基于预测控制的PHEV能源管理策略
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摘要
提出一种基于预测控制的PHEV在线能源管理策略。它利用BP神经网络构建旅途预测模型,并采用遗传-粒子群混合优化算法提升预测模型的车速预测精度;在此基础上,为保证预测模型对工况的适应性和策略的实时性,设计了基于动态规划的预测控制策略;最后以实际工况数据对提出的策略进行了仿真验证。结果表明,设计的旅途预测模型可有效地进行车速预测,预测精度超过93%;同时,与现有的实时策略和全局优化策略相比,采用提出的策略时油耗、排放和实时性得到了改善。
An online energy management strategy for PHEV based on predictive control is proposed. It utilizes BPNN to construct a trip prediction model, and uses genetic/particle swarm hybrid optimization algorithm to improve the vehicle-speed prediction accuracy of the trip prediction model. On this basis, a dynamic programming-based predictive control strategy is designed to ensure the adaptability of the trip prediction model to trip conditions and the real-time performance of the strategy. Finally, a verification simulation is conducted on the strategy proposed based on trip condition data. The results show that the trip prediction model designed can effectively predict vehicle-speeds with an accuracy higher than 93%, and the fuel consumption, emissions and real-time performance with the proposed strategy are improved compared with the existing real-time strategies and global optimization strategies.
An online energy management strategy for PHEV based on predictive control is proposed. It utilizes BPNN to construct a trip prediction model, and uses genetic/particle swarm hybrid optimization algorithm to improve the vehicle-speed prediction accuracy of the trip prediction model. On this basis, a dynamic programming-based predictive control strategy is designed to ensure the adaptability of the trip prediction model to trip conditions and the real-time performance of the strategy. Finally, a verification simulation is conducted on the strategy proposed based on trip condition data. The results show that the trip prediction model designed can effectively predict vehicle-speeds with an accuracy higher than 93%, and the fuel consumption, emissions and real-time performance with the proposed strategy are improved compared with the existing real-time strategies and global optimization strategies.
引文
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[2] LIU Jichao, CHEN Yangzhou, LI Wei, et al. Hybrid trip-model-based energy management of PHEV with computation-optimized dynamic programming[J]. IEEE Transactions on Vehicular Technology,2018,67(1):338-353.
[3] KIM Namwook, CHA Sukwon, PENG Huei. Optimal control of hybrid electric vehicles based on Pontryagin’s minimum principle[J]. IEEE Transactions on Control Systems Technology,2011,19(5):1279-1287.
[4] ZHANG Yi, LIU Heping, GUO Qiang. Varying-domain optimal management strategy for parallel hybrid electric vehicles [J]. IEEE Transactions on Vehicular Technology,2014,63(2):603-616.
[5] 司远,钱立军,邱利宏,等.基于等效油耗最小的四驱混合动力汽车能量管理[J].中国机械工程,2017,28(9):1112-1117.
[6] LI Liang, YOU Sixiong, YANG Chao, et al. Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses[J]. Applied Energy,2016,162:868-879.
[7] 刘吉超,陈阳舟.基于GA-PSO混合优化的BPNN车速预测方法[J].交通运输系统工程与信息,2017,17(6):40-47.