Effect of route topography on real driving emissions based on neural network models

doi:10.11835/j.issn.1000-582X.2022.130

Home > Archive>Volume 47, Issue 1, 2024 >31-40. DOI:10.11835/j.issn.1000-582X.2022.130

Effect of route topography on real driving emissions based on neural network models
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                        10.11835/j.issn.1000-582X.2022.130
                    
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                        CHANG Hong1CHANG Hong
China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, P. R. China
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WU Dongmei2WU Dongmei
School of Mechanical and Automotive Engineering, Chongqing University, Chongqing 400044, P. R. China
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ZHANG Li2ZHANG Li
School of Mechanical and Automotive Engineering, Chongqing University, Chongqing 400044, P. R. China
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GONG Xiangkun1GONG Xiangkun
China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, P. R. China
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XU Hualong1XU Hualong
China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, P. R. China
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FU Mingming2FU Mingming
School of Mechanical and Automotive Engineering, Chongqing University, Chongqing 400044, P. R. China
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Affiliation:1.China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, P. R. China;2.School of Mechanical and Automotive Engineering, Chongqing University, Chongqing 400044, P. R. China
Clc Number:U448.213
Fund Project:Supported by National Key R&D Program (2018YFB0106404), the Chongqing Technology Innovation and Application Development Project (cstc2019jscx-msxmX0016), and China General Technology (Group) China Automobile Research and Testing Division Innovation Project (JCCXKT-2021-002).

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Abstract:

It is difficult to separate the effect of route topography from that of other test boundaries in real driving emission (RDE) tests. We proposed an artificial neural network (ANN) weight method to quantitatively evaluate the impact of route topography on RDE tests. Based on 37 256 data window samples of RDE tests in Chongqing, a factor analysis method was used to reduce data and eliminate information overlap between test boundaries. Additionally, a neural network model was also established to predict pollutant emissions and calculate the relative importance of input variables. The results show that route topography significantly affects CO₂ emissions, with its relative importance far exceeding that of other test boundaries. Moreover, the influence of the route topography cannot be ignored for CO, PN (particle number), and NO_x emissions, having an impact on vehicle driving emissions comparable to that of trip dynamics, especially under high-speed driving conditions. However, the existing regulatory emission standards seriously underestimate the impact of the route topography on vehicle driving emissions.

Key words:RDE (real driving emission);emission model;artificial neural network;route topography;trip dynamics

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常虹,吴冬梅,张力,龚香坤,徐划龙,付明明.神经网络模型度量地形对实际行驶排放的影响[J].重庆大学学报,2024,47(1):31~40

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Received:October 10,2022
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Online: January 19,2024
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