重庆大学学报2024，Vol.47Issue(1)：31-40,10.DOI:10.11835/j.issn.1000-582X.2022.130
神经网络模型度量地形对实际行驶排放的影响
Effect of route topography on real driving emissions based on neural network models
摘要
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 NOx 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.关键词
实际行驶排放/排放模型/神经网络/地形/行程动力学Key words
RDE(real driving emission)/emission model/artificial neural network/route topography/trip dynamics分类
交通工程引用本文复制引用
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):31-40,10.基金项目
国家重点研发计划资助项目(2018YFB0106404) （2018YFB0106404）
重庆市技术创新与应用发展专项项目(cstc2019jscx-msxmX0016) （cstc2019jscx-msxmX0016）
通用技术中国汽研检测事业部创新课题项目(JCCXKT-2021-002).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). （JCCXKT-2021-002）
