Analysis of real-driving emissions from light-duty gasoline vehicles: A comparison of different evaluation methods with considering cold-start emissions

2021 
Abstract Cold-start emissions from road vehicles are an important source of air pollution. However, how to evaluate them in the real driving emission (RDE) test is controversial. Although the Commission Regulation (EU) 2017/1154 and 2018/1832 successively adopt the moving averaging window (MAW) method and the cumulative averaging (CA) method to calculate the emission factors that include the cold-start emissions in the RDE tests, both the methods have been found to be flawed when processing the RDE data including cold-starts. To demonstrate the problem, the RDE test data from three light-duty gasoline vehicles were comparatively analyzed using the MAW method and the CA method. Results reveal that the MAW method unreasonably underestimates cold-start emissions in the urban and total trip emission evaluation, and the CA method tends to increase the inconsistency of the RDE evaluation results. Considering the inherently different characteristics between the cold-start emissions and the non-cold-start emissions, this paper proposes that the entire RDE test trip should be divided into a cold-start trip and a non-cold-start trip, and the cold-start emissions are evaluated by the CA method while the non-cold-start emissions are evaluated by the MAW method. Furthermore, the pollutant emission factors in the cold-start trip are corrected referring to the CO2 emissions emitted during the cold-start period of Worldwide harmonized Light vehicles Test Cycle (WLTC). Using this proposed approach, the aforementioned RDE tests were further analyzed and evaluated again. It is verified that the consistency of the test evaluation results in the RDE tests can be improved. The results of this study could be used to improve future emission regulations for the more reasonable evaluation of cold-start emissions in RDE tests.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    41
    References
    4
    Citations
    NaN
    KQI
    []