Meeting of the Steering Group on Simulation (SGS) Back-translation: implementation and accuracy implications Munich - 24 April 2014 Biagio.Ciuffo@jrc.ec.europa.eu Alessandro.Marotta@jrc.ec.europa.eu Jelica.Pavlovic@jrc.ec.europa.eu Simone.Serra@jrc.ec.europa.eu Disclaimer: The views expressed are purely those of the writer and may not in any circumstance be regarded as stating an official position of the European Commission

Motivation The introduction of WLTP in the type-approval of LDV requires the definition of a methodology to assess the compliance of OEMs to the 2015 and 2020 CO2 targets which are now based on measured carried out on the NEDC At least during the transition period 2017-2019 it is most plausible that a WLTP->NEDC translation-back approach will be adopted in order to keep the NEDC-based targets The approach is based on the development of a WLTP-NEDC correlation function

Issues The translation-back approach will produce a not negligible implementation burden This has a potential effect on the accuracy that is possible to achieve with the use of the correlation function Objective Objective of this presentation is to have a preliminary insight into the implementation and accuracy implication of the approach

The translation-back approach Starting on September 2017 all new vehicle types (PC+N1 class 1) are type-approved on WLTP How the equivalent NEDC figure for CO2 will be estimated? Option 1. Direct estimation at type-approval by TA authority Option 2. Estimation at a later stage by other authority (COM, EEA, JRC, ???)

Implications Option 1 The correlation function coded in a well-defined and user friendly computer software After the tests, the TA authorities will input all the necessary details into the software to evaluate the correlation factor for that vehicle Which kind of inputs can be provided the TA authority with? Are the TA authorities available to deal with this additional burden?

Implications Option 2 The correlation function handled by a different authority (translation authority, TrA) and used, e.g. at the end of the year with the entire vehicle db The necessary inputs are provided to the TA authority at TA and just registered and transmitted to the TrA Which kind of inputs can be transmitted to the TrA? How can the TrA link the data coming from TA to the vehicles in the monitoring db? This option very unlikely

Implications Options 1 seems the most straightforward way Still to be seen if TA authorities available/possible to carry out the job Possible problem of transparency on very technical information provided by OEMs that TA cannot verify and that in principle are not publicly available (also because possibly confidential) It is likely that the information will be those already provided now to be complemented with indication on the presence of certain technologies (in order to get a reasonable translated effect)

Implications Info already available at TA per each variant/version/model/body-style/engine/n.pow.axles: No RLs! Other data not considered in the list? What can be added? Transmission and gear-box type, ratios Engine capacity Wheel-base Idling speed Axles track Maximum net power Length Fuel type Width Type of fuel feed Heigth Type of emission control systems Max and running order mass (distr. among axles) Maximum design speed N. of cylinder Types of tires and wheels

Implications How can the ±2-3g/km accuracy can be respected? Which is the effect of a certain inaccuracy on the performance by a specific OEM (or of a specific pool of them)?

Accuracy implications How can the ±2-3g/km accuracy can be respected? Waiting for the results of SA and first simulations Which is the effect of a certain inaccuracy on the performance by a specific OEM (or of a specific pool of them)? A theoretical assessment is possible

Analysis of the effect of the inaccuracy distribution on the performance of a single OEM (or pool of them) The analysis was carried out using the final EEA database 2013 (passenger car market 2012) 3 different levels of un-biased random error have been added to the vehicles included in the db Each level has been assessed 10 times to take into account the effect of the random sampling Per each pool of OEMs the distribution of the error on the average CO2 performance has been evaluated

Step 1 - Extraction of data from the EEA 2013 db SQL query (posted here for transparency): SELECT MP, Ft, [m (kg)], [e (g/km)], [ec (cm3)], [ep (KW)], Sum(r) AS SumOfr INTO [Vehicle DB] FROM CO2_passenger_cars_v6 GROUP BY MP, Ft, [m (kg)], [e (g/km)], [ec (cm3)], [ep (KW)] (to group all the vehicles from the pool with the same characteristics) HAVING (((MP)<>"") AND ((Ft)="Diesel" Or (Ft)="Petrol") AND (([m (kg)])>0) AND (([e (g/km)])>0) AND (([ec (cm3)])>0) AND (([ep (KW)])>0) AND ((Sum(r))>0)) (to remove vehicles with missing fields) ORDER BY MP;

Step 1 - Extraction of data from the EEA 2013 db Result: a table with less than 30.000 rows with CO2 emissions and registrations for all vehicles of 13 pools of OEMs Some of the pools were also for OEMs having derogation to the CO2 regulation The average CO2 performance of each pool was easily calculated

Step 2 – Selection of 3 levels of random error 3 distributions of un-biased random errors selected N(0,1) - Normal distribution with 0 mean and 1g/km standard deviation N(0,2) - Normal distribution with 0 mean and 2g/km standard deviation N(0,3) - Normal distribution with 0 mean and 3g/km standard deviation

Step 3 – Error propagation A random error sampled from the three distributions is assigned to each line of the table The resulting effect on the average CO2 emission per pool was calculated 10 repetitions were carried out to see the effect of the random sampling The distribution of the error on the average CO2 performance was evaluated per each pool of OEMs

Step 4 – Analysis of the results Results are shown in terms of candlestick plots in which is shown the 80% confidence interval of the distributions of the error per Pool of OEMs and the extrema of the same distributions Since the error are weighted on the basis of the number of registration of each vehicle, the distribution of the average values is only “quasi” unbiased

N(0,1) 100% -3 3

N(0,2) >90% -6 6

N(0,3) >75% -9 9

2017 – Average target: 130g/km Only new types of vehicles are Type-approved in WLTP in the period Sep-Dec Average CO2 emissions around 110g/km No effect on compliance of possible inaccuracy of the correlation

2018 – Average target: 130g/km Only new types of vehicles are Type-approved in WLTP in the period Jan-Aug All types Sep-Dec Average CO2 emissions around 105g/km No effect on compliance of possible inaccuracy of the correlation

2019 – Average target: 130g/km All models WLTP TA Average CO2 emissions around 100g/km Only an average bias in the correlation of around 30g/km can affect the compliance to target

2020 – Average target: 95g/km All models WLTP TA Average CO2 emissions around 95g/km Compliance to target potentially strongly influenced by accuracy in the correlation if WLTP values are translated back to NEDC

Conclusions Back-translation at TA seems the most suitable solution if the additional burden is limited Possible inaccuracies at the level of the single vehicle are not fully removed when evaluating the Pool’s average but significantly reduced Higher reductions can be achieved if the accuracy for “best- seller” technologies is high It is important to select an un-biased correlation function to guarantee the limited effect on the Pool’s average For the transition period the translation-back approach should be able to meet the overall accuracy requirements