China VI heavy-duty moving average window (MAW) method: Quantitative analysis of the problem, causes, and impacts based on the real driving data

Sheng Su, Yang Ge, Pan Hou, Xin Wang, Yachao Wang, Tao Lyu, Wanyou Luo, Yitu Lai, Yunshan Ge and Liqun Lyu

Energy, 2021, vol. 225, issue C

Abstract: The heavy-duty moving average window (MAW) method, used for heavy-duty diesel vehicle (HDDV) real driving emission certification, has been long criticized for its unreasonable results. To quantitively analyze the problem, causes, and impacts of the MAW method, five China VI HDDVs were tested under real driving conditions. The specific method and MAW method with different boundaries are applied for data analysis. The results illustrate that cold start occupied 40.82 ± 11.22% of the total NOx emission within 5.77 ± 1.21% of the duration. Compared to the specific method, the MAW result gap is observed varying from −16.92% to 100.24% and didn’t show any pattern. Three reasons could explain biased MAW results: the 20% power threshold excludes the cold data; the 90th accumulative percentile window brings large uncertainty to the result and leaves the highest 10% window without supervision; the initial data gets low utilization. The MAW method could lead to ineffective NOx supervision and exhaust cheating. The future emission limits and emission inventories based on these results are also less reasonable. The above-discussed three reasons and the cold start data exclusion should be considered together to consummate the MAW method. These results could be used for future emission legislation and NOx control optimization.

Keywords: China VI heavy-Duty diesel vehicles; Real driving tests; Moving average window method; NOx; Biased results; Ineffective supervision (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:225:y:2021:i:c:s0360544221005442

DOI: 10.1016/j.energy.2021.120295

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