王志红,袁雨,王少博,吴鹏辉,严浩,胡杰.重型柴油车实际道路NOx排放预测模型研究[J].内燃机工程,2019,40(6):9-14.
重型柴油车实际道路NOx排放预测模型研究
Research on Nox Emissions Prediction Model for Heavy Duty Diesel Vehicles
DOI：10.13949/j.cnki.nrjgc.2019.06.002
关键词:NOx排放  便携式车载排放测试系统  自适应学习速率法  排放预测
Key Words:NOx emissions  portable emissions measurement system  adaptive learning rate method  emissions prediction
基金项目:国家重点研发计划项目(2017YFC0211203)
作者单位
王志红,袁雨,王少博,吴鹏辉,严浩,胡杰 1.现代汽车零部件技术湖北省重点实验室（武汉理工大学）,武汉 430070； 2.汽车零部件技术湖北省协同创新中心,武汉 430070 
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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NOx 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NOx 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NOx排放的实时预测,具有一定的工程应用价值。
Abstract: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 NOx 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 NOx 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 NOx emissions of this heavy-duty diesel vehicle, which has certain engineering application value.
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