1. WLTP CoP Procedure for CO2/FC
Analysis of CoP data and JRC Proposal assessment
B. Ciuffo, A. Marotta, M. Ktistakis
WLTP CoP TF telco
June 24, 2019

2. Conformity of production (CoP) (background info)
CoP is a means of evidencing the ability to produce a series of products that comply with the specifications, performance and marking requirements demonstrated and outlined in the type approval documentation.
CoP should thus be sufficiently robust to ensure the conformity of what is produced with what has been demonstrated during type- approval
Given the complexity of the “vehicle” product, CoP is not a trivial task as there are many uncertainties
In the production process
In the testing method

3. Conformity of production (CoP) (background info)
Managing the existing uncertainty is a task that should not be underestimated
It is necessary to ensure sufficient evidence to base the conformity upon
It is important to follow sound statistical procedures in order to have sufficient confidence on the results of the assessment
In particular, what we want to achieve at the end of the CoP is sufficient confidence that there is no significant difference between the emissions and CO2/FC measured during the type- approval process (thus representative of the entire population) and what is measured from the population sample

4. Conformity of production (CoP). Conditions (background info)
Population. During the emission type approval, CO2 results are compared with a declared value (DV). DV is accepted if the resulting CO2 is lower than DV
When it comes to CoP, there are 3 reasons why DV should be higher than the population mean:
Customer protection (the CO2/FC declared on the CoC should be higher than the individual vehicle CO2/FC value under WLTP, for the majority of the consumers);
Imposing a margin between DV and population mean creates an incentive at production level to improve the quality and decrease the CO2/FC spread;
Assuming that DV could be equal to the population mean would penalise manufacturers with higher production quality standards.
Confidence Interval. In the hypothesis that the different CoP samples are uncorrelated and that the production process does not change over time, confidence intervals can be the right tool to assess whether the population is in line with the expectation with a certain confidence
Confidence interval around the mean with 90% or 95% confidence can be used

5. Conformity of production (CoP). Approach (background info)
In order to use a pragmatic approach, 16 can still be the maximum sample size
The boundaries of the confidence interval are calculated using a Student distribution with (N-1) degrees of freedom and the standard deviation of the sample
DV is assumed to be the upper bound of the acceptance region in case of 16 tests
The assumption on DV leads to the identification of the theoretical mean of the CO2 distribution. This depends on the confidence level and the standard deviation of the distribution (either the theoretical value or the sample value)

6. Conformity of production (CoP). Acceptance-Rejection Region (s=3%)
(background info)
m=DV-(t90,(16-1)*s/√16) DV m

7. Assessment of the method (1) (background info)
The method was tested using a simulation approach. In particular
combinations of 16 randomly generated results of CoP tests have been derived from a population following a Normal distribution having
mean (m)
standard deviation (s)
Simulation results derived in terms of
Probability for a vehicle to have CO2 higher than the declared value (Pr{x<DV}): Defective rate
Overall Pass/Fail probability
Pass/Fail rate at the end of each CoP step

8. Assessment of the method (2)
The method was also tested using the CoP data provided by France, Germany and The Netherlands. In particular
combinations of 16 results of CoP tests have been randomly sampled from the available data
Simulation results derived in terms of
Overall Pass/Fail probability
Pass/Fail rate at the end of each CoP step
Before applying the method, the three datasets were analysed and centred around a common mean value to remove the variability due to the selection of the declared value for the specific family

9. Assessment of the method (2.1)
During June 6 telco it was requested to JRC to make additional analyses:
Centering data around additional values (not just 100, 99, and 98)
See the effect of different max number of tests on the pass/fail results
See the results of applying the current method on the same data

10. Histograms of data cantered around 100%
Overview of CoP data distributions
Histograms of raw data
Histograms of data cantered around 100%

11. Number of families which have 16,17,…,172 vehicles
French dataset. Preprocessing
We use 611 vehicles from the 11 families which have at least 16 vehicles
Number of families which have 16,17,…,172 vehicles 16 21 25 27 37 44 58 83 96
172 2 1
Raw data
Data shifted to 100%
min
1st Qu
median
mean
3rd Qu
max sd
83.40
92.50
94.70
94.60
96.80
102.50
3.10
min
1st Qu
median
mean
3rd Qu
max sd
88.34
98.66
100.07
100.00
101.48
107.28
2.31

12. French dataset. Assessment of JRC method
Raw data
N. tests
Pass Rate
(%)
Fail Rate 3
46.71
0.00 4
20.92
Cumulative
67.63 5
13.36 6
8.05 7
4.85 8
2.85
96.75 9
1.52 10
0.85 11
0.41 12
0.26
99.78 13
0.12 14
0.05 15
0.02 16
Total
100.00
Data centered around 95%
N. tests
Pass Rate
(%)
Fail Rate 3
67.66
0.00 4
18.13
Cumulative
85.79 5
7.64 6
3.37 7
1.80 8
0.82
99.42 9
0.34 10
0.14 11
0.06 12
0.02
99.99 13
0.01 14 15 16
Total
100.00
Data centered around 96%
N. tests
Pass Rate
(%)
Fail Rate 3
52.38
0.00 4
20.63
Cumulative
73.02 5
10.41 6
6.22 7
3.83 8
2.40
95.88 9
1.61 10
1.01 11
0.58 12
0.36
99.45 13
0.24 14
0.13 15
0.08 16
0.10
Total
100.00
Data centered around 97%
N. tests
Pass Rate
(%)
Fail Rate 3
35.42
0.02 4
16.85
0.01
Cumulative
52.27 5
11.80 6
8.19
0.00 7
5.79 8
4.66
82.71
0.03 9
3.49 10
2.78 11
2.22 12
1.80
92.99 13
1.47 14
1.17 15
0.97 16
3.37
Total
99.97

13. French dataset. Assessment of JRC method
Data centered around 98%
N. tests
Pass Rate
(%)
Fail Rate 3
19.81
0.29 4
9.83
0.11
Cumulative
29.64
0.40 5
7.20
0.06 6
5.87
0.04 7
4.73
0.03 8
4.29
0.02
51.74
0.55 9
3.62
0.01 10
3.31 11
2.96 12
2.56
64.19
0.59 13
2.33
0.00 14
2.23 15
2.03 16
28.62
Total
99.41
Data centered around 99%
N. tests
Pass Rate
(%)
Fail Rate 3
8.54
1.99 4
3.65
1.29
Cumulative
12.18
3.28 5
2.46
1.15 6
1.80
1.02 7
1.44
0.88 8
1.20
0.71
19.08
7.04 9
0.65 10
0.94
0.62 11
0.81
0.51 12
0.68
0.49
22.53
9.31 13
0.59
0.45 14
0.57
0.39 15
0.55 16
64.84
0.38
Total
89.08
10.92
Data centered around 100%
N. tests
Pass Rate
(%)
Fail Rate 3
2.39
9.10 4
0.79
7.30
Cumulative
3.18
16.4 5
0.40
6.75 6
0.21
6.01 7
0.16
5.30 8
0.09
4.83
4.04
39.29 9
0.07
4.13 10
0.04
3.82 11
0.03
3.41 12
0.01
3.11
4.19
53.75 13
0.02
2.92 14
2.75 15
2.53 16
31.47
2.35
Total
35.69
64.31
Data centered around 101%
N. tests
Pass Rate
(%)
Fail Rate 3
0.50
24.69 4
0.11
18.69
Cumulative
0.61
43.38 5
0.03
14.15 6
0.01
10.24 7
0.00
7.48 8
5.70
0.66
80.95 9
4.27 10
3.14 11
2.56 12
1.96
92.87 13
1.56 14
1.16 15
0.89 16
2.17
0.69
Total
2.83
97.17

14. Assessment of JRC method
𝑙𝑏 𝑖 =1− 𝑡 𝑖−1,𝑐𝐿 𝑖 + 𝑡 𝑁−1,𝑐𝐿 𝑁 ∗𝑠 𝑖
𝑢𝑏(𝑖)=1+ 𝑡 𝑖−1,𝑐𝐿 𝑖 − 𝑡 𝑁−1,𝑐𝐿 𝑁 ∗𝑠(𝑖)

15. French dataset. Assessment of JRC method
Raw data
N. tests
Pass Rate
(%)
Fail Rate 3
44.24
0.00 4
19.80
Cumulative
64.03 5
13.40 6
8.40 7
5.56 8
3.35
94.74 9
2.16 10
1.31 11
0.77 12
1.02
Total
100.00
Data centered around 95%
N. tests
Pass Rate
(%)
Fail Rate 3
64.19
0.00 4
18.56
Cumulative
82.75 5
8.10 6
4.40 7
2.43 8
1.15
98.83 9
0.62 10
0.29 11
0.12 12
0.14
Total
100.00
Data centered around 96%
N. tests
Pass Rate
(%)
Fail Rate 3
49.47
0.00 4
20.71
Cumulative
70.18 5
10.68 6
6.59 7
4.16 8
2.71
94.32 9
2.13 10
1.27 11
0.73 12
1.55
Total
100.00
Data centered around 97%
N. tests
Pass Rate
(%)
Fail Rate 3
34.72
0.01 4
15.02
Cumulative
49.74 5
10.81
0.02 6
7.95 7
5.83 8
4.70
79.03
0.04 9
3.98 10
2.62 11
2.52 12
11.81
Total
99.96

16. French dataset. Assessment of JRC method
Data centered around 98%
N. tests
Pass Rate
(%)
Fail Rate 3
18.21
0.38 4
8.44
0.11
Cumulative
26.65
0.49 5
6.57 6
5.03
0.08 7
4.31
0.05 8
3.79
0.03
46.35
0.76 9
3.33
0.06 10
2.96
0.02 11
2.76 12
43.73
0.01
Total
99.13
0.87
Data centered around 99%
N. tests
Pass Rate
(%)
Fail Rate 3
7.49
2.14 4
3.14
1.89
Cumulative
10.63
4.03 5
1.90
1.80 6
1.38
1.44 7
1.21
1.08 8
1.15
1.00
16.27
9.35 9
0.90
1.07 10
0.62
0.99 11
0.61
0.96 12
68.45
0.78
Total
86.85
13.15
Data centered around 100%
N. tests
Pass Rate
(%)
Fail Rate 3
2.33
9.96 4
0.71
8.72
Cumulative
3.04
18.68 5
0.39
7.56 6
0.19
7.35 7
0.10
6.33 8
0.03
5.39
3.75
45.31 9
0.08
4.84 10
0.00
4.24 11
0.02
4.15 12
34.18
3.43
Total
38.03
61.97
Data centered around 101%
N. tests
Pass Rate
(%)
Fail Rate 3
0.41
26.86 4
0.09
21.04
Cumulative
0.50
47.90 5
0.02
14.89 6
0.01
10.29 7
0.00
7.41 8
5.07
0.53
85.56 9
4.06 10
2.58 11
2.04 12
3.75
1.48
Total
4.28
95.72

17. French dataset. Assessment of JRC method
Raw data
N. tests
Pass Rate
(%)
Fail Rate 3
40.50
0.00 4
17.63
Cumulative
58.13 5
12.02 6
8.60 7
6.04 8
15.21
Total
100.00
Data centered around 95%
N. tests
Pass Rate
(%)
Fail Rate 3
60.74
0.00 4
18.78
Cumulative
79.52 5
8.38 6
4.99 7
2.77 8
4.33
Total
100.00
Data centered around 96%
N. tests
Pass Rate
(%)
Fail Rate 3
45.85
0.00 4
18.41
Cumulative
64.26 5
10.88 6
6.70 7
4.62 8
13.54
Total
100.00
Data centered around 97%
N. tests
Pass Rate
(%)
Fail Rate 3
30.34
0.03 4
13.51
0.02
Cumulative
43.85
0.04 5
9.64
0.01 6
7.36
0.00 7
5.38 8
33.71
Total
99.94
0.06

18. French dataset. Assessment of JRC method
Data centered around 98%
N. tests
Pass Rate
(%)
Fail Rate 3
16.76
0.35 4
7.23
0.30
Cumulative
23.99
0.65 5
4.94
0.21 6
3.69
0.24 7
3.08
0.18 8
62.89
0.13
Total
98.59
1.41
Data centered around 99%
N. tests
Pass Rate
(%)
Fail Rate 3
6.87
2.74 4
2.51
2.67
Cumulative
9.38
5.41 5
1.56
2.73 6
0.98 7
0.77
2.57 8
71.72
2.36
Total
84.41
15.59
Data centered around 100%
N. tests
Pass Rate
(%)
Fail Rate 3
2.06
11.82 4
0.50
11.85
Cumulative
2.56
23.67 5
0.25
11.29 6
0.13
9.49 7
0.06
7.89 8
38.28
6.39
Total
41.27
58.73
Data centered around 101%
N. tests
Pass Rate
(%)
Fail Rate 3
0.41
32.16 4
0.06
23.90
Cumulative
0.47
56.07 5
0.01
15.62 6
0.00
9.71 7
6.38 8
7.63
4.10
Total
8.12
91.88

19. French dataset. Assessment of JRC method
Cumulative
Ν=16
Ν=12
Ν=8
Raw
data 4
67.63
64.03
58.13 8
96.75
94.74
100.00 12
99.78 - 16
95% centered data
85.79
82.75
79.52
99.42
98.83
99.99
96% centered data
73.02
70.18
64.26
95.88
94.32
99.45
97% centered data
52.27
49.74
43.85
82.71
79.03
99.94
92.99
99.96
99.97
Cumulative
Ν=16
Ν=12
Ν=8
98% centered data 4
29.64
26.65
23.99 8
51.74
46.35
98.59 12
64.19
99.13 - 16
99.41
99% centered data
12.18
10.63
9.38
19.08
16.27
84.41
22.53
86.85
89.08
100% centered data
3.18
3.04
2.56
4.04
3.75
41.27
4.19
38.03
35.69
101% centered data
0.61
0.50
0.47
0.66
0.53
8.12
4.28
2.83

20. French dataset. Comparison with current CoP method

21. French dataset. Assessment of Current method
Raw data
N. tests
Pass Rate
(%)
Fail Rate 3
100.00
0.00 4
Cumulative 5 6 7 8 9 10 11 12 13 14 15 16
Total
Data centered around 95%
N. tests
Pass Rate
(%)
Fail Rate 3
100.00
0.00 4
Cumulative 5 6 7 8 9 10 11 12 13 14 15 16
Total
Data centered around 96%
N. tests
Pass Rate
(%)
Fail Rate 3
99.99
0.01 4
0.00
Cumulative 5 6 7 8 9 10 11 12 13 14 15 16
Total
Data centered around 97%
N. tests
Pass Rate
(%)
Fail Rate 3
99.85
0.12 4
0.03
Cumulative
99.88 5
0.00 6 7 8 9 10 11 12 13 14 15 16
Total

22. French dataset. Assessment of Current method
Data centered around 98%
N. tests
Pass Rate
(%)
Fail Rate 3
99.04
0.85 4
0.10
0.01
Cumulative
99.14
0.86 5
0.00 6 7 8 9 10 11 12 13 14 15 16
Total
Data centered around 99%
N. tests
Pass Rate
(%)
Fail Rate 3
93.74
5.78 4
0.42
0.04
Cumulative
94.16
5.83 5
0.01 6
0.00 7 8
94.17 9 10 11 12 13 14 15 16
Total
Data centered around 100%
N. tests
Pass Rate
(%)
Fail Rate 3
77.04
21.91 4
0.69
0.32
Cumulative
77.72
22.23 5
0.03
0.01 6 7
0.00 8
77.76
22.24 9 10 11 12 13 14 15 16
Total
Data centered around 101%
N. tests
Pass Rate
(%)
Fail Rate 3
47.06
51.63 4
0.60
0.63
Cumulative
47.66
52.26 5
0.02
0.05 6
0.01 7
0.00 8
47.69
52.31 9 10 11 12 13 14 15 16
Total

23. French dataset. Comparison of methods
Cumulative
JRC
N=8
N=12
N=16
Current
Raw
data 4
58.13
64.03
67.63
100.00 8
94.74
96.75 12 -
99.78 16
95% centered data
79.52
82.75
85.79
98.83
99.42
99.99
96% centered data
64.26
70.18
73.02
94.32
95.88
99.45
97% centered data
43.85
49.74
52.27
99.85
99.94
79.03
82.71
99.88
99.96
92.99
99.97
Cumulative
JRC
N=8
N=12
N=16
Current
98% centered data 4
23.99
26.65
29.64
99.14 8
98.59
46.35
51.74 12 -
99.13
64.19 16
99.41
99% centered data
9.38
10.63
12.18
94.16
84.41
16.27
19.08
94.17
86.85
22.53
89.08
100% centered data
2.56
3.04
3.18
77.72
41.27
3.75
4.04
77.76
38.03
4.19
35.69
101% centered data
0.47
0.50
0.61
47.66
8.12
0.53
0.66
47.69
4.28
2.83

24. Assessment of JRC method

25. French dataset. Assessment of JRC method (A=1.01)
Raw data
N. tests
Pass Rate
(%)
Fail Rate 3
58.43
0.00 4
22.96
Cumulative
81.38 5
10.78 6
4.75 7
1.96 8
0.73
99.60 9
0.27 10
0.09 11
0.02 12
100.00 13 14 15 16
Total
Data centered around 95%
N. tests
Pass Rate
(%)
Fail Rate 3
78.94
0.00 4
14.15
Cumulative
93.08 5
4.52 6
1.75 7
0.44 8
0.14
99.93 9
0.05 10
0.01 11 12
100.00 13 14 15 16
Total
Data centered around 96%
N. tests
Pass Rate
(%)
Fail Rate 3
68.02
0.00 4
18.16
Cumulative
86.18 5
7.41 6
3.31 7
1.73 8
0.89
99.52 9
0.30 10
0.07 11 12
0.02
99.98 13
0.01 14 15 16
Total
100.00
Data centered around 97%
N. tests
Pass Rate
(%)
Fail Rate 3
52.22
0.00 4
20.52
Cumulative
72.74 5
10.28 6
6.40 7
4.06 8
2.40
95.88 9
1.53 10
1.08 11
0.61 12
0.37
99.47 13
0.24 14
0.12 15
0.05 16
0.10
Total
100.00

26. French dataset. Assessment of JRC method (A=1.01)
Data centered around 98%
N. tests
Pass Rate
(%)
Fail Rate 3
35.75
0.00 4
17.06
Cumulative
52.81 5
11.77 6
8.00 7
5.87 8
4.58
83.02
0.01 9
3.55 10
2.75 11
2.22 12
1.68
93.21 13
1.29 14
1.12 15
0.89 16
3.48
Total
99.99
Data centered around 99%
N. tests
Pass Rate
(%)
Fail Rate 3
20.06
0.27 4
9.78
0.13
Cumulative
29.84
0.4 5
7.14
0.11 6
5.97
0.06 7
5.03
0.03 8
4.24
0.02
52.22
0.61 9
3.61 10
3.21
0.00 11
2.85
0.01 12
2.68
64.57
0.65 13
2.31 14
2.17 15
1.93 16
28.36
Total
99.33
0.67
Data centered around 100%
N. tests
Pass Rate
(%)
Fail Rate 3
8.75
1.96 4
3.51
1.35
Cumulative
12.26
3.31 5
2.36
1.3 6
1.79
1.06 7
1.48
0.87 8
1.18
0.80
19.07
7.33 9
1.09
0.77 10
0.82
0.64 11
0.52 12
0.66
0.45
22.40
9.71 13
0.56
0.41 14
0.53
0.42 15
0.60
0.43 16
64.55
0.39
Total
88.64
11.36
Data centered around 101%
N. tests
Pass Rate
(%)
Fail Rate 3
2.38
8.79 4
0.72
7.20
Cumulative
3.10
15.99 5
0.45
6.8 6
0.26
5.82 7
0.10
5.42 8
0.12
4.63
4.03
38.67 9
0.03
4.26 10
0.02
3.82 11
3.53 12
0.01
3.17
4.12
53.45 13
3.02 14
0.00
2.69 15
2.44 16
32.01
2.25
Total
36.16
63.84

27. French dataset. Assessment of JRC method
Cumulative
A=1.00
A=1.01
Raw
data 4
67.63
81.38 8
96.75
99.60 12
99.78
100.00 16
95% centered data
85.79
93.08
99.42
99.93
99.99
96% centered data
73.02
86.18
95.88
99.52
99.45
99.98
97% centered data
52.27
72.74
82.71
92.99
99.47
99.97
Cumulative
A=1.00
A=1.01
98% centered data 4
29.64
52.81 8
51.74
83.02 12
64.19
93.21 16
99.41
99.99
99% centered data
12.18
29.84
19.08
52.22
22.53
64.57
89.08
99.33
100% centered data
3.18
12.26
4.04
19.07
4.19
22.40
35.69
88.64
101% centered data
0.61
3.10
0.66
4.03
4.12
2.83
36.16

28. Number of families which have 3,4,…,18 vehicles
German dataset. Preprocessing
We use 987 vehicles from the 186 families which have at least 3 vehicles
Number of families which have 3,4,…,18 vehicles 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 95 2
Raw data
Data shifted to 100%
min
1st Qu
median
mean
3rd Qu
max sd
85.47
92.58
94.86
95.01
97.27
105.26
3.44
min
1st Qu
median
mean
3rd Qu
max sd
93.55
99.02
100.03
100.00
100.99
106.22
1.70

29. German dataset. Assessment of JRC method (16 vehicle max)
Raw data
N. tests
Pass Rate
(%)
Fail Rate 3
38.97
0.04 4
16.55
0.00
Cumulative
55.52 5
10.96 6
8.23 7
6.29 8
4.82
85.82
0.05 9
3.47 10
2.68 11
2.12 12
1.56
95.65 13
1.16 14
0.87 15
0.60 16
1.67
Total
99.95
Data centered around 95%
N. tests
Pass Rate
(%)
Fail Rate 3
83.70
0.00 4
11.88
Cumulative
95.57 5
3.23 6
0.97 7
0.19 8
0.03
99.99 9
0.01 10 11 12
100.00 13 14 15 16
Total
Data centered around 96%
N. tests
Pass Rate
(%)
Fail Rate 3
71.14
0.00 4
17.02
Cumulative
88.16 5
6.61 6
3.00 7
1.30 8
0.57
99.65 9
0.22 10
0.09 11
0.02 12
0.01
100.00 13 14 15 16
Total
Data centered around 97%
N. tests
Pass Rate
(%)
Fail Rate 3
53.42
0.00 4
19.31
Cumulative
72.73 5
10.44 6
6.06 7
3.80 8
2.52
95.55 9
1.65 10
1.01 11
0.70 12
0.42
99.32 13
0.25 14
0.17 15
0.09 16
Total
100.00

30. German dataset. Assessment of JRC method (16 vehicle max)
Data centered around 98%
N. tests
Pass Rate
(%)
Fail Rate 3
31.34
0.06 4
15.22
0.02
Cumulative
46.56
0.08 5
10.08
0.01 6
7.70
0.00 7
5.87 8
4.73
74.93
0.09 9
3.83 10
3.20 11
2.65 12
2.27
86.89 13
1.93 14
1.60 15
1.39 16
8.10
Total
99.91
Data centered around 99%
N. tests
Pass Rate
(%)
Fail Rate 3
12.01
0.83 4
5.62
0.58
Cumulative
17.62
1.41 5
4.26
0.49 6
3.33
0.43 7
2.67
0.34 8
2.30
0.32
30.18
2.99 9
2.00
0.26 10
1.81
0.20 11
1.62
0.19 12
1.47
0.16
37.09
3.81 13
1.31
0.14 14
1.17
0.12 15
1.18
0.10 16
54.99
Total
95.73
4.27
Data centered around 100%
N. tests
Pass Rate
(%)
Fail Rate 3
2.46
8.08 4
0.87
7.17
Cumulative
3.33
15.24 5
0.48
6.54 6
0.24
5.70 7
0.17
5.10 8
0.11
4.76
4.34
37.34 9
0.09
4.10 10
0.06
3.72 11
0.04
3.59 12
0.03
3.19
4.56
51.94 13
0.02
2.9 14
2.79 15
2.66 16
32.70
2.4
Total
37.32
62.68
Data centered around 101%
N. tests
Pass Rate
(%)
Fail Rate 3
0.40
31.43 4
0.06
20.31
Cumulative
0.45
51.74 5
0.01
14.92 6
0.00
10.49 7
6.93 8
4.78
0.47
88.87 9
3.33 10
2.32 11
1.64 12
1.11
97.27 13
0.75 14
0.51 15
0.36 16
0.43
0.21
Total
0.90
99.10

31. German dataset. Assessment of JRC method (12 vehicle max)
Raw data
N. tests
Pass Rate
(%)
Fail Rate 3
36.91
0.03 4
15.76
0.00
Cumulative
52.67 5
10.05
0.01 6
7.85 7
6.59 8
5.04
82.22
0.04 9
3.88 10
2.82 11
2.28 12
8.77
Total
99.96
Data centered around 95%
N. tests
Pass Rate
(%)
Fail Rate 3
82.06
0.00 4
12.75
Cumulative
94.82 5
3.58 6
1.14 7
0.37 8
0.08
99.98 9
0.01 10 11 12
Total
100.00
Data centered around 96%
N. tests
Pass Rate
(%)
Fail Rate 3
69.11
0.00 4
17.42
Cumulative
86.54 5
6.85 6
3.36 7
1.77 8
0.80
99.32 9
0.40 10
0.15 11
0.07 12
0.06
Total
100.00
Data centered around 97%
N. tests
Pass Rate
(%)
Fail Rate 3
50.88
0.00 4
18.72
Cumulative
69.60 5
10.99 6
6.29 7
4.13 8
2.80
93.80 9
2.09 10
1.40 11
0.86 12
1.85
Total
100.00

32. German dataset. Assessment of JRC method (12 vehicle max)
Data centered around 98%
N. tests
Pass Rate
(%)
Fail Rate 3
29.54
0.05 4
14.08
0.03
Cumulative
43.62
0.08 5
9.69
0.01 6
7.01 7
5.56 8
4.56
70.44
0.11 9
3.69
0.00 10
3.17 11
2.88 12
19.70
Total
99.88
0.12
Data centered around 99%
N. tests
Pass Rate
(%)
Fail Rate 3
11.18
0.86 4
4.95
0.76
Cumulative
16.13
1.62 5
3.66
0.72 6
2.65
0.59 7
2.20
0.51 8
1.83
0.5
26.47
3.94 9
1.58
0.49 10
1.33
0.34 11
1.19
0.33 12
64.01
Total
94.58
5.42
Data centered around 100%
N. tests
Pass Rate
(%)
Fail Rate 3
2.29
9.25 4
0.73
8.91
Cumulative
3.02
18.17 5
0.36
7.71 6
0.19
6.85 7
0.13
6.16 8
0.05
5.59
3.75
44.48 9
0.04
4.89 10
0.02
4.38 11
0.03
4.17 12
34.80
3.43
Total
38.65
61.35
Data centered around 101%
N. tests
Pass Rate
(%)
Fail Rate 3
0.34
34.46 4
0.07
21.54
Cumulative
0.41 56 5
0.02
15.29 6
0.01
9.99 7
0.00
6.67 8
4.28
0.43
92.22 9
2.63 10
1.78 11
1.14 12
1.08
0.71
Total
1.52
98.48

33. German dataset. Assessment of JRC method (8 vehicle max)
Raw data
N. tests
Pass Rate
(%)
Fail Rate 3
33.46
0.06 4
13.90
0.01
Cumulative
47.36
0.07 5
9.37 6
6.92
0.00 7
5.72 8
30.54
Total
99.92
0.08
Data centered around 95%
N. tests
Pass Rate
(%)
Fail Rate 3
78.33
0.00 4
13.80
Cumulative
92.13 5
4.80 6
1.92 7
0.77 8
0.37
Total
100.00
Data centered around 96%
N. tests
Pass Rate
(%)
Fail Rate 3
65.25
0.00 4
17.59
Cumulative
82.84 5
7.94 6
3.99 7
2.33 8
2.90
Total
100.00
Data centered around 97%
N. tests
Pass Rate
(%)
Fail Rate 3
47.30
0.00 4
17.92
Cumulative
65.22 5
10.14 6
6.72 7
4.57 8
13.35
Total
100.00

34. German dataset. Assessment of JRC method (8 vehicle max)
Data centered around 98%
N. tests
Pass Rate
(%)
Fail Rate 3
26.79
0.09 4
11.92
0.05
Cumulative
38.71
0.15 5
8.18 6
6.04
0.04 7
4.78
0.02 8
42.03
0.01
Total
99.73
0.27
Data centered around 99%
N. tests
Pass Rate
(%)
Fail Rate 3
9.91
1.31 4
3.98
1.43
Cumulative
13.89
2.73 5
2.63
1.46 6
1.91
1.34 7
1.50
1.23 8
72.11
1.21
Total
92.04
7.96
Data centered around 100%
N. tests
Pass Rate
(%)
Fail Rate 3
2.08
11.56 4
0.49
12.17
Cumulative
2.57
23.73 5
0.26
10.88 6
0.16
9.24 7
0.07
7.58 8
38.97
6.54
Total
42.03
57.97
Data centered around 101%
N. tests
Pass Rate
(%)
Fail Rate 3
0.31
39.32 4
0.05
25.18
Cumulative
0.35
64.50 5
0.01
15.71 6
0.00
8.49 7
5.01 8
3.35
2.58
Total
3.71
96.29

35. Number of families which have 3,4,…,47 vehicles
Dutch dataset. Preprocessing
We use 471 vehicles from the 49 families which have at least 3 vehicles
Number of families which have 3,4,…,47 vehicles 3 4 5 6 7 8 9 10 11 12 13 16 17 21 25 32 34 47 1 2
Raw data
Data shifted to 100%
min
1st Qu
median
mean
3rd Qu
max sd
84.03
92.45
94.73
94.70
96.85
102.46
2.75
min
1st Qu
median
mean
3rd Qu
max sd
90.88
98.88
99.98
100.00
101.34
105.40
1.85

36. Dutch dataset. Assessment of JRC method (16 vehicle max)
Raw data
N. tests
Pass Rate
(%)
Fail Rate 3
50.72
0.00 4
23.23
Cumulative
73.95 5
13.59 6
6.46 7
3.17 8
1.52
98.69 9
0.73 10
0.33 11
0.14 12
0.06
99.94 13
0.04 14
0.02 15 16
Total
100.00
Data centered around 95%
N. tests
Pass Rate
(%)
Fail Rate 3
80.98
0.00 4
13.73
Cumulative
94.71 5
3.99 6
1.03 7
0.22 8
0.05
100.00 9 10 11 12 13 14 15 16
Total
Data centered around 96%
N. tests
Pass Rate
(%)
Fail Rate 3
65.36
0.00 4
21.36
Cumulative
86.72 5
7.19 6
3.39 7
1.67 8
0.65
99.62 9
0.22 10
0.11 11
0.03 12
0.01
99.99 13 14 15 16
Total
100.00
Data centered around 97%
N. tests
Pass Rate
(%)
Fail Rate 3
44.21
0.00 4
22.09
Cumulative
66.30 5
12.85 6
7.54 7
4.52 8
2.88
94.10 9
2.08 10
1.35 11
0.95 12
0.60
99.07 13
0.36 14
0.24 15
0.13 16
0.21
Total
100.00

37. Dutch dataset. Assessment of JRC method (16 vehicle max)
Data centered around 98%
N. tests
Pass Rate
(%)
Fail Rate 3
25.49
0.1 4
11.48
0.04
Cumulative
36.97
0.15 5
9.81
0.02 6
8.04 7
6.54 8
5.28
66.63
0.17 9
4.61 10
3.79 11
3.29 12
2.85
81.18 13
2.46 14
2.12 15
1.84 16
12.23
Total
99.83
Data centered around 99%
N. tests
Pass Rate
(%)
Fail Rate 3
11.30
1.70 4
4.88
0.82
Cumulative
16.19
2.53 5
3.15
0.66 6
2.29
0.59 7
1.90
0.44 8
1.71
0.38
25.25
4.60 9
1.52
0.30 10
1.40
0.27 11
1.23
0.25 12
1.06
0.21
30.45
5.63 13
0.99
0.15 14
0.94
0.18 15
0.88
0.16 16
60.48
0.14
Total
93.75
6.25
Data centered around 100%
N. tests
Pass Rate
(%)
Fail Rate 3
2.55
8.19 4
0.89
7.23
Cumulative
3.44
15.43 5
0.47
6.62 6
0.28
5.86 7
0.18
5.35 8
0.12
4.76
4.50
38.01 9
0.08
4.21 10
0.05
3.91 11
0.03
3.45 12
3.29
4.68
52.87 13
0.01
2.98 14
2.72 15
2.59 16
31.74
2.39
Total
36.45
63.55
Data centered around 101%
N. tests
Pass Rate
(%)
Fail Rate 3
0.43
28.76 4
0.06
20.32
Cumulative
0.49
49.08 5
0.02
15.57 6
0.00
10.26 7
7.05 8
4.84
0.53
86.81 9
3.39 10
2.51 11
1.96 12
1.36
96.03 13
0.97 14
0.72 15 16
0.88
0.35
Total
1.41
98.59

38. Dutch dataset. Assessment of JRC method (12 vehicle max)
Raw data
N. tests
Pass Rate
(%)
Fail Rate 3
47.96
0.00 4
22.74
Cumulative
70.70 5
13.43 6
7.57 7
4.01 8
2.04
97.75 9
1.12 10
0.60 11
0.27 12
0.26
Total
100.00
Data centered around 95%
N. tests
Pass Rate
(%)
Fail Rate 3
79.01
0.00 4
14.63
Cumulative
93.63 5
4.35 6
1.55 7
0.32 8
0.13
99.98 9
0.01 10 11 12
Total
100.00
Data centered around 96%
N. tests
Pass Rate
(%)
Fail Rate 3
62.03
0.00 4
22.06
Cumulative
84.09 5
8.06 6
3.83 7
2.25 8
1.02
99.25 9
0.39 10
0.22 11
0.07 12
Total
100.00
Data centered around 97%
N. tests
Pass Rate
(%)
Fail Rate 3
41.98
0.00 4
20.65
Cumulative
62.63 5
12.95 6
7.91 7
5.01 8
3.46
91.96 9
2.27 10
1.70 11
1.29 12
2.78
Total
100.00

39. Dutch dataset. Assessment of JRC method (12 vehicle max)
Data centered around 98%
N. tests
Pass Rate
(%)
Fail Rate 3
24.24
0.10 4
10.49
0.05
Cumulative
34.73
0.15 5
8.49
0.02 6
7.14
0.04 7
6.03
0.00 8
5.10
61.49
0.21 9
4.39 10
3.62 11
3.08 12
27.21
Total
99.78
0.22
Data centered around 99%
N. tests
Pass Rate
(%)
Fail Rate 3
10.17
1.92 4
4.35
0.99
Cumulative
14.52
2.91 5
2.71
0.96 6
2.16
0.83 7
1.56
0.62 8
1.24
0.56
22.18
5.88 9
1.10
0.63 10
1.01
0.46 11
0.90
0.42 12
67.02
0.39
Total
92.21
7.79
Data centered around 100%
N. tests
Pass Rate
(%)
Fail Rate 3
2.46
9.03 4
0.83
Cumulative
3.29
18.06 5
0.40
8.05 6
0.20
7.05 7
0.13
6.41 8
0.06
5.48
4.06
45.05 9
4.77 10
0.02
4.47 11
0.03
4.04 12
33.82
3.68
Total
37.99
62.01
Data centered around 101%
N. tests
Pass Rate
(%)
Fail Rate 3
0.43
30.61 4
0.05
22.18
Cumulative
0.48
52.79 5
0.02
16.23 6
0.00
9.89 7
6.62 8
4.49
0.49
90.02 9
2.97 10
2.01 11
1.45 12
1.04
Total
2.51
97.49

40. Dutch dataset. Assessment of JRC method (8 vehicle max)
Raw data
N. tests
Pass Rate
(%)
Fail Rate 3
43.65
0.00 4
20.57
Cumulative
64.22 5
13.26 6
8.52 7
5.30 8
8.71
Total
100.00
Data centered around 95%
N. tests
Pass Rate
(%)
Fail Rate 3
75.16
0.00 4
15.81
Cumulative
90.97 5
5.11 6
2.60 7
0.83 8
0.49
Total
100.00
Data centered around 96%
N. tests
Pass Rate
(%)
Fail Rate 3
57.10
0.00 4
21.81
Cumulative
78.91 5
9.84 6
4.55 7
2.80 8
3.89
Total
100.00
Data centered around 97%
N. tests
Pass Rate
(%)
Fail Rate 3
38.46
0.00 4
17.67
Cumulative
56.12 5
12.36 6
8.18 7
5.80 8
17.53
Total
100.00

41. Dutch dataset. Assessment of JRC method (8 vehicle max)
Data centered around 98%
N. tests
Pass Rate
(%)
Fail Rate 3
21.66
0.17 4
8.22
0.07
Cumulative
29.89
0.24 5
6.79
0.05 6
5.86
0.03 7
4.44
0.02 8
52.67
0.01
Total
99.65
0.35
Data centered around 99%
N. tests
Pass Rate
(%)
Fail Rate 3
9.44
2.31 4
3.63
1.58
Cumulative
13.07
3.88 5
2.08
1.68 6
1.32
1.51 7
1.04
1.42 8
72.62
1.39
Total
90.12
9.88
Data centered around 100%
N. tests
Pass Rate
(%)
Fail Rate 3
2.28
11.07 4
0.56
12.01
Cumulative
2.84
23.08 5
0.23 6
0.11
9.42 7
0.08
7.68 8
38.78
6.70
Total
42.05
57.95
Data centered around 101%
N. tests
Pass Rate
(%)
Fail Rate 3
0.38
36.62 4
0.05
26.22
Cumulative
0.43
62.84 5
0.01
15.54 6
0.00
8.81 7
5.01 8
4.27
3.08
Total
4.71
95.29

42. Conclusions
The presentation provides a more extensive analysis of the CoP data submitted by France, Germany and The Netherlands in particular for what concerns the assessment of the JRC proposal for the CoP of CO2/FC.
Main results in addition to previous analysis:
JRC approach appears to confirm its robustness and the higher flexibility compared to the current CO2 CoP procedure (trade-off between margin of safety and number of tests)
As the mean of the distribution gets closer to the pass limit, the JRC approach allows a slightly better protection from false fails
The JRC also provides a better protection from false passes
On the bases of the CoP test data that have been submitted and analysed, changing the max number of tests to 12 and 8 does not significantly affect the results, suggesting that also a lower than 16 max number of tests could be taken for the final pass/fail decision

43. Thanks
Questions?
You can find me at