90-95 | 3.74619] 0.83254]. 4.3383] 2.3782). a
} CHE ILOGT1 [90-95 | 2.29939] 0.47273] 1.1019] 1.1903] 3.3532].
| Caz [LOGT1_[30-95. 4.7181} 0.48708}_ 1.0162} 4.02] 2.3871] 1.5186].
| ee ILOGT1_ [90-95 | 1.20254] 0.23609] 1.0388] 0.9035] 1.4703] 1.2205] 1.9615] a
| ILOGT1 [90-95 | 1.13572} 0.20105). 1.0447] 0.9125] 1.3168] 1.3287[ 1.3674] 2.3637) a
fea7, LOGT1 [90-95 |" 1.04495] 0.01286). 1.0374} 0.8678] 1.2457] 1.3709] 1.2968] 1.8468] 2.5009] q
fers ILOGT1 [90-95 | 0.65189] 0.0106]. 0.9551] 0.9717] 1.0606] 1.2615] 0.8351] 1.7471] 1.7371] 1.3845].
| [LOGT1 {90-95 0.219] ~ 0.00146) 0.968] 0.9605] 0.9969] 0.9364[ 0.9681} 0.9047] 1.5264) 0.9898] 1.012]



8e-0

|M147 Composite Regression Coefficients, NOx, 1990 to 1995 Model Year LDGV's
Regression Coefficients
[Segment RMS —|Reg.
pene [Error Constant |C1 ic2 ica. ica ics icé ic7 ica ico C10 cit © [C12 C13. C14 jc15 C16 \C17 C18 ic19
PT DGV__ [90-95 | 0.85265] 0.98234] 2602).
fP2 [LOGVJ90-95 | 0.55676) 0.37911] 3.4685] 3.9198] a n
Ps. JLOGV_ [90-95 0.5187] 0.31019} 2.4287] 2.702] 6.9157}. a
[Pa iLOGV_{90-95 | 0.51384] 0.29256] 2.566] 2.669] 5.4346] 4.5289] i
[PS [LOGV_[90-95 | 0.47026] 0.27475[ 0.7261) 2.0325] 3.4764] 2.5823] 7.1639]. a
IPE ILDGV__ [90-95 | 0.46835] 0.27776] 0.0795] 2.0153] 3.3673] 1.5718] 6.6904] 3.6255].
[PT ILOGV__ [90-95 | 0.43598] 0.24653] -0.2787] 1.6476] 1.7867] 2.607] 2.986] 4.5264] 4.7362] a
PB ILDGV__}90-95 | 0.42397] 0.22613] 0.8668) 4.576] 1.4189] 1.0315] 2.7698] 1.4176] 3.8634] 4.4221].
Po. ILDGV__[90-95 | 0.41414] 0.21779] 0.4251] 1.6155] 0.9945] 0.7619] 2.1999] 0.108] 3.4947] 3.5743] 5.7316] a 7
JP 10. LDGV_ [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].
[Pat ILOGV_ {90-95 |" 0.32541] 0.07608] 0.4951) 4.01] 0.8419] 0.3247] 2.3515] 0.3887] 1.0667[ 2.0267] 3.9523] 1.1292] 1.4038]. .
Piz ]LOGV_J90-95 | 0.27382[ 0.05866] 1.1507] 0.9457] 0.7041] 0.1603] 1.9823] 6.7329] 1.4374} 0.757| 2.5555] 1.2857] 0.8276] 3.2666) .
[Ps 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] 2.4078].
[P14 JUOGv_ [90-95 [0.11701] 0.02975] 1.1371] 0.7436) 0.97] 0.8665] 0.7991] 0.5198] 0.7343} 0.5517] 1.73671 0.4654] 0.6168] 1.0241] 1.4687] 1.7969]. .
[P1S JLOGV__ [90-95 | 0.07219] 0.00927] 0.8725] _0.7153] 0.8392] 0.9635] 0.7945] 0.6765] 0.7708] 0.6708] 4.0226] 0.5631] 0.7013] 0.8864] 0.8237] 1.0439] 1.3285].
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] 0.7254] 0.8265] 0.8134] 0.9715} 1.0589] 1.191]
P17 ILDGV_ [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]. .
| ILOGV__|90-95 | 0.03251] 0.00233] 0.6937] 0.7144] 0.6856] 0.936: 0.7529] 0.9301] 0.619] 0.8318} 0.7798] 0.5858] 0.6898] 0.8309] 0.7014] 0.849] 0.7786] 1.1002} 0.83] 0.8865].
pps [LOGV_ {90-95 | 0.00903] 0.00112] 0.8195] 0.7065] 0.733] 0.7558] 0.7072] 0.8124] 0.7212] 0.7331f 0.7165] 0.7193] 07121] 0.7215] 0.718 0.7075] 0.7149] 0.7316) 0.7374] 0.7123] 0.7172)
IM147 Phase 2 Regression Coefficients, NOx, 1990 to 1995 Model Year LDGV's
‘Regression Coefficients

[Segment IRMS — Reg. q

lumber {Error {Constant [C1 ic2 \c3 \c4 ics ice \c7 ice ice }C10 Cit [C12 C13. jC14 jc1S. C16 C17 fal C19

1 TOGV_ [90-05 | 04055] 0.16763]. Z 7646]
B12 ILOGV__|90-95 | 0.39806} 0.10265]. 1.5279} 4.9874].
B13 ILOGV_ {90-95 | 0.21598] 0.0764]. 0.9775] 2.036] 3.2274]. a
B14 ILOGV__{90-95 | 0.16191] 0.04867] 0.8655] 1.4947] 2.0303] 2.4727[-
B15 ILOGV_ {90-95 | 0.08916} 0.01709] 0.9685) 1.262] 4.1221] 1.4177] 1.8321].
[B16 ILOGV__{90-95 | 0.08828) 0.01425] 4.0137] 4.1226) 1.1413] 1.30017 1.4762} 1.565]. .
{B17 ILOGV__J90-95 | 0.07057] 0.00619. 0.9986] 1.1256] 1.132] 1.322] 1.3198] 1.5162] 2.0499]. i
| CSE ILOGV__ [90-95 | 0.04511] 0.00552]. 0.9273] 1.1868] 0.9219] 1.1679] 1.0538] 1.5928] 1.0608] 1.1805].
| EI LOGV_ {90-95 | 0.01076] 0.00217]. 0.0663] 0.9854] 0.87 0.9566] 0.9719] 1.0002] 7.022] 0.0682] 0.9807]



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.

Segment7- Comp_Std

Convergence_ Ratio = Segment\9- Comp_ Std [5-2]

Predicted emissions after segment 7 (Composite Regression)
Predicted emissions after segment 19 (Composite Regression)
Composite Cutpoint for the specific emission

Where: Segment7
Segment19
Comp_Std

Common sense would dictate that 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, might suggest 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
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. 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.

-42-


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% | 0 | 70 | 0.0% | O { 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 12824 | 15.6% 1394 12953} 11.8% 1518 | 2829 | 15.5% 1952 12395 | 28.4%

rd IM147

Failure Rate By Model Year Grouping - Thi
al Cutpoints
HC co NOx OVERALL
V Type} Year| Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass
52_| 50.5% | 25 | 80 | 23.8% | 32 | 73 | 30.5% | 67 | 38
137 | 39.9% | 88 | 140 | 38.6% | 81 | 147 | 35.5% | 156| 72
325 | 23.5% | 76 | 349 | 17.9% | 96 | 329 | 22.6% | 171 | 254
916 | 3.8% | 50 | 902 | 5.3% | 68 | 884 | 7.1% }112| 840
70 | 0.0% 1 69 1.4% 0 | 70 | 0.0% 1 69
177 | 31.9% | 57 | 203 | 21.9% | 61 | 199 | 23.5% | 137 | 123
191 | 14.0% | 24 | 198 | 10.8% | 36 | 186 | 16.2% | 65 | 157
444 [ 1.3% | 4 [446 | 0.9% | 29 [ 421 | 6.4% | 36 | 414 | 8.0% ]]
32_| 0.0% 0 | 32 | 0.0% 1 31 | 3.1% 1 31 3.1%
50 | 46.8% | 41 | 53_| 43.6% | 31 | 63 | 33.0% | 73 | 21 | 77.7%
48 | 25.0% { 11 | 53_| 17.2% | 17 | 47 | 26.6% | 32 | 32 | 50.0%
414 | 3.0% 4 | 423 | 0.9% | 34 | 393 | 8.0% | 44 | 383 | 10.3%
18 | 0.0% ) 18 | 0.0% 0 18 | 0.0% ) 18 | 0.0%

2874 | 14.1% 138112! 11.4% | 486 12861] 14.5% | 895 12452 | 26.7%

B-8


from an emissions perspective, this results in a fairly substantial decrease in the projected
failure rate.

There is also a second factor affecting the projected failure rates shown in Table 1-1.
Review of the individual results for each of the three sequential IM147s shows that
roughly 80 vehicles (i.e., about 2.5% of the total sample) failed the third IM147 after
having passed the first test cycle. The reason for this degradation in emissions over the
three IM147s is unknown, but appears to be an artifact of the test protocol. Since these
vehicles would have passed out as a result of the first IM147, they are considered false
failures that would not occur with the addition of the integrated algorithms.

Table 1-2 shows the effect on projected failure rates of applying the algorithms to the
3,347-vehicle sample. The failure rates shown in the table have also been adjusted by
normalizing the results to the vehicle fleet distribution contained in 2% random sample
data collected in the Arizona program from July 1997 through March 1998. (These were
the most recent vehicle type-specific data readily available to Sierra.) This fleet
distribution is detailed in Appendix G.

: . Table 1-2
Predicted Arizona IM147 Failure Rates with
MaxCO Cutpoints and Integrated Algorithms

Vehicle Class Predicted Failure Rate”
LDGV 16.7%

LDGT1 11.4%
LDGT2 10.4%

14.8%

* Normalized to Arizona fleet distribution obtained from 2% random
sample data for the period July 1997 - March 1998.

Overall

Historical Arizona IM240 test results also show a significant seasonal effect. This is
believed to be caused by two primary factors: the impact of changes in ambient
temperatures on the purging of fuel vapors to the canister (in some older models these
vapors are emitted directly to the engine), and the wintertime use of oxygenated gasoline. .
Both factors will act to reduce wintertime failure rates. At the high ambient temperatures
typically experienced in Phoenix in the summer and fall, high purge rates from vehicles
idling in the queue lead to canister overloading and breakthrough. This in tum results in
higher emissions and an increase in projected failure rates. Older-technology vehicles are
particularly susceptible to this phenomenon, due to the vehicles’ poorer fuel delivery
capabilities. This effect will not occur nearly as often during colder temperatures, thus

-5-


Table of Contents (continued)

page

6. Development of IM147 Variation Limits ............... 0 0c cece eee eee ee 55
Existing Tolerance Limits ... 2.0.0.6... cece cece eee e eens 55
Previous Analysis of Speed Variation Limits ......... 1. 57
Development of Positive Power-Based Variation Limits +... 58
Evaluation of IM147 Variation Limits on Test Time ....................004. 66

7. Integration of CPP Variation Limits ............ 0.0.0.0 2 cece eee eee ee 68
8. References ......... 0. ccc cece teen eee e tenes 71

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

Appendix F - Regression Summaries

Appendix G - Fleet Distribution Data

-ii-


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

60

50 :

40-44 |

304:

Speed (mph)

20-4!

10 4

0 ‘aiualial referee ere Pe He TEETH

oO 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

-39-


Figure 7-1
Process for Integrating CPP Variation Limits

FAST-PASS
Partial Test CPP 4
< Lower Limits? pres_,|
‘CONTINUE TEST
NO
PASS FAIL
Valid Fast-Pass #1
NO | Full Test CPP Full Test CPP [NO Retest? NO.
< Lower Limit? > Upper Limit? etest?

YES | YES YES
Valid Pass #1 —TM) Invalid Test Valid Fail #4
~ FAST-PASS. FAST-FAIL
Partial Test CPP Partial Test CPP.
<Lower Limits?) YES YES! > Upper Limits?
CONTINUE TEST ‘CONTINUE TEST

PASS FAIL,
NO | Full Test CPP Full Test CPP | NO Retest? NO
< Lower Limit? > Upper Limit? ‘etest?

YES YES YE:

‘S
Valid Pass #2 Le) Invalid Test Valid Fail #2
FAST-PASS | FAST-FAIL
Partial Test CPP Partial Test CPP
<Lower Limits? PES YES} < Lower Limits?
CONTINUE TEST CONTINUE TEST
Valid Fast-Pass #3

PASS FAIL

NO | Full Test CPP Full Test CPP [NO Retest? NO.
< Lower Limit? > Upper Limit?

YES YES YE:

S
Valid Pass #3 “*) Invalid Test Valid Fail #3

Repeat IM147 #3 Test

Hi

-70-


Failure Rate
Startup Cutpoints

HC co NOx OVERALL
VType [Fail [Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail
First TMMi47t—‘“‘(‘;..!O!O!O!OOCO!C*@*S

LDGV 180 | 1600 | 10.1% | 179 [1601 | 10.1% | 216 | 1564 | 12.1% | 416 | 1364 | 23.4%

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] 85% | 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% [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%

All 191 13156} 5.7% {193 13154 |_ 5.8% 121713130 | 6.5% | 456 | 2891 | 13.6%

Failure Rate By Model Year Grouping - First IM147

Startup Cutpoints

V Type

HC

co NOx OVERALL
Pass | % Fail | Fail | Pass [% Fail | Fail | Pass | % Fail | Fail | Pass | % Fail

13 | 92 | 12.4%

75 | 153 | 32.9%

50 | 375 | 11.8%

37 | 915 | 3.9%

4 | 66 | 5.7%

24 | 236 | 9.2%

11 | 211 | 5.0%

2 | 448 | 0.4%
0 | 32 | 0.0%
26 | 68 | 27.7%
6 | 58 | 9.4%

422 | 1.2% | 29 | 398 | 6.8% | 42 | 385 | 9.8%

O | 18 | 0.0% | O | 18 | 0.0% | 0 | 18 | 0.0%

253 13094 | 7.6% 1345 13002 | 10.3% | 685 12662 | 20.5%

B-3



sierra research

Report No. SRO0-03-01

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

prepared for:

U.S. Environmental Protection Agency

March 2000

prepared by:

Sierra Research, Inc.

1801 J Street

Sacramento, California 95814
(916) 444-6666


02-9

IM147 Composite Regression Coefficients, CO, 1988 to 1995 Model Year LDGT2's
| yession Coefficients
segment IRMS —|Reg.
Number Error [Constant [C1 ic2 C3 ica ics. icé C7 ice ico C10 (al C12 ic13 C14 [Cis C16 [C17 C18 ic19
[Pt [C0Gra fen-ss | 7reas3]aszeai] 0.6075 r
[Pz [LOGT2 88-95 [5.72678] 5.74122] 0.9189] 2.3772]
Ps ]LOGT2 [88-95 [5.24439] 4.67052] -1.3811] 1.1541] 5.5635].
@ ILOGT2 [88-95 | 5.14826] 4.26813] -1.2272] 1.0883[ 3.6162] 4.9841[-
[PS ILOGT2 [88-95 | 5.09801] 4.25023] -1.2746] 0.9561] 3.0791, 3.914] 1.7536].
[PG (LOGT2 [86-95 | 5.03227] 430487] 1,045} 275] 2.5651} 1.1445] 4.8242] a
PT LOGT2 [88-95 | 4.94785] 3.9903] -1.6248] 0.9734] 2.2915] 2.261) 0.0539] 4.0718] 2.4087]. .
PB |LOGT2 [88-95 | 4.89866] 3.89264] -1.7403] 1.0233] 1,931] 1.7839] 0.2408] 2.7207] 1.5017] 2.1928].
[Ps LOGT2 [88-95 | 4.85108] 3.81187| -1.2151] 1.0106] 1.7096] 1.0917] 0.0267] 1.4417] 1.4322] 1.6155| 3.0089].
P10 LOGT2 [88-95 | 4.82605] 3.68452] -1.0168] 1.0461] 1.7253] 1.7644] 0.2401] 1.2295] 1.1312] 1.6015] 1.909] 2.3455]. .
P41 (LOGT2 [88-95 | 3.00685! 1.74476| 1.0382] 0.6435] 22076| 0.8361 0.297] 0.8814] 1.2011 0.12] 2.4158] 1.0536] 1.0849]. .
P12 jLOGT2 [98-95 [2.71202] 1.40261] 1.3589] 0.5437] 1.8138] 1.0668] 0.3264] 0.7761] 1.4576] 0.0633] 1.6352] 0.8125] 0.9020] 1.3647].
[P13 ILOGT2 [88-95 1.9294] 0.60523] 1.2096] 0.6003] 1.3401] 0.909] 0.56447 1.2411] 0.8508] 0.3162] 0.575] 0.1662] 0.8851] 0.7097] 2.7113].
P14 [LOGT2 [98-95 |" 1.26478] 0.42753] 1.2507] 0.6101 4.151] 0.906} 0.608} 3.707[ 0.8161] 0.593] 0.3194] 0.7475] 0.7497] 0.8072] 1.7136] 1.2385] a
[P15 ILOGT2 [88-95. 0.8779} 0.28841] 0.9697] 0.6415] 1.0695] 0.7999 0.5885] 1.284] 0.8121] 0.5778] 0.5467] 0.957] 0.7335] 0.8178} 1.057] 0.8384] 1.2288]. a
P16 CDGT2 [88-95 [0.79251] 0.16425[ 7.0108] 0.6516{ 1,0689| 0.8061! 0.5356] 1.3495] 0.6303| 0.6371] 0.4534] 0.9417] 0.7116] 0.8113] 1.0192] 0.8943] 0.9962] 1.2531]
Py? LOGT2 ]88-95 | 0.69731} 0.07985] 0.8509] 0.7135] 0.8792] 0.7703] 0.5294] 1.3068] 0.6755] 0.6246] 0.4121] 0.958} 0.7118] 0.8086] 0.9187] 0.9121] 0.9794] 0.751] 2.056)
[P18 ILDGT2 [88-95 | 0.52944] -0.00314] 0.7855] 0.7161] 0.8485] 0.6381, 0.64] 4.2114] 0.5735] 0.7301] 0.4666] 0.8616] 0.7036] 0.7599] 0.6897] 0.6686] 0.9015] 0.8581] 1.2682] 1.0027].
[Prs LOGT2 [68-95 [0.18228] 0.02029] 1.0088[ 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] 0.7311
___!M147 Phase 2 Regression Coefficients, CO, 1988 to 1995 Model Year LDGT2's
Regression Coefficients
Segment IRMS — |Reg.
Number Error Constant |C1 jc2 C3 \Ca iC5 C6 ic7 ice ico C10 C11 C12 C13 C14 C1S C16 C17 ic18 \c19
[Bit ]tGr2 [es-55 | Ssoasi| 400126), 7.4506]. "
fez ]UOGT2 [88-95 [3.81315] 2.54349]. 1.2451] 2.0945] a
| XEN {LOGT2 {68-95 | 2.63707] 0.83295) 1.1848] 0.9524] 3.6626].
[B14 JLOGT2 [88-95 [1.77124] 0.72633] 1.0256] 1.1004] 2.4663] ~ 1.6377] a
[815 {UOGT2 [88-95 | 1.22283] 0.50627]. 4.0014] 1.1489] 1.5439] 1.0872] 1.7128). a
| ILOGT2 {88-95 | 1.11757] 0.38075}. 0.9754] 1.1094] 1.4934] 1.1553] 1.4158] 1.6429) a
fea7. ILOGT2 [88-95 | 0.95396] 0.13503)" 0.9673] 1.1001] 1.2482] 1.2114] 1.3643] 0.9484] 2.8337]. .
| Eee ILOGT2 [88-95 | 0.72512] -0.00576]- 0.9541] 1.0331] 0.9195] 4,158] 1.2515] 1.1276] 1.7748] 1.3609].
| Ee LOGT2 [86-95 | 0.28372| 0.00704] 0.9656] 0.9834] 0.9474] 0.9412] 1.0187] 0.882 1.65) 0.9988] 0.9646}



Excess Emissions with Fast-pass Disabled
Max CO Cutpoints
(Data Not Normalized for Model Year Distribution)

Excess { Excess HC Excess | Excess CO Excess | Excess NO
Vehicle |Model Year| IM240 HC | w/o fast- | Excess HC | IM240CO | w/o 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% 177 0 0.0% 1.65 1.65 100.0%
90-95 0 0 NA 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% 4.11 2.31 56.2%
88-89 0.21 0.21 100.0% 26.15 26.15 100.0% 0.68 0.38 55.9%
LDGT1 90-95 0 0 N/A 0 0 N/A 0 0 NIA
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 TATOT 95.0% 23.61 23.04 97.6%
ALL 68.99 65.08 94.3% 2680.86 98.2% 58.56 53.94 92.1%



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

Table of Contents

page
Summary 2... ee eee ee eee eee eee ee 1
Scope of Work 2.0.6... 6. eee nett eae

Projected IM147 Failure Rates ....
Modal IM147. Fast-Pass Standards .
Predictive Retest Algorithms ...... an
Modal Fast-Fail Criteria... 0... eee eee eee e es
Integrated Fast-Pass, Retest, and Fast-Fail Algorithm Results .................. 9
Segment 2 Revised Integrated Algorithms Results
Development of IM147 Driver Variation Standards
Integration of CPP Variation Limits ............ 6. cece cece c eee eee
SIP Credit Analysis 2.0.0... 0c cece eee eee eee eee e nee ene
Need for Follow-Up Analysis . 0.0... 0.6.0 ec bec cee ce beeen nee

. Introduction 2.0... eee cent e nnn eens 15

SCOPE 6. nee teen teen ete 18
Organization of the Report...............0055 Lecce cece eee eee eee eens 20

» Test Data... ce eee 21

. IM147 Cutpoint Analysis ..... 0.0.00 ences 26

IM147 Test Length ......... 0c. cece cece e eee Lele cece eee ete 26
Cutpoint Analysis
Comparison of Failure Rates 2.0.00... cece cee eee eee nee 30

. Optimized IM147 Test Criteria ...... Lecce cece eee eee eee eee e eens 36

Modal Fast-Pass Standards ....... 6... ccc cece eee eee
Predictive Retest Algorithms ......... 0.0.0.0 cece eee eee eee

Modal Fast-Fail Criteria... 2.2... cece eee eens
Integration of Fast-Pass, Retest, and Fast-Fail Algorithms . :
Segment 2 Revised Integrated Algorithms Results .......... 0... e cee seen eee
SIP Credit Analysis... 0.0.0... cece ccc eee nee e teenies
Need for Follow-Up Analysis ....... 6. e eee ce eect een eens

“ie


1@-D

1M147 Composite Regression Coefficients, CO, 1996 and Newer Model Year LDGT2's

Regression Coefficents
ISegment RMS Reg.
Number Error {Constant ca__|ca__fes__fce__cr_|cs_|co _|éw _|crs_|cr2_fera_|ora _|ors__|ers_|ci7_|cis__|cro
7 ToGT2 [ser 7,086] 1.42622]
PZ TLOGT2 [6+ 1.1479] 1.00342)
Ps [LoGr2 |s6- | 0.98949] 0.98019] TON
[Ps LDGT2 [96+ | 0.82952] 0.97186] 0.0693] 9.4052].
|F= LOGT2 |e6+_{_0.82272| 0.92541 0.0838] 7.9052] 1.1172).
[rs LOGr2_|96+__| 0.80854] 0.03687] 0.9927] 5.0074| 0.2744] 51496].
fez. ILOGT2 [96+ 0.78612] 0.93219] 0.867] 2.2463] -0.6382] 7.4747] 1.8026]. .
\Ps LOGY? |e6+ | 0.7922) 0.8145] 7.1613] 0.1172] 0.5476] 6.6914] 1.4725] 1.1768 E
[Ps LOGT2 |96+ | 0.79439] 0.90764] 1.57] -1.980i] 0.584] 6.2734] 1,261] 1.4763] 1.8463 r
JP10, LOGT2 [96+ 0.80176] 0.89411! 4.1925] -2.1872] 0.6255] 7.6689} 1.3637] 1.6415] 0.9728] 1.2038]. a a
| C2 LOGT2 [96+ | 0.45629] 0.43587] 4.1761[ 4.6902" -0.051] 7.5376] 0.5427| 2.8880] 0.9224[ 1.6589] 0.7053) -
Piz ILOGT2 [96+ 0.43361] 0.27262 4.1808] -3.2179} 0,1827{ 6.7307] 0.6851] 2.4296] 1.0559 0.404] 0.7027] 1.0882] .
|P13____[Locr2 |o6+ 10.5545] _ 0.13528] 0.5668] 2,970] 0.8935] 3.5682] 0.4381] 0.8157] 0.0928] 0.4580] 0.7161] 0.608] 1.0107] a
: aed LOGT2 [96+ 0.30207} 0.07328) 0.4318] 1.9157] 1.1728] 1.7862] 0.501] 0.7493] 0.097] 1.2673} 0.6916] 0.617] 1.1886] 1.8756]. a
[Pis___{tocre {e+ [0.11059] 0.01946 9676] 0.1605] 1.423] 0.6812] 2.4687] 0.9513] 0.6769] 0.0759] 1.097 0.7126] 0.6574] 1.1486] 0.504] 1.6003). a
{P16 [tocre [ese [0.09136] 0.01277] 0. 8203] 0.1463] 2.0115] 0.7771] 2.1495] 0.9056] 0.4482] 0.4595] 0.6343] 0.7138] 0.6364] 1.0267] 0.626] 1.3436] 7.1000]. E
|P17 —TtoGr2 |e6e 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] 0.0450] 0.5905] 1.3506] _ 0.864] 1.1062 i
Pr, LOGT2 [o6+ 0.07115] 0.00155] 0.1596] 0.9576] 0.1434) 4.207] 0.6318] 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].
pPis LOGT2 [96+ 0.02689] -0.00249] -0.2617| 0.9243] 0.5171] 0.6083] 0.8825} 4.183] 0.5554) 0.926] 0.5734] 0.7532) 0.715] 0.657] 0.7458} 0.7682| 0.6645] 0.7015] 0.6289] 0.6613] 0.7521

IM147 Phase 2 Regression Coefficients, CO, 1996 and Newer Model Year LDGT2's
Regression iS

iSegment RMS Reg.
Number fEror__|Constant_|C1 ce__|es__|ea__|es__|ce__|cr_|cs__|co _|cw _|cms_|crz fers _jcra fers cre fer _|cra__|cro
m0 OGT2 [ser | 1.20408] 1.12220). 0.7608].
; EXE JLOGT2 [96+ 0.62558] 0.40305}. 0.9685] 2.1567]. .
{e13___[LoGr2 les+ [0.54166 _ 0.23979]. 716] 1.5278| 2.1489 :
Bia |UDGT2 [e+ | 0.44605] 0.15037). 0.9303] 1.3086] 1.0695] 2074]. i
Isis |LOGT2 [96+ | 0.24307] 0.03103} (0.9846 1.2679] 1.1880] 0.9274] 2.4187] r
[B16—locr2 [oe+—_| 0.21899] 0.021121. (0.9894 1.1748] 0.9741] 1.2313] 1.0455] 1.756].. r
[B17 ILDGT2 [96+ 0.19501] 0.00313]. 0.9536] 1.2274] 0.7628] 1.1104] 1.0911] 0.8779] 2.8520]. .
|p18____|LoGr? fase 0.14168) 0.00707] 0.9522] 1.0935] 0.6831] 1.2308] 1.576] 1.0600] 0.7732] 1.602].
pis ILDGT2 [96+ 0.06636] 0.01816]. 0.9663} 0.9491] 0.8971] 1.0203] 0.7701] 0.8001] 1.1191] 1.0266] 1.261:



Figure 4-1
IM147 Trace
Phase 1 and Phase 2

60

147 PHase | —————> +. 1m 147 PHASE «>

50 4

404

304

Speed (mph)

20

ATTRA PTAT TTT PPTTT APTA ETTT

O 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.)

-27-


ie)

APPENDIX C

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

Regression Coefficients

RMS —[Reg.
fEor__|Constant C1 co fca_—cs_ cscs ~— cr? ics — co ——fcro—fcrs_—fcrz_for3._fora_—_fcis_fere_fcrz_fcrs_|cr9
COGT1 [eres | 0.7s608] 0.95039] 155112].

LOGT1 Ja1-85 | 0.52614] 0.34801] 2.7265] 55143].

LOGT [61-85 | 0.40809] 0.23152] 0.5647] 3.2006] 6.2124

LOGT1 [81-85 | 0.38717] 0.22535| -1.0106} 3.4095] 3.2694] 3.5979].

DGT1 [61-85 | 0.36872] 0.23976] -0.6175| 2.5630] 3.1216] 20146| 3.4755).
LOGT1 [61-85 | 0.34976] 0.25562] -2.6842| 2.7609] 1.6633] 1.5965] 2.3446] 3.2626].
LOGT1 [81-85 | 0.33920] 0.24395] -2.5969] 2.4215] 2.0512] 1.644] 0.5816] 2.2806] 2.0109] i
LDGT1 181-85 | 0.30707] 0.21404] 0.9821] 1.8007] 1.4784) __ 1.745] 0.0989] 0.6055] 2.4381] 4.82 -
LOGT+ {61-85 | 0.30603] 0.21485] -0.9303| 1.7873] 1.3395] 1.7754] 0.0281] 0.6238] 2.4701] 4.5115] 0.6007]
LOGT# [61-85 | 0.30246] 0.21468] -1.160] _1.8114| 1.1577] 2.1168] 0.3265] 0.5235] 2.6637]  4.265| 0.5876) 2.1284]. s
LOGTs [81-85 | 0.25475] 0.05761] 0.2203] 0.7411] 1.0904] 1.5866] 0.0582 1.3727] 1.9974] 2.4699] 0.622| 1.4541] 1.8008] a
LoGTi_ [61-85 | 0.24251] 0.05503 -0.1605] 0.7753] 0.9307] 1.635] 0.0864] 1.936] 2.0956] 2.1337] 0.2754] 2.1378] 1.2063] 1.1100)
COGT1_[61-85 | 0.19345] 0.05679] 1.3617] 0.2166] 0.9320] 1.5508] -0.1316] 12355] 2.3977] 1.6874] 0.0101] 0.9701| 0.9488] 0.4982] S.2a51]. "
LOGT1 [81-85 | 0.1691] 0.04219| 1.279] 0.1384] 1.4759] 1.4082] 0.0948] 1.1156] 2.0131| 1.2038] 0.729] 1.1342] 0.4477] 0.3274] 1.562] 4.0745].
LOGT1 [61-85 | 0.13928] 0.03346 1.6241] 0.0799] 1.2164] 1.2299] 0.2827] 0.8749] 2.2975] 0.6681 0.8756] 0.7239] 0.5659] 0.1723] 0.7424] 1.1987| 2.7671).

LOGTs for-85 | 0.13703] 0.0021] 1.6766] _ 0.023| 1.2823] 1.1606] 0.305] 0.6514] 2.2773] 0.5758] 0.0491] 0.7430] 0.5775] 0.2639] 0.6400] 1.2728] 2.0001] 2.1607]. -
LOGT+ [81-85 | 0.09132] _0.00875| 1.4179] 0.434] 0.6904] 0.7838] 1.0571] 0.616] _ 1.5938] 0.7516] 0.2064] 0.8616] 0.5896] 0.4328] 0.7951] 1.3837] 1.3066] 0.6628] 2.7633). -
LOGT1 [61-85 | 0.073141 0.00235] 1.5086] 0.5696) 0.7494| 0.8068] O.es63| 0.5804| 1.2972| 0.5907| 0.40571 _0.6221| 0.5658] 0.564 0.7038] 0.6587| 0.988] .3277| 1.7201 1.7883]

LOGT1 [81-85 | 0.06979] -0.00621] 1.375] 0.6281] 0.6996] 0.852} 0.9084] 0.5029] 1.4414] 0.5441] 0.3942] 0.569] 0.6012] 0.6081| 0.7093] 0.7313] 0.6296] 0.2443] 1.7266] 1.5860] 0.7:

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

Regression Coefficients

Segment RMS Rea.

eve lEmor _|Constant {C1 cz ica es_——cs_—sfce ~—sicr_—ifca fos ero fers fcr2z__—fers_ fers fcrs_—_—cre—cr7_—fcrs_—_—icia
it TOGTs [e105 | 0.5023] 0.12361 " 5 C C s C aaaaT " C r a
\e12__[tocrs e185 [ 0.48187] 0.110131. i a C a i 5 3.3244] 1.9633}. a

[e13___|tocrr_e1-85 | 0.35264] 0.07757]. 5 5 a 4 i a 1.7904] _-1.1796] 5.9021]

aia Lots |e1-85 | 0.32195] 0.06184). . . . . . . . o.g97a| -1.0185] 3.8122} 5.5161}. .

BIS LOGTT |e1-05 | 0.2626] 0.04064] q a a i - a = i i 1.0928 0.0499] 1.7408} 0.7784] 4.7049]. a

B16 |LOGT# |e1-85 | 6.27957] 0.0348]. i a i a a 7.0816] 0.2105] 1.5379] _0.952a| 3.4207] 3.6478]. 4

Is17___ |LOGT1 [81-85 | 0.14692] 0.01627. a 5 a i i i 7.0035] 0.5208] 1.1766] 2.2402] 1.4703] 0.4909] 4.6019]. i
i818 |LOGT1 [61-85 | 0.11692[ 0.02022]. i i i ‘o.si77] 0.8016] 0.9222) 15324] 0.2911{ 0.0868] 3.252] 2.7650].

B19 [LOGT# [61-85 | 0.11286] -0.02209]. a _L a i 0.9799] 0.8216] 0.9206] 1.4305] 0.4043] 0.0284] 28156) 2.4808) 0.9608]



Failure Rate
Intermediate Cutpoints

HC co NOx OVERALL
VType | Fail | Pass | % Fail | Fail | Pass | % Fail Fail | Pass
First M147
LDGV 286 | 1494 | 16.1% | 238 | 1542 | 13.4% | 281 | 1499 | 15.8% | 533 | 1247] 29.9%
LDGT1 121 | 843 | 12.6% | 62 | 902 | 6.4% | 122 | 842 | 12.7% | 247] 717 | 25.6%
LDGT2 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
LDGV 170 | 1610} 9.6% | 161] 1619] 9.0% | 214 | 1566 | 12.0% | 386 | 1394] 21.7%
LDGT1 87 | 877 | 9.0% | 57 | 907 | 5.9% | 78 | 886 | 8.1% {176} 788 | 18.3%
LDGT2 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 IM147
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 13070 |_8.3% _| 256 |3091 | 7.6% | 311 | 3036 | 9.3% | 625 | 2722 | 18.7%

Failure Rate By Model Year Grouping - First IM147
Intermediate Cutpoints

HC co NOx OVERALL

53

B-S


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 3,347-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 1-6 shows the net effect of the procedures on test time and excess emissions.

The table shows that excess emission identification using the Max CO cutpoints with the
fast-pass, retest, or fail-fail algorithms enabled is 95.9%, 93.1%, and 97.0% for HC, CO,
and NOx, respectively. (For comparison, the respective identification rates are 97.8%,
96.3%, and 95.4% 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. First, 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 and normalized
to the vehicle inspection fleet distribution for the period July 1997 to March 1998. Asa
result, the vehicle distribution was skewed toward older vehicles relative to that in the
previous 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. It was therefore decided not to pursue this latter option.

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.

-9-


“Table 5-6
Comparison of Integrated Algorithms (No CPP) vs. Standard IM147
Impact on Test Time and Excess IM240 Emissions (Max CO Cutpoints) Lost

Model Mean Test Mean Test % Excess Emissions Identified”
Year Sample Time Time w/
Group Size* Standard® Algorithms* HC co NOx
1981-82 286.4 140.1 - 87.6% 100.0%
1983-85 228 311.2 169.4 100.0% 97.6% 100.0%
1986-89 425 248.4 112.0 99.2% 97.1% 100.0%
LDGV
1990-95 952 184.8 78.3 96.8% 85.3% 91.2%
1996+ 70 154.3 37.1 - - :
All” 1780 221.0 100.0 98.2% 93.3% 96.8%
1981-85 260 306.6 158.7 78.5% 97.3% 99.4%
1986-89 222 230.8 101.6 93.8% 90.8% 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 83.4% 92.6% 99.5%
1981-85 94 307.5 158.8 100.0% 100.0% 77.2%
1986-87 64 253.2 101.4 100.0% 100.0% 100.0%
LDGT2 | 1988-95 427 166.5 54.2 - 0% 44.4%
1996+ 18 146.0 28.0 - - -
All 603 197.1 74.7 100.0% 93.8%
Weighted Average 3,347 217.0 94.0 95.9% 93.1%

* Mean test time standard refers to the average dynamometer test time without the algorithms enabled.
This was determinéd using the 3,347-vehicle sample.

> Percent of IM240 (Max CO) excess emissions identified with the integrated algorithms enabled. This
was determined using the 835-vehicle sample and normalized to the Arizona 2% random sample fleet
distribution data, July 1997 to March 1998.

-51-



8D

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

Regression
Segment RMS — Reg.
Number Emor {Constant C1 oz es ce cs fos —cr@_ caso fcto cts —fcr2 fora. |ora_—_—fcts_—fcre_—for_fcrs_|cra
a OGY [sex | o.0rzas] Ooevo] 102). i
fez COGT2 [96+ [0.04564] 0.0237] 7.2812] 4.6776].
{3 LOGT2 }96+ | 0.04313] 0.03223] 2.4032] 1.004] 6.1404 i
[Pa jLOGT2 [56+ 0.03729} 0.03292] -1.4437]_ 2.1679] -2.2327] 9.7739] .
[Ps [LoGr2 |96+ [0.02867] 0.03311] 7.1815] 1.5592] 9.6084] -6.097| 6.5012].
[Ps LOGT2 [96+ | 0.02468] 0.03246| 5.2125] 2.8958] -0.7092[__-3.162] 2.3757] 7.6986]
Jez LDGT2 [96+ | 0.02471] 0.03212] -5.6003] 2.6260] -0.3486| _-3.879| 1.8571] _ 6.769] 2.1598].
Ps LOGT2 [96+ 0.02458] 0.03221] _-5.7203]_ 2.2642] 1.1436] -7.0745] 3.5654] 5.3631] 0.5718] 2.5776].
[Pe LOGT2 [96+_| G.0z153] 0.02755] -5.6174| 3.2239] 0.1186] 6.6505] 2.7670] 1.6063] -1.0747| 0.5927] 6.5733]
[P10 LOGT2 [96+ 6.02175] 0.0276] -5.5557}_ 3.1793] 0.113] 6.663] 2.9877] 1.5294] -1.4181] 0.9223] 7.6307] 0.9647). .
PAY ILOGT2 [96+ 6.01561] 0.01512] 0.1593] 1.3225] 0.8970] -3.5644] 1.4176] 3.2321] -2.66i]_ 2.4201] 3.7658] 1.2056] 7.069] a
[P12 LOGT2 [96+ 0.01535] 0.01155] 0.1184] 1.3108] 0.2317] -1.9686] 1.0103] 3.2791] -1.9360] 2.0526] 2.5266] 1.346] 0.9439] 0.9442] 5
[P13 LOGT2 [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 |LOGT2 |ee+ | 0.00894] 0.00333] 0.9204] 0.6687] 0.8295] 0.2036] 0.5645] 4.0004] _0.2628{ 1.1646] -0.1226| 0.2024] 0.7031] 0.6864] 2.9870] 2.5080]
[Pas |LOGT2_|96+ 0.00519} 0.00298] 0.5728] 0.3247] 2.2863] -2.5268] 2.2831] 4.4541] -0.658] 2.6037] 0.4889] 1.5224] 0.6251] 0.0309] 1.4354] 0.4248 1.7293]. 5
{P16 |toGr2 [ser 0.00341| 0.00091] 0.6938] 0.6433] 0.9494] 0.2435] 1.2108] 1.0848] 0.1134] 1.3632] 0.4148] 0.7129] 0.7604] 0.2037] 1.1446] 0.6148] 0.7145] 4.0420).
P17 LDGT2 [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].
[Pi ILDGT2 [96+ 0.00228] 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] 0.3526]
pis [LDGT2 [96+ 0.00148] 0.00015] 0.5489] 0.7737] 0.6253] 0.5368] 0.7319] 1.0865] 0.7396] O.777| 0.6586| 06472} 0714] 0.5043] 0.6109] 0.8951| 0.552] 1.6026] 0.688}
M147 Phase 2 Regression Coefficients, HC, 1996 and Newer Model Year LDGT2's
Regression Coefficients
[Segment RMS — Reg.
Number lEmor__|Constant |C1 cz fcs_—fca cs cafe — cacao —fcto ets |crz_ cra fers, ets fcte_ ci’, fcxs.— cto
|Ei;_[toarz ser | G.0zee7] o.01s 7.9495] r
B12 [UOGT2 Ja6+ | 0.02266] — 0.00456] 7.3011] 3.138
fa13__|LoGr2 }9ee__|_0.01837| -0.00086]. 1.3623[ 0.2518] 4.0037].
[B14 ]LOGT2 [se+ 0.015] -0.00298] 1.1501] 0.0589} 2.9165] 3.3772]. .
1815 |LOGT2 |s6+__| 0.01016] 0.00336]. 1.1443] 0.9415] _1.7337| 0.5943] 2.5641
[e716 JLoGT2 [96+ [0.00753 0.00463]. 1.0628] 1.0417] 1.2120] 0.903] 1.0508] 6.8124].
la17_|toGr2 |s6+ | 0.00686] 0.00476 1.1331{ 1.0977] 1.0456] 0.7683] 1.3074] 4.7654] 2.3041]. a
[B18 LOGT2 [56+ 0.00371] -0.00109] 0.9732] 1.1893] 0.7137|- 0.4116] 1.4972] 2.8064} -0.0005] 2.4412]
Bis. [eoGT2 [se+ 0.00243] 0.00127) 0.9785] 0.9384] 0.9374. 1. 0.8374, 1.4572] 0.714] 1.4339 1.370:



Appendix A

Startup, Intermediate, and Final IM240 and IM147 Cutpoints


Figure 4-3
Failure Rate by Consecutive IM147

-¢E-

Startup Cutpoints
HC Failure Rate by Vehicle Type ~ CO Failure Rate by Vehicle Type
12.0% 12.0%
10.0% 11 10.0% 44H
A
¢ ul Tl] Fil g i :
2 8.0% mi 7 | li Dist tmi4a7 EZ 8.0% THI Hl Dist 1m147
2 ff Hi .° 7
3 6.0% 1 i m2ndimi4a7 3 6.0% Hi - i mand iM47
2 3 Hi
0% H = ea3rd M147 6 4.0% 4 - 3rd 1M147
= k o i
2.0% Hi || 2.0% H = l kt
i ' i
0.0% Ue ul ih 0.0% LL i i Hh
Pc LDTI—LDT2 aut Pc LoT! = LDT2 All
Vehicle Type Vehicle Type
NOx Failure Rate by Vehicle Type 7 Overall Failure Rate by Vehicle Type
14.0% (15.0%
i
_ 20% fi ge 20.0% Le 7m
10.0% tI a He
ri 8.0% 1 i Qi st M147 £ 15.0% fT it [Dist M147
ga 1 2 Hi
2 il [ ond imta7 é ti i m2ndiMi47
6.0% T+] — = | |_|} |_|
it I th @3rdimia7 B 10.0% TH | ea3rd M147
2 4.0% 1 i) s . H
2.0% 3 5.0% | |
0.0% H | 0.0% i ul
PC LDTI —-LDT2 All Pc LDTL DT? All
Vebicle Type Vehicle Type



Figure 5-3

Retest Algorithm During Second IM147 - LDGV
Max CO Cutpoints

UM 147 TEST

|

yes 5 No
Vehicle Fast Passes After
Segment 19
¥
NO | Mode 7 Predicted HC < STD * 1
or
Mode 19 Predicted HC < STD * 1
YES
NO q " YES
le. Mode’7 Predicted HC -STD_ , 9 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 .
NO | Mode 7 Predicted NOx < STD * 1.2
and
Mode 19 Predicted NOx < STD * 1.2
YES
y
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.

-45-



6¢-D

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

Regression Coefficients
RMS Reg. -
JError Constant jC1 ic2 ic3 ic4 ics ice ic7 ice ice C10 C14 C12 ic13 C14 cis. C16 C17 C18 C19
LOGT1 [90-95 | 1.15988] 1.24128] 32.9634]. .
(COGT1 [90-95 | 0.72181] 0.40146} 0.679] 4.5914] a
]LOGT1 [90-95 | 0.69216) 0.32475] 2.1542] 3.4912] 6.7253] . a
{LOGT1 90-95 | 0.68749) 0.3123] 2.1371] 3.4737] 5.023] 5.8563]. a
(LDGT1 [80-95 | 0.62455] 0.33205| 3.4043] 2.3634] 1.7239| 5.0562] 6.7446. a
ILDGT1_ [90-95 | 0.61978] 0.33046] 2.2722] 2.3005] 1.5599] 2.9584} 8.2913] 6.1514]
LDGT1 [80-85 [0.58792] 0.20740[ 2.0622] 1.8473] 0.0555] 3.6238] 4.2818] 7.9809] 5.3164]. a
[LOGT1_ [90-95 [0.55746] 0.26129] 5.9179] 1.7445] 0.5662) 1.235] 4.3563} 0.782) 3.7446] 7.1976]. a
[LOGT1_ [90-95 | 0.54008] 0.24653] 4.1085] 1.8313] -1.1591] 2.0187] 2.9278] 0.7348] 3.4121] 5.4986] 7.6688]. .
LOGT1 [90-95 [0.53603] 0.2342] 2.920] 1.8441] -1.4993] 2.6442] 2.6552] 0.0076] 2.7177] 5.458] 6.0386] 2.7176].
TLOGT# [90-95 | 0.45579] 0.05778] 3.2806 1.051] -0.2096] 0.7143) 2.8133] 1.4291] 0.9563] 2.9071] 6.0179] 0.9428] 1.4469] a .
LOGT1 [30-95 | 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]. a
JLOGT1 [90-95 | 0.17758] 0.04529] 0.6553] 0.7331| 0.4708] 1.1424] 1.2661] 0.9419] 0.3362] 0.8] 1.2658] 0.277] 0.7426] 1.2309] 2.6602]- i
]LOGT1 [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]. a
{LOGT1 [90-95 | 0.07822] 0.01763] 1.1762] 0.6831] 0.7996] 1.3552] 0.7806] 0.9482] 0.6863] 0.8420] 1.0081] 0.3774] 0.6949] 0.7426] 0.8865] 1.0954] 1.3651]. a
]LOGT1 [90-95 | 0.06969] 0.01628) 1,367] 0.7128] 0.8247 1.0691] 0.8408] 0.6506] 0.711] 0.7051 0.814} 0.6359] 0.7085] 0.724] 0.8452] 1.0797] 1.0435] 1.2099] a
TLOGT1 [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] 1.1224] 1.216]. a
]LOGT1 |90-95 T 0.03909} 0.0008] 0.7599] 0.7108] 0.6532] 1.2612] 0.7148] 4.122] 0.5737] 0.837] 0.7408) 0.6827! 0.6897] 0.8094] 0.6826] 0.8525] 0.8302] 1.1087] 0.8134] 0.8578].
[LOGT1 [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) 0.728} 0.72021 0.7122] 0.7434) 0.6941 0.73}
IM147 Phase 2 Regression Coefficients, NOx, 1990 to 1995 Model Year LDGT1's
} ‘Regression Coefficients
Segment IRMS = |Reg. | :
[Number |Error Constant {C1 ic2 ica ica C6 ic? ica ico C10 (ikl (C12 C13 C14 Cis C16 C17 C18 ic19
17 [COGri [00-05 | 0.70725] 0.11508), ERE
[B12 ILOGT1 [90-95 | 0.54083] 0.02981]. 1.5474] 5.8806]. a
| CEE ILOGT1_ [90-95 | 0.24246] 0.06072]. 0.9747} 1.7935) 3.6]. a
| EXE ILOGT1_ [90-95 0.1753] 0.03555]. 0.8927] 1.2292] 2.2351] 2.4522} .
[eis [LOGT? [90-95 | 0.10866] 0.02903] 0.9331] 1.1278] 1.1969] 1.4839] 1.8483} .
[B16 [LOGT1 [90-95 [0.09571] 0.02747]. 0.9823] 1.0076] 1.1844] 1.4428, 1.4242] 1.6458]. 5
| EXE TLOGT1 [S0-95 | 0.08918] 0.01127]. 0.9705] 1.0643] 1.1573] 1.445] 1.3597} 1.5734] 1.73411. a
| EXE ]LOGT1 [90-95 | 0.05491|" 0.00454} 0.9276] 1.1839] 0.9094] 1.1571] 1.0953] 1.6597} 1.2012] 1.1201].
{819 [LOGTs [90-95 | 0.01259} 0.0022] 0.9623] 0.9896] 0.9651] 0.9817| 0.9732] 0.9769] 1.063] 0.9408] 0.0026]



Table 5-5
Fast-Fail Algorithm

Second IM147 Third IM147 False
Fast-Failures Fast-Failures Failures

Vehicle Class

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.

The excess emissions identification rate shown in Table 5-6 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 and then normalized to
the inspection fleet, based on 2% random sample data collected from July 1997 to March
1998. This skews the model 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 7 of the 3,347 vehicles were tested over the full
duration of all three IM147 tests.

-50-


Regression Summary - Composite HC, IM240 to IM147

Model Years 1981-1985

All Vehicle Types
SUMMARY OUTPUT
Regression Statistics
MultipleR 0.981278
R Square 0.962907
Adjusted R 0.962636
Square
Standard 0.331332
Error
Observations 139
ANOVA
of ss MS F Significance F
Regression 1 390.4283 390.4283 3556.441 6.9E-100
Residual 137 15.03995 0.109781
Total 138 405.4682
Coefficients Standard t Stat P-value Lower 95% Upper 95%
Error
intercept 0.110694 0.036049 3.070666 ~ 0.002576 0.03941 0.181979
{M240 0.896629 0.015035 59.6359 6.9E-100 0.866899 0.92636
1M240 Line Fit Plot
@ IM147

1M147

IM240

15

—— Predicted IM147

F-1


Figure 5.

4

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

VM 147 TEST

YES

Vehicle Fast Passes After
Segment 19

NO

NO

Mode 7 Predicted HC < STD * 1.2
or

Mode 19 Predicted HC < STD * 1.2

YES

Mode 7 Predicted CO < STD * 1.3
or
Mode 19 Predicted CO < STD * 1.3

YES

NO YES
Mode 7 Predicted HC - STD. >09
Mode 19 Predicted HC - STD °
NO
NO YES
Mode 7 Predicted CO-STD_ 05
Mode 19 Predicted CO - STD .
NO

Y
NO RETEST

Note:

STD = Composite Standard for the specific pollutant.

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

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.

-46-



Final 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 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 - 0.80/0.50 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

LDGT2

13.0/10.0 - 41.0/ 6.0

1.8 - 2.7/0.8

1981-83 3.40/2.00 - 3.10/2.10 | 70.0/56.0 - 67.0/35.0 45-4918
1984-86 1.60/1.00- 1.70/1.00 | 40.0/32.0 - 43.0/20.0 45-4918

1987 1.60/1.00 - 1.70/0.80 | 40.0/32.0 - 43.0/20.0 45-49/1.7
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

A-3

15.0/12. 1.0/7.0

2.0 - 3.8/0.9



Appendix C

IM147 Regression Coefficients


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

No Fast-Pass Fast-Pass Enabled
Excess HC Identified 97.8% 95.7%
Excess CO Identified 96.3% 92.7%
Excess NOx Identified 95.4% 82.4%
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.

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.

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

-40-


Excess Emissions with Fast-Pass, Retest, Fast-Fail Enabled
Max CO Cutpoints
(Data Not Normalized for Model Year Distribution)

D-1

Excess HC Excess | Excess CO Excess ke 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 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 15.7% 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% 1,77 0 0.0% 1.65 1.65 100.0%
96+ 0 0 N/A 0 0 NA 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% 4.11 2.91 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%
ALL 68.99 65.13 94.4% 2680.86 2557.44 95.4% 58.56 56.07 95.7%



St-O

1M147 Composite Regression Coefficients, CO, 1990 to 1995 Model Year LDGV's
‘Regression Coefficients.
Segment IRMS — Reg.
Number |Ecror Constant |C+ cz ic3 ica ics icé C7 J ico C10 \c1t C12 C13 IC14 cis: |c16 ca? C18 ic19
| (oa CDGV_ [90-85 | 5.5629] 4.52493] 10.561]. r
Pz, LOGV_ [90-95 [4.19016] 2.55934] -2.4176| 3.5176]. "
7P3. ILOGV__ [90-95 [3.59909] 1.7089] -2.0167] 1.8381] 5.8658]. .
[Pa ILOGV__ [90-95 | 3.52751] 1.57279] -2.1878] 1.7555] 4.1493] 4.0887].
PPs lLOGV__[90-95 [3.35967] 1.52742] -1.6847] 1.3658] 3.0822] 2.7595] 3.0675]. 5
[Pe ILOGV__[90-95 [3.30911] 1.53896] -2.1747| 1.433] 2.5763] 2.1518] 2.309] 3.2307}
Ga LDGV_ [90-95 | 3.20465] 1.37753] -1.7351] 1.2677] 2.4869] 1.7618] 0.6153] 2.8007] 3.0019) a
@ iLOGV_ [90-95 | 3.08075] 1.25584] 0.8703] 1.3957] 1.5808] 1.0639] 0.6544] 1.6621] 1.8801] 3.4334] a
Po iLOGV_]90-95 | 3.03308] 1.28742] -0.9409] 1.3472] 1.2620] 1.2814] 0.1146) 0.9574) 2.0383] 25679| 2.6687]. a
P10. ILOGV_ [90-95 | 2.99284] 1.20175] 0.6667] 1.2006] 1.1937] 1.4946] 0.2007] 0.8633] 1.7797| 2.4995] 0.7394] 2.5847]. a
yPat LOGV__ [90-95 |" 1.79334] 0.39573] 0.8907} 0.875] 0.9738] 0.6525] 0.6724] 1.4441] 0.9505] 1.8704] 1.0336] 4.2774] 0.946]. .
[Piz LOGY [50-95 [7.46201] 0.27031] 0.8772) 0.7005] 0.9191] 0.6699} 0.6683] 0.8288] 0.9734] 1.6847] 0.9938] 1.2059] 0.7634] 1.2265).
P33. ILOGV__ {90-95 | 1.22124] 0.18956] 0.9352] 0.7049] 1.1123] 0.7843] 0.6333] 0.6174] 0.819] 1.3469] 0.8633] 0.9048] 0.7518] 0.6615] 1.6924] _ .
[Pia 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]. 5
P15. ILOGV_ [90-95 | 0.67059] 0.09713] 1.1865] 0.6235] 0.8229] 0.6645] 0.7023] 0.9149] 0.9983] 0.9575] 1.0487 0.5316] 0.7254] 0.7636] 0.6921] 0.7821] 1.3034]. a
[P16 ILDGV__ [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]. .
iP v7 ILOGV_ {90-95 | 0.56769] 0.04406] 1.1034] 0.6685] 0.6934] 0.7551| 0.6966] 0.8027] 0.9189] 0.7923] 0.9076] 0.6476] 0.7299] 0.7393] 0.8716] 0.8032] 0.8822] 1.1592] 1.0586). a
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] 0.7651] 0.7468] 1.0451] 1.2236] 0.9766].
P19 LDGV__}90-95 [0.13949] 0.01779] 0.77847 0.7029] 0.7108] 0.7686] 0.6798] 0.8583 0.733] 0.6745| 0.7337 0.685] 0.7139] 0.7148] 0.744] 0.7002] 0.7152] 0.6362] 0.8674] 0.7322] 0.764.
1M147 Phase 2 Regression Coefficients, CO, 1990 to 1995 Model Year LDGV's
Regres Coefficients.

Segment RMS Reg.
Number |Error iConstant }C1 ic2 ic3 C4 ics ice C7 ice (J C10 fa C12 C13 C14 IC15 C16 ald C18 C19
{eit ILOGV__|90-95. 4.103] - 2.35754]. 3.4957]. .
| EXE ILDGV__[90-95 | 2.32229] 0.91944} 4.1523] 2.0251)
{813 ILOGV_ [90-95 | 1.76794] 0.5229] - 41,0723] 1.2322} 2.7642]_
[B14 [LOGV_ [90-95 | 1.40264] 0.44364]. 0.9979] 1.1201] 2.0763] 1.6438} a
{B15 ILOGV__ [90-95 | 0.97281] 0.25022]. 41,0003] 1.0639] 1.4071] 1.0542] 1.8686].
5 Cy ILOGV_}90-95 | 0.88873] 0.20771]. 4.0023] 1.0561] 1.3449] 1.0920] 1.2856] *2.5533] a
fez ILDGV_ [80-95 | 0.79003] 0.10796]. 0.8981) 1,009] 1.2809] 1.0795] ¥.1947[" 1.6393} 3.0349]. a
B18 ILDGV_ [90-95 | 0.54771} 0.04247]. 0.9734] 0.9933] 1.0674] 1.0348] 1.0249] 1.4049] 2.0212] 1.3189].
aa ILOGV__ [80-95 | 0.19036) 0.9671| 0.8711] 0.9708] 0.9442{ 0.0644] 0.8616] 1.3588] 0.9845] 1.0760)



ve-O

|M147 Composite Regression Coefficients, NOx, 1996 and | Newer Model Year LDGT2's
. i ficients

Regression
Segment IRMs — |Reg. . |
Number Error Constant {C1 ic2 ic3. C4 ics ice ic7 ica ico |c10 C14 ICi2 ak} ic14 cis C16 C17 C18 ic19
kr oGT2 [oor D576] 0.71370] 15.4054] r
Pz, [LDGT2 [96+ 0.33821 0.37061] -21.218] - 3.256]. a
JP3. LOGT2 [96+ 0.33307] 0.33021] -17.4693] 2.5344] 3.8906]. a
Pa ILOGT2 [96+ 0.30416] 0.30038] -14.5682] 2.2242] 11.6872] -12.2270]. a
Ps ILDGT2 [96+ 0.28072] 0.28842] -17.6088] 1.8185} 9.3908] -10.8536] 4.734).
[Pe LOGT2 [96+ 0.2883] 0.30337 -11.6965] 1.745} 9.8903] -10.7171] 5.8457] -7.1022} i.
{Pz ILOGT2 [96+ 0.24718] 0.25136] -20.0849] 1.7221] 5.3396] -2.4287] 0.5675) -9.9875] 8.4455} ‘
Po, LOGT2 [96+ 0.23492] 0.2519] -22.4763] 1.5623] 5.2794] -3.3995] -2.4191] -8.9711] 6.8514] 7.9536} a
i DGT2 [96+ | 0.23608] 0.24316] -22.27| 1.5055] 5.3167] -3.4761] -3.0309| -9.6937] 66514] 6.1317| 3.1602]. 2
P10 ILDGT2 [96+ 0.2359] 0.23077} _-20.9734] 1.5584] 5.6716] -3.4051] -4.2894] -6.7832] 6.6235] 6.3126] 1.8606] 3.9221] . >
| (oR ILOGT2 [96+ 0.18371] 0.16444} -10.5542] 0.5675] 3.1636] -1.1457] -3.3023] 0.5825] 3.4287] 3.1947] 4.4137] 2.9738} 1.3085]. .
pPiz ILDGT2 [96+ 0.18578] 0.16354} -9.0999] 0.6160] 2.8048] -0.7267] -3.2312] 0.1935] 3.5982] 3.0439] 4.2462] 2.9303] 1.2191] 0.2662)
;Pi3. [LDGT2 [96+ 0.12127] 0.05817] -4.2485{ 0.6116] 4.4173] -3.0746] -1.9566] -5.1095] 2.1593] 3.236] 5.8951] 2.3221] 1.0537] -0.1452{ 1.6328] a
| a ‘[COGT2 [e6+ 0.06953] 0.0183, 4.3668] 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]
Ps, ]LOGT2 [96+ 0.05854] 0.01128] 2.8175} 0.7637] 1.5804] 1.7608] 0.6221] -2.7325] 2.4484] 0.2815] 0.0086] 2.3246] 0.5592] 1.0307] 0.7388] 1.1608] 0.7986] .
[Pic JUOGT2 [96+ 0.05584] 0.00873] 1.9619] 0.7799] 1.6432} 1.5678] 0.4135] 0.2982] 1.9348] 0.6182] 0.9336] 0.7441] 1.0862] 0.7146] 4.3475]. ~
Piz _ {COGT2 eer 0.05251] 0.00847] 0.351] 0.7816] 1.4739) 1.5467] 0.3431] 0.4812] 4.4451] 0.723] 0.6611] 0.7370] 1.0331] 0.6536] 1.3684) 2.1742].
[P18 ]LOGT2 [o6+ 0.01918] 0.00084] 1.8364] 0.7759] 1.0390] 1.1145] 0.2474] 0.6723] 1.4583{ 0.5956 0.9049] 0.7647] 0.8462] 0.6884] 1.3503) 0.5973] 0.8893]
[P19 [LOGT2 [96+ 0.00325] 0.00226," 0.7097) 0.7313, 0.8044] 0.6612) 0.6632] 0.5647] 0.8016] 0.72] 0.6732] 0.7116] 0.7105] 0.7272} 0.7857] 0.7169] 0.7106] 0.6863}
IM147 Phase 2 Regression Coefficients, NOx, 1996 and Newer Model Year LDGT2's

Regression Coefficients”
iSegment IRMS —|Reg.
[Number [Error [Constant {C1 > jc2 ica C4 cS ice ic? ica ico C10 cr [C12 C13 IC14 cis iC16 C17 C18 cio
Ferree aero POON :
| XE |LOGT2 [96+ 0.25427] 0.26879]. 1.8738] 1.3389].
| CEE [LOGT2 [96+ 0.18429] 0.10134]. 1.6678] 0.8038] 2.0982]. a
| EXC JLOGT2 [96+ 0.44213] 0.05754] 1.2535] 1.3129} 14,0561] 1.9394]. a
[B15 ]LOGT2 [96+ 0.08972] 0.02934) 1.1523] 0.0849} 1.0364] 1.4467] __ 1.3152] ~ a
B16 [LOGT2 {96+ 0.08172] 0.02161}. 1.1412] 0.9687} 1.0064] 1.2969] 1.0936] 2.5063] _ a
B17 [LOGT2 [96+ 0.07624] 0.01744]. 1.1996] 0.6743] 0.9948] 4.205} 1.0153] 2.0425] 3.1694]. a
B18. LDGT2 [96+ 0.03345] 0.0095) 0.929] 1.0616] 1.0355] 1.1034] 0.9077 1,821] 0.1023] 1.2122]
[Bis LOGT2 [96+ 0.00415] 0.0038]. 0.9821] 0.9175] 0.9608] 0.9467[ 0.9808] 1.0103] 1.1121[ 0.9437] 0.9983}



6£-0

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

Regression Coefficients
ISegment RMS — Reg.
Number Error [Constant {C1 ic2 ic3 C4 ics ice \c7 ica ice iC10 (all jC12 ic13 [C14 ic1s C16 C17 ici ic19
| ca OGY foo 0.29865] O.a70ti] 11,7736). a"
fez LOGV_ [96+ 0.22677] 0.24297] -3.0327| 2.4026]. 5
ps ILOGV_ [96+ 0.21591 0.222 -0.4545] 1.4421! 5.448]
[Pa ILDGV__ [96+ 0.21456] 0.2173] _ 0.7302] 1.4606] 4.3233] 3.1071) a
[Fs [toGv— |96+ | 0.20702] 0.21364] 1,060] 1.2242] 3.8404] 0.6109] 43710]. r
yPs. LOGY a6+ 0.20746] 0.2141] 0.6213] 1.2154] 3.841] 0.6842] 4.2145] 0.9383] a
\P7 toGV— [98+ | 0.20382] 0.20285] 2.5242] 1.0207] 3.6971] -1.4266] 3.1047] 0.3945] 2683]. i
[Ps JLOGV__ [96+ 0.19807] 0.19482] 4.1563] 0.9074] 3.4911] -2.0343] 2.3803] 0.0536] 2.1013] _ 3.3361
Po LOGV_ [96+ 0.19619] 0.19096] 1.8739] 1.0364] 2.2721] _ -1.119] 2.141] 0.5391] 2.1297] 2:5845) 3.318]
|Pi0. LOGV_ [98+ 0.1835] 0.18673} 1.493] 0.998] 2.3525] -1.4944] 2.0734] 0.242] 1.6938] 2.8999] 1.2192] 1.9845].
Pat LOGV__ [96+ 0.15206] 0.11563] 6.6686] 0.3811, 2.506] -1.1863] 0.7057] 0.2193] 2.4209} 0.8267] 1.5013] 0.5498] _ 1.1615]
[Piz___[toGv— [eee 0.14242] 0.00025, ”_7.3264[ 0.4696] 2.0937] -0.2105] 0.3797] 0.6436 2.1032] 0.8737] 0.8521] 0.6422] 0.984] 1.6062).
| ILDGV_ [96+ 0.147] 0.0621] 4.8271] 0.6696] 2.0895] -2.2591] 0.9164] -0.565] 1.4492] 0.1321] 0.1274] 0.7773] 0.8735} 0.8257] 2.0634]. a
P14 |LOGV_ |a6+ | 0.0761] 0.04141] 3.4134] 0.5916] _1.3165| 0.2005] 1.3675] 0.2080] 1.4185] 0.0989] 0.2565] 0.3003] 0.7549] _o.ee41[ 1.280] 1.631]. a
[P15 LOGV_ [96+ 0.05148] 0.02187] 2.4596] 0.6558) 1.17] 0.6931] "0.7565] 0.2435] 0.7215] 0.5341] 0.5256] 0.6843 0.7209] 0.6775] 1.1075] 0.9069} 1.122]. .
[P16 ILDGV__ [96+ 0.04778] 0.01987] 3.1395] 0.6091) 1.203] 0.5425] 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
Pa? ILOGV__|96+ 0.04525] 0.01326] 3.3943[ 0.6187] 0.8612] 0.7678] 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].
Pia |toGV— |o8+ [0.02353] 0.00022] 1.5178] 0.6970] _0.7651| 0.8288) 0.6991] 0.3426] 0.7096] 0.6796] 0.6084] 0.5847] 0.7147] 0.6429 0.6623[ 0.895] 0.6746] 0.9705] 0.7a13] 1.0086].
[Ps LOGV__|96+ 0.004) 0.00038} 0.7846] 0.7169] 0.7176] O7276] 0.6851, 0.7511] 0.7279] 0.6879] 0.768i| 0.6817] 07174) 0.7317] 0.7201[ 0.7203} O.7125| 0.7336[ 0.7584, 0.7113] 07181

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

Regression Coefficients

Segment Rms — |Reg.
Number fEror {constant |c1__fcz_fcs_|ea._|cs _|cs_|cr__|cs__|co _|cw for _|crz__|crs_|or crs few _|cr7_ crs _|cw
[Bit [W0GV_ Joes Jo, 200551 0, 16572 : : r : 170
| Ee ILDGV_ [96+ 0.19448] 0.14434]. a a . 1.3687] 2.7382]. .
Je13_—_|toGv— Jose [-_0.155] 0.09500]. : : : : i : : : 1.1262] 1.7948] 2.6037]. :
{eta ILDGV_ [96+ 0.10659] 0.06065]. a a a . a . . 0.9685] 0.9898] 1.6977] 2.2378]. .

LOGV_ [96+ 0.06912] 0.03298} - a a q a . a 0.9526] 0.8932] 1.4872 1.252] 1.5305]. .
| ed LOGY [96+ 0.0649] 0.03049] . a . . . a . . 0.9772] 0.9069] 1.4267] 1.2614] 4.1325] 1.7334]
[B17 |L0Gv__|96+—| 0.06203] 0.02204]. : i : : i : a 0.s604| _0.9375|_1.3118] 1.3984] 1.0703] 1.6717] 1.273). i
(B18 ILOGV__ [96+ 0.03172] 0.00214) . . . a a a a 0.9508] 1.1071] 0.9089] 1.1952] 0.9328] 1.2014] 1.0022] 1.4675]

jis [Loc |96r|0.00523[ 0.00117]. 5 5 r 0.3672] 0.0874] 0.9855] 0.9701] 0.0075} 0.838] 1.0369] 0.6660] 0.075



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.


Regression Summary - Composite CO, IM240 to IM147
Model Years 1990-1995

All Vehicle Types
SUMMARY OUTPUT
Regression Statistics
MultipleR 0.916596
R Square 0.840149
Adjusted R 0.839803
Square
Standard 3.35827
Error
Observations 464
ANOVA
of ss MS F Significance F
Regression 1 27384.98 27384.98 2428.183 4.6E-186
Residual 462 5210.424 11.27797
Total 463 32595.41
Coefficients Standard t Stat P-value Lower'95% Upper 95%
Error
Intercept 0.392486 0.180702 2.172 0.030364 0.037385 0.747586
IM240 1.037836 0.021061 49.2766 4.6E-186 0.996448 1.079224
IM240 Line Fit Plot
@ IM147

IM147

150

—— Predicted IM147

F-8


81-0

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

tegression Coefficienis
Segment RMS = |Reg. y
Number [Error Constant |C1 ic2 ic3. jc4 ics ice \c7 ics ice C10 jc. [C12 jC13 C14 |c15 C16 C17 C18 C19
‘a DGr2 [e165 | 16.0705] 25.7173] 7 sete]. r
fez LDGT2 [81-85 [12.3795] 12.9451] 3.4376] 3.6374) a
[es LOGT2 [81-85 | 11.5598] 10.6148] 1.0212] 2.3546] 5.6508” a 5
Pa. LOGT2 [81-85 | 11.1259] 9.72659/ 0.5375] 2.5842) 25322] 5.5716]. .
[Ps [LOGT2 [81-85 [11.1623] 9.79162[" 0.516] 2.5693] 2.415} 5.44] 0.2344]. a =
i (od ILOGT2 [81-85 | 10.5815] 10.5149] -5.1729] 2.5377] 2.4642] 3.5854] -2.439] 9.5373} a At
[Pz LOGT2 [61-85 | 10.5066] 9.63447{ 5.1208] 2.3487] 2.1758] 3.1663] -2.7919] 8.8001] 1.9586]. a 2b
1 LOGT2 [81-85 | 10.4986] 9.46232] -4.5397] 2.2016] 2.2264] 2.9027] -2.935] 8.1749] 4.56] 1.182]. “| ‘
Ps iLDGT2 [81-85 10.534] 9.45853] 4.6546] 2.2745] 2.3176] 2.9007| -2.96] 8.3683] 1.5537] 1.2902] -0.3956]
;P10 LOGT2 [81-85 | 10.5104] 9.14764] -4.0569] 2.3269] 1.7791] 3.0871] -3.3367] 7.9403] 1.0377] 1.7694] -1.8655| 2.3687]
JPat ILDGT2 [81-85 | 6.70683] 1.00692] 0.9625] 0.8295] 2.0212] 0.599] -1.3472] 6.2535] 1.0658] 1.6592] -0.2765|" 1.4949] 1.0962]. a
P12 [WOGT2 [61-85 | 5.8444i| 1.64077| 0.9579} 0.6376] 20719| 1.290i| -1.7228| 6.1373] 1.3423| 1.3993] -0.7286| 1.7599] 0.6956] 1.5409).
[P13 ILOGT2 {81-85 | 3.26892] 0.61651] 1.3225] 0.6113] 0.8575] 1.1276] 0.3105] 2.4597] 0.7224] 0.0984] 0.8037[ 1.0488] 0.8185] 0.7462] 26417]. .
[P14 JLOGT2 [61-85 | 2.22626] 0.53462[ 1.2795] 0.6654| 0.9477|_ 7.2845|" 05586] 7.7615] 0.7222] 0.4197] 1.0753] 0.8413) 0.6372| 0.836] 1.8815] 1.2908]. a
[P1S: JLOGT2 [81-85 | 1.43068] 0.16253[ 1.4763] 0.6916] 0.8805] 1.0498] 0.7614) 4.593] 0.6161] 0.2931] 0.9481] 0.6408] 0.759] 0.6640] 1.0119] 0.9471] 1.4604]. .
[P16 [toGT2 {61-65 | 1.39761] 0.09202] 1.4913 0.6618] 0.9628] 0.8632) 0.8115] 1.4561] 0.5963] 0.4052] 0.7374] 0.7242] 0.7696] 0.6414] 0.9815] 0.0546] 1.1597| 1.2077]. a
fea, LOGT2 [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] 1.1886] 1.5709].
pPis. [LOGT2 [81-85 | 0.98169] -0.23459] 4.3573] 0.7081] 0.8658] 0.8401] 0.6551] 1.4524] 0.5751] 0.6074] 0.7206] 0.4241] 0.7757] 0.6063| 0.7425] 0.6257] 0.8673] 1.0036] 1.3323] 0.6645).
[PIs [COGT2 [81-85 | 0.s0sss] 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] 0.7046] 0.6711] 0.7256] 0.4192] 1.146 0.8223] 0.747'
1M147 Phase 2 Regression Coefficients, CO, 1981 to 1985 Model Year LDGT2's
| Regression Coefficients, :
Segment IRMS —|Reg. t
|Number |Error Constant [C1 ic2 ica C4 ics: icé \c7 ics ico Ic10 Cit C12 C13 C14 C15 C16 (C17 C18 C19
fan _T eras | 13.6062] 10.3110). T5ae7]. C
B12 LOGT2 [81-85 | 9.5454] 6.3219]. 0.9685] 2.6838] .
EXE ILOGT2 [81-85 | 4.74433] 1.25606] 1.0757] 0.9968] 4.0692] _ .
B14 LOGT2 [81-85 | 3.50162] 1.57213} 0.8395] 1.1681] 2.9369] 1.7248]
815 LOGT2 [81-85 | 2.41832] 0.59238]. 1.0109} 0.918] 1.4255} 1.2686] 2.1564]. a
B16 ILOGT2 [81-85 [2.31489] 0.51543]. 1.0284] 0.8761] 1.3639] 1.2777] 1.5125] 2.5385] a
| Cd ILOGT2 [81-85 | 2.02742] -0.13816] 1.0611] 0.7865] 1.3133] 1.3648] 1.0331] 2.4287] 2.7411 a
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]. it] 0.8755] 0.9246] 0.9223] 0.7526] 1.3895] 2.0596] 1.205] 1.0768}



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 S12
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.

-63-


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 these criteria, 99 trucks and 124 cars are fast-failed after segment 7 of the second
IM147. Different fast-fail criteria were used during the third IM147, since 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 the error term being added 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= Cnt 2% {San x Xnm} - (Me x En) [5-3]

Where: 240, = Predicted emissions after completing n segments
Cc, = Regression intercept for equation n
Sam = Regression coefficient for segment m in equation n 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

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 were 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 time. 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.

-49-


[a ee)

1M147 Composite Regression Coefficients, HC, 1996 and Newer Model Year LDGV's

Regression Cooficents
[Segment RMS — {Reg.
Number feror__|Constant_|c1 c2__|cs_fca_- jes {ce _—_|cr_{ca__|ce_ _|cro {crs _|cr2__jcrs_ fers fers [crs fci7_—fcis_cro
[Pr CDGV_[96+ | 0.10036] 0.04026] 20.2462]. 5
Pz coGV__[96+ [0.07371] 0.01788] 5.9162] 4.0658]. i
Ps toGV— [86+ _|_0.06ee4| 0.01874] 2.0638] 1.9564] S560].
j GZ LOGV_fo8+ 0.06132] 0.01973] 2.7332] 1.6433 3.0543] 4.4161] -
[Ps toGv— ser _{_0.06046| —o.0z1e2[ 2.8327] 1.2424] 2.314] 3.7830] 1.604).
| Gd LOGV 96+ [0.05435] 0.02387" 1.2376] 1.2522[0.457[3.4031[ -1.2143| 7.4758]
ev, eOGv_Jos+ {0.05447 0.0241{ 1.0091] 1.2768] 0.4420] 3.4792] -4.2783] 7.8471] 0.2285] |
[Ps LOGV— [96+ | 0.0535] 0.02337] 0.0941] 0.7720] 0.7794] 3.2658] 2.1201 r
{Pa LoGV— [96+ | 0.05270] 0.02482] 2.6220] 0.6273] 0.6458] 3.3105] 1.1123] 4.6464] 0. 1966] 0.768] 4.1005]. i
[Pio |tpGv—|s6+ [0.05143] 0.02476] 1.0359] 0.4556] 1.1796] 3.0156] -1.3707| _3.8216| 0.108] 0.823] -0.5809] 6.6862 a
|Pi1_—_[tocv— [eee [0.03867] 0.00936] 5.4115] -0.1934] 1.9861] 0.9553] -0.8572{ 0.8886] 0.8704] 0.1062] 0.020 6.6498] _1,6503). i
[P12 [tpGv—Jos—[-o.o214| 0.009] 3.1804] 0.1934] 1.3914] 1.1882] 0.032] 0.1178] 0.7255] _1.0187| 0.0701] 4.5106] 0.6495] 2.1154). i
[P13 [LGV |a6+__| 0.01901] 0.00675] 3.9120] 0.3208] 1.0647] 0.8952] 0.4206] 0.6088] 0.9736] 0.1471] 0.7025] 3.5491] 0.6163] 1.6571] 1.3163] a
[P14 |LOGV_|a6+_| 0.01746] 0.00454] 4.3463] 0.3067] 1.0782] 1.0439] 0.3355] 1.3016] 1.0866] 0.2129] 0.7694] 2.9703] 0.5207] 1.3494 1.2261] 1.5726]. 5
IPs |LOGV— [ser | 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] 1.5226. i
P16 |LOGV__ fer | 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] 0.1067] 2.1338] 2.0163]. i
|P17____|LDGvV—|o6+—|0.01236| 0.00351] 2.9817] 0.5075] o.60ti| 1.1216] 0.3316] 1.6187] 0.8518] 0.5096] 0.0325] 1.425] 0.5667] 1.2205] 0.6224] _1.0087| 1.5045] _-2.3070] 2.031]. i
Pie |LoGV__{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] 0.52021 1.4602] 1.0165] _ 1.033] 1.7225].
P19 JtbGv__|9e+_[-0.008<3[_0.00092[ 1.469] 0.6076] 0.4864] 0.7865] o.6192| 1.1938] 0.534] 0.2013] 0.6084] 1.9759] 0.6416] 0.624] 0.4897] 0.7037 2a] 0.0797] 0.3611] 1.5812] 0.6053]
M147 Phase 2 Regression Coefficients, HC, 1996 and Newer Model Year LDGV's
Regression Coefficients
Segment ams — |Reg.
Number JEror__{Constant [C1 c2__jes_—{cs_—_—fes_—fce_—_—ifcr_|ca__fca_|cwo fem |orz fers_fora eis fers err fore fcro
fatt fser | 0.06231] 0.01367]. 72a,
B12 |LoGv— [96+ | 0.03471] 0.01289 1.0956] 3.2165].
B13 |LOGV__ [6+ | 0.02909] 0.00972]. 0.7958] 2.4462] 2.013}.
B14 LDGV_fo6+ "| "0.02639] 0.00805]. 0.7353] 2.2615[_1,8431| 1.2834].
Bis |LOGV— [6+ [0.02692] 0.00988 0.8024] 2.2022] 1.7552] 1.4225] 1.6689]. i
[316 |LDGV 98+ | 0.02668] 0.010421. 0.7459] 2.48941 1.4033] 0.6725] 2.5744] 35714].
{B17 |toGv—|ee+ |" 0.0192] 0.004421. 0.9885[ 1.714] 0.9711] 0.0804] 1.8655] 2.5604] 3.663]. a
[818 [CoGv~ Joss [0.01449] 0.00185] 0.7232{_ 1.6635] 1.0171] 0.5616] 1.7084] 1.9280] 2.0001] 2.3916).
[s—_[tocv—Je6+__ [0.01043] 0.00074) 0.92] 1.1468] 0.7067] 0.6882] 1.2025] -0.0572] 0.6503] 2.302] 1.1734



+ 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 2,518- 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 2,518- and 543-vehicle samples was administration of
the IM240 test at the conclusion of the IM147 test. For the 2,518-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 2,512 vehicles (1,360 cars and 1,152
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. Asa 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

-23-


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 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,
midpoint, Max CO and final cutpoints.” The analytical approach used in the 1997

“The original workplan called for this latest analysis to use the 16,581-vehicle data set from Sierra’s 1997
PKE study for EPA (SR98-02-01); however, as explained later in this report, a “time realignment” (of
emissions versus vehicle speed) was incorporated into the analysis, which made use of the previous data
problematic. It was determined that the effort required to adjust the data could not be justified in terms of a
significant increase in the accuracy of the results; thus, it was decided not to use these previous data.

-19-


LI-O

996 and Newer Model Year LDGT1's

|M147 Composite Regression Coefficients, CO, 1
if

Regression Coefficients.
ISegment RMS: Reg. .
[Number Error Constant ica jca cS. icé C7 ice ico C10 jc11 ic12 C13 C14 C15 ic16 C17 C18 C19
i ‘1_ {96+ 4.82409] 1.39419]

[P2. ILOGT? [96+ 1.63165] 0.95561) . a

Po LDGTt [96+ 4.57733] 0.85422} 3.8856].

Pa DGT1 [96+ 1.57696] 0.84862} 1.4232] 4.1058}

PS ILDGT1 [96+ 1.55655] 0.77387 1.4375] 1.5669] 3.3072]

PE LDGT1_ [96+ 1.48358] 0.83226] 0.2011] -2.3153] 2.1554] 10.6111] a

iP? ILOGT1 [96+ 1.47504] 0.7148] 0.9914] 0.8568] 0.4965] 7.7306] 2.5842] a ~

PB [LDGT1 [96+ 1.48311] 0.71859} 0.9277] 0.9266} 0.871) 8.0227] 2621] 0.2793].

@ [LOGT1 [96+ 1.49155] 0.72084} -1,0008] 0.6946] 0.7565] 7.9469] 2.63] 0.1935] 0.3333]. .
[Pio JLOGT1 [96+ 1.48528] 0.70223} 0.9574] 0.075] 1.1515] 8.3687] 2.439] 0.1927] -2.1271] 1.4542]
Pit (LOGT1 [96+ 0.71796] 0.22242] 0.5359] -1.1903] 0.4023] 2.2155} _ 2.7505] 0.2649] 0.6008] 1.0433] 0.8928]. a
[Piz [LOGT1 [96+ 0.54429] 0.13226] 0.7868) . 2.482] 1.6269] -0.3682] 2.2202] 0.4979] 0.8781] 1.6265] a
[PIs ILDGT1_ [96+ 0.43766] 0.08336] 1.0816] -1.4102] 0.8701] 2.4778] 1.5414] 0.0111] 0.8915] 0.5019] 0.6812] 0.3603] 3.2131].

[P14 ILDGT1 [96+ 0.27254) 0.0707} 0.7684] 0.6826] 0.5074] 1.8189] 1.5858] 0.2076] 0.9221] 0.5798] 0.729] 0.6284] 2.4591] 0.7018]. a

LDGT1 [96+ 0.19651] 0.03007] 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}. .

[Pig ILDGT1 [96+ 0.17615] 0.02979] 0.7323| 0.205] 0.5366] 2.4872] 0.79947 0.4735] 0.5618] 0.7865] 0.7275] 0.7712] 1.4017] 0.6818] 0.9833] 2.2195]. .

Pa7. ILOGT1 [96+ 0.13011] 0.05988] 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] 1.6762]. a
P18 ILOGT1 [96+ 0.68591] 0.02837] 0.7295] 0.2618] 0.8191] 1.2599] 0.5828) 0.7744] 0.6755] 0.6858] 0.712] 0.7269] 0.7236] 0.711%] 0.0386] 0.4881] 1.7121] 1.0654].
P19 JLOGT? jo6+ 0.03308} 0.00956) 0.7481) 061] 0.6377] 0.8223} 0.7051] 0.6935) 0.8481] 0.6648] 0.7101] 0.7232] 0.6944, 0.7189} 0.6125] 0.226] 1.1367] 0.7471] 0.7019}

IM147 Phase 2 Regression Coefficients, CO, 1996 and Newer Model Year LDGT1's
= Regression Coefficienis
[Segment IRMS —|Reg.
[Number Error ‘Constant }C1 C2 ics C4 ics ice icy ica ico C10 cit ic12 ic13 C14 \c1s IC16 Caz \c18 C19
11 TOGTt |s6+ | 1.2004] 0.00623] OG &

B12 LOGT1_|s6+ 0.74363] 0.24386]. 1.1933] 2.1843}. a

(B13 ILOGT1 [96+ 0.60473] 0.1527]. 1.2056] 0.5562] 4.3384]

B14 LOGT1 [96+ 0.40405] 0.15643}. 0.9921] 0.9282] 3.3758] 0.9673]. .

[B15 LOGT1_[96+ 0.30462] 0.06538]. 0.9969] 1.0801] 1.9431] 0.914] 2.0105]. a

[B16 ILOGT1 [96+ 0.26097] 0.03614]. 0.9954) 1.08] 1.8842] 0.9365] 1.1866] 3.7438)"

| ke ILOGT1 [96+ 0.2048] 0.0653]. 0.9914] 1.0331] 1.5352] 0.9586] 1.2475] 1.026] 2.448]. a

| XE ILOGT1 [96+ 0.12681] 0.03391]. 0.8631] 0.9575] 0.9469] 0.9698] 1.2044] 0.8083] 2.4102] 1.4239].
| CHEN [LDGT1 [96+ 0.04352[ 0.01126]. 0,9604[ 0.9595] 0.9395] 0.9708] 1.1345] 0.2607] 1.4873] 1.0202] 0.9453]



Table 4-4.

Failure Rates, Third IM147

Vehicle HC co NOx OVERALL
Type Fait | Pass |% Fail] Fait L Pass | % Fail} Fail | Pass |%Fail} Fail | Pass | % Fail
Max CO Cutpoints
LDGV 114 | 1666 | 6.4% | 250 | 1530 |14.0%| 155 | 1625 | 8.7%
LDGT1 49 | 915 | 5.1% | 156 | 808 |16.2%] 70 | 894 | 7.3%
LDGT2 25 | 578 [4.1% | 63 | 540 [10.4%] 30 | 573 | 5.0%
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% | 67 536 411.1%
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%]| 507 | 1273 |28.5%
LDGT1 120 | 844 |12.4%| 85 879 | 8.8% | 127 | 837 |13.2%] 239 | 725 |24.8%
LDGT2 73 530 547 |9.3% | 82 521 |13.6%| 149 | 454 {24.7%
All 473_| 2874 2966 |11.4%| 486 | 2861 14.5%] 895 _| 2452 |26.7%

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 3,347.(4.8%) are tested beyond the second IM147.

31-


40-0

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

._Regression Coefficients:
[Segment RMS Reg.
Error Constant {C1 ic2 ica C4 ics: C6 \c7 ics ico c10 il [C12 C13. C14 C15 fa \C17 C18 ic19
[Poa farses real acese oars
(Pz, LOGT1 [81-85 | 0.82038] 0.84089] 0.5552] 5.7627]. i
[Ps [LOGT1 [81-85 | 0.76521] 0.73117] 5829} 4.5821] 7.1986] a
[Pa (LDGTT [61-85 | 0.75286] 0.70493] 1236] 4.4969] 4.9474] 8.0174] .
[Ps ILOGT1_ [81-85 | 0.74049] 0.73769] -3.3308] 4.0049] 3.8497] 6.663] 3.702, a
{Pe LOGT1 [61-85 | 0.73674] 0.71486] -3.0127| 4.0869] 3.6046] 3.6966] 3.0011] 4.8137] "
[P7 LOGT1 [81-85 | 0.71739] 0.69385] -3.0212] | 3.5234] 3.5081] 3.9937] 0.1792] 6.3496] 3.9431].
LOGT1 [81-85 | 0.70652] 0.67353] -2.6651] 3.5435] 2.1822] 2.6706] 0.0115] 3.2004] 2.6238] 3.9683]
[Po 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]- a
10. [LOGT1 [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]. a
Pad ILOGT1 [81-85 | 0.61346] 0.34120) 0.06] 2.6963] 1.5635] 2.4383] 1.5201 5.655] 0.7401] 2.8173] -2.4441] 1.0401] 1.3101] 5
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]. a
ILOGT1 [81-85 | 0.27799] 0.12098] 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]. a
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] 1.75827 a
LOGT1 [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] 0.6141] 1.2315) 1.3505} a
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]. a
LOGT1 61-85 | 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] 0.77621 0.8511] 1.1597] 1.0383] 1.0222] 1.0523]. i.
LDGT1_ {81-85 | 0.05721] 0.00436] 0.6442] 0.68171 1.0499] 1.0725] 0.6056) 1.15] 0.5722] 0.7474] 0.8465] 0.5131] 0.6842] 0.8341] 0.6429] 0.922] 0.8319] 1.1396] 0.7657] 0.8484]
[LOGT1 {81-85 0.0165] 0.00076] 0.7099] 0.6986{ 0.7653] 0.7747] 0.7168} 0.800% 0.7285} 0.7007] 0.7342] 0.6359] _ 0.7093) 0.736] 0.7337] 0.6964] 0.7033] 0.7238] 0.7471] 0.7212] 0.7201
IM147 Phase 2 Regression Coefficients, NOx, 1981 to 1985 Model Year LDGT1's
- . Regression Coefficients
Segment RMS Reg. 7
|Number |Error Constant {C1 ic2 ica C4 ics ice ic? ica ico C10 ica C12 jc13 jc14 cas C16 C17 cis ici9
i [LoGT1 Jore5 | tore] 0.65050) EXEED
| Eke ILOGT1 [81-85 | 1.00048] 0.67351]. 2.4254) 3.4463} a
| CEES ILOGT1 {81-85 | 0.38989] 0.20314]. 0.8889] 1.8075] 3.7573] .
| Ce LDGT1 [61-85 | 0.27037] 0.089%]. 0.8213] 1.2937] 2.454] 2.3704]. a
[eis (LOGT1 [81-85 | 0.15297] 0.04337] 0.8745} 3.292] 1.0958] 1.7178] 1.8359) a
[B16 JLOGT+ [61-85 | 0.13123] 0.01886]. 0.9513] 1.0861] 1.1153] 1.5763] 1.4991] 1.5837] a
{ei7 (LOGT1 [81-85 | 0.12525] 0.01412]. 0.9423} 1.0698] 1.428] 1.6173] 1.4024] 1.4526} 1.3599]. .
| Ce iLOGT? [81-85 | 0.08127) 0.0094]. 0.8966] 1.2134] 0.8469] 1.3163] 1.0875] 1.7522] 1.1639] 1.0764].
{B19 COGTT ]61-85 | 0.01759] 0.00132] 0.0552] 1.0069] 0.9919] 0.9568] 0.9493] 1.0003] 1.0401] 0.9479] 7



IM147 CUMULATIVE POSITIVE POWER (CPP) VARIATION CUTPOINTS

1M147 REFERENCE DATA

TIME SPEED CPP
{sec) {mph} (mph2/sec)
138 27.0 4617.34

139 23.5 4617.34

140 20.0 4617.34

141 16.5 4617.34

142 13.0 4617.34

143 95 4617.34

144 6.0 4617.34

145 25 4617.34

146 0.0 4617.34

Cycle Sums 4617.34

POWER VARIATION CUTPOINTS (mph2/sec)
CPP LIMITS

"BASE" MULT. VARYING

DELTA FACTOR DELTA
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

1.000

E-4

Low
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

HIGH

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


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
1IM147 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 J/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.®

-55-


Ze)

{M147 Composite Regression Coefficients, CO, 1986 to 1989 Model Year LDGV's
Regression Coeffic is
Segment RMS —-[Reg.
Number Error [Constant {C1 ic2 ic3 ica ics ice ic7 ice ica C10 Cat C12 jc13 cia Crs C16 \C17 cise C19
iP Tocv 186-89 | 4.67926} 5.71776) 10.6405] . t
[P2 JLOGV_ [86-89 | 3.51709[ 3.81788] 0.1364) 2.695). a
Ps [LOGV_[a6-89 [3.15842] 3.11069] 1.0124] 1.6169] 3.9459]. a i
Pa LOGV [86-89 | 3.14894] 3.04972] 0.8613[ 1.649) 3.2671] 1.3569] - a :
[PS OGV_ [86-89 | 3.10204." 3.03267] 0.7486] 1.4903} 2.3894] 0.8299] 1.8322]. a +
PS LOGV_ [86-89 | 3.09685] 3.08465} 0.6252] 1.4634] 2.3373] 0.5195] 1.5731] 0.9056]. a
iP? LOGV_[86-89 | 3.02573] 2.86107] 1.1098] 1.2652] 1.9531] 0.4921] 0.7359] 0.8227] 2.1087].
PS JLOGV__ [86-89 | 2.94297] 2.68329] 0.7601] 1.2178] 1.8801! 0.6138] 0.7593[ 0.8902] 1.5027] 2.0203]. a ‘
[Ps [LOGv_ [86-89 [2.87652] 2.54621] 0.0543] 1.2024] 1.523] 1.0828] 0.2360] -1.106] 4.5137] 1.3545] 3.0417].
[Pio [LoGv 96-89 [2.74734] 2.37808) -0.6515[ 1.4027] 1.4303] 1.1934] 0.1403] 0.5027] 1.2073] 0.8038] 0.333. 4.4623] a
]LOGV_ [86-89 | 1.80566] 1.04515] 0.0874] 0.8282] 1.4645] 1.1694[ 0.8248] 0.2636] 0.6251] 1.0042] 1.4625| 2.0759] 0.6630). .
-[LOGv_ [a6-80 [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
ILOGV__ [86-89 | 1.06067] 0.36461} 0.8207] 0.6529] 0.9226} 4.272] 0.8321] 0.1223] 0.6425] 0.8893] 0.7679[ 1.6076] 0.7379] 0.7345] 2.1093]. a
ILOGV_ [86-89 | 0.82231] 0.26307] 0.8376] 0.5953] 0.933] _ 1.0658} 0.8038] 0.4366] 0.872] 0.8768] 0.8378] 1.4217] 0.7134] 0.7172] 1.4716] 1.4516] a
LOGV_ [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].
pie 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] 7.1743] 1.3482]. a
P17 LOGV__ [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] 1.6464]. a
P18 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].
pis 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] O.7177| 0.7096] 0.7249] 0.7202] 0.6852] 0.9434] 0.7022] 0.6271]
1M147 Phase 2 Regression Coefficients, CO, 1986 to 1989 Model Year LDGV's
Regression Coefficients
[Segment IRMS — Reg. Q
Number Error [Constant }C1 ic2 ic3 C4 ic5 C6 C7 ic8 ica C10 Cit IC12 C13 C14 C15 Ic16 C17 C18 C19
11 (86-85 4.18172] 3.9913} 4.1684) 5
Bi2 ILOGV_}86-89 [2.21032] 1.63478]. 1.0475] 1.964].
[B13 LOGV_ [86-89 1.5326] 0.82575]. 1.0148] 1.0859] 3.0948 a
B44 LOGY ]86-89 } 1.21271) 0.6268] - 0.9705} 1.0467 2.17] 2.0218].
815 ILOGV__ [86-89 | 0.82448] 0.406011. 0.9653] 0.9766] 1.3403] 1.2022] 2.2818].
[ere ]LOGV_ [86-89 [0.77323] 0.3766]. 0.9636] 0.9886] 1.3495] 1.1956] 1.6328] 2.1301]. a
[B17 [LoGv_]86-89 | 0.67252] 0.23667]. 0.9624] 0.9661] 1.2888] 1.1945] 1.4722] 1.3414] 2.4799] _ a
B18 [LOGV__ [86-89 | 0.40383] 0.10653} 0.9566} 0.984] 1.0723] 0.9316] 1.1674] 1.5464] 1.9865] 1.1802].
B19 {LOGV_ [86-89 |" 0.20724] 0.05209} 0.9611! 0.965] 0.9597[ 0.9776) 0.9801] 0.9162} 1.3409] 0.9424] 1.1303}



Startup IM240 Cutpoints and IM147 Cutpoints”
(Composite/Phase 2 Cutpoints in g/mi, [M240 - IM147)

Model Years
LDGV
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 2.5-2.71.0
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

“Developed for SR99-10-02

60.0/48.0 - 61.0/29.0

4.0 - 4.3/1.7


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, Gordon Darby 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.

-25-


99

IM147 Composite Regression Coefficients, HC, 1986 to 1987 Model Year LDGT2's
Regression Coafficents
Segment RMS — Reg. "
[Number Error Constant {C1 ic2 ica C4 ics. icé C7 ic8 ico C10 C11 \C12 C13 1C14 cis C16 \C17 C18 C19
PT MOGT2 [6-67 | 076767] 0.70081] 14.62] :
PQ ILOGT2 {86-87 | 0.53753] 0.28237] 1.2627] 6.2363]. a w
[P3_ ILOGT2 {86-87 | 0.38485] 0.18393] 0.7945] 3.7354] 5.7608].
[Pa LOGT2 {86-87 | 0.34875] 0.16586] -2.6149] 3.7629] 2.8334] 4.1762]
[PS |LOGT2 {86-87 j 0.34617] 0.17953] -3.6873] 3.212] 2.761] 3.4285] 2.4018] 5
{Pe lLOGT2 [86-87 |" 0.20325] 0.20542] -3.5626[" 3.7025] 1.7413] 0.4872] 1.0679] 4.3236]. “
[Pr |LOGT2 [86-87 0.2934} 0.19684] -: 3.6876] 1.7296] 0.6143] 0.2317] 4.2624] 0.8683].
7Ps. [LDGT2 [86-87 | 0.28213] 0.17396] -1.6292[ 3.3926] 1.1562] 0.6702] 0.0208] 3.449] 0.3557] 2.8062] a
iPS |LDGT2 {86-87 | 0.27183} 0.1983] -1.1117] 3.3764] 1.0296} 0.699] 0.2722] 2.4556] 0.8353} 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] o.0120/ 1.3566] 1.7368].
Pv LOGT2 [86-87 | 0.23982] 0.0819] 0.4024] 2.3049] 0.6701] 0.0523] 1.2178] 2.4136} 0.3361] 0.0779] 0.9104] 1.690%] 1.4014].
Piz ILOGT2 [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] 1.0229[ 2.1489].
[P13 [LOGT2 [86-87 | 0.17953] 0.04669] 1.6471] 0.9949] 0.685] 1.0020] 0.4324] 1.1786] 0.7455] 0.9956] -0.3607] 1.9373] 1.0646} 0.024] 2.6969].
P14 LDGT2_ [86-67 | 0.16923] 0.01715] 2.1778] 0.6591] 1.0222] 0.7528] 0.0545] 1.7038) 0.8256] 0.5799] 0.1446] 1.6426] 1.1863] 0.1661] 1.154] 2.4636].
| (ES 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] 1.5026] 1.0456] 0.1761] 0.9342] 1.5234] 1.2274] .
P16 LOGT2 [86-87 | 0.15721] 0.03244] 2.1199] 0.4169] 0.672] 0.9335] 0.3563] 1.582} 1.3979] 0.0804] 0.2026] 1.6162] 1.1907] 0.4448] 0.6092) 0.8887] 0.3523[ 3.6537]. a
IGT2 [86-87 | 0.08118} 0.01059] 0.6472) 0.7516] 0.3931] 0.9394] 1.5188] 0.882) 0.3132] 0.5162] 0.8422] 0.3101] 0.8207] 0.6645] 0.7572] 1.2216] 1.2293} 3.38]. a
[P18 LOGT2 [86-87 | 0.06878] -0.02245| 0.747] 0.8866] 0.3561] 0.7928] 1.5497] 0.9638] 0.0777] 0.7405] 0.8591] 0.4782] 0.7233] 0.7721] 0.6208[ 0.8129] 0.1007] 2.9199[ 1.2143"
P19 LOGT2 [66-87 | 0.05744] 0.02534] 0.6277[ 0.9324) 0.4772[ 0.6834| 1.4838] 0.7072] 0.6021] 05: 0.8642) 0.1132[ 0.7642) 0.7717] 0.5785] 0.3706, 0.4851] 0.0561[ 2.1533] 0.9364] 1.4322)
1M147-Phase 2 Regression Coefficients, HC, 1986 to 1987 Model Year LDGT2's
T "Regression Coefficients
Segment IRMS —|Reg. 4
Number Error [Constant [C1 ic2 ic3 \C4 ics ice ic7 cB ice IC10 C11 C12 C13. ic14 }C15 C16 C17 ad C18
B11 mDGT2 6-87 | 0.42595] 0.04024] EXPO "
[B12 ILOGT2 [86-87 | 0.34262] 0.01535]. 2.3444] 4.5554] a
| EXE ILOGT2 [86-87 |" 0.26401] 0.08727]. 1.8518] 0.0098] 4.3001].
| CX LOGT2 {66-87 | 0.26019] 0.04305). 1.8614] 0.0618] 3.3888) 4.611}. a
| CHE [WOGT2 [66-87 [0.24666] 0.06334). 1.6413] 0.0316] 2.594} 0.5597] 1.9278] .
[B16 |LOGT2 [86-87 | 0.24422} 0.06216] 1,731] 0.2275] 2.4543)" -0.1869] 1.1753] 3.2006]. a
{817 [LOGT2 [86-87 [0.11773] 0.01838]. 1.1424) 0.827] 1.1411] 1.9999) 0.6793} 1,223] 4.3846].
B18 ILOGT2 [86-87 | 0.10087} 0.03741]. 1.0229] 1.0633] 1.2587] 1.1759] 0.8269} 0.1785] 3.8251] 1.6179].
Ne LOGT2 [86-87 | 0.09638] -0.03963- 4.0707[ 1.0296] 7.0107] 1.2016] 0.7080] 0.201] 3.2092] 1.6708] 0.605



contributing to lower wintertime failure rates. The use of oxygenated gasoline reduces -
CO emissions, leading to further decreases in failure rates.

A review of historic failure rate data for the Arizona program shows that this seasonal
effect appears to have a 2%-2.5% impact on failure rates. For example, the IM240 failure
rate in January 1999 was 14.0% versus a failure rate of roughly 16%-16.5% during the
July-September 1999 period. Since most of the data analyzed in this study were collected
during late summer, the wintertime failure rate would be expected to be significantly
lower than the projections shown in Table 1-2.

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.

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 (3,347 vehicles) and the Max CO
standards. Table 1-3 details the results of this analysis. ,

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 (3,347 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.

-6-


intermediate IM240 Cutpoints and IM147 Cutpoints Developed in This Study

EE

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

LDGT1

Model Years HC
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

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 -3.2/1.1

1994-95

2.00/1.25 - 2.00/1.00

50.0/40.0 - $1.0/25.0

2.8 - 3.0/1.1

1996+ (Tier 1

0.90/0.57 - 1.30/0.60

LDGT2

17.0/13.0 - 31.0/8.0

2.2 - 2.7/1.0

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

4.0 - 4.3/1.6

1996+ (Tier 1

1.60/1.00 - 2.00/0.90

38.0/30.0 - 51.0/18.0

A-2

3.0 - 4.1/1.3



Failure Rate By Model Year Grouping - Second IM147

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 | 24 | 81 | 22.9% |-13 | 92 | 12.4% | 18 | 87 | 17.1% | 38 | 67 | 36.2%

83-85 | 35 | 193 | 15.4% | 53 | 175 | 23.2% | 43 | 185 | 18.9% | 92 | 136 | 40.4%

LDGV | 86-89 | 36 | 389 | 8.5% | 42 | 383 | 9.9% | 55 | 370 | 12.9% | 101 | 324 | 23.8%
90-95 | 14 | 938 | 1.5% | 25 | 927 | 2.6% | 36 | 916 | 3.8% | 60 | 892 | 63%

96+ | 0 | 70 | 0.0% | O | 70 | 0.0% | 0 | 70 | 0.0% | 0 | 70 | 0.0%

81-85 | 31 | 229 | 11.9% | 23 | 237 | 8.8% | 18 | 242 | 6.9% | 58 | 202 | 22.3%

LpeT1 86-89 | 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+ | O | 32 | 0.0% 32 | 0.0% 1 31 3.1% 1 31 | 3.1%

81-85 | 20 | 74 | 21.3% | 23 | 71 | 24.5% 85 | 9.6% | 35 | 59 | 37.2%

LocT2 86-89 | 8 56 | 12.5% | 5 59 | 7.8% 56 | 12.5% | 16 | 48 | 25.0%
88-95 423 | 0.9% | 3 | 424 | 0.7% | 17 | 410 | 4.0% | 23 | 404 | 5.4%

96+ | 0 18 | 0.0% | 0 18 | 0.0% | 0 18 | 0.0% | 0 18 | 0.0%

5.7% 6.1% _{ 237 ]3110 | _ 7.1% 14.3%

Startup Cutpoints
HC co NOx OVERALL
Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass
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 | 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%
LocT1 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+ 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%
LDGT2 86-89 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+ 18 | 0.0% | 0 18 | 0.0% | 0 18 | 0.0% 0 18 | 0.0%
ALL [191 |3156|_ 5.7% _| 193 [3154 | 5.8% _}217 13130] 6.5% | 456 |2891]| 13.6%

B-4



established. 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 2,518 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 2,512 vehicles, respectively (i.e.,
3,347 vehicles total). All of the data were collected by Gordon-Darby I/M lanes in
Phoenix, Arizona.

a Superscripts denote references listed in Section 8.

-2-


€t-9

IM147 Composite Regression Coefficients, CO, 1983 to 1985 Model Year LDGV's
_| ‘Regression Coefficients

Segment IRMS- Reg.
[Number Error \Constant }C1 ic2 ica ica cS icé ic7 ica ico jc10 yet [C12 C13 C14 cis. C16 C17 ic18 ic19
fr GV [80-65 | 5.05667] 6.71077] 100773)
[Pz {LOGV_Je3-85 | 4.39498] 6.05704] 2.7634) 2.4038].
Ps TLOGV_[e3-85 | 3.797047 5.05452] 2.432} 1.2201] 4.6404]. _ |
[Pa ~_JUOGV_ Jas85 [3.62371] 4.67808] 0.5952) 1.3042] 2.354] 5.4862] _ i.
[PS ILOGV_ [83-85 | 3.59484] 4.60345] 0.0971] 1.2134] 2.0391] 4.9125] 1,269].
[Pe L.OGv_ [83-85 | 3.59268] 4.59476] 0.3543] 1.2106] 2.0275] 4.62] 1.1146] 1.0934].
[P7_ |LOGV__ [83-85 | 3.44031] 4.32493] 0.5781] 1.2446] 0.8896] 4.3893] 0.61747 1.6598] 1.5993} a
Ps. LOGV__ [83-85 | 3.36343] 4.2564] 0.0459] 1.093] 0.9848] 3.5609] 0.5789] 0.9952] 1.2147] 1.5455].
| LOGV_ [83-85 | 3.330207 4.1852] -0.1659] 1.1245] 0.9751] 3.3158] 0.1104] 0.6866] 1.2398] 1.3047] 2.0264] a

lLOGV__ [93-85 [3.29987] 4.12599] 0.0405] 1.1476] 0.8196] 3.3153] 0.2624] 0.8442] 1.1246) 1.22] 1.1895} 1,552] .

ILOGV_ {83-85 | 2.20201] 2.25511] 0.3918] 0.6788] 0.9189] 1.6168] 0.932] 1.9937] 0.5168] 0.7853] 1.9632] 1.1107] 0.7316]

LOGV_J63-85 [1.87972 1.58471] 0.5439[ 0.7415] 0.6536] 1.6537] 0.727] 1.925] 0.7033] 0.7588] 1.3654] 1.0997] 0.6415] 1.1026]. a

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] a

LOGV_ [63-85 | 1.03127] 0.58797! 0.6489] 0.7423] 0.3578] 1.9308] 0.4662] 1.5147] 0.8643] 0.6344] 1.5628] 0.5435] 0.6468] 0.7632] 1.5457| 1.1794) .

LOGV__ [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]. a

[LOGV__ [83-85 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

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] 1.6092). .

{LOGV_"Ja3-85 [0.35809] 0.01542] 1.0268] 0.6988 0.5932[ 7.2395] 0.5512] 1.1677] 0.6596] 0.7122] 0.8022] 0.6293] 0.6933] 0.7668| 0.6573| 0.8204] 1.0268] 0.6176] 1.4839] 0.9520).
t [LOGV_ _|83-85 | 0.25947] 0.01001] 1.0127| 0.7023] 0.6463] 1.13447 0.5259] 1.0927] 0.704] 0.6265] 0.7024] 0.6521] 0.7068] 0.755] 0.7026] 0.74038] 0.743] 0.4739] 1.3579] 0.7294] 0.7327}

|M147 Phase 2 Regression Coefficients, CO, 1983 to 1985 Model Year LDGV's
Regression Coefficients

Segment RMS —-|Reg.
|Number Error [Constant {C1 ic2 ic3 ica CS C6 ic7 ica ico C10 C11 C12 C13 C14 C15 IC16 C17 C18 C19
| CXR LOGV [83-85 | 4.69212] 5.7524] 1.0243]. a
[B12 ILDGV_ 183-85 | 2.83315] 3.12116]. 0.9251] 1.6564]. a
| EXE ILOGV_ [83-85 | 1.86354] 1.56942]. 0.9295] 1.1338] 2.6182]_ .
| EXES ILOGV_ [83-85 | 1.55825] 1.15742] 0.9063] 1.0736] 2.325] 1.5629].
[B15 LOGV__[83-85 | 1.07233] 0.69467]. 0.943] 0.9688] 1.4054] 1.0868] 2.1751]. .
[B16 ILOGV__[83-85 [1.05389] 0.68187]. 0.9437] 0.9647] 1.3864] 1.0625] 1.8313] 1.3471]. i
[B17 ILDGV_{83-85 | 0.89995] 0.27297]. 0.9612] 0.9964] 1.3316] 1.1059] 1.5922[" 0.6992] 2.8516). a
1818 LOGV_ [83-85 | 0.54856] 0.0412]. 0.9415] 1.0459] 0.8624) 4.195] 1.3817] 1.1042] 2.1846] 1.3008]
[319 LOGY 163-85 0.4078) 0.005} 0.9594] 1.0223] 0.9206] 0.9952] 0.9616] 0.6242] 1.9266] 0.9809] 1.0636]



Failure Rate By Model Year Grouping - Second IM147

Max CO Cutpoints
HC co NOx OVERALL

V Type | Year [Fail] Pass | % Fail [Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass
13 [ 92 | 12.4% | 29 [ 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 | 88 | 6.7% | 38 [ 914 | 4.0% |100| 852 | 10.5%
0 | 70 | 0.0% | o | 70 | 00% | 0 | 70 | 0.0% | o | 70 | 0.0%
33 | 227 | 12.7% | 99 | 161 | 38.1% | 48 [ 212 | 18.5% [139] 121 | 53.5%
19 | 203 | 8.6% | 48 [174 | 21.6% | 10 | 212 | 4.5% | so | 163 | 26.6%
7 [443 | 1.6% | 20 | 430 | 4.4% | 11 | 439 | 2.4% | 32 | ais | 7.1%
96+ | 0 | 32 | 0.0% | 0 | 32 | 00% | 1 | 31 | 3.1% [1 [31 | 3.1%
81-85 | 16 | 78 | 17.0% | 40 | 54 | 42.6% | 14 | 80 | 14.9% | 53 | 41 | 56.4%
wore | 8689-5 [59 [7.8% [in [53 [172% [a] 53 [17.2% [22 [42 [34.436
88-95 424 | 0.7% [10 | 417 | 2.3% | 6 | 421 | 1.4% | 16 | 411 | 3.7%
96+ 18 [ 0.0% | 0 [ 18 | 0.0% | o [| 18 | 0.0% | 0 | 18 | 0.0%
Aut [209 [3138] 6.2% [484 ]2803[ 14.5% [267 [3080] 8.0% [728 [2619] 21.8%

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

HC co NOx OVERALL

V Type | Year | Fail] Pass | % Fail | Fail | Pass | % Fail | Fail] Pass | % Fail Fail | Pass % Fail
[- | gi-82] 13 | 92 [124%] 29 | 76 [27.6% | 16 | 89 [15.2%] 46 | 59 | 43.8%
83-85 | 29 | 199 | 12.7% | 79 | 149 | 34.6% | 39 | 189 | 17.1% | 110] 118 | 48.2%

LDGV | 86-89 | 48 | 377 | 11.3% | 77 | 348 | 18.1% | 65 | 360 | 15.3% | 137] 288 | 32.2%
90-95 | 24 | 928 | 2.5% | 64 | 888 | 6.7% | 35 | 917 | 3.7% | 96 | 856 | 10.1%

96+ | 0 | 70 | 0.0% | 1 | 69 | 14% | 0 | 70 | 0.0% | 1 | 69 | 1.4%

81-85 | 31 | 229 | 11.9% | 95 | 165 | 36.5% | 47 | 213 | 18.1% | 134] 126 | 51.5%
LDGTI | 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% | o | 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%
LDGT2 | 86-89] 6 | 58 | 9.4% | 13 | 51 | 20.3%] 10 | 54 | 15.6% | 23 | 41 | 35.9%
83-95 | 3 | 424 | 0.7% | 10 | 417 | 2.3% | 7 | 420 | 1.6% | 17 | 410 | 4.0%

96+ | 0 | 18 | 0.0% | o | 18 | 0.0% | 0 | 18 | 0.0% | 0 | 18 | 0.0%

ALL [188 13159 | 5.6% {469 | 2878 | 14.0% [255 [3092 | 7.6% | 702 | 2645 | 21.0%

B-2


61-0

|M147 Composite Regression Coefficients, CO, 1986 to 1987 Model Year LDGT2's

Regression Coefficients,

RMS —jReg.
Error ___|Constant {C1 ic2 ica @ ics ce ic? ica ico Icio jews ciz_—cia_— feta —icis_ fers —fci7_fcwe__fcio
COGT2 fees? | to.0saa] 12.0035] S.00zT

LOGT2 {66-67 | 7.55854] 7.07406] 5.0552] 2.7507].
LoGr2 |ee-07 | 6.09501] 6.13276] —4.0ts2] 1.6000) 44048].

LOGT2 [96-87 | 6.97238] 5.95324] 3.4920] 1.8483] 3.4182] 2.3575] -
COGT2 |66-87 | 6.96833] 6.05169| 3.0277] 1.6681] 2.9579] 1.9105] 1.5754].

LOGT2 [66-87 | 6.92215] 5.98054| 2.189] 1.8432] 2.2804] 1.5316] 0.9365] 3.0663] -
LOGT2 [66-87 | 6.94695] 5.96146] 2.2344] 1.7097] 2.1735] 1.5149] 0.7182] 3.2075] _ 0,349] a
COGT2 86-87 | 6.80254] 5.02020] 3.5275] 1.4609] 2.7997] 0.5921] 0.3135]. 2.0870] 0.2127] 2.0542).
LOGt2 |e6-87 | 6.87158] 5.05307] 3.8626| 1.5007| 2.5734] 0.597] 0.1327] 1.6367] 0.0355] 1.5977] 1.3024]. -
LOGT2 [86-87 | 6.8465] 5.84494] 3.610) 1.5958] 2.1327] 0.6932] 0.2752] 1.0075] -0.5524| 1.769] -0.4369] 2.5615]. -
lLoGT2 [e6-87 | 4.00509] 1.79665] 7.9731] 0.449] 2.1242[ -1.7461] 2.0304] 1.3946] 0.7103] 1.246] 1.7513] 2.4636] 1.0775). 5
LOGT2 [66-87 | 3.20981] 1.45021] 1.8521] 0.2077| 1.3666] 1.06] 2.0745] 0.8491] 0.2216| 1.3232] 1.1353] 1.6136] 0.6802] 1.8765],
LOGT2 [86-87 | 2.16993] 0.93628] 1.6364] 0.6089] 1.2038] 1.0515] 0.3685] -0.5328| -0.0587| 1.2006] 0.4256] 1.2367] 0.6922) 1.2207] 2.0554].

toGT? |a6-s7 | 1.37534] 0.49406] 1.2757] 0.6254] 1.0865] 0.9774] 0.576] 0,036] 0.198] 1.1141] 0.5629] 1.3507] 0.6742] 0.7400] 1.5811] 1.3708].

LOGT2 [86-87 | 0.62074] 0.15633] 1.0946] 0.6111[ 0.9745] 0.7593] 0.7578] 1.1728] 0.4226] 1.0261] 0.3048] 1.0886] 0.7145] 0.6425] 0.9896] 1.02] 1.5300]

tOGT2 {06-87 | 0.69514| 0.03757] 1.0745] 0.6025] 1.0008] 0.7342] 0.6475] 1.2043] 0.645] 0.395] _0.3072| 0.8668] 0.7284] 0.6071] 0.9474] 1.1426] 0.9559] 2.2045]

LOGT2 [66-87 {0.60214 -0.0821| 1.1799] 0.6279| 0.8854| 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].

OGT2 [e6-87 | 0.44333] 0.03364] 1.1598] 0.6606] 0.7253] 0.5353] 0.0864] 1.1754] 0.4085] 0.9965] 0.5489] 0.8032] 0.7002] 0.7326] 0.6560] 1.0226| 0,838] 0.6221] 1.7755] 0.6044].

iP x x x
Fe [toct2 js-s7 | 0.1065] 0.04526] 7.0637] 0.7123[ 0.7400] 0.8109] 0.662] 0.9665] 0.652] 0.7496] 0.6309] 0.6545] 0.715] 0.7245] 0.7123] 0.6921] 0.7437] 0.6817] _ 1.153] 0.6078]

IM147 Phase 2 Regression Coefficients, CO, 1986 to 1987 Model Year LDGT2's
- Regression Coefficients.

Segment RMS —|Reg.
INumber Error [Constant }C1 ic2 ic3 C4 ics. C6 C7 ic8 ico C10 au |C12 C13. C14 C15 C16 C17 C18 C19
Bit oGr2 [e687 | 7.76562) 5.60047]. C a 5 74380]
B12 LDGT2 [86-87 | 4.68466] 3.16394]. a a a a 5 a a 0.9308] 2.6872]. a
B13 ILDGT2 {86-87 | 3.05474] 1.67487]. a . . ql . . 0.9606] 1.5982] 2.7862].
B14 ILOGT2 [86-87 | 2.02059] 1.1201]. a a a 5 a a a . 0.8908} 1.1326] 2.3405] 1.8259].
[B15 ILOGT2 [86-87 | 1.24999] 0.47151]. a a . . . a a a a 0.9544] 0.9442] 1.4229] 1.3063] 2.1977]. .
B16 ILOGT2 [86-87 | 1.08317] 0.16747). 5 a a 5 . q . 0.9833] 0.8605] 1.3578] 1,508] 1.2803] 3.3341. a
B17. ILOGT2 [86-87 | 0.90457] -0.06369) ql a a a . . . . a 0.9757} 0.9354] 1.2019] 1.5282] 1.2917] 2.1953] A754}. .
B18 LDGT2 [86-87 | 0.76129] -0.02736]. . . a a . a . . . 0.9433] 1.0434] 0.99627 1.3721] 1.2582] 1.4319] 0.8181].
B19 LOGT2 [66-87 [ 0.34719] — -0.0285]- . a a . 0.9781 0.9885] 0.9663] 0.6916] 0.9566] 0.9606] 0.947} 1.0918}



ae)

M147 Composite Regression Coefficients, HC, 1996 and N

lewer Model Year LDGT1's
nts

Regression, ie

|Segment RMS Reg.

[Number Error |Constant [C1 ic2 ic3 ica ics icé C7 ica ico C10 cit ic12 jc13 [C14 C15 C16 c17 C18 C18
PT [DGT1 [ser | 0.05316] 0.03402] 16,1995].

Pe. ILDGT1_[96+ 0.04307] 0.02411] 0.2121 2.417].

iP ILOGT1_ [96+ 0.03308] 0.02193] 2.6278] 1.1433] 2.9158} a

iP4 ILOGT1 [96+ 0.0321] 0.02215] 3.4627] 1.1835] 1.5689] 1.9266].

PS ILOGT1 [96+ 0.0267] 0.01828] 1.068] 0.5294] 2.0247] -3.6687| 6.4714]. a

PS ILDGT1 [96+ 0.02641[ 0.01933] 0.5547 0.4887| 1.8979] 4.0225] 7.8031] 1.8996]. a

iP? ILOGT1 [98+ 0.02534] 0.01654] 0.953{ 0.3268] 2.2496] 3.2672] 4.7342] 1.2515] 3.3214].

Pa ILDGT1_ [96+ 0.02524] 0.01597] -1.5963] 0.6417] 2.0178} 4.3447] 3.8465] 1.9882] 2.8209] 1.9699]

PS 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 LOGT1_[e6+ 0.02529] 0.01642] -1.4673] 0.5347] 2.3503] -5.1056] 4.7009] 2.0105] 2.7607! 2.6563] -1.6108] -0.7547]. .
[Pit ILOGT1_ [96+ 0.01755] 0.00739] 3.4599] 0.1081 1.652] -1.1947] 3.4359] 0.4622] 0.6672} 0.794] 0.5304] -0.2246] 1.7144].

Piz LOGT1_ [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. . a
pa 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] a
Pia ILOGT1 [96+ 0.01176} 0.00367] 4.875] 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
{PIs COGT1 [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]. 5
[LOGT1 [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]. .
pPiz 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]. .
| LOGT1_ [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].
[pis ILOGT1 [96+ 0.00416} 0.00114] 1.271! 0.6218] 0.8437[ 1.0272] 0.6835] 0.4345] 0.8099] 0.3982] 0.7078} 0.75} 0.6649} 0.7239] 06761} 0.8112] 0.5347] 0.2273] 22233) 1.3046] 0:
IM147 Phase 2 Regression Coefficients, HC, 1996 and Newer Model Year LDGT1
tegression Coefficients
Segment RMS. Reg.
Ez Error \Constant jC? ic2 ics \c4 ICS~ ice ic7 ics ico C10 C14 \c12 C13 C14 \C15 C16 C17 Ic18 C19
i DCT! [96+ | 0.02393] 0.00865] 2.4226)

B12 ILDGT1 [96+ 0.02095} 0.00863} 1.6528] 1.9763]. a

B13 |LOGT1 [96+ 0.01824] 0.00691]. 4.444] 0.667] 2.5512]. .

B14 LOGTt [56+ 0.01653] 0.00607}. 4.2007] 0.3983) 2.3614] 1.9099) .

[B15 ILDGT1_ [96+ 0.01077] 0.00256]. 1.0854)" 0.6091} 1.8127] 0.4665] 3.016]. .

B16 ILOGT1_ [96+ 0.01076] 0.00241) 1.0549] 0.6405] 1.7899] 0.4709] 2.8141) 0.9908] _ a

B17 ILDGT1 [96+ 0.00794] 0.00414]. 0.8756] 0.7439] 1.2805]_1.0073] 1.458] 1.4709] 3.9228]. a
[B18 ILDGT1_ [96+ 0.00581] 0.00161]. 0.8235] 0.95347 1.0124] 1.0609] 1.0031] 0.8433] 3.018] 2.4076].
Gi [LOGT1 [96+ 0.00549] 0.00161}. 0.623] 1.0394[ 0.8747| 1.0551] 0.8342] 0.2958] 25227| 1.8686] 0.7675)



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.

-57-


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
(i.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

-53-


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 warmed 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 IM 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

-15-


Figure 5-2

Retest Algorithm During First 1M147 - LDGV
Max CO Cutpoints

VM 147 TEST

|

ES
* 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
Le 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
le Mode 7 Predicted CO-STD_ | 0.4
Mode 19 Predicted CO - STD .
y
NO | Mode 7 Predicted NOx < STD * 2
i and
Mode 19 Predicted NOx < STD * 2
YES
Y
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.

-44-



Complete Tests

Table 5-4 |
Retest Algorithm Results
LDGV LDGT1, LDGT2
———|
Total Number of 1567 1780

# of Failures Without

327 (20.9% of 1567)

273 (15.3% of 1780)

Retest Algorithm
# of Correctly Identified
Faitwes! 245 (74.9% of 327) 180 (65.9% of 273)°
# 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 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.

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
the drive trace.” To maintain uniformity for lane software programmers, the error term
was not included in the prediction of 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)

“tis 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 this issue to
be evaluated.

-48-


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%
LDGTI | 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%
LDGTI | 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% | 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%

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

HC co NOx

Year | Fail | Pass |% Fail | Fail | Pass |% Fail | Fail | Pass | % Fai
81-82 | 19 | 86 | 18.1% | 32 | 73 | 30.5% | 24 | 81 | 22.9%

83-85 | 48 | 180 | 21.1% | 108 | 120 | 47.4% | 53 | 175 | 23.2%

86-89 | 85 | 340 | 20.0% | 96 | 329 | 22.6% | 87 20.5%

90-95 | 55 | 897 | 5.8% | 112] 840 | 11.8% | 72 7.6%

96+ | O | 70 | 0.0% | 4 | 66 | 5.7% | 0 0.0%

81-85 | 38 | 222 | 14.6% | 109 | 151 | 41.9% | 54 20.8%

86-89 | 22 | 200 | 9.9% | 57 | 165 | 25.7% | 17 7.7%

90-95 434 | 3.6% | 38 | 412 | 8.4% | 11 2.4%

96+ 32 | 0.0% | 0 | 32 | 0.0% | 1 3.1%

81-85 76 {19.1% | 44 | 50 | 46.8% | 16 17.0%

86-89 12.5% | 13 | 51 | 20.3% 20.3%

88-95 1.2% | 30 7.0% 3.5%

0.0% | 0 | 18 | 0.0% 0.0%

ALL 9.4% 19.2% 10.8%

B-1



Regression Summary - Composite CO, IM240 to IM147

Model Years 1986-1989

All Vehicle Types
SUMMARY OUTPUT
Regression Statistics
MultipleR 0.984568
R Square 0.969374
Adjusted R 0.969218
Square
Standard 5.123058
Error
Observations 198
ANOVA
of. Ss MS F Significance F
Regression 1 162823.5 162823.5 6203.812 2.5E-150
Residual 196 5144.161 26.24572
Total 197 167967.7
Coefficients Standard t Stat P-value ~ Lower95% Upper 95%
Error
Intercept 1.679632 0.403976 4.157756 4.8E-05 0.882936 2.476328
IM240 0.939067 0.011922 78.76428 2.5E-150 0.915554 0.96258
IM240 Line Fit Plot
350

IM147-

@ IM147
—— Predicted IM147

F-5


IM147 CUMULATIVE POSITIVE POWER (CPP) VARIATION CUTPOINTS

1M147 REFERENCE DATA
TIME SPEED CPP
(sec) (mph)
92 53.2 3715.41
93 54.1 3811.98
94 54.6 3866.33
95 54.9 3899.18
96 55.0 3910.17
97 54.9 3910.17
98 54.6 3910.17
99 54.6 3910.17
100 54.8 3932.05
101 55.1 3965.02
102 55.5 4009.26
103 55.7 4031.50
104 56.1 4076.22
105 56.3 4098.70
106 56.6 4132.57
107 56.7 4143.90
108 56.7 4143.90
109 56.3 4143.90
110 56.0 4143.90
111 55.0 4143.90
112 53.4 4143.90
113 51.6 4143.90
114 51.8 4164.58
115 52.1 4195.75
116 52.5 4237.59
117 53.0 4290.34
118 53.5 4343.59
119 54.0 4397.34
120 54.9 4495.35
121 55.4 4550.50
122 55.6 4572.70
123 56.0 4617.34
124 56.0 4617.34
125 55.8 4617.34
126 55.2 4617.34
127 54.5 4617.34
128 53.6 4617.34
129 52.5 4617.34
130 51.5 4617.34
131 50.5 4617.34
132 48.0 4617.34
133 44.5 4617.34
134 41.0 4617.34
135 37.5 4617.34
136 34.0 4617.34
137 30.5 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


Fast Pass
2,490 Vehicles @
46.8 Seconds

Figure 1-2

Integrated Test Results
Maximum CO Cutpoints Without CPP Limits

Fast Pass
260 Vehicles @
199.8 Seconds

Fast Pass
40 Vehicles @
360.8 Seconds

Pass Fail
8 Vehicles @ 189 Vehicles @
146.0 Seconds 132.0 Seconds
y
No Decision
660 Vehicles
Fast Fail
92 Vehicles @
194.0 Seconds
Pass Fail
1 Vehicle @ 145 Vehicles @
292.0 Seconds 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

-10-

TOTAL FAIL = 545

TOTAL FALSE FAILURES = 3



Appendix F

Regression Summaries


Fast Pass
2,490 Vehicles @
46.8 Seconds

Figure 5-6

Integrated Test Results
Maximum CO Cutpoints Without CPP Limits

Fast Pass
260 Vehicles @
199.8 Seconds

Fast Pass
40 Vehicles @
360.8 Seconds

Pass Fail”
8 Vehicles @ 189 Vehicles @
146.0 Seconds 132.0 Seconds
No Decision
660 Vehicles
Fast Fail
92 Vehicles @
194.0 Seconds
Pass Fail
1 Vehicle @ 145 Vehicles @
292.0 Seconds 278.0 Seconds
No Decision
162 Vehicles
Fast Fail
115 Vehicles @
n
Pass Fail 3825 Seconds
3 Vehicles @ 4 Vehicles @
438.0 Seconds 438.0 Seconds

TOTAL PASS = 2,802

-52-

TOTAL FAIL = 545

TOTAL FALSE FAILURES = 3



41

. (With and Without Fast-Pass)

List of Tables

page
Failure Rates, Third IM147......... 00. ce cece ee ccc eee etree eet e eee neee 3
Predicted Arizona IM147 Failure Rates with MaxCO Cutpoints
and Integrated Algorithms ........ cece cece cere ee tence eee nent nee 5
Modeled Fast-Pass Results Excess Emissions vs. Average Test Time . 7
Retest Algorithm Results ... 0.0... 60. e ese e cece eee e eens 8
Fast-Fail Algorithm .. 0.6.00... cece cnn ene 8
Comparison of Integrated Algorithms vs. Standard IM147 Impact on .
Test Time and Excess IM240 Emissions (MaxCO Cutpoints) Lost............ 11
Average Test Time by Test Time Reduction Methodology and CPP .......... 13
Comparison of IM147 Max CO Cutpoints to IM240 Final Standards
Impact on Excess Emissions Lost ......... 00000 e eee eee eee eee eee 13
IM240 to IM147 Composite Regression Equation Coefficients............... 28
IM147 Composite to Phase 2 Regression Equation Coefficients .............. 29
Revised IM147 Max CO Cutpoints (Composite/Phase 2) ................00. 30
Failure Rates, Third IM147........ 0.0... ccc cece ect tenn eee 31
Revised IM147 Segments

Modeled Fast-Pass Results Excess Emissions vs. Average Test Time
Excess IM240 (Max CO Cutpoints) Emissions Identified

Retest Algorithm Results ........ .
Fast-Fail Algorithm .. 2.0.0... ccc cee ee eee teen eee eens
Comparison of Integrated Algorithms (No CPP) vs. Standard IM147

Impact on Test Time and Excess IM240 Emissions (Max CO Cutpoints) Lost ... 51
Comparison of IM147 Max CO Cutpoints to IM240 Final Standards

Impact on Excess Emissions Lost .... 2... 2.0 c cece cece eee eee 54
Preliminary CPP Cutpoints Based on Top-50% Drivers .............0.00005 63
Centered CPP Cutpoints and Resulting Test Abort Rates ................06. 64

End Test Decisions Affected by CPP Errors ........ 0... c cece e eee eee eens 68

“iii


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 powet
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 3,347-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 +2 mph speed limits detailed in the
IM240 guidance, vehicles already failing the +2 mph speed limits were eliminated from
the sample since they would be aborted regardless of the CPP outcome. Of the original
3,347 vehicles, 3,006 remained after these vehicles were eliminated 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 3,006-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 3,006 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.

-69-


Figure 3-2

Model Year Distribution of
Sample Fleet and AZ Overall Fleet

20%

15%

10%

ANNY

Percent 1998-1999

5%

RSOHAAassg
RM SOOO

RRMA MOO sy
ESSE SR | fo\

0%
81 82 83 84 85 8 87 88 89 90 91 92 93
Model Year

HE 2512 Vehicle Sample 535 Vehicle Sample 300 Vehicle Sample —#— YTD October 1999

according to their T90 responsé 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.

-24-


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
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 = 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].

-67-


Regression Summary - Composite NOx, IM240 to IM147

Model Years 1990-1995

All Vehicle Types
SUMMARY OUTPUT
Regression Statistics
MultipleR 0.958073
R Square 0.917905
Adjusted R 0.917727
Square
Standard 0.306825
Error
Observations 464
ANOVA
of ss MS. FE Significance F
Regression 1 486.2964 486.2964 5165.597 6.2E-253
Residual 462 43.49332 0.094141
Total 463 529.7897
Coefficients “Standard t Stat P-value Lower 95% Upper 95%
Error
Intercept 0.048771 0.020544 2.374 0.018004 0.0084 0.089143
IM240 1.102698 0.015343 71.87209 6.2E-253 1.072548 1.132848
IM240 Line Fit Plot
9

IM147

@ IM147
—— Predicted IM147

F-9


Figure 6-1

Comparison of M147 Variation Limit Metrics

60 30,000

Speed —__, ~
50 25,000 ©
Cumulative PKE (CPKE) \ 3
= 40 20,000 &
z a gy
Gi id
20 15,000 &
3 rh aad Cumulative Positive Power (CPP) \ e
F 39 10,000 £
/ NY WV 8
10 + 5,000 a
—— : 000

°o = \ oO

oO 10 20 30 40 50 60 70 80 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 from a full modal
analysis of the test data sample.

-60-


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 concem 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 I/M 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 of a
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
were developed in the 1997 study. Therefore, further analysis was needed to develop
similar speed variation criteria for the IM147.

-17-


Figure 4-4
Failure Rate by Consecutive IM147

“pee

Intermediate Cutpoints
HC Failure Rate by Vehicle Type CO Failure Rate by Vehicle Type
18.0% 16.0%
16.0% 14.0% 7
14.0% 2 12.0% Hil
E 12.0% Distimia7 = 100% H 7 Gist M147
g 10.0% 2 7
2 0% 2nd IM 147 2 8.0% +]Ii li 2nd IM147
= 6.0% 3rd M147 e 6.0% Hh Based M147
= 4.0% Oo 4.0% THI Hil
2.0% 2.0% THI] i
0.0% 0.0%
PC PC LDTI ~—sLDT2 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% 7 2 5 fl
3 25.0% Ty} i
& 12.0% fi i & i il
Ist IM147 2 iH HT
§ 10.0% f | Bis § 20.0% | | Ciist M147
a ! i W2nd iM147 2 Hi WH 2nd 1M147
E 8.0% i \ = 15.0% Hil] i =
S 6.0% iH Ca3rd M147 $ | | @3rd M147
3 {i S 10.0% 41H!
4.0% 1 Hi] 6 ]
2.0% ll ll 5.0% i IH | i
0.0% be z 0.0% LU Ul i
PC LpTt LDT2 All pc LDTt LDT2 All
Vehicle Type Vehicle Type



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 95.2% 96.2% 84.8% 91.9% 95.1% 85.7%
LDGT1 80.5% 100.0% 70.6% 68.4% 98.1% 81.2%
" LDGT2 87.9% 100.0% 46.6% |. 98.5% 97.7% 73.0%

Weighted
Average

91.3% 97.3% 79.9% 86.7% 95.9%

1 __

81.6%

* 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.

HE

-54-


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 tun 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.

HE

-20-


Regression Summary - Composite HC, IM240 to IM147
Model Years 1990-1995

All Vehicle Types

SUMMARY OUTPUT

Regression Statistics
MultipleR 0.974081
R Square 0.948834
Adjusted R 0.948723
Square
Standard 0.100407
Error
Observations 464
ANOVA

of ss MS F Significance F
Regression 1 86.37186 86.37186 8567.38 2.3E-300
Residual 462 4.657643 0.010081
Total 463 91.0295
Coefficients Standard t Stat P-value Lower 95% Upper 95%
Error
Intercept 0.026672 0.00526 5.071064 5.74E-07 0.016336 0.037007
IM240 0.963839 0.010413 92.56014 2.3E-300 0.943376 0.984302
IM240 Line Fit Plot
7
3 ° IM147
= —— Predicted IM147
8

F-7


roe)

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

Regression Coefficients,
Segment RMS, Reg. :
Number Error Constant C1 C2 ic3 ica ics ice ic? ice ico ic10 cat C12 [C13 C14 Cis C16 \C17 C18 C19
PT [CoGTt [essa | o40aze] 0.5423] 17 20231.
[P2. LOGT1 [86-89 | 0.37436) 0.2465] 2.2738] 4.5192} . :
jP3. LDGT1_ [86-89 | 0.33313] 0.22224] 0.5252] 2.5885] 4.6599] a i A
Pa LOGT1 [86-89 [ 0.29595] 0.20426] 0.5943] 2.3203] 2.3375] 3.7634) a
Ps: ILOGT1 [86-89 0.2038] 0.19895] 0.6763] 2.0377] 2.3025] 2.9749] 1.8038] a
Ps. ILOGT1 [86-89 [0.28058] 0.20644] -0.6191] 2.2069] 1.2069] 2.4549] -0.1084] 4.2386].
ez LOGT1 [86-89 | 0.27458] 0.19491] 0.0117] 2.0167] 1.3562] 2.7382] -1.5023] 2.6456] 2.6231. a
[Ps [LOGT1 [86-89 [0.26273] 0.17858] 0.9768] 1.9562] 1.0027] 2.2073] -1.5776] 1.5977} 2.003] 3.2606] .
[Ps ILOGT1 [86-89 | 0.25683] 0.17484 0.544] 2.0132] 0.9204] 2.1628] -2.2132] 0.7045] 2.0543] 2.3838] 3.4294)
P10. LOGT1 [86-89 [0.25153] 0.17214] 0.4457] 2.0253] 0.8623] 1.9864] -2.2523] 0.4119] 2.2232] 1.6726] 1.6076] 3.6006]. .
Pat LOGT1 [86-89 [0.19659] 0.07476] 2.1608] 0.6208] 1.0463] 1.2006] -1.6683] 1.7041] 1.4349] 0.7533] 1.4174] 2.3957] _ 1.957]
Piz LOGI [66-89 | 17271] 0.05605] 2.4645{ 0.4889] 05378 1.0905] 0.6636] 1.2017| 1.9698] 1.1634) 0.8772) 3.332] 1.2131] 1.5195) .
| ED [COGT1 86-89 | 0.13816) 0.05647] 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] i.
pris ILOGT1 [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]. a
pis. 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].
[P16 iLOGT1 [86-89 | 0.10769] 0.03525] 1.9852} 0.3076] 0.7867] 1.4374] 0.022] 1.1859] 1.1325] 0.515] 0.1541] 2.0989] 0.7572] 0.7681] 0.784] 0.499] 0.7608] 4.7323] a
Gi LOGT1 [86-89 | 0.05356] 0.01318] 1.0985] 0.6236] 0.7621] 0.7653] 0.5658] 1.0327] 0.7036] 0.4123] 0.7975] 1.2148] 0.7601] 0.6019] 0.6454 0.937} 0.7894] 0.9132] 3.009} i.
1P18 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 LOGT1 }86-89 | 0.03395] 0.00397} 1.0933] 0.6168] 0.695] 0.9026] 0.4507] 1.0319] 0.9511] 0.5369| 0.7983{ 0.6339] 0.6758] 0.6909] 0.7163] 0.7503] 0.7572| 0.3355] 1.6677| 1.2005] 0.6459]
IM147 Phase 2 Regression Coefficients, HC, 1986 to 1989 Model Year LDGT1's
J J Regression Coefficients
[Segment RMS Reg. %
|Number |Error [Constant |C1 ic2 ica ica ics. icé C7 C8 ico jc10 au C12 \c13 C14 cis C16 ald C18 cia
{Bir Jtoctt [oo-eo | 0.20034] 0.1156). 3.3457. :
| ILOGT1 [86-89 | 0.26979] 0.09053] 2.3987] 1.9564].
| CX LOGT1 [86-89 | 0.20617] 0.08073]. 1.4658] 0.0492] 4.1671]. a
[B14 LOGT1 [86-89 [0.19052] 0.06515)" 1.1799] 0.3012] 2.4794] 3.6519] a
815° ILOGT1 [86-89 [| 0.16882] 0.04542] 4.1829] 0.6531] 1.4949] 0.6859] 3.2881].
B16. ILDGT1_[86-89 | 0.16083] 0.04255]. 1.4483] 0.9014] 1.2632] 0.3783] 1.3376} 6.662]. a
B17 ILOGT1 [86-89 | 0.07626] 0.01566]. 1.0548] 0.7801| 1.1751] 1.2709] 1.2392] 0.9883] 4.2486]. a
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 LOGT1 [86-89 | 0.04869] 0.00402]. 0.9313] 0.8308] O9901[ 0.9768[ 1.1033] 0.6703] 2.4005] 1.5697] 1.23]



Table 5-3
Excess IM240 (Max.CO Cutpoints) Emissions Identified*
(With and Without Fast-Pass)

No Fast-Pass Fast-Pass Enabled

Vehicle Excess HC | Excess CO | Excess NOx | Excess HC | ExcessCO | Excess NOx
Class Identified _| Identified _| Identified | Identified | Identified | Identified

81-82 N/A 87.6 100.0 N/A 87.6% 100.0%
83-85 100.0% 97.6 100.0 100.0% 97.6% 100.0%
86-89 100.0% 99.8 100.0 99.2% 97.1% 100.0%
90-95 99.0% 86.3 91.2 96.7% 85.3% 46.0%

96+ N/A N/A N/A N/A N/A NIA
ALL 99.6% 94.8 96.8 98.2% 93.3% 80.6%

81-85] 78.5 99.6 99.4 78.5% 92.0% 99.4%
88-89 | 100.0 99.0 113 93.8% 90.8% 13%
90-95 | 100.0 0.0 100.0 0.0% 0.0% 100.0%
96+ NIA N/A 100.0 N/A NA 0.0%
ALL 90.0 99.0 94.6 83.4% 91.0% 92.6%
81-85 92.3 100.0 | 79.7 | 923% | 100.0% 79.1%
86-87 | 100.0 100.0 53.1 100.0% 100.0% 53.1%
88-95 | 100.0 100.0 65.7 100.0% 36.2% 56.6%
96+ NA NIA N/A N/A N/A NIA
ALL 96.1 100.0 65.8 96.1% 95.2% 64.5%

‘otal . .. .! fo fo 4 /o
95.7% 92.7% 82.4%

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

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 predicted test
outcomes using a combination of mass and concentration readings during specific modes
of the IM147 test. 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 concem, as previously mentioned, was that the modes did
not align with the fast-pass 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

-41-


Failure Rate By Model Year Grouping - Second IM147
Intermediate Cutpoints
HC co NOx OVERALL
Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail
36 | 69 | 34.3% | 15 | 90 | 14.3% | 23 | 82 | 21.9% } 49 | 56 | 46.7%
51 | 177 | 22.4% | 66 | 162 | 28.9% | 60 | 168 | 26.3% | 118] 110 | 51.8%
61 | 364 | 14.4% | 50 | 375 | 11.8% | 76 | 349 | 17.9% | 134 | 291 | 31.5%
22 | 930 | 2.3% | 30 | 922 | 3.2% | 55 | 897 | 5.8% | 85 | 867 | 8.9%
O | 70 | 0.0% | 0 | 70 | 0.0% | 0 | 70 | 0.0% | 0 | 70 | 0.0%
57_| 203 | 21.9% | 34 | 226 | 13.1% | 31 | 229 | 11.9% | 95 | 165 | 36.5%
24 | 198 | 10.8% | 18 | 204 | 8.1% | 25 | 197 | 11.3% | 52 | 170° | 23.4%
6 | 444 | 1.3% | 5 | 445 | 1.1% | 21 | 429 | 4.7% | 28 | 422 | 6.2%
oO | 32 0.0% 0 32_| 0.0% 1 31 3.1% 1 31 3.1%
29 | 65 | 30.9% | 32 | 62 | 34.0% | 15 | 79 | 16.0% | 50 | 44 | 53.2%
10 | 54 | 15.6% | 5 | 59 | 7.8% | 11 | 53 | 17.2% | 19 | 45 | 29.7%
7_| 420 | 16% 3_ | 424 | 0.7% | 22 | 405 | 5.2% | 31 | 396 | 7.3%
) 18 | 0.0% 0 18 | 0.0% 0 18 0.0% 0 18 | 0.0%
303 13044 | 9.1% | 258 13089 |_ 7.7% 1340 13007 | 10.2% | 662 |2685 | 19.8%

Failure Rate By Model Year Grouping - Third IM147
Intermediate Cutpoints
HC co NOx OVERALL
V Type | Year |Fail| Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail | Fail | Pass | % Fail
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%
90-95 | 19 | 933 | 2.0% | 29 | 923 | 3.0% | 46 | 906 | 4.8% | 77 | 875 | 8.1%
96+ | 0 | 70 | 0.0% | 1 | 69 | 1.4% | 0 | 70 | 0.0% | 1 | 69 | 1.4%
81-85 | 56 | 204 | 21.5% | 34 | 226 | 13.1% | 29 | 231 | 11.2% | 92 | 168 | 35.4%
86-89 | 21 | 201 | 9.5% | 16 | 206 | 7.2% | 25 | 197 | 11.3% | 50 | 172 | 22.5%

LDGTI 90-95 | 3 | 447 | 0.7% | 3 | 447 | 0.7% | 18 | 432 | 4.0% | 22 | 428 | 4.9%
96+ | 0 | 32 | 0.0% | O | 32 | 0.0% 1 | 31 | 3.1% 1 | 31 | 3.1%
81-85 | 25 | 69 | 26.6% | 31 | 63 | 33.0% | 14 | 80 | 14.9% | 47 | 47 | 50.0%
LDGT2 86-89 | 10 | 54 | 15.6% | 8 | 56 | 12.5% | 8 | 56 | 12.5% | 19 | 45 | 29.7%
88-95 | 5 [422 | 1.2% | 3 | 424 | 0.7% | 20 | 407 | 4.7% | 27 | 400 | 6.3%

96+ | O | 18 | 0.0% | O | 18 | 0.0% | O | 18 | 0.0% | 0 | 18 | 0.0%
ALL 127713070} 8.3% 1256 13091 | 7.6% 1311 13036} 9.3% | 625 }2722 | 18.7%

B-6


x PP,
CPKE(x) = =2— (6-1]
fac
co)

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

Vi? -(W-)? whenV, >V,.,

i
PP = £0. when, £V,, [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 t¢ over a transient driving cycle of T seconds, or

re [6-3]
CPP(T) = >. PP

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.

-59-


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 [1281] 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 44 [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% | $07 [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 82 | 521 [ 13.6% | 149 | 454
All 473 [2874 | 14.1% 1381 [2966 436 12861 | 14.5% [895 [2452

Failure Rate By Model Year Grouping - First IM147
Final Cutpoints

HC

co

NOx

OVERALL

Fail % Fail

Fail

Pass | % Fail

Fail

Pass

Fail

Pass

% Fail

70 | 35 | 66.7%

30

75_| 28.6%

39

66

2

27

74.3%

148 | 80 | 64.9%

120

108 | 52.6%

81

147

183

45

80.3%

179 42.1%

102

323 | 24.0%

144

281

247

178

100 10.5%

90

862 | 9.5%

132

820

220

732

2 2.9%

ry

66 | 5.7%

0

70

5

65

116 44.6%

69

191 | 26.5%

70

190

165

95

71 32.0%

29

193 | 13.1%

53

169

104

23 5.1%

ll

439 | 2.4%

59

391

77

0 0.0%

0

32_| 0.0%

2

30

2

50 53.2%

41

53_| 43.6%

32

62

73

29 45.3%

14

50_| 21.9%

20

44

42

56 13.1%

bf

416 | 2.6%

68

104

0 0.0%

0

18 | 0.0%

0

18

0

844 25.2%

2826 | 15.6%

B-7



££-0

IM147 Composite Regression Coefficients, NOx,

1988 to 1995 Model Year LDGT2's

Regression Coefficients.
IRMS Reg. ‘
lErmor__|Constant cafes —fce_—fer_fes_feo _|cro_|ers_fer2__|ors_|ers _fors__|cre_|er7_|cra__|cro
TOGT2 [65-95 | 1.24676] 2.35108) a
toGr2 [e8-95 | 0.2176] 0.87411
COGT2 88-95 [0.77645] k
|LDGT2 |88-95 0.7742] 0.70093] 5.1308]. ‘
[LOGT2 |88-05 [0.73556] 0.71204) 3.2699} 6.2663]
[LOGT2 68-95 | 0.72738] 0.72308} 0.5835} 5.6308] 6.8957) a
[toGr2 [88-95 | 0.70212] 0.63963 0.5084] 1.7183] 8.4446] 5.0667]. i
“[tocrT2 {66-95 | 0.68022[ 0.60537) ~1.474] 4.1716] 2.9862] 4.0341] 6.3227]. J
ILOGT2 [88-95 | 0.67704] 0.59719] 0.993] 0.5073] 2.3082] 3.7294] 5.9607] 3.1202]. £
P10 JLDGT2 [88-95 | 0.66525} 0.59249] 0.3777) 0.1781 2.4339] 2.4689] 6.3272] 0.1222] 4.5356]. .
[Pr |LoGr? Jas-95 [0.49566] — 0.16456] 7.6143] 0.2178] 0.8508] 0.2398] 3.1105] 1.8601] 2.7668] 1.0044]. r
P12 LOGT2 [88-95 | 0.39011} 0.13915] 0.7011] 0.5649] -0.3036] 0.9763] 0.3149] 0.4631] 3.3575] 0.9895] 3.9381].
[P13 tocr? |ee-95 [0.22071] 0.13586] 1.6722] 0.4435] _0.7176| 0.4615] 0.651] 1.5772] 0.5428] 0.7152 1.6198] 2.5117].
peas LOGT2 [88-95 [0.16814] 0.08085) 1.54] 0.4306] 1.0374] 0.8469] 0.7753] 1.5915] 0.5229] 0.5567] 1.0483] 1.735] 1.6545]. a
pis. LDGT2 {88-95 | 0.09792] 0.04274! 1.5301] 0.7496] 0.6925{ 0.7213] 0.5876] 1.3142] 0.4284] 0.6686] 0.9379] 0.6221] 1.0317] 1.4681]. .
P16 |tOGr2 }oe-05 | 0.08515] 0.04] 1.2443] 0.777 0.5902] 0.9112] 0.3659] 1.1635] 0.6339] 0.6006] 0.6562] 0.8431] 0.0855] _1.1161] 1.2364].
PAT {LOGT2 [86-95 0.0793) 0.03} 4.166] 0.8211] 0.5607] 0.8664} 0.4784] 1.1288] 0.4527] 0.7155] 0.8591] 0.6406] 0.9915] 1.0422] 1.1105] _ 1.2887]. a
JP1s. TLOGT2 [88-95 | 0.04482] 0.00528} 0.9762] 0.6066] 1.0589] 0.5961] 0.8411] 0.7658] 0.5158] 0.703] 0.8891] 0.6603] 0.8286] 0.8216] 1.0348] 0.9346] 0.6702]
| a [LOGT2 [ee 0.0136) 0.00252] 0.7215 0.711) ‘O.747] 0.8258] 0.7089] 0.7055| 0.696] 0.744] 0.73207 0.7233 715] 0.7362] 0.7138} 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
ISegment RMS,
Number lEror _|Constant |ct c2_fcs_—fea_—fcs_ ca fcr _—ifca_—cs cro crs cr2__fer3-_— erasers. — fers fer7_— erecta
iH 2 [68-95 | 0.71642] 0.34014] ERIN r
{eiz [LOGT2 [s8-95 [0.55127] 0.22451] 1.6945] 5.1988]. a
|e13___[Locr2 Jes-95 [0.30245] 0.20374]. 0.8854] 2.4317] 3.3783] i
| EXEy [LOGT2 [88-95 [0.23377] 0.14045] 0.7529} 1.6903] 2.356] 2.1936]. .
Isis [L0GT2 [es-05 | 0.13683] 0.07379]. 0.881] 1.4099] 1.0647| 1.3754] 2.0165]. ___|.
B16 LOGT2 [88-95 [0.11912] 0.06757]. 0.9498] 1.1559] 1.1515] 1.31] 1.5401] 1.6409]. .
B17 DGT2 [88-95 [0.10916] 0.04612]. 0.9645] 1.1668] 1.1296] 1.3343] 1.4075} 1.494] 1.8722]. 5
18 ILDGT2 [88-95 0.0835] 0.01503]. 0.9291] 1.3099] 0.8518] 1.1259] 4,093] 1.5612] 1.2713] 1.1432].
18 __foGT2 [28-95 | 0.0r4z3] 0.0016] 0:9872[ 1.0082] 0.9647] 0,96] 0.9598] 1.0003] 1.0227] 0.8463] 1.0006]



9E-D

IM147 Composite Regression Coefficients, NOx, 1983 to 1985 Model Year LDGV's
ression Coefficients
[Segment IRMS — Reg.
Number |Error [Constant {C1 ic2 ic3. ca ics: ice ic7 ice ico IC10 C1 C12 Ic13 C14 C15 C16 [C17 C18 Ic19
Pt ]UDGV_]o3-05 | 0.Se403] 1.60521] 16.7416].
Pz [LOGV__[e3-85 | 0.58177] 0.74717) 0.782}
Ps. LOGV__J83-85 | 0.57164] 0.69924] 1.7395] .
[Pa ILOGV_ [63-85 | 0.56866] 0.69182] 2.2485] a a
pes LOGV__ [83-85 | 0.54912] 0.68329] 0.0309} 4.4539}
Ps. ILOGV_ [83-85 | 0.54056] 0.65766] _-0.2605] 3.3123] 6.9261].
iP? LOGV__ [83-85 [0.524787 0.64771] -1.2424] 1.2743] 8.0478] 3.3901]. a
IPS LOGV__[63-85 | 0.49039] 0.64355] -0.2025; 0.9327} 0.9917] 2.648] 6.9554] _ .
Po LOGV_ [83-85 | 0.49081] 0.6366] -0.2479| 0.7709} 0.8195] 2.6479} 6.804] 0.8258]. a
[P10 ILOGV__[83-85 | 0.48198} 0.62502] -0.6852} 0.3327] 1.3097] 1.8572] 6.9911] -1.6918] 3.7996].
[Pay ILOGV_[63-85 | 0.43238] 0.38649] 0.6471 14,3594] 1.4974] 0.1122] 6.1798] 0.33] 1.9052] 1.0346]. a
Piz LOGV_ 83-85 [0.35372] 0.24907] 1.0365] 1.5953] 0.2612] 1.2403] 3.835)" 0.566] 1.7683] 0.4528] 3.3405]
| AEY ILOGV [83-85 | 0.22239] 0.11604] 1.4435] 0.6475] 1.7479] 0.451] 1.0221] 0.5773] 0.455] 0.4514] 1.7854] 2.2469].
[Pid LOGV__ [83-85 | 0.16685] 0.04752] 1.1189} 0.3301] 2.1868] 0.9638] 0.6226] 1.2885] 1.0545] 0.5328] 0.7862] 1.2774] 2.0142|
P15 ILDGV_ [83-85 | 0.09344] 0.00442] _ 0.6972 0.647] 1.6301] 0.8766] 0.4133] 1.7045] 0.185] 0.7132] 0.6701] 0.6719] 1.0248] 7.5087] a
P16 LOGV__ [63-85 | 0.07618] 0.01155] 1.4799] 0.7603] 0.9115] 4.0175] 0.3506] 1.3353] 0.4874] 0.7501] 0.6536] 0.7419] 0.6969] 1.1713] 1.4857] a
[P17 ILOGV_ [83-85 | 0.07136] 0.00873] 1.0722} 0.6004] 0.9688] 0.8579] 0.6022] 0.9517] 0.6109] 0.7611] 0.5902] 0.7747] 0.9329] 1.1217] 1.2072] 1.6127]. .
P18 LOGY [83-85 | 0.03952] 0.0055] ~ 0.6259] 0.6208] 1.1765] 0.512] 0.9231] 0.7662] 0.5965] 0.7047] 0.7878] 0.6504] 0.8038] 0.8335] 1.1663] 1.2587) 0.9322].
[P19 [LOGV_ [83-85 | 0.012247 0.00128] 0.8422} 0.7372] 0.8013] 0.7199] 0.7142] 06943, 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
ssion Coefficients
[Segment IRMS —|Reg. :
lumber |Error [Constant |C1 ic2 ica C4 ics. C6 \c7 ic8 ico C10 (onl C12 Ic13 cis Cis C16 C17 C18 cia
; CX 0.73374] 0.81776). 2.43821.
B12 JLOGV__]63-85 | 0.54834] 0.39309] 1.1577] 5.767].
[B13 [LOGV" 763-85 | 0.31629] 0.20822) 0.6375] 2.8066] 3.1603]
B14 [LOGV_[a3-85 | 0.24412] 0.1176] _ 0.8023] 1.3747] 1.0218] 2.6998] a
[B15 [LOGV__J93-85 [0.13375] 0.03665) 0.9663] 1.0509] 0.9033] 1.3245] 2.2612] ~ .
{816 ]LOGV_J83-85 [0.10708] 0.03554] 1.0501] 0.8984] 1.0487] 1.1265] 1.6402] 2.0329} a
B17 [L0GV_ 63-85 | 0.09638] 0.02013] 1.0496] 0.8104] 1.0655] 1.2345] 1.6252] 1.6710) 2.3080]. a
B18 [LOGV_ Je3-65 [0.05614] 0.0163} 0.9253] 1.1246] 0.8614) 1.096] 1.1113] 1.8248] 1.4559] 1.1012].
| CX [LOGV" [63-85 | 0.01369] " “0.0022] 0.0686] 0.6844] 0.0642) 0.0854[ 0.062] O8116| 1.1138] 0.0360] 1.0241



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
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. Coefficients of correlation (7° values) are also shown
for each of the regression equations. As expected, the r values shown in the table
demonstrate good correlation between the IM240 and IM147 cutpoints. (More detailed
regression results, including graphical plots, are shown in Appendix F.) 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

Correlation
Model Year Emission Coefficient
Grou Constituent Slope Intercept ta value)

HC 0.896629 0.110694 0.963
1981-1985 co 1.020463 0.858255 0.979
NOx 1.065128 0.085613 0.978
HC 0.933646 0.056509 0.976
1986-1989 co 0.939067 1.679632 0.969
NOx 1.077932 0.058971 0.956
HC 0.963839 0.026672 0.949
1990+ co 1.037836 0.392486 0.840
NOx 1.102698 0.048771 0.918

-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%
g 30.0% Ta
2 rs0% 1
ge 0% TT Dist imi47
2 20.0% 1 Ta
E 15.0% i | (2nd IM147
% 10.0% 1 Zoe 3rd M147
§
5 5.0% L",
0.0% Ad ba:
PC LDT1 LDT2 All
Vehicle Type

Later in this report, implementation of fast-pass, fast-fail, and retest algorithms will be
discussed. The addition of the algorithms has a significant impact on failure rate. In
predicting which vehicles will ultimately pass the test, minor predictive errors associated
with the fast-pass algorithm lead to.a small fraction of false passes and in turn a reduction
in the failure rate. The fast-fail and retest algorithms, which are used to predict failing
vehicles, will err on the side of false failures, hence acting to increase the failure rate.

One of the goals in developing the revised Max CO cutpoints was to minimize the
projected false failure rate; however, false passes were treated somewhat differently.
Rather than attempting to keep the number of false passes to a minimum, the study
instead focused on maximizing excess emissions identification while also minimizing test
time. As a result, the fast-pass algorithms allow a number of marginal vehicles that have
little impact on the overall excess emissions to falsely pass. While relatively insignificant

-4-


Regression Summary - Composite CO, IM240 to IM147
Model Years 1981-1985

All Vehicle Types
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.989621
R Square 0.979349
Adjusted R0.979198
Square
Standard 5.150236
Error
Observations 139
ANOVA
df ss MS. F Significance F.
Regression 1 172333.7 _ 172333.7,  6497.045 2.6E-117
Residual 137 3633.916 26.52493
Total 138 175967.6
Coefficients | Standard t Stat P-value Lower 95% Upper 95%
Error
Intercept 0.858255 0.536573 1.599511 0.112011 -0.20278 1.919292
(M240 1.020463 0.01266 80.60425 2.6E-117 0.995428 1.045497
IM240 Line Fit Plot
250
200
150
o © M147
= —— Predicted IM147
= 100
50
0
300

F-2


Appendix G

Fleet Distribution Data


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 are judged
by their ability to predict emissions on the current IM147, an easier task. The retest
criteria must therefore be 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 shows the
number of false fails as a result of the retest procedure. The criterion for false failing is
quite simple: a false failure occurs any time a vehicle that would go on to 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 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
IM147 tests, the fast-fail algorithm can terminate tests during an IM147 test. Like 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 was 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.

-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 3,347-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 | rai | pass |%rait | Fai | pass | %Fait | Fait | Pass |oFail| Fail | pass | 9% Fail
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 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%
LDGTI 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 | 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 |83%]| 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%| 507 | 1273 |28.5%|
LDGT1 120 | 844 |12.4%| 85 879 | 8.8% | 127 | 837 |13.2%] 239 | 725 |24.8%
LDGT2 73 | 530 j12.1%] 56 547 19.3% | 82 521 |13.6%] 149 | 454 124.7%
All 473 | 2874 114.1%] 381 | 2966 |11.4%] 486_| 2861 |14.5%| 895 | 2452 |26.7%

-3-


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 one-second recordings
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 the table shows, 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.

-37-


CED

1M147 Composite Regression Coefficients, NOx, 1986 to 1987 Model Year LDGT2's

Regression Cositcents
ISegment RMS — |Reg.
lumber feror {Constant |C1 cz fcs_— ca cs cw —fcm?_——ca_—fcao’— cto fers forz__—fera_|cra__fcrs__fcre_|cr7_|cis__|cro
ie LOGT2 [eo-67 | 1.61176] 3.13063] _s1.o40a]
[Pz LOGr2 [s6-87 | 0.97705] 1.10862[ 3.0631
yes LOGT2 [66-87 |" 0.94502] 0.9639] 5.0759}
[P= LoGT2 [26-87 | 0.93501] 0.63053] -9.3825] i
LOGT2 {86-87 | 0.85994] 0.96285] 4.403] 5.1673].
LoGr2 [86-87 | 0.84757] 0.91259 4.4095] 8.3678] 6.6507).
LoGr2 {e6-87 | 0.85020] o.01803] 4.0853] 7.8929] 10.0876] 0.7099]. 5
LoGr2 [s6-87 | 0.05275] 0.2104] -3.8044| 7.7612) 9.5053] 0.6261[ 1.3726]. ‘|.
LoGr2 [se-87 | 0.8317e{ 0.86005] -3.778| 4.6162] 6.3808] 0.6741] 0.2851] 0.0702). a
LOGT2 [26-87 | 0.80985] 0.90764] _-7.0196| 2.6936] 10.9511] -1.1615] 0.3123] 3.2549] 6.6621}. "
LoGr2 [20-87 | 0.76148] 0.62169] 6.9566] 5.15| 9.0243] 2.4765] 0.213] 3.9069] 6.4604] 0.8602]. i
oGT2 [26-87 | 0.59003] 0.3735] -2.4217] 2224] 1.9821] 1.3263 -2] 4.7639] 5.4191] 0.2735] 5.406]. a
LoGT2 [86-87 | 0.33385] 0.19007] 0.2814 ~0.1077| 2.4058] 0.5387] 1.7614] 4.2622] 0.1798] 0.5637] 0.4608] 3.2023]. i
LOGT2 [66-87 | 0.20099] 0.13611] 1.8759) 0.9302} 1.5194] 0.7032] 0.6974] 2.2407[ 0.0121] 0.4793] 0.2025] 1.9049] _ 2.1646] a
LoGr2 |e6-67 | 0.1036] 0.0461] 0.3765) 1.0648] 1.097] 0.2424] 0.0264 1.7158] 0.0601] 0.6449] 0.9104] 1.0307] 1.1876] 1.3178].
LOGT2 [26-67 | 0.09657] 0.03818] 0.6854! 0.6222] 1.3221] 0.5089] 0.0753] 1.3097| 0.8115] 0.675] 0.0002] 0.9762] 1.1823] 1.0924] 0.8604].
LoGr2 [66-87 | 0.08202] 0.02243] 0.73411 0.6071] 1.3461] 0.4168] 0.5635] 1.6121] 0.0273] 0.695] 0.6097] 0.9541 1.1481| 1.01571 _0.8336| 1.7625].
LoGr2 [26-87 [0.05719] 0.02206] 0.9248] 0.6674] _1.7312{ 0.3068[ 0.8156] 1.3908] 0.0199] 0.6845] 0.0323] 0.8095] 0.6223[ 0.7408] 0.9324] _ 1.241] 0.7403}.
[toGr2 [26-87 | 0.01482] 0.00786] 0.6527] 0.7944] 0.7706[ 0.6813] 0.8969] 0.6883] 0.6733] 0.71] 0.7008] 0.7385] 0.7569] 0.6670] 0.7207] 0.8026] 0.7243] 0.7;
M147 Phase 2 Regression Coefficients, NOx, 1986 to 1987 Model Year LDGT2's
Rograssion Coefficients
Segment RMS —|Reg.
Number fervor __|Constant |c1 cz jes fes_—fcs_ fos —sfcv_—- cs —fca_—fcro —_|err_—fcr2_fcr3-_— fers fcrs_—|cte—er7_— crac
BIT oGr2 [ee-7 | 1.44062) 1.6077) 2507]
Biz |toGr2 [e687 | 0.9z6zi[ 0.61792]. 0.4543] 9.6700). a
{e13___[toGr2 |ee-87 | 0.45405] 0.32209]. 0.7862] 0.5919] 4.3568 i
fei4___[toGra fee-87 | 0.27745] 0.22123). ‘O6tre] 0.2412] 2.551] 2.0603}. i
[B15 |toGr2 |ee-87 {0.14699} 0.0967]. 0.8032] 1.2698] 1.2876] t.et05] terra. |.
[are LoGr2 [e6-67 | 0.1382] 0.07565). 0.859] 1.1449] 1.2732] 1.5831] 1.6009] 0.9183].
Bt LoGr2 (86-87 | 0.1172) 0.05283]. 0.8945] 1.2281] 1.135] 1.5637] 1.3545] 1.3403] 2.1304).
fate |LoGr2 |86-07 | 0.08824] 0.05293) ‘o.8ee7| 1.3239] 0.9624{ 1.2543] 1.0361] 1.5212] 1.2098] 0.5040].
B19 ‘OGT2 ]e8-87 | 0.0176] 0.00876]. 0.958] 0.9848] 0.9632] 0.9799] 0.9647] 0.0458] 1.1217] 0.0723] 0.904



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.

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

-16-


Table 1-6
Comparison of Integrated Algorithms vs. Standard IM147
Impact on Test Time and Excess IM240 Emissions (Max CO Cutpoints) Lost*

Model Mean Test | MeanTest | % Excess Emissions Identified®
Year | Sample Time 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% | 97.6% 100.0%
1986-89 | 425 248.4 112.0 99.2% 97.1% 100.0%
tDsv 1990-95 | 952 184.8 78.3 96.8% 85.3% 91.2%
1996+ | 70 154.3 37.1 - - -
All 1780 221.0 100.0 98.2% 93.3% 96.8%
1981-85 | 260 306.6 158.7 78.5% | 97.3% 99.4%
1986-89 | 222 230.8 101.6 93.8% 90.8% 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 83.4% 92.6% 99.5%
1981-85 94 307.5 158.8 100.0% | 100.0% 77.2%
1986-87 64 253.2 101.4 100.0% | 100.0% | 100.0%
LDGT2 | 1988-95 427 166.5 54.2 - 0% 44.4% |
1996+ 18 146.0 28.0 - - -
All 603 197.1 74.7 100.0% | 93.8%

Weighted Average

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

217.0

94.0

95.9%

> Mean test time standard refers to the average dynamometer test time without the algorithms enabled.
This was determined using the 3,347-vehicle sample.
© Percent of IM240 (Max CO) excess emissions identified with the integrated algorithms enabled. This
was determined using the 835-vehicle sample and normalized to the Arizona 2% random sample fleet
distribution data, July 1997 to March 1998.

-1l-


List of Figures

page
Modal IM147 Fast-Pass Standards ........... 000 c cece eee cee eens 4
Integrated Test Results Maximum CO Cutpoints Without CPP Limits ......... 10
Test Sequence Used to Investigate Triplicate
IM147 Tests in Arizona Test Lanes ........... 0.0.00 ccc cece cece 21
Model Year Distribution of Sample Fleet-and AZ Overall Fleet .............. 24
IM147 Trace Phase 1 and Phase 2 ........ 0. c cece eects 27
Failure Rate by Consecutive IM147 Max CO Cutpoints .................0.. 32
Failure Rate by Consecutive IM147 Startup Cutpoints ... . 33
Failure.Rate by Consecutive IM147 Intermediate Cutpoints : ..34
Failure Rate by Consecutive IM147 Final Cutpoints ........... 00sec sevens 35
IM147 Test Segments Used for Fast-Pass Cutpoint Development ............. 39
Retest Algorithm During First IM147-LDGV . .. 44
Retest Algorithm During Second IM147- LDGV beeeeeee .45
Retest Algorithm During First IM147-LDGT1, LDGT2 . -- 46
Retest Algorithm During Second IM147-LDGT1, LDGT2 . . 47
Integrated Test Results Max CO Cutpoints Without CPP Limits beeen ee eee 52

Comparison of IM147 Variation Limit Metrics ............ 00.00 e cece eens
Distribution of Arizona IM147 Positive Power Differences
Illustration of Second-by-Second IM147 Cumulative Positive

Power Variation Limits ..... 2.00.0... cece cence e eens 65
Effect of Initial CPP Limits Multiplier on Effective IM147 Abort Rate ........ 66
Process for Integrating CPP Variation Limits ..............0. 00s eee eeee 70 ,

-iv-


Appendix E

Second-by-Second CPP Variation Limits


(eee)

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

ession Coefficients.
7
RMS — Reg. i
Error Constant ica ica C5 icé ic7 ics ico iC10 C14 C12 ic13 C14 C15. C16 C17 ic18 c19
[81-82 | 7.99839] 11.889:
81-82 | 6.58035] 8.47137] . 1
181-82 | 5.95523] 7.35499) 4.3273}. €
(81-62 | 5.84107] 6.94384] 2.5147] 4.4184]
81-62 | 5.67429] 6.92733} 1.0082] 3.8467] 3.2208} a
(61-82 | 5.60045} 7.2234! 0.1894] 3.6997] 2.3343] 2.4304] . o
(83-82 | 5.42769] 6.90375] 0.2076] 2.5448] 1.0667/ 3.0396) 2.007) a a
181-82 | 5.17703] 6.82475) 0.567] 2.793} 1.0784] 2.1889] 0.6894! 3.583]. a my
81-82 | 5.11543] 6.5602} 0.049] 1.6424] 0.3327] 1.1075] 0.5581! 3.277] 3.6636). =
81-82 | 5.09166] 6.36664) 0.4412] 1.7282] 0.1522] 1.3964] 0.1539} 3.943] 1.4796] 2.6588]. + .
181-82 | 3.36176] 2.42133} 0.3844] -1.067] 0.3333] 1.7489] 0.1534] 1.8436] 3.6102] 2.5886] 0.995] a a
81-82 | 2.45431] 1.51197 0.3739] 0.1848] 0.3965] 1.261] 0.4926] 1.5431] 2.6906] 2.8681] 0.6782] 1.37481. a
81-82 | 1.64222]  0.81857| 0.5366] 1.486] 0.1283] 1.4627] 0.5971] 1.0752] 1.2221] 2.1211] 0.6873] 0.6834] 2.0065]. a
(81-82 | 1.30098} 0.6002} 0.4632] 0.8416] -0.3448] 2.0537] 0.7512] 1.0435] 0.8919] 2.1412] 0.6644] 0.6957] 1.6479] 1.2133}
81-82 | 0.99586] 0.48274 0.5819] 1.4446] 0.1117] 1.6713] 0.9146] 0.8677] 0.8203] 0.9725] 0.6913] 0.6885] 1.0364] 0.6235) 1.3336]
(81-82 | 0.92334] 0.37973} 0.6059] 1.5811f 0.0371] 1.4292] 1.0143] 0.8284] 0.7229] 0.8179] 0.6963] 0.6941] 0.9588] 0.9625] 0.9091] 1.8136]. .
81-82 | 0.64726] 0.19448} 0.544] 1.2283] 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
181-82 | 0.52289] 0.04744} 0.3484] 1.2653] 0.3087] 1.1707] 0.6643] 0.8253] 0.7848] .0.8903] 0.6988] 0.7311] 0.7096] 0.8592] 0.8362| 1.0156] 1.7867] 0.8424].
81-82 | 0.31963] -0.00493} 0.5675] 1.1578] 0.3797] 0.9834] 0.7238] 0.6876) 0.994] 0.7576] 0.7005] 0.7001] 0.7374] 0.7792] 0.7056 0.244] 1.232] 0.646] 0.9017
IM147 Phase 2 Regression Coefficients, CO, 1981 to 1982 Model Year LDGV's
Regi Coefficients.
fessor s
Segment RMS — jeg. “
lumber Error {Constant [Ct Ica ica. ica cs ice ic? ica ico Icio fet fc12z_—sfcra-_—s feta fers sete fes7—s cra [cto
fe COGV__{et-a2 | 7.67561) 6.04656). z 7.2055). 5
pBiz LCOGV—_[e1-82 | 4.28681] 4.12649]. 0.942i[ 1.967]. a
5 CHEN TOGV [61-82 | 2.91132) 1.85708} 0.9717] 1.0199] 3.5026]
| Cr LOGV [81-82 | 2.35783] 1.43426] 0.0278] 0.7999] 2.9182] 1.5914].
| ILOGV__]81-82 | 1.81322] 0.97781]. 0.9942] 0.8184] 1.529) 0.995] 2.1475] a
[816 ILOGV__[81-82 | 1.64673] 0.74808]. 1.0076] 0.8215] 1.3625] 1.3316] 1.1639 3.4285].
| xe 1LOGV__ [81-82 | 1.35105] 0.12268) 1.0045} 0.858] 1.3822] 4.20] 1.2026] 1.7464] 4.1093] a
| xe JLOGV [81-82 | 0.83894] 0.09966} 0.9679] 0.9648] 0.9202] 1.1549] 1.0845] 1.59497 3.5169] 1.2191]
| EEN [Locv_ 81-82 | 0.57427] 0.10409]. 0.9662] 0.9148} 0.9743] 1.0447] 0.9099 0.3172] 2.66] 0.9357] 1.311



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)

[Lower Limit Aborts Upper Limit Aborts |}

20 25 3.0 35 40 45 5.0
Initial CPP Limits Multiplier

RR

10%

Effective Abort Rate (%)

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.)

-66-


3. TEST DATA

Data used in this study are divided into three distinct groups consisting of 304, 543, and
2,518 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 4
10 +
t- >| + >
0
5-15 minutes First Second Third Hot
in queue (M147 test M147 test M147 test IM240 Test
Time

-21-


Regression Summary - Composite NOx, IM240 to IM147

Model Years 1986-1989

All Vehicle Types

SUMMARY OUTPUT

Regression Statistics
MultipleR 0.977553
RSquare 0.955611
Adjusted R0.955384
Square
Standard 0.298767
Error
Observations 198
ANOVA

of ss MS F Significance F
Regression . 1 376,638 376.638  4219.491 1.6E-134
Residual 196 17.49525 0.089261
Total 197 394.1332
Coefficients Standard t Stat P-value Lower 95% Upper 95%
Error
Intercept 0.058971 0.035732 1.650379 0.100467 -0.0115 0.129439
IM240 1.077932 0.016594 64.95761 1.6E-134 1.045206 1.110659
IM240 Line Fit Plot
@ IM147

IM147

10

—— Predicted IM147

F-6


Table 1-3
Modeled Fast-Pass Results
Excess IM240 Emissions* (w/ MaxCO Cutpoints) vs. Average Test Time
No Fast-Pass? Fast-Pass Enabled
Excess HC Identified 97.8% 95.7%
Excess CO Identified 96.3% 92.7%
Excess NOx Identified 95.4% 82.4%
= Average Test Time‘ 217 seconds 125 seconds

Excess emissions normalized to Arizona 2% random sample fleet distribution data, July 1997 to March
1998.

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.

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
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 1-4 details further results of the retest analysis.

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.


Regression Summary - Composite HC, IM240 to IM147

Model Years 1986-1989

All Vehicle Types
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.987927
R Square 0.975999
Adjusted R 0.975876
Square
Standard 0.189037
Error
Observations 198
ANOVA
df ss MS F Significance F
Regression 1 284.8199 284.8199 7970.309 1.1E-160
Residual 196 7.004082 0.035735
Total 197 291.824
Coefficients Standard t Stat P-value Lower 95% Upper 95%
Error

Intercept 0.056509
IM240 0.933646

0.015868 3.561206 0.000463
0.010458 89.27659 4.1E-160

0.025215 0,087802
0.913021 0.95427

IM240 Line Fit Plot

12

M147

@ IM147
—— Predicted IM147

15

IM240

F-4


4. IM147 CUTPOINT ANALYSIS

This section of the report discusses the application of the IM147 Max CO cutpoints to the
3,347-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 3,347-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. (Figure 4-1 shows the speed/time profile of the IM147 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.

-26-


01-0

1M147 Composite Regression Coefficients, HC, 1983 to 1985 Model Year LDGV's

Regression Coefficients
Segment IRMS — | Reg.
[Number Error ‘Constant [C1 jc2 ic3 C4 ics ice C7 ic ico C10 cat C12 C13 jC14 C15 C16 C17 C18 ic19
PT ToGV_fes-65 | O40ri2] O46122| 11.2061 a
PZ. ILDGV_ [83-85 [0.28584] 0.23584] 0.3793] 4.0312].
PS LOGV— {e3-85 | 0.2251] 0.18104] 0.1504] 1.6625] 5.8835).
Pg LOGV__ {83-85 | 0.20949} 0.4636] -0.6566} 1.69] 3.4063] 3.0941] a
{Ps LOGV—Ja3-85 | 0.19447] 0.16094] -1.0863] 1.2885] 2.6860] 2.1201] 3.7766].
PS LOGV_ 83-85 [0.19153] 0.16531] -1.4624] 1.3044] 2.1907] 1.8106] 3.1209] 2.0852].
7 ILOGV__ [83.85 | 0.18355] 0.15427 -1.1191] 1.2382] 2.1956] 1.3759] 2.2879] 1.3053] 2.1756] a
PB ILOGV_[83.85. 0.1742] 0.14505] -1.1020] 1.1144] 1.7862] 0.8880] 2.4267] 1.2368] 1.6895] 1.9949]
Po. CoGv—Ja3-85 | 0.16499] 0.14815] 0.7220] 1.073] 1.8176] 0.7769] 1.4705] 0.6194] 1.4169] 0.9084] 4.175] a
P10 JL0GV|a3-85 | 0.16024] 0.14347] -0. 7243] 1.1373] 1.5662] 0.8427] 0.8657] 0.8605] 1.1993] 0.834] 2.645] 2.0158].
[Pit [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 a
{P12 [LoGv—Je3-as | 0.10785] 0.05022] 0.4878] 0.4094] 0.8846] 0.5066] 1.0774] 1.8031] 0.736] 0.5491] 1.8603] 2.2810] 1.0106] 1.6327]. i
P13. [COGV"Ja3-85 | 0.07061 0.0331] __ 0.7767] 0.4332] 0.8179] 0.8806] 1.0015] 1.2108] 0.561] 0.4915] 1.5593] 1.6272] 0.8246] 0.3997] 2.5228].
P14 JLOGV"Jé3-65 0.088] 0.02445; 0.861] 0.3782] 0.9405] 0.9692] 0.8471] 1.0556] 0.6664] 0.4702] 1.5692} 1.559] 0.6216] 0.5139] 1.483] 2.5758].
[P15 LOGV_ [83-85 | 0.05617] 0.01633] 0.6641] 0.3847" 0.9494] 0.9339] 0.8876] 1.1718] 0.6875] 0.3691] 1.2553] 1.3076] 0.7499] 0.5579] 0.6795] 1.1332] 2.2877, .
[P16 {L0GV__|a3-a5 { 0.05545] 0.01675] 0.7233] 0.3931 0.9564] 0.9026] 0.9463] 1.1024] 0.7194] 0.867] 1.2701] 1.19771 0.7436] 0.5865] 0.627 1.1212] 1.8334] 1.417] i
Paz LOGV_ [63-85 [0.03879] 0.00847) 0.893} 0.5126] 0.7936] 0.6136] 1.0495] 0.8218] 0.8047] 0.6078] 0.984] 0.9734] 0.7209] 0.7276] 0.5005] 1.2506] 1.4269] 0.4287] 2.417]. .
Pia [LOGV_ ]e3-85 | 0.02039] -0.0015] 0.9063] 0.6276] 0.7336] 0.769] 0.6401] 0.7809] _0.¢547| 0.6256] 1.0592] 0.5762 0.7013] _ 0.748] 0.5944] 0.8615 1.0441] 0.6308] 1.7862] 1.3705].
ac [LOGV [83-85 0.0264] -0.00036[ 0.6248] 0.6354)" 0.7403[ 0.7849] 0.8885| 0.8016) O.6772| 0.6005] 1.0662| 0.4753] 0.7239] 0.7376] 06163] 0.7249] 0.9182] 0.3306] 1.7224] 1.1415] 0.5047]
IM147 Phase 2 Regression Coefficients, HC, 1983 to 1985 Model Year LDGV's
Regression Coefficients
Segment RMS: Reg.
Number Error [Constant {C1 ic2 ic3 C4 ics C6 ic? ice ico \C10 C14 C12 C13 C14 iC1S C16 C17 C18 C19
eit [COGV—fas-e5 | 0.2057] 0.10209]. 2646). "
B12 JLOGV_fe3-85 [0.17708 0.0755]. 7.8316] 2.7713].
| EE [LOGV [83-85 | 0.12398] 0.04472]. 1.2782] 0.5298] 4.0757]. a
fers TLOGV_fe3-85 [0.10965] 0.03254]. 0.9666] 0.713] 2.5673] 3.6248. -
{eis {LoGv—Je3-85 [0.08809] 0.01672]. 7.0598] 0.7837| 1.0694] 1.2338] —S.6614]. a
fae [LOGV_ 183-85 [0.08613] 0.02003] 4.0528] 0.8555} 0.9553] 1.2083] 2.7852] 2.7887}
|es7——tocv [3-85 | 0.05674] 0.00853]. 10.0868) 0.0946] 0.7356] 1.5594] 2.1184] 0.6020] 3.6306]. i
| EXE ILOGV_ [83-85 [0.04106] 0.0038] 0.9461] 1.0589] 0.7763] 1.1008] 1.4725] 0.8392] 2.602] 1.9484)
| aE LOGV_ [63-85 | 0.03964] 0.00206)" 0.9706] 1.0465] 0.8189} 0.9395] 1.277) 0.41) 2.4945] 1.6256] 0.6907]



Regression Summary - Composite NOx, IM240 to IM147

Model Years 1981-1985

All Vehicle Types
SUMMARY OUTPUT
Regression Statistics
MultipleR 0.988936
R Square 0.977995
Adjusted R 0.977835
Square
Standard 0.322696
Error
Observations 139
ANOVA
: of ss MS F Significance F
Regression 1 634.0613 634.0613 6088.969 2E-115
Residual 137 14.26619 0.104133
Total 138 648.3275
Coefficients Standard t Stat P-value Lower 95% Upper 95%
Error
Intercept 0.085613 0.045469 1.882899 0.061834 -0.0043 0.175525
IM240 1.065128 0.01365 78.03185 2E-115 1.038136 1.09212
IM240 Line Fit Plot
14

IM147

oa

—— Predicted IM147

IM147

15

IM240

F-3


£9

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

Regression Coefficients

Segment IRMS — [Reg.

[Number |Error. Constant }C1 [cz ic3 ica ics C6 ic7 cB ico C10 (al \C12 \c13 C14 c1s C16 jc17 C18 C19
{Pi___[C0Grs [50-85 | 037406] 0.22616) 17-1654). r
fP2. ILOGT1 [90-95 | 0.23507] 0.05902] 2.145] 5.1262].
[es LOGT1 |90-95 | 0.21189] 0.05784] _-1.1729] 3.3619] 3.4507]. -
Pa, LDGT1 [90-95 | 0.20276] 0.05212] -2.7245] 3.2374] 1.6019] 3.7987].
| JLOGT1 [90-95 | 0.19505] 0.05057] -3.0392[ 2.7597] 1.1351 2.24] 3.8993] |.

@ LOGT1 [90-95 [0.18478] 0.05162] -3.3501] 2.6388] 0.6397] 1.4629] 1.9249] 4.9984]

[PZ JLOGT1 [90-85 [0.17282] 0.04206] -2.9083| 2.3805| 0.6434] 1.9124] 0.9621 3.0176] 4.1577] a

IPB |LOGT1 [90-95 | 0.16217] 0.03882] -1.4065] 1.7566] 1.0327| 1.3221] 0.6046] 1.7837] 2.9574] 4.5516]. a

[Po [LOGT1 [90-95 | 0.15997] 0.03954] -1.5284] 1.8223] 0.7356] 1.6157] -0.8447] 0.9682] 2.5193] 3.7565] 3.0158]. i

P10. LDGT1 [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].

P11 LOGT1 [90-95 | 0.1242] 0.00888] 0.4575] 0.8942} 1.2216] 1.078] -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] 0.0978] 1.8755]. a

[P13 |LOGT1_[90-95 | 0.08846] 0.01744] 0.8836] 0.5089] 0.8095] 1.7266] 0.0115] 0.5537] 1.4386] 0.4008] 0.5776} 4.904] 0.7549] 0.8219] 2.3703]. a
| Ga JLOGTs [90-95 | 0.07835] 0.01772) 0.858] 0.474] 0.6951] 1.5244] 0.3476] 0.7542] 1.4394] 0.4383] 0.7477] 2.2305] 0.5943] 0.0555] 0.9132] 2.1616].
Pts, JLDGT1 [90-95 | 0.06853] 0.01326] 0.7115] 05394] 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 JLOGT1 [90-95 | 0.0593)" 0.01319] 0.6956] 0.6235, 0.517] 1.2012] _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 LDGT1 [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] 0.041] 2.2135] 2.3062]. .
[P18 JLOGT1 [90-95 [0.03216] 0.00497] 0.6809] 0.6034] 0.583a[ 0.0437] 0.759] 0.6708) 0.356] 0.8599] 0.6671] 0.8103] 0.5884] 1.0269] 0.3620) 1.3443] 1.5874] 1.5359].
[P19 {LOGT1 [90-85 | 0.02955] 0.00468] 0.7103} 0.6964] 0.6123] 0.9353] 0.834] 0.5442[ 0.0225] 0.8944)" 0.4112] 0.6891] 0.6898[ 0.7038] 0.6905] 0.7638] 0.3123] 0.6669] 1.5046| 1.3036] 0.7468]

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

Segment RMS Reg.

[Number Error Constant |Ct ic2 ic3 ica cS icé ic7 icé ico C10 Cit C12 C13 cia C15 C16 C17 C18 C19
Tt [COGT1 [90-95 | 0.19826) 0.0sa6). EXEC "

| EXE 0.16235] 0.0226]. 4.7885] 3.1368] a

| CXEY 0.12683] 0.02868}. 4.4305] 1.1678] 3.5627].

eis 0.11708] 0.02986)" 0.9821] 1.3215] 2.042] 2.4823]. q
pis 0.09769} 0.0205} 1,024] 1.124] 1.0503] 0.5085] 3.5526} a
| Gs 0.08453] _ 0.02072} 1.0421] 1.0995] 1.3341] 0.4695] 0.6556] 7.7639]

(B17. 0.05719] 0.01154]. 41,0027 0.9977] 1.0045] 0.9826! 1.4994] 2.7725{ 3.1834] _ .
818 0.04481 0.008]. 0.9558] 1.0711] 0.8811] 1.2053] 0.5862) 1.7403] 2.1672] 2.144]

B19 0.04108] 0.00785]. 0.9823] 0.9356] 1.0185] 0.9737 0.499} 0.8303] 2.0505f 1.8394] 1.0152]



Table 1-4
Retest Algorithm Results

LDGV LDGT1, LDGT2
Total Number of Complete 1567 1780
ests
# of Failures Without o,
Retest Algorithm 327 (20.9% of 1567) 273 (15.3% of 1780)
# of Conectly Identified 245 (74.9% of 327)? 180 (65.9% of 273)°
ailures’

# 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 9
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 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.

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
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 1-5 shows the results of the fast-fail algorithms when applied to the 3,347-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 1-5
Fast-Fail Algorithm

Second IM147 Fast- Third IM147 Fast- False Failures
Failures Failures :

211

182



69

1M147 Composite Regression Coefficients, HC, 1981 to 1982 Model Year LDGV's
"Regression Coefficients

Segment IRMS — Reg.

INumber Error [Constant |C1 ic2 ica C4 ics \C6 ic? J ico C10 C14 C12 C13 C14 cis C16 C17 C18 C19
a TOGV [61-62 | 0.40216] 0.57705] 15,1354]

iP2 LOGV_ [81-82 | 0.33532] 0.27587) 2.7958] 4.3107].

PS LOGV_[81-82 | 0.28385] 0.2387] 3.237] 1.8573] 5.3824]. :

Pg ILOGV__ {81-82 | 0.26651] 0.22287] 1.6681] 2.0142] 3.3342] 3.0522]. .

iPS (LOGV_ [81-82 [0.25039] 0.21722] 0.3542] 1.7343] 3.0584] -0.4158] 5.9309] a
Pe {LOGv_fet-82 | 0.23492] 0.22612] -1.1998] 1.7931] 20663) 0.0624] 2.4987] 4.6232].
[Pz {UOGV_ [81-82 [0.22127] 0.21033] -1.4452] 1.5589] 1.4953] 0.1839] 1.2039] 3.8754] 3.5496].
[Ps TLOGV__fat-82 [0.21552] 0.19445[ 0.9213] 1.4891] 1.3035] 0.0672] 1.0624] 2.967] 2.9559] 2.5679} a
Po. ILOGV__ [81-82 0.2067] 0.18041] -1.0513] 1.5796] 1.3012] -0.0519} 1.564] -0.0917] 1.2979] 2.0321] 6.4454]. a
[Po iLOGV {81-82 [0.18913] 0.16356] -1.3284] 1.5976] 1.0833] 0.3527] 0.1824] 0.3708] 1.1991] 2.1788] 0.884] 7.9922]. - a

| CXR LOGV_ [81-82 | 0.15452] 0.0563] 0.0087} 0.5393] 1.7119] -0.1012| 0.6423] 0.9746] 7.0961, 0.177] 1.9283] 6.7153] 1.4557]. a

Piz LOGV [81-82 [0.13848] 0.03382] 0.3059] 0.4616] 1.2406] 0.2523] 0.8742] 0.2381] 0.6512] 1.0074] 1.6257] 7.4] 0.8496] 1.7116].

| CAE LOGV__[81-82 [0.11063] 0.03458] 1.1018] 0.2871] 1.8035] 0.9765] -0.5314] 0.4907] 1.1534] 0.6704] 1.3477] 4.1066] 0.6837] 0.0626] 3.0307]. a
[P14 ILOGV__ [81-82 | 0.10379] 0.02583] 0.937} 0.3096} 4.715] 1.0249] -0.5161] 0.4784] 1.2033] 0.6542] 1.7892] 3.4911] 0.4746] 0.3833] 2.0844] 1.6075]
Jers. ILOGV__[81-82 | 0.09549] 0.02733] 1.3271] 0.2571] 1.7659] 0.7304] 0.2147] 0.1823] 1.1956] 0.9924] 1.0719] 2.8131] 0.4955] 0.6194] 1.2124] 0.4439] 2.0986] a
{PIs LOGV_ [81-82 [0.07832] 0.0312] 4.0918] 0.4768] 1.3033] 0.8784] 0.0472} 0.402} 1.6192) 4,449] 0.2368] 2.1792] 0.4806] 0.6063] 1.0279] 0.7352] 0.5135] 5.3144) a
| (ed LOGV_ [81-82 | 0.05521] 0.00814] 1.2935] 0.6849] 0.6167) 0.5737] 0.616] 0.8743] 0.9448} 1.724] 0.1427) 0.6803] 0.6243[ 0.6678] 0.6972] 1.4033] 0.6874] 2.3373] 2.2858] a

(LOGV [81-82 [0.04359] 0.00235] "1.4765 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) 1,817] 1.4148] 1.4078}
;Pi9 LOGV_ [81-82 | 0.03591] 0.00148[ 1.2093] 0.7134] 0.6447 0.8407| 0.2702] 0.9872] 0.8646] 0.9518] 0.1384] 1.1208] 0.6920] 0.6275] 0.8021] 0.5739] 0.7675} -0.2237| 1.2067] 0.0834] 1.1392]
IM147 Phase 2 Regression Coefficients, HC, 1981 to 1982 Model Year LDGV's
Regression Coefficients

Segment RMS —|Reg.

Number Error jConstant [C1 jc2 C3 C4 cS. (C6 ic7 ica ico C10 al jC12 C13. C14 Cis: C16 fon ic18 C19
(XN SLC SEP MOYERS ERE) r

fei2_s JLOGV_ 81-82 [0.29163] 0.08753]. 1.8437] 3.4587]. a

| EXE LOGV__ [81-82 | 0.18796] 0.06684]. 0.9136] 0.374] 6.0595]

[4 ]LOGV_J61-82 [0.1799] 0.05326)" 0.6172] 0.1991] 4.6274] 2.6027] a

[815 iLOGV__[81-82 | 0.15358] 0.052777. 0.6903] 0.7618] 2.1672] -0.1274] 4.2479]- a

B16 ILOGV__ {81-82 | 0.12174] 0.04997) 0.8196] 0.78621 1.6353] 0.667] 1.0053] 8.5142] a

(B17 ILOGV_ {81-82 | 0.08162] 0.00913]. 4.0095] 0.7175] 1.2863] 1.8724] 0.9087] 3.3081] 3.148] .
[B18 ILDGV_ [81-82 | 0.06421] 0.00073} 0.9515] 0.8623] 1.1518] 0.998] 0.9777] 2.5859] 1.9343] 1.9181].
{B19 LDGV_ [61-82 | 0.05142] 0.00017]. ~ | 0.9854] 0.8032] 1.1865] 0.7374] 4.1) 0.3037] 1.7543] 1.3113] 1.698;



Lame)

IM147 Composite Regression Coefficients, CO, 1981 to 1985 Model Year LDGT1's
ression Coefficients.
Segment RMS |Reg. 7 a
Number Error |Constant {C1 ic2 ic3 ics id icé id ica ico C10 C14 C12 [C13 (C14 cis Ic16 [C17 ice jc19
{Pi ]tOGrt [or-05 | 10.1754] 145276] 12.5442],
jP2. |LOGT1 [81-85 | 7.53286] 8.22078] 5.3429] 3.5563] a
[PS LOGTS [81-85 | 6.63268] 6.60978] 2.7805] 2.1525] 5.3312}
[Pa LOGT1 [81-85 | 6.52317] 6.63083] 1.5125] 2.2786] 3.2782] 3.8618] i. 4
PS: |LOGT1_ [81-85 6.508] 6.69361} 1.2349] 2.1748] 2.865] 3.4811] 1.0432). ny
PS LOGT1 [81-85 [6.43582] 6.71221] 0.1813] 2.3646] 2.9243] 1.5307] 0.1722] 2.7427]
{Pz LOGT1 [81-85 [6.19445] 6.37324] 0.5635, 1.9693] 2.7172] 0.6361] -1.561} 3.2843] 2.9955]. 5
iPS ILOGT1 [81-85 | 5.94027] 5.98064] 1.2118] 1.6393] 2.7167] 0.5356] -1.4442] 1.3759] 1.7876] 3.6004]. . A
[Po ILOGT1 [81-85 5.9353] 5.97383] 1.0059] 1.6737] 2.5314] 0.6549] -1.7102] 0.8508] 1.7967] 3.4358] 1.1942] et
[P10 ILOGT1_ [81-85 | 5.85738] 6.02848) TA77]_ 1.7285] 2.0533] 0.7067] -2.0802] 1.0202] 1.2427] 3.6953] -1.2444F 3.1472] “
P11 ILOGT1 [81-85 | 4.17486] 2.67355] 0.0251] 1.2531] 0.5349[ 0.8814] -0.4647| 21824] 0.0334) 2.27) 1.2267] 2.243] 1.0397] .
Pa: 2. ILOGT1 [81-85 | 3.65635] 1.97698] 0.5472] 1.2512] 0.2635] 2.0162] 0.5276] 2.225] 0.3841] 1.8143] 0.5359] 2.6593] 0.7892] 4.1045) .
P13. LOGT1 [81-85 | 2.30258] 0.92158] 0.7798} 0.5868] 0.3445] 2.2218] 0.3028] 1.3536] 0.523] 1.1833] 0.7632] 1.2554] 0.7979] 0.6638] 2.4441] _ a
[P14 LOGT? [81-85 [1.68789] 0.51912] 1.7468] 0.429] 0.6515] 1.5816] 0.7182] 0.8416} 0.48] 1.2506] 1,093] 1.1172] 0.6993] 0.5859] 1.7776] 1.5319} .
[P15 ILOGT1 [81-85 | 1.13559] 0.36037) 1.4986} 0.481] 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 a
[P16 ILOGT$ {81-85 [1.03437] 0.37532] 1.4149] 0.5205] 0.4715] 1.4258) 0.7753] 0.6646] 0.8775] 0.7646] 0.5543[ 1.4051] 0.6989] 0.7734] 0.9487] 0.9689] 0.6955] 1.6125]. .
PAT LDGT1 [81-85 | 0.96568] 0.20115] 1.2423] 0.5923] 0.2634] 1:3236] 0.9825] 0.7034] 0.7693} 0.7682] 0.1979] 1.3783]_0.7136] 0.7407] 0.9773] 0.9693] 0.8911] 1.2362] 1.2653]. A
P18 ILOGT1 [81-85 | 0.75949] 0.06889] 1.1529] 0.5685] 0.4385] 1.1423] 0.7942] 0.99431 0.7711] 0.6846] 0.5815] 1.031] 0.7017} 0.7499] 0.8098] 0.8097] 0.847] 0.846] 14.2494] 0.9829!
P19 |LOGT1 [81-85 [ 0.34973] 0.06051] 1.1508] 0.6559] 0.4115] 1.1472} 0.7599] 0.9619] 0.799] 0.6744] 0.5327] 0s 0.7106] 0.7294, 0.7378) 0.6891] 0.6385] 0.7063] 1.0232] 0.6907] 0.8306]
M147 Phase 2 Regression Coefficients, CO, 1981 to 1985 Model Year LDGT1's

} tegression. ficients.
[Segment [RMS Reg. ny
Number Error Constant [C1 jc2 ic3 ica ics. ice ic? cB ico C10 Cat C12 C13 C14 cis C16 Cv7 jC18 C19
pease Tors | O21] 9.1100: [e108]. r
B12 [LOGT# [61-85 | 5.96613] 4.72873) 1.1535] 7.7914]
B13 ILOGTs [81-85 | 3.55873] 1.89825]. 1.0491) 0.955] 3.6598]. a
B14 ILDGT1 [81-85 | 2.92833} 1.4436]. 0.9077] 0.9194] 2.7661) 1.929].
(B15, ILOGT1 [81-85 | 1.93926} 0.86159]. 0.8453] 1.0943] 1.32] 1.0292] 2.3486] .
1816 ILOGT1 [81-85 | 1.76086] 0.83361). 0.9517] 1.4151] 1.2896] 1.1023f 1.3595] 2.6847]. a
pz JLOGT1 [81-85 | 1.44658] 0.21699] 0.9768] 0.9803] 1.3491] 1.2314} 1.27} 1.5265] 2.526) a
fee ILDGT1 [81-85 1.172] 0.01644]. 0.9407] 1.0108] 1.0591] 1.0119] 1.2102] 1.0895) 2.58] 1.3347]
| EXE LOGT1 [61-85 [ 0.69767] -0.12536]- 6.8656[ 0.9707] 0.9936) 0.8576] 0.0082] 0.6708] 2.1203] 0.9645] 1.1513)


0£-D

1M147 Composite Regression Coefficients, NOx, 1996 and Newer Model Year LDGT1's

Regression cosfficrenis

ISegment RMS — Reg.

Number lemor__|Constant_[c1 c2_fes_—_fes_ es —_—ifce__iicz_—ifca fea _—_fcro_fo_ferz__|ers_ few _feis_fcie__fci7_|c1e_|cr9
| NS SLL CC SS OEY OT

fez LOG [es+ | 0.46673] 0.16401 12.1233] 9.0933]. i

qP3. LOGT1 fo+ | 0.46374[ 0.16268] 12.749] 2.6461] 3.1462] i

Fa LOGTr [96+ | 0.46514] 0.16338] ¥2.1028[ 2.534] 2.3048] 4.0422]

5 Locrs [see | 0.3791| 0.14765] 16.1604] 1.5554] 1.8687 0.8812] 12.6955]. a

PS. LoGr1 [96+ | 0.38121{ 0.14804] 16.193] 1.5564] 1.9020 1.0072] 12.8058] 0.5274] i

P7 LoGT1 [96+ | 0.3284] 0.12947] 19.8606] 1.0854] -0.0898| 6.0504] 5.5367 -2.3077| 0.6773}. J

PS LOGT1 [96+ __{ 0.31082] 0.14076] 16.6826] 0.9749] 1.2061] -.4908] 2.7593] 4.5733] 4.9464] 6.5776].

Po LOGT1 leer | 0.32062] 0.15138] 18.5988] 0.8971] _1.6707| 4.2603] 2.4773] -3.9369] 4.9682] 9.0390 2.7402].

P10 |LOGT1 lose | 0.24301] 0.13404] 9.3292] 0.7620] 2.6158] 11,0976] 1.3803] 2.9882] 1.7674 11.2329 a
; a LOGT1 [98+ | 0.18099] 0.10785] 0.7088) 0.5528] 3.4731{ 8.0995] -0.2732| 4.7615] 0.0782] 7.2458] Zz 1.26]
[P12 tors Jes+— [0.17159] 0.09436 0.6219] 0.5365] 2.653] 3.0512] 0.8472] 1.9087] 0.1596] _6.9805| 2.7130] 3.1761] 0.681a| 1.0803).
]P13.__|tocr1_[26+—|-0.12809} —o.07os7| 1.7268] 0.2591 2.536| 1.5628] 0.5764] -2.7771] 1.4626] 2.9783] 1.5186] 1.4469] 0.7662] 1.075] 1.7081
{P14 tocrt_{sex_| 0.10769] 0.06233 1.0403] _0.4537{ 1.0505] 0.0036] 0.7498 1.6955] 0.3857] 3.6594 0.892] 0.9085] 0.7212] 0.4778 1.0256| 1.5265}. 5
P15 |LOGT1 |96+ | 0.08499] 0.02733] 0.039 0.6102] 1.5776] 0.9616] 0.0528] 0.1089] 1.1166] 2.0576] -0.8366] 1.0631 0.7396] 0.86] 0.6974] 0.7671] 1.3416] 5
Pie tLOGT1 [e+ | 0.05153} _0.02¥83] 0.4719] 0.5741] 2.1516] 0.4167] 0.1408] 0.60 0.7472] 2.1867] 0.0185] 0.9315| 0.8149] 0.7897] _0.707| 0.6081| 0.6861] 2.2402).
P17 |LOGr1 |96+ | 0.04794] 0.01463] 6.175 0.6024] 1.5796] 0.5006] 0.714] -1.1576] 0.8449] 2.0981] -0.6903] 1.1098] 0.7149] 0.9709] 0.8286] 0.7074] 0.7195] _1.7387| 1.6076].
18 |LdGTt |96+ | 0.02762] 0.00211] 0.6747] 0.6416] 1.5148] 0.1216] 0.5752] 0.4924] 0.7534] 1.8426] 0.2646] 0.9695] 0.7007] 0.83] 0.7552] _0.7376| 0.7684] 1.1270] 0.6032] 0.766].
P19___[toGrt [ase __[ 0.00403] 0.00098] 0.7432] 0.7168] 0.7163] 0.6233] 0.7282| 1.0286] 0.6325| 0.8521] 0.6402] 0.7217] 0.7125] 0.7112] 0.7066| 0.7343] 0.6734] 0.7676] 0.7274] 0.7236] 0.7481]

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

Regression Cooficrents
Segment RMS — Reg.
Number JError__ {Constant [C1 cz jes_—fca_—fcs_—fcw_——ifcv_—ice~—feao—fcro ses —fcrz_ sera. fers, —|cte feria. cro
ist LOST foes | 0.28558] 0.12007]. i C r a Zany.
,B12_—]toGr+_|ee+—[-0.24770| 0.09313}. a i L i r 1.6406] 3.4138] r
jes ___]toGr1_[s6+ {0.18123} 0.08816}. - 5 i 4 1.252] 1.6471] 2.2975).
| CRE LOGTs_[os+ [0.15739] 0.06574] a a a 0.9727)" 1.3922] 1.725] 1.7148} 5
{B15 [toGr1_ [ser | 0.08057] 0.02458]. a 5 i a 5 a 7.0721] 1.1839] 0.9796] 1.028] _ 1.025]. i
{e16___[LOGr1 [96+ [0.07613] 0.02379]. i i i a q 1.1061] 1.2115] 1019s] 0.9869] 1.1519] 2.6649].
{817 [tOGT1 [e+ [0.07139] 0.00843] r a a a a 4.0352[ 7.3901] 7.1173|_0.956[ 1.1159] 2.3796] 2.0304]
{a18___|CoGr1_|s8+ [0.04333] 0.00582]. 5 5 i i a 5 i 0.9841] 1.2098 1.0418] 0.9720] 1.1585] 1.4094] 0.0884] 1.1153).

[B19____ [tort {96+ [0.005e5[ 0.00179]. 5 5 a r 0.9761] 0.0740] 0.6703] 0.9737] 0.6206] 1.0067] 0.0432] 0.9717] 1.0089]



Figure 6-2

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

4% 4

Frequency (% of observations)

2% 4

0% 4

10% 9% 8% -7% -Bh% -S% 4% -Bh -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 grouped by individual drivers, one for each of the
231 drivers that were found in the sample. Driver groups containing fewer than 25 test
records 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) indicate 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.

-62-


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 faimess. 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

-68-


Table 1-7
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 58

Table 1-8 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.

Table 1-8
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)

HC | co NOx HC co NOx
LDGV 95.2% 96.2% 84.8% 91.9% 95.1% | 85.7% |
LDGT1 80.5% 100.0% 70.6% 68.4% 98.1% 81.2%
LDGT2 87.9% 100.0% 46.6% 98.5% 97.7% 73.0%

Weighed | 913% | 97.3% 79.9% | 86.7% | 959% | 81.6%

® Percent of IM240 (Final Standards) excess emissions identified was determined using the 835 vehicle
sample. Data normalized to Arizona 2% random sample fleet distribution data, July 1997 to March
1998. .

-13-


11-0

IM147 Composite Regression Coefficients, HC, 1986 to 1989 Model Year LDGV's
Regression Coefficients:
Segment IRMS — | Reg.
[Number Error Constant }C1 ic2 ic3 ica ics icé ic? ic8 ico C10 Cat C12 C13 C14 C15 C16 C17 C18 ic19
PT OSV [e085 | 0.31427] 0.27001] 16.5205] :
P2. LOGV_Ja6-89 [0.23056] 0.12663[ 1.6181] 4.2149] a
iPS LOGV__ {86-89 | 0.19626] 0.11002{ 2.0358 2.3971] 3.9903].
Ps ILOGV__ [86-89 | 0.18598] 0.09989] 0.6425] (2.546) 1.7035] 3.3548]. a
jPS_ ILOGV__[86-89 | 0.17584] 0.09767] 0.5819} 4.852] 1.5615} 1.555] 3.8429].
[Ps LOGV__ [86-89 | 0.16772] 0.10204[" 0.8376) 1.9082] 1.0601] 1.0965] 1.0459] 3.8412]. a
PT LOGV_ [86-89 | 0.16444) 0.09594] 0.6742] 1.6725] 0.9995] 1.3082] 0.7661] 3.2631] 23581, a
PB ILOGV_ [86-89 | 0.15806] 0.08909 0.1422] 1.5556] 0.6844] 0.9331] 1.0208] 2.2001] 1.5379] 2.5805]
Po ILOGV__ [86-89 0.1519] 0.08696] 0.1948] 1.5757] 0.7441] 1.0414] 0.6635] 1.0744] 1.3289] 1.468] 3.5915]
[P10 LOGV_ [86-89] 0.t4467[ 0.0797] 0.48947 1.5393] 0.9657] 0.7863] 0.5873] 1.2938] 1.3345] 1.1773] 0.2863] 4.8853] a
pPat LOGV_ [86-89 | 0.11095] 0.02972] 0.2469 0.721] 1.1728] 0.8987] 0.4133] 1.8423] 0.6973] 0.4156] 0.9936] 2.4194] 1.6263]. a
[Piz LOGV_[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] 1.1042] - 1.7602] a
[P13 LOGV_J86-89 [0.06902] 0.02184] 1.3196] 0.4061] 0.8931] 0.6959] 0.5820] 1.0567] 0.7867] 0.7859] 0.5864] 1.9088] 0.9881] 0.201] 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]
fP35, 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]. .
P16 LDGV_ [86-89 | 0.04861] 0.01432] 1.1646] 0.4732] 0.8017] 0.9250] 0.7055] 0.8742] 0.7619] 0.7564] 0.5576] 1.5488] 0.7021] 0.6507| 0.7862] 0.9721] 1.2367] 2.5815]. .
pPiz. LOGV__ [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] 14.0692] 1.1351] 1.0816] 2.2376]. .
pis ILOGV_ [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].
Ps LOGV__[66-89 | 0.02176{ 0.00312] 1.0887] 0.6218] 0.766 0.6703} 0.7668] 0.6802] 0.7991] 0.5736] 0.7502] 0.9406] 0.7447) 0.674f 0.6947] 0.6605] 0.6164] 0.1544] 1.3072] 0.0142] 0.9739]
M147 Phase 2 Regression Coefficients, HC, 1986 to 1989 Model Year LDGV's
Regression Coefficients
|Segment RMS: (Reg.
[Number |Error [Constant |Ct ic2 \c3 ica ics ice ic7 ics ico C10 C11 jC12 ic13 C14 Cis. IC16 C17 C18 C19
in TOG [66-89 | 0.17572] 0.05913]. 75626].

[Biz ILOGV_ [86-89 | 0.14079] 0.04187} 1.8474] 2.837]. 5
{Bis LOGV__ [86-89 | 0.10283] 0.03308} 1.4781] 0.4901] 3.5664)
B14 ILOGV_ [86-89 | 0.09666] 0.02888) 4.2557] 0.8541] 2.0611] 2.8208]. a
B15, LOGV_[86-89 [0.07943] 0.02543] 14,0638] 0.9286] 1.331] 0.6468] 3.2335]
[B16 ILOGV_ [86-89 | 0.07243] 0.02198]. 1.0486] 0.9587] 1.3534] 0.8765] 1.5227] a
1817 [LOGV_ 66-89 | 0.04689] 0.01029] 1.0457] 0.8812] 1.097] 1.3615} 1.4856} 3.2261]. 5
; EXE [LOGV_ [a6-89 [0.03789] 0.00478]. 0.0974] 0.9025] 1.0724] 1.0277] 1.1846] 1.1391] 2.3713] 1.8028].
| EXE) [LOGV_ {86-89 |" 0.03036] 0.00369) 1.0225] 0.8952] 0.9808] 0.8231) Ti] 0.3076] 7.8483] 1.2733] 1.3385)



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

-18-


Figure 5-5

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

UM 147 TEST

YES

Segment 19

Vehicle Fast Passes After

NO

NO

Mode 7 Predicted HC < STD * 1.1
or
Mode 19 Predicted HC < STD * 1.1

y
NO RETEST

Note: STD = Composite

Standard for the specific pollutant.

YES
NO Yes
Mode 7 Predicted HC-STD_ 12
Mode 19 Predicted HC - STD :
NO] Mode 7 Predicted CO < STD* 1
or
Mode 19 Predicted CO < STD* 1
YES
No YES
Mode 7 Predicted CO-STO_ | 07
Mode 19 Predicted CO - STD .
NO | Mode 7 Predicted NOx < STD * 1.2
and
Mode 19 Predicted NOx < STD * 1.2

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.

-47-



he)

(M147 Composite Regression Coefficients, HC, 1988 to 1995 Model Year LDGT2's
Regression Coefficents.
Segment RMS Reg.
|Number Error Constant |C1 ic2 ica. C4 ics C6 ic? ica ico C10 jc11 IC12 C13 C14 IC15. C16 (C17 C18 ic19
w DGt2 [68-95 | 0.46034] 0.5235] 11.3071]. a"
2 LOGT2 [88-95 | 0.33713] 0.21906; -2.96] 4.9422]. a
[Ps TUOGT2 [88-95 | 0.20651] 0.19834] -1.7489] 2.6353] 4.9489]. .
[Pa LOGT2 [86-95 | 0.28747] 0.18080] -1.9477]_ 2.7951 2.9733] 3.2647]_
jPS {UOGT2 [e8-95 | 0.27016] 0.17705] -2.2098] 7.8312] 2.2325] 1.406}
iP ILOGT2 [88-95 | 0.25421] 0.19481] 2.5612] 2.2441] 0.5267] 0.4177]
[P7 [LDGT2 [88-95 | 0.24477| 0.17308] -2.1598] 1,867] 0.6968] 0.5785} 4.2662). a
Pa ILDGT2 188-95 | 0.22604] 0.14292] -1.1587]_ 1.5845] 0.5456] 0.1762] 3.7472] 4.4401 a
[Ps (LOGT2 [88-05 | 0.22226] 0.14551] -1.2733] 1.703} 0.284} 0.1479] 3.6501 3.448] 2.3522] .
[P10 ILOGT2 [88-95 | 0.21442] 0.13617] 0.7091] 1.5866] 0.3214] 0.3077] 3.9046] 2.8806] 0.9464] 5.4516]. a
fPat ILOGT2 [88-95 0.1654] . 0.04125] 0.6538} 0.773] 0.5132] 0.6898} 2.0621] 1.1813] 0.4048] 2.5588] 2.1807].
[Piz LOGT2 [88-95 [ 0.14608] 0.03783] 1.0502] 0.5966] 0.307] 0.8399] 0. 1.7804] 1.3774) 0.14] 2.8081] 1.4104] 2.3284)" a
| (ES LOGT2 [88-95 | 0.08638] 0.03352] 1.3017] 0.5004] 0.8055] 0.8896] . 0.5695] 0.5182] 0.3106] 1.2433] 0.9247] -0.2803] 4.1519. .
peas ILOGT2 [88-95 | 0.07556] 0.02491] 1.2879] 0.524] 0.7653] 0.8982] 0.3475] 1.5181] 0.5104 0.7567| 0.2081] 1.4094] 0.6811] 0.0851] 2.7444] 1.9985]. a
| aE LOGT2 [88-95 | 0.06718] 0.02356] 1.1938] 0.5183] 0.6186] 0.8118) 0.379] 1.4304] 0.7007] 0.85127 0.4013] 1.2797] 0.6663] 0.2998] 1.6768] 0.8504] 1.7815) q
[P16 ILOGT2 88-95 | 0.06237] 0.02258] 1.1224] 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). a
pea ]LOGT2 [88-95 [0.04421] 0.01056] 0.9076] 0.7137] 0.6003] 0.6133[ 0.8089| 0.9512] _0.7459| 0.7414] 0.5867| 0.9827| 0.6061] 0.6414] 0.972] 0.6458] 1.1687] 1.7368] 1.9774]. a
[Pie [LOGT2 [6-55 [0.03716] 0.00462 0.9099] 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].
Pio {LOGT2 [88-95 | 0.03217] 0.00297] 0.8625] 0.7085] 0.7059[ 0.6951| 0.7723, 0.6271] 0.7424| 0.7667] 0.703} 0.8462] 0.6346] 0.6886] 0.6361, 0.620i[ 0.7448! 0.7476] 1.2353, 1.0716] 0.9076]
1M147 Phase 2 Regression Coefficients, HC, 1988 to 1995 Model Year LDGT2's
"Regression Coefficients.

ISegment Iams — Reg. '
[Number Error Constant {C1 C2 ic3. C4 ics ice ic7 ica ico (C10 cit C12 C13 C14 C15. C16 C17 C18 C19
fr [CDGt2 feos | 0.25751] 0.05700]. 3.87591. a
[eiz [LOGT2 [88-95 0.2067] 0.04911) 2.4274] 3.3621] a
| CHEN ILOGT2 {88-95 0.1199] 0.03897] 1.2412] 0.4078} 5.5892]. a
614 ILDGT2 [88-95 | 0.10757] 0.02785]. 41,0283] 0.0365] 3.9664] 2.4645]. a
(B15. [LOGT2 [88-95 | 0.09668] 0.02657]. 1.0392] 0.3282] 2.5887] 0.8805] 2.4135) a
[B16 iLDGT2 |88-05 | 0.09084] 0.02471]. 41,0542] 0.5363] 1.9743] 0.7835] 1.5325] 4.1018} .
[B17 ILDGT2 [88-95 | 0.06106] 0.01248]. 0.8883} 0.8408] 1.4659] 1.0499] 1.5851] 2.3095] 2.7427]. a
| ILDGT2 {88-95 | 0.05212] 0.00425] 0.9142] 0.9654] 1.0256] 0.9252] 1.4081] 1.2094] 2.0957] 1.7973}
} EXE LDGT2 [88-95 | 0.0449] 0.00374]. 0.9209[ 0.9243] 1.0204] 0.748] 0.9948] 0.9323] 1.7813] 1.4026] 1.2714]



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+ © 1.57/1.15 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 (3,347 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 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
pollutant-specific failure rates for each cutpoint category (e.g., the pollutant-specific
failure rates for the startup cutpoints range from 5.7% to 6.5%). Both the pollutant-
specific and overall failure rates 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.

-30-


IM147 CUMULATIVE POSITIVE POWER (CPP) VARIATION CUTPOINTS

1M147 REFERENCE DATA
TIME SPEED CPP
(sec) {mph)
O 0.0 0.00
4 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
31 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
41 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

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
784.74
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


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 95.9% (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 little more than a 1% reduction in the
excess emissions identification rate for CO. (As noted above, the addition of the
algorithms reduces 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.

-14-


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 (i.e. +2 mph) 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 the details of how these IM240-developed methodologies were adapted for the
IM147 test are discussed, 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-baséd
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: : :

-58-


Excess Emissions with Fast-pass Enabled
Max CO Cutpoints
(Data Not Normalized for Model Year Distribution)

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) jpass (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 NA ) 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 75.7% 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 11.3%
LDGT1 90-95 0.42 0 0.0% 1.77 0 0.0% 1.65 1.65 100.0%
96+ 0 0 NA 0 0 NA 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 NIA 0 0 N/A 0 0 NA
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%



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

Regression Coeficients
Segment IRMS = [Reg.
Number |Erros Constant }C1 ic2 (J ic7 C8 ico C10 ic11 C12 ic13 C14 cis Ic16 C17 C18 C19
F [LOGV_[se+ | 4.2602] 1.81004] Sa.0at4]. r —
JLOGV"|o6+ [2.79375] 0.66889] 6.9133] 5.3042] 7
LOGv_ fas 2.34951] 0.49886] -22.4698] 2.9121)
LOGV__ [96+ 2.30066] 0.48852] -20.2357] 2.6582] 1
ILOGV__ [96+ 2.29856] 0.4874] -19.0504{ 2.2906]
ILOGV_ [96+ 2.17485] 0.36586 -18.1249] 2.1863} a
ILOGV_ [96+ 2.17872] 0.34724] -17.6869] 2.1129} 0.3983]
[LOGV [96+ 2.17729] 0.32839] -15.8387] 1.9759] 0.2201] 1.4309] a
TLOGV__Jo6+ 2.16612} 0.4024} -11.8473] 1.4566} 0.3128] 0.4287] 3.2042]. a
TUOGv_Joe+ 2.17081f 0.40258] -11.4985] 1.3861 0.2871] 0.3698] 2.6985] 0.5987}
[LOGY [98+ 1.3457] 0.04872] -1:5411] 1.3212] 0.7158] 0.2276] 1.3044] 1.2284) 4.4171]. a
ILOGV_ [96+ 0.94601] 0.03677} 0.1569] 1.1325] 0.8581] 0.5253] -0.3667] 2.4476] 0.8375] 1.6915]. . .
ILDGV [96+ 0.83127] 0.04161] 6.5433] 0.7725) 0.8026] 1.0385] -0.6814] 0.7591] 0.8719[ 1.3576] 1.4743]. a
LOGV_ 96+ 0.36237} 0.07318] -1.7846] 0.7477} 0.6876] 0.4145] 1.11047 0.4144] 0.6973] 0.7747] 0.877] 1.6748). .
ILOGV__ [96+ 0.20315] 0.05053] -0.3484] 0.5009} 0.8321] 0.2796] 0.0113] 0.3684] 0.7271] 0.7433] 4.1116] 1.0359] 0.8817]. a
ILOGV_ [96+ 0.29205] 0.04924] 0.0274] 0.4967] 0.8654] 0.1479] 0.1851] 0.3987] 0.7276] 0.7477| 1.1209} 1.0535] 0.7862] 0.5132] .
ILOGV_ [96+ 0.23867] 0.02554] 0.2222] 0.7177) 0.7007] _ 0.7511] 0.1642] 0.6983] 0.7129] 0.7751] 0.8196] 1.3698] 0.5666] 0.1481] 2.2487].
LDGVo6+ 0.211] 0.02076] 1.8186] 0.5321) 0.6247] 0.8096] -0.3981] 0.9301] 0.7173] 0.7777] 0.8774] 0.7831] 1.0214] -0.0380] 1.7607] 0.6392].
LOGV_ [96+ 0.0541! 0.00995] 0.7614] 0.7041 0.6972] 0.728} 0.6052{ 0.6829] 0.7065] 0.7486] 0.7235] 0.6863} 7334] 0.6843] 0.7333] 0.7391] 0.7203]
M147 Phase 2 Regression Coefficients, CO, 1996 and Newer Model Year LDGV's
Regression Coefficients
Segment RMS .
Number Error Constant [C1 ic2 ics ic7 ice ico C10 Cit C12 ic13 C14 Cis. C16 C17 C18 ic19
Bit TOGV__|eor Ze152] 0.6365]. Tese9]- r
B12 ILOGV__ [96+ 4.55476] 0.22139} 1.2687] 2.444].
(B13 LOGV_ [96+ 4.21697] 0.12297} 1.4955] 1.6069] 1.8026) a
[B14 ILOGV__}96+ 0.54556] 0.13004] 0.9864} 1.1698] 1.3748] 2.3969}
1815. |LOGV__ [96+ 0.4806] 0.1147] 0.9893] 1.0617] 1.3564] 1.8394] 0.7573]
B16 COGV_ [96+ 0.4633] 0.09077]. 0.885} 1.0604] 1.4116] 1.8485] 0.4981] 4.6802] a
B17 LOGV__ [96+ 0.33866)" 0.04768} 0.968] 1.0626] 1.1629] 1.9325] 0.5734] 0.6152] 3.1735]_
B18 ILOGV__ [96+ 0.30627[ 0.03898) 0.9596] 1.0743] 1.0207) 1.4501] 0.9473] 0.6378] 2.7247] 0.6646].
B19 [LOGV_ [56+ 0.07437} 0.01481}. 0.9598] 1.0088] 0.9451] 0.066] 0.9371] 1.0884] 1.159] 0.8807{ 0.9897



82-0

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

tegression ficients,
Segment RMS — ‘Reg.
[Number Error Constant }Ct ic2 ica ica ics C6 ic7 ice ico C10 C111 JC12 (C13 ic14 C15 ic16 ald ic18 ic19
| Cr 511 [o6-69 | 1.23003] 2.06604] 23.5015] i
Pz ILOGT1 [86-89 | 0.70187] 0.74907] 1.2554] 4.6182]. a
[Ps LOGT1 [66-89 | 0.65768] 0.66459] 1.9511] 3.4741] 6.6849] a
[Pa ILOGT1 [86-89 | 0.65847] 06622] 2.0121] 3.4718] 6.5638] 0.4801]
[Ps iLOGT1 [86-89 | 0.60304] 0.65314] 0.8979] 2.5888] 3.7778} 0.693} 7.82].
| ILOGT1 [86-89 | 0.60221] 0.64909] 0.8986] 2.5939] 3.720] -0.3487] 7.391] 2.7912]
[P7_ [LOGT1 [86-89 | 0.58554] 0.63588 0.2658] 2.2631! 2.7626] 0.2519] 46311 4814) 3.3278].
IPB [LDGT1 [86-89 | 0.57068] 0.60583[ 0.1372] 2.1042] 2.0548] -1.463[ 4.181] 1.0084] 2.9915] 4.4107] .
9 LOGT1 [86-89 [0.57122] 0.60597/ 0.1342] 2.1057] 2.0299] -1.4503] 4.1619] 0.9698] 2.989] 4.3952] 0.1053]. i
[Pio ILOGTt [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].
Pir ILDGT1 [86-89 | 0.44918] 0.31619] 0.8774) 1.547] 5 3.7047] 1.8503} 0.2997 3.4571] 0.7772] 1.6757] 1.4183] 5
P12 LDGT1_ [86-89 | 0.39015] 0.23116] 2.1899] 1.3771, E 2.7841 0.6668] 0.7794] 1.8436] -0.1039] 1.861] 0.955] 2.7651 a
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] a
[Pia LOGT1 ]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)
{Pas ILOGT1 [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]
[P16 LOGT1 [86-69 | 0.09624] 0.03427[ 0.4994] 0.6974] 0.6351] 1.2145] 0.5601] 0.5254] 1.017] 0.3771} 0.9662] 0.7877] 0.7267] 0.8275] 0.9401] 1.0394] 0.9564] 1.2214] a
pei. LOGT1 [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] 0.71G3] 0.847] 0.8964) 1.08; 0.896] 1.1054) 1.2517]. .
Pi [LOGT1 [66-69 | 0.05194] 0.01155] 0.561] 0.6874] 0.7247 0.8944 0.4555] 1.1866] 0.7413] 1.03] 0.6717] 0.4644] 0.7014] 0.8099] 0.637] 0.8736] 0.8444] 0.9947] 0.7536] 0.9208].
[P19 LOGT1 [66-89 | 0.01446) 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] O.7131| 0.7176] 0.7067) O.7115| 0.6001; 0.7158] 0.7420}
IM147 Phase 2 Regression Coefficients, NOx, 1986 to 1989 Model Year LDGT1's
Regression Coefficients
iSegment RMS Reg.
[Number Error Constant C1 ic2 ic3 ic4 ics ice ic7 ica ico }C10 cit C12 C13 C14 Cis. C16 ‘C17 ic18 jc19
fe ILOGT1. 0.71447] 0.56214]. 2.9488]. a
| 3E LOGT1 0.58562] 0.33666]. 1.7796] 4.5068] a
| EXE {LOGT1 0.28253] 0.15935] 1.0449] 2.1599] 3.1651)
[B14 {LoGTT 0.22004] 0.08924] 0.9001] 1.3657] 2.486] 1.9841]. .
| EXES ILOGT1 0.14504) 0.05}. 0.9776] 4.164] 1.1562] 1.5604] 1.7468}
[Bie |LDGT4 0.13142] 0.04594]. 1.0201] 1.0708] 1.2975] 1.3765] 1.3303] 1.5302].
(B17 |LOGT1 0.12255] 0.04125]. 0.977] 1.1131] 1.218] 1.4703] 1.2356] 1.4639] 1.602]. a
818 |LOGT1 0.0715] 0.02293] 0.937} 1.1335] 0.8403] 1.2196] 1.0929] 1.5766] 0.9306] 1.2095].
[B19 ILOGT1 0.01559} 0.00168] 0.9648] 0.9858] 0.9551} 0.9715{ 0.9562] 1.0147] 0.9809] 0.9807] 0.9956]



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 concems 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.
These data, in combination with the 101 vehicle data, were 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

-l-


{M147 Composite Regression Coefficients, HC, 1981 to 1985 Model Year LDGT2's

Regression Coefticrents
Segment IRMS = |Reg.
Number fError__|Constant [C1 cz |ca_fca__jos__fcs_—_fer_|cs _fco _|cro fem _|erz__fera_ fers _fers__—_fcre_fer7_fcrs_—_|cro
7 [LOGT2 [e185 | 0.029] 1.06950] 14.1310]. C
\Pz [toGr2 [a1-85 | 0.49433] 0.24032] 2.1062] 6.4378].
j GE JtoGr2 Jer-as |"0.47a7i 0.22561] 0.921] 5.3132] 27042].
@ (LOGT2 [81-85 | 0.43631] 0.17782] -1.7872] 5.4763] 0.3261) 3.8518].
[PS LDGT2 [81-85 | 0.42908] 0.2114] -1.5166] 4.7372] 0.0105] 2.9709] 2.5245} a
[PS ILDGT2 [81-85 | 0.41687] 0.23473] -3.8569] 4.5338] -0.9000] 2.7375] 1.9498] 3.2209]
1P7 ILOGT2 [81-85 | 0.41066] 0.23366] -3.2663] 4.2499] -1.1231} 2.3552] 1.2612] 2.4743] 2.1991
[Pa LDGT2 [81-85 0.3672 0.16688) 6179] 3.5835] -0.8816] 1.6341] 0.5481] 3.1967] 0.3748] 5.7221F- .
Po. LOGr2 [1-85 | 0.3566] 0.18117] -1.9t27] 3.5055] 0.0416] 2.6495] 0.9804] 1.9891] 0.0338] 4.0901] 3.7721 a
P10 ILOGT2 [81-85 | 0.34151] 0.16655] _-1.8705] 3.3075] 0.9637] 3.3159] -1.7891 1.43} 0.0942] 4.2235] 1.3603} 3.899].
[P11 [ocr [eras | 0.2931] 0.04548] 1.0748] 1.8020] -1.2122| 2.864] 0.6499] 0.7308] 0.2678] 3.2607] 0.113] 4.0698] 1.6325]

P12 [toGr2 {61-5 | 0.2758] 0.01823] 0.763] 1.8538] 0.084] 2.60771 0.7763] 0.9790] 0.773] 2.1956] 0.3985] 4.4575] — 0.673 2.4503]. a
[Pia UG fa1-85 | 6.21405] 0.01142] 0.4126] 1.5075] 0.0583] 2.6141] 0.5004] 0.917] 1.449] 0.2757] 1.0903] 2.7846] 0.8500] 0.03] 3.3529] i
[P14 ILDGT2 [61-85 [0.17574] 0.01255] 0.2502] 14.2847] -0.0742| 1.733] 0.4325] 1.3417] 1.2187] 0.2611] 1.1197] 1.7719] 0.3758] 0.7006] 0.8312] 4.2084]. a
[P15 LDGT2 {81-85 | 0.15062] 0.02221] 0.8141 1.325] 0.0587] 1.4355] 0.2426] 1.2096] 1.3459} 0.694] 0.5531, 1.836] 0.507] 0.8254] 0.1245] 2.1165] 2.3277]. a
P16 |toGr2 [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]. a
PIT, LOGT2 ]61-85 | 0.11644] 0.04735 1.4605] 1.1844| 0.1561] 0.8181] 0.2508] 0.6122] 1.2664[ 7.4097] -0.9806| 2.1461] 0.6223] 0.997| -0.2513] 1.5706] 1.3565] t.6707| 2.2361].
1@__|tDGr2 [1-85 | 0.10411] 0.05875] 1.9421] 1.2153] 0.2284] 0.6248] 0.1763] 0.8731] 0.9762] 1.0864] 0.3199] _1.6679| 0.7@65| 0.7901| 0.01] 1.0203] 0.9553] 1.6502] 1.5044] 1.4188].
Pia [ubGT2 [a1-85 | 0.08606) 0.02861] 1.9168 1.1188] 0.3074] 0.5037] 0.6515] 0.9558] 0.5634] 1.1722] 0.4072] 1.0016] 0.7179] 0.564] 0.4013] 1.517] 0.5651] 0.0166] 1.2033] 0.0607] 1.3405}

{M147 Phase 2 Regression Coefficients, HC, 1981 to 1985 Model Year LDGT2's

Regression Coefficients
Segment RMS —_|Reg.
Number ferror_|Constant [C1 cz jes__|ca cs jes, —c7__fee__fca _fcro_[er_|crz__fcrs_ fers cis —_|ere_—fcrz_ fers _—_ cro
{en [LOGT2 eres | 0.5360<] 0.067 aE 5 i r i 47635).
[B12___|CoGr2 fe1-85 | 0.50867] -0.03629] 5 a i a 5 a i 2.9814 3.9743). a
J213__ |C0GT2 far-a5 | 0.37516} 0.03573] i i i 5 a 2.2833] 1.1494] 5.7782]
[are LOGT2 61-85 | 0.31715] 0.05881]. i i i i a 5 7.5613] 0.0866] 1.7691] 6.5887] i
[B15 toGr2 eres [0.20115] 0.05673]. 5 i i i a 7.6798{ 0.2156] 0.484] 3.6578] 3.2614
[ere LoGT2 |e1-85 | 0.28506] 0.05551. i i 5 i i a a 7.6963 0.4373] 0.1942] 3.4658] 1.7593] 4.9957]. a
{a17 LOGT2 a1-85 | 0.19206] 0.07062]. i i i J i i 5 a a 1.2418[ 0.7375] 0.4405[ 2.7164] 1.6887] 1.1505] 4.1633].
[are LOGr2 |a1-85 | 0.17741] 0.07525). i i i i 5 5 a 1.3222] 0.5566] 6.4852] 2.1651] 0.9953] 1.6325] 3.2654] 1.6763]

| XI LOGT2 [61-85 | 0.15121] 0.03936]. . 5 a 7.1587] 0.4814] 1.1413] 2.1958] 0.3955] -1,7440] 2.7608] 1.0076] 1.0001]



-S¢-

Figure 4-5

Failure Rate by Consecutive IM147
Final Cutpoints

HC Failure Rate by Vehicle Type

CO Failure Rate by Vehicle Type

Vehicle Type

Vebicle Type

30.0% 25.0%
25.0% | 20.0% :
g i iT 2
2 20.0% : ti ist M147 a 15.0% Dist 1M 147
2 15.0% Hi H W2nd M147 2 m2nd IM 147
a EZ 10.0% |
o 10.0% i | 3rd M147 o i N Ba3rd 1M 147
= ti Osun Hi
alll a “TH |
i ] i
0.0% +e l 0.0% .
Pc Lov) LpT2 au Pc Loti LDT2 an
Vehicle Type Vehicle Type
NOx Failure Rate by Vehicle Type Overall Failure Rate by Vehicle Type
25.0% 45.0%
40.0% =
ye 20.0% {it £ 35.0% 1 fi
s ! i od |
mom i il ise i147 py 30.0% Hi i plist 1m147
5 2 25.0% i
2 | / 2nd iM147 2 | i 2nd M147
= 10.0% i = 20.0% i He i
co i 231d M147 2 15.0% =|. 2 H Ht 3rd imia7
]
2 5.0% / | S 10.0% / i]
5.0% _ |
ll ! li
0.0% 0.0%
PC LpT1 LDT2 "All PC tpt LDT2 All



Le

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

Regression Coefficients

Segment IRMS — Reg.
iNumber JError Constant {C1 ic2 ic3 ica ics C6 ic7 ica ico C10 C11 C12 |C13 C14 IC1s C16 C17 C18 C19
qPx LOGV_ [86-89 | 0.78: 1.40846] 22.9983]
jP2 ILDGV__ [86-89 | 0.57644] 0.77237| 8.21] 3.0925] a
[P3 ILOGV_ [86-89 | 0.53576] 0.63136] 9.1703} 2.0307] 7.0173]. a
[Ps LGV. 0.61276] 9.5621] 2.0154] 5.1612] 5.5354] a

iPS LOG 0.58411} 7.6422] 1.5437] 3.1981] 4.1123] 5.8762]. a

ILOGV. 0.58] 4.0676) 1.525] 2.9935] 0.4973] 4.8986] 10.1263].
JLOGV. 0.51948] 2.5722] 1.2222] 2.2025] 1.1125] 2.3363] 11.7286] 3.4574]. .
ILDGV_ 0.46953] 3.5526] 1.2073] 1.7483] 0.3064] 2.0313] 6.7259] 2.6637] 5.3318].
ILOGV_ 0.45426] 3.3018] 1,301) 4.139] 0.1369} 1.5033] 5.9904] 2.5273] 4.8613] 3.2834) q
ILOGV_ 0.44434] 2.7682] 1.279] 0.8669] 0.8265] 1.1624] 5.9839] 1.9554] 4.98247 2.1016] 2.39040] a
LOGV_ 0.16273] 1.6969] 0.9234) 0.2026] 1.5843] 1.5047] 5.7857] 0.6453 3.2517] 2.4719] 1.4771] 1.3713]. a
[LOGV_ 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]. a
ILOGV_ 0.05351] 1.5839] 0.7587] 0.7809] 1.2166] 0.7145] 1.5691] 0.0527 4.0643] 1.0876} 0.6012] 0.7526} 1.15] 2.3208]. .
‘[toGV. 0.04173] 0.3316] 0.7154] 4.0709] 0.6518] 0.7486] 1.2646] 0.8718] 0.8749] 0.8522] 0.9520] 0.6065] 0.9816] 1.5163] 1.6439] a
LDGV. 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]. .
{LOGV. 0.01317] 0.5535] 0.7013] 0.7623] 0.918] 0.6392 4.068} 0.9735] 0.7864] 0.2568] 0.9626] 0.7416] 0.7472] 0.8532] 0.8939] 1.013] 4.487}. a
TLOGV, 0.04125} 0.3901] 0.7232] 0.6166] 0.8153] 0.6598] 1.0147] 0.8687] 0.6487] 0.2077] 0.8136] 0.7444] O.7667[ 0.847] 0.9405] 0.9462] 1.3241] 1.4006]. .
jiocv. 0.00394] 0.7351] 0.6866] 0.7439] 1.0847] 0.6838] 1.2044] 0.6292] 0.8002] 0.5201) 0.672] 0.6852] 0.8818] 0.7191) 0.82] 0.7757] 1.2183] 0.9059] 0.8386]
[LOGV_ 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]
\M147 Phase 2 Regression Coefficients, NOx, 1986 to 1989 Model Year LDGV's
regression Cooficanis

Segment IRMS —-|Reg.

Number {Error {Constant }C1 ic2. ic3 C4 ics ice ic7 ica ico C10 C14 C12 Ic13 C14 C15 C16 ald C18 C19
| CoE 186-89 | 0.52925] 0.42098) . a . a . a 2.5542]. . a I. a a . a
[Biz [LDGV_ [86-89 | 0.43224] 0.27635]. a a a a a a . 1.6545] 3.9022} a
{813 LDGV_ [86-89 | 0.23852] 0.0957]. a a a . a a a 1.0648] 1.7542] 3.3487]. a
| x LOGV__ [86-89 | 0.18118] 0.07257| a . a . 5 q a 0.867 1.4108!" 2.1959] 2.261. a
| ILOGV_ [86-89 | 0.11203] 0.02849]. a a a i a . . . 0.9505] 1.1788] 1.2539] 1.4295] 1.7612]. .

{B16 ILDGV . [86-89 0.0963] 0.02351]. . a a . . a 4.0428] 0.9898] 1.2047] 1.2246] 1.4141] 1.9452].

[Bi7 ILOGV__ [86-89 | 0.08734] 0.01558]. . i. . a a a q . 1.0275] 1.0114] 1.167] 1.2958] 1.2756] 1.7751 .
| CEE JWOGV_Ta6-89 [0.05354] 0.0073] . . . . a . 5 0.0133] 1.2514] 0.867} 1.1377] 1.0194] 1.8001] 1.0893].
| CE [LOGV_ [ee-89 | O.0T1s3] 0.00104]. a a a r r 0.964] 0.9967| 0.973] 0.9592] 0.9531] 0.9973| 1.0544) 0.0572] 1.0152]



(awe)

(M147 Composite Regression Coefficients, HC, 1990 to 1995 Model Year LOGV's

Regression Coefficients
ISegment RMS Reg.
Number lEror {Constant [c1 cz jca_—es cs fee —fcz_ ics fea — cto —scrs—fcrz__—fcra. crs fcrs_—fcre_—ferz_—cre_—_cro
Pt OSV 00-95 | 0.2071] 0.16705] 15,7046]. C r i" C _
wa [LoGv— [90-95 | 0.19863] 0.05505] 1.9351] 4.6837]. i
P3 [LOGV {90-95 | 0.16828] 0.03974] -1.seeo] 2.5546] 5.6414. 5
Pa LOGV |o0-95 | 0.1584] 0.03534] -1.9531| 2.652] 3.0550] 3.5632].
[Ps lLoGV— [90-95 | 0.14861] 0.0377] _-1.7859] 2.1399] 2.3054] 1.5053] 9.6706]. a i a
[Ps LOGV_|90-95 | 0.1428] 0.04157] 2.1978] 2.3528] 1.9307) 1.1611 1.6243] 4.1705 i aa
[P7 LOGV— ]90-95 | 0.13017] 0.03006] 1.447] 1.6386] 1.3743] 1.2125] -0.2629[ 2.1347] 4.0626 i i Et
{Pa LOG 90-95 | 0.12213] 0.03722] 0.5405} 1.6099] _1.2031[ 0.3856] 0.1326] 1.4892] 3.3678] 3.4765 i
[Po LOGV— ]90-95 | 0.11733] 0.03754] 0.7341] 1.7237} 1.0051] 0.4924] 0.9573] 0.6324] 3.2462] 2.3004] 3.0101 n
[P10 ___|LDGV_|90-95 | 0.11526] 0.0368] 0.8148] 1.6847] 1.071] 0.425] _-1.176] 0.676] 2.845| 2.502] 1.4335] 2.9572)
Pi [LOGV— {20-95 |-0.08741] 0.00996] 0.6446] 0.8311] 1.0111] 0.5067] 0.4113] 1.2288] 2.2201] 1.2696] 1.6991] 1.3218] 1.5051

Piz |LOGV__ {90-95 |“0.07472] 0.0105] 0.6979] 0.7423] 0.706] 0.5223] 0.0564] 1.3101] 1.9432] 1.266] 1.2008] 1.3807] 0.6836] 1.6454). "
P13 |LoGv_[90-95 [0.05916] 0.0139] 1.3045] 0.4375] 0.9242] 0.6499] 0.0689] 4.2742] 1.493| 1.04] 1.3582] 0.4913] 0.6544] 0.3112] 2.5560).
Pia [LOGV__ [90-95 | 0.05362] 0.01549[__1.386[ 0.3479] 4.0903] 0.5466] 0.252] 1.3522] 1.424] 0.6349] 1.5658]  0.679| 0.6813] 0.3943] 1.6017| 2.0376]

P15 |LOGv_ [90-95 [0.04337] 0.01025] 1.2116[ 0.4485] 0.9328] 0.7036] 0.5036] 1.3664] 7.1003] 0.7903] 1.3103] 0.2473] 0.6803] 0.7135] 0.7193] 0.5976] 2.3136] a
Pie |LOGV_ [90-95 | 0.04152] 0.00959] 4.1338] 0.4558] 0.9892] 0.6217] 0.5423] 1.2542] _0,9416| 0.7770] 1.3289] _0.1876| 0.6808] 0.7232| 0.692] o.608i| 1.451] 2.8503].
P17 |LOGV_ [80-95 | 0.0313] 0.00573| 0.9123] 0.6301] 0.5621] 0.8487] 0.6701 1.1772] 0.6709| 0.7111] 1.0362 _0.425| 0.686] 0.7574] O.6s63| 0.8447] 1.2024] 1.3540] 2.4390] -
P18 [LGV [80-95 0.02304] 0.004] 1.0116| 0.6089] 0.6873] 0.6706] 0.6772] 1.1438] 0.7472] 0.6904] 0.8929] 0.5431] 0.6856] 0.7093] 0.6728] 0.682] _ 1.042] 0.6819] 1.3735] 1.501].
Pig [LoGv__fs0-95 | 0.01920] 0.00280] 0.874] 0.6682] 0.6242} 0.6993] 0.6995] 1.2226] 0.716] 0.6058] 0.6359] 0.606] 0.6915| _ 0.724] 0.6677] 0.6623] 0.8334] 0.1215] 1.1562] 1.238] 0.0207|

M147 Phase 2 Regression Coefficients, HC, 1990 to 1995 Model Year LDGV's

Regression Coefficients
Segment RMS — Reg. : :
Number |Ercor [Constant [C1 ic2 ica C4 ics ice ic7 ica ico (C10 C11 jc12 C13 C14 Cis C16 C17 C18 cia
fei1 _UOGV—J90-55 [0.15011] 00203]. i : r : : r 25776 :
B12 [LoGV— [00-95 | 0.11712] 0.01783 : : : 1.7491] 2.8654]. :
[B13 LDGV__[90-95 | 0.08624] 0.01945] 5 a a . a a 1.2881} 0.3333] _ 3.9332]. a
B14 JLDGV— [90-95 | 0.08064) 0.02128 : : : : 1.0669] 0.4262] 2.8321] 2.9028). :
B15 [LOGV_[90-95 | 0.06433] 0.0142]. 5 . a . a 4.0083] 0.9586] 1.2508] 0.4227] 3.4379}
[B16 ILOGV_ {90-95 | 0.06126] 0.01375]. . a a a 0.9905] 0.9989] 1.1536] 0.6257] 2.0703] 4.3157] .
1817. JLOGV__ {90-95 | 0.04442] 0.00749] . a . a . 0.9692] 0.997] 1.0495] 1.0206] 1.6198] 2.0719] 3.5584] .
[B16 ILOGV__ [90-85 | 0.03245] 0.00457] 5 a . . 0.9306] 0.9602] 0.9645] 0.8007] 1.3774] 1.0699] 1.0911] 2.2266].

[e19__[Locv— [30-95 [0.02708] 0.00366] i a a a - - 0.9309] 0.9723] 0.9484 0.8169[ 1.0622] 0.3307] 1.6177] 1.7198] 1.2786]



Table 5-1
Revised IM147 Segments

-

Initial Second Data

Final Second Data

3 17 22
4 23 28
5 29 34
6 35 42
7 43 48
8 49 54
9 55 60
10 61 66
11 67 16
12 77 82
13 83 92
14 93 98
15 99 108
16 109 112
17 113 116

-38-