| Issue |
E3S Web Conf.
Volume 268, 2021
2020 6th International Symposium on Vehicle Emission Supervision and Environment Protection (VESEP2020)
|
|
|---|---|---|
| Article Number | 01022 | |
| Number of page(s) | 14 | |
| DOI | https://doi.org/10.1051/e3sconf/202126801022 | |
| Published online | 11 June 2021 | |
Analysis of the influence of RDE test data processing methods on the emission results of China 6 light duty vehicles
1 Hubei Key Laboratory of Advanced Technology for Automotive Components (Wuhan University of Technology), Wuhan, Hubei 430070, China
2 Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan, Hubei 430070, China
3 Xiangfan Da An Automobile Test Center, Xiangyang, Hubei 441004, China
* Corresponding author: zhangyuanjun@nast.com.cn
The CO2 moving average window(MAW) method is used to process RDE (real drive emissions) emissions data in China 6 light duty vehicle emissions regulations, while the Euro 6 light duty vehicle emission regulations allow to use both of MAW and power binning(PB) method to deal with RDE emission data. In order to study the difference between the two data processing methods and analyze the differences in the emission results, 10 different types of light duty vehicles are conducted RDE test with PEMS (portable emissions measurement system), and the test data are processed by the two methods separately. The results show that there is a little difference between MAW and PB, while both of them can satisfy the vehicle emission assessment. The PB method calculates the emission factors higher than the MAW method. After removing the cold start and idle condition data, the results of PB is similar to MAW. Besides, reducing the average speed limit of urban working conditions in PB has a greater impact on the urban driving condition emission factor, but less on the whole cycle emission factor.
Key words: China 6 light duty vehicle / CO2 moving averaging window method / power binning method / RDE
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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