基于PSO-SVR的重型柴油车NO_(x)排放预测  

Prediction of NO_(x) Emissions of a Heavy-Duty Diesel Vehicle Based on PSO-SVR

作　　者：王志红[1] 董梦龙 张远军 胡杰[1] Wang Zhihong;Dong Menglong;Zhang Yuanjun;Hu Jie(School of Automotive Engineering,Wuhan University of Technology,Wuhan 430070,China;Xiang Yang DA’AN Automobile Testing Center Limited,Xiangyang 441004,China)

机构地区：[1]武汉理工大学汽车工程学院,湖北武汉430070 [2]襄阳达安汽车检测中心有限公司,湖北襄阳441004

出　　处：《内燃机学报》2023年第6期524-531,共8页Transactions of Csice

基　　金：国家重点研发计划资助项目(2017YFC0211203)。

摘　　要：结合重型汽车国Ⅵ污染物排放法规,采用车载便携式排放测试设备(PEMS)进行了某重型柴油车实际道路排放测试.对测试数据进行数据对齐,剔除无效数据后,采用灰色关联分析提取了对NO_(x)排放影响较大的参数,引入主成分分析(PCA)对输入数据进行降维,引入非线性递减惯性权重粒子群算法(PSO)对支持向量回归(SVR)模型进行优化,最终得到重型柴油车实际道路NO_(x)排放预测模型,测试集均方根误差(RMSE)为1.381 6 mg/s,平均绝对百分比误差(MAPE)为19.88%,决定系数R^(2)为0.908 1.该研究为车载NO_(x)传感器故障诊断以及重型车NO_(x)排放在线监管提供一种可能性方法.Followed with ChinaⅥemission 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 NO_(x) 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 NO_(x) 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 1.3816 mg/s,the mean absolute percentage error(MAPE)is 19.88% and the R^(2) is 0.9081.This study provides a possible method for on-board NO_(x) sensor fault diagnosis and on-line NO_(x) emission monitoring for heavy-duty diesel vehicles.

关 键 词：重型柴油车 便携式排放测试设备 主成分分析 粒子群算法 支持向量回归 

分 类 号：TK421.5[动力工程及工程热物理—动力机械及工程]