Prediction of NOx Emissions of a Heavy-Duty Diesel Vehicle Based on PSO-SVR

被引：0
|
作者：
Wang Z. [1 ]
Dong M. [1 ]
Zhang Y. [2 ]
Hu J. [1 ]
机构：
[1] School of Automotive Engineering, Wuhan University of Technology, Wuhan
[2] Xiang Yang DA’AN Automobile Testing Center Limited, Xiangyang
来源：
Neiranji Xuebao/Transactions of CSICE (Chinese Society for Internal Combustion Engines) | 2023年 / 41卷 / 06期
关键词：
heavy-duty diesel vehicle; particle swarm optimization(PSO); portable emission measurement system; principal component analysis; support vector regression(SVR);
D O I：
10.16236/j.cnki.nrjxb.202306062
中图分类号：
学科分类号：
摘要：
Followed with China Ⅵ emission regulations for heavy-duty vehicles，a portable emission measurement system(PEMS) was used to measure the real-world emissions of a heavy-duty diesel vehicle. After aligning the test data and removing the invalid data，the parameters with great impact on NOx emissions were extracted by gray correlation analysis. Then，principal component analysis(PCA) was introduced to reduce the dimension of input data，and particle swarm optimization(PSO) was introduced to optimize the support vector regression(SVR) model. Finally，a real-world NOx emission prediction model of the heavy-duty diesel vehicle was obtained. The results show that the root mean square error(RMSE) of the test dataset is 1.381 6 mg/s，the mean absolute percentage error(MAPE) is 19.88% and the R2 is 0.908 1. This study provides a possible method for on-board NOx sensor fault diagnosis and on-line NOx emission monitoring for heavy-duty diesel vehicles. © 2023 Chinese Society for Internal Combustion Engines. All rights reserved.
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页码：524 / 531
页数：7
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