摘要
基于便携式车载排放测试系统(portable emission measurement system,PEMS),对某型号重型柴油车进行实际道路排放测试,分别利用车辆比功率(VSP)和车辆牵引力(VA)对NO x排放值进行拟合。以这两个因子作为输入参数,应用自适应学习速率法改进后的双隐含层反向传播(BP)神经网络来训练和预测NO x的排放情况。与原BP网络预测情况相比,预测值与实际值的皮尔逊相关系数提高了0.1136,相对误差降低了0.6621%,改进后的神经网络预测准确度有所提升,泛化能力较强,可以用于该款重型柴油车NO x排放的实时预测,具有一定的工程应用价值。
Based on the portable emissions measurement system(PEMS),an actual road emissions test was performed on a certain type of heavy-duty diesel vehicle,and NO x emissions were fitted by the vehicle specific power(VSP)and vehicle traction force(VA)respectively.Using these two factors VSP and VA as input parameters,a double-hierarchical BP neural network improved by adaptive learning rate method was used to train and predict the NO x emissions.Compared with the original BP network prediction,the Pearson correlation coefficient between a predicted value and an actual value was increased by 0.1136,and the relative error was reduced by 0.6621%.The accuracy of the improved neural network prediction was promoted,and the generalization ability was strong.The improved neural network is applicable for the real-time prediction of the NO x emissions of this heavy-duty diesel vehicle,which has certain engineering application value.
作者
王志红
袁雨
王少博
吴鹏辉
严浩
胡杰
WANG Zhihong;YUAN Yu;WANG Shaobo;WU Penghui;YAN Hao;HU Jie(Hubei Key Laboratory of Advanced Technology for Automotive Components(Wuhan University of Technology),Wuhan 430070,China;Hubei Collaborative Innovation Center for Automotive Components Technology,Wuhan 430070,China)
出处
《内燃机工程》
EI
CAS
CSCD
北大核心
2019年第6期9-14,23,共7页
Chinese Internal Combustion Engine Engineering
基金
国家重点研发计划项目(2017YFC0211203)~~
