基于MPC的PHEV转矩分配控制策略研究  

Research on torque allocation control strategy of PHEV based on MPC

作　　者：尹安东 王伟 YIN Andong;WANG Wei(School of Automobile and Traffic Engineering,Hefei University of Technology,Hefei 230009,China;National and Local Joint Engineering Research Center for Automotive Technology and Equipment,Hefei 230009,China)

机构地区：[1]合肥工业大学汽车与交通工程学院,安徽合肥230009 [2]汽车技术与装备国家地方联合工程研究中心,安徽合肥230009

出　　处：《合肥工业大学学报（自然科学版）》2021年第4期440-445,490,共7页Journal of Hefei University of Technology：Natural Science

基　　金：国家新能源汽车重点研发计划资助项目(2017YFB0103204)。

摘　　要：为提高并联混合动力汽车(parallel hybrid electric vehicle,PHEV)的燃油经济性,文章提出了一种基于模型预测控制(model pedictive control,MPC)的PHEV转矩分配控制策略。首先建立马尔柯夫模型,在预测时域内预测汽车的速度和加速度,为提高预测准确性,提出一种利用工况速度差的改进方法;然后将马尔柯夫模型与动态规划算法相结合,搭建基于模型预测控制的转矩分配控制策略;最后基于ADVISOR建立PHEV模型进行仿真对比分析。结果表明,该文提出的改进方法能够提高预测准确性;提出的转矩分配控制策略与基于规则的逻辑门限控制策略相比,整车的百公里燃油消耗率降低了7.4%。In order to improve the fuel economy of parallel hybrid electric vehicle(PHEV),a torque allocation control strategy of PHEV based on model predictive control(MPC)is proposed.Firstly,the Markov model is established to predict the speed and acceleration of the vehicle in the prediction horizon.In order to improve the accuracy of prediction,an improvement method is proposed by using the speed difference of the working condition.Then the Markov model and dynamic programming algorithm are combined to construct a torque allocation control strategy based on MPC.Finally,the PHEV model based on ADVISOR is established for simulation and comparative analysis.The results show that the proposed improvement method can improve the accuracy of prediction,and compared with the rule-based logical threshold control strategy,the proposed torque allocation control strategy reduces the fuel consumption per 100 km by 7.4%.

关 键 词：并联混合动力汽车(PHEV) 马尔柯夫模型 动态规划算法 模型预测控制(MPC) 控制策略 

分 类 号：U469.72[机械工程—车辆工程]