汽车工程

汽车工程 ›› 2019, Vol. 41 ›› Issue (3): 275-282.doi: 10.19562/j.chinasae.qcgc.2019.03.006

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基于预测控制的PHEV能源管理策略*

刘吉超1,陈阳舟2   

  1. 1.北京工业大学,北京市交通工程重点实验室,北京 100124;
    2.北京工业大学人工智能与自动化学院,北京 100124
  • 收稿日期:2018-01-29 出版日期:2019-03-25 发布日期:2019-03-25
  • 通讯作者: 陈阳舟,教授,博士,E-mail:yzchen@bjut.edu.cn
  • 基金资助:
    国家自然科学基金(61573030)和北京市自然科学基金交控科技轨道交通联合基金(L171001)资助

Energy Management Strategy for PHEV Based on Predictive Control

Liu Jichao1 & Chen Yangzhou2   

  1. 1.Beijing University of Technology, Beijing Key Laboratory of Transportation Engineering, Beijing 100124;
    2.College of Artificial Intelligence and Automation, Beijing University of Technology, Beijing 100124
  • Received:2018-01-29 Online:2019-03-25 Published:2019-03-25

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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。

关键词: PHEV, 能源管理策略, 预测控制, 旅途预测

Abstract: 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

Key words: PHEV, energy management strategy, predictive control, trip prediction