TY - JOUR
T1 - China VI heavy-duty moving average window (MAW) method
T2 - Quantitative analysis of the problem, causes, and impacts based on the real driving data
AU - Su, Sheng
AU - Ge, Yang
AU - Hou, Pan
AU - Wang, Xin
AU - Wang, Yachao
AU - Lyu, Tao
AU - Luo, Wanyou
AU - Lai, Yitu
AU - Ge, Yunshan
AU - Lyu, Liqun
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/6/15
Y1 - 2021/6/15
N2 - 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.
AB - 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.
KW - Biased results
KW - China VI heavy-Duty diesel vehicles
KW - Ineffective supervision
KW - Moving average window method
KW - NOx
KW - Real driving tests
UR - http://www.scopus.com/inward/record.url?scp=85102369647&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2021.120295
DO - 10.1016/j.energy.2021.120295
M3 - Article
AN - SCOPUS:85102369647
SN - 0360-5442
VL - 225
JO - Energy
JF - Energy
M1 - 120295
ER -