基于智能优化规则的并联混合动力汽车能量管理策略  

Parallel hybrid electric vehicle energy management strategy based on intelligent optimization rules

作　　者：李萍 朱晓璐 焦晓红[1] LI Ping;ZHU Xiaolu;JIAO Xiaohong(School of Electric Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China)

机构地区：[1]燕山大学电气工程学院

出　　处：《燕山大学学报》2019年第6期546-553,共8页Journal of Yanshan University

基　　金：国家自然科学基金资助项目(61573304);河北省自然科学基金资助项目(F2017203210)

摘　　要：针对并联型混合动力汽车,为了提高燃油经济性,在满足驾驶性能和车辆动力要求的前提下,提出了一种基于智能优化规则的能量管理策略。首先,考虑发动机最优工作区和电池的荷电状态,根据一定的工程经验,选取合适的发动机最优工作区转矩和电池荷电状态的阈值,设计了基于规则的能量管理控制策略。然后,考虑到规则控制中一些阈值参数不确定的问题,应用了一种智能优化算法——粒子群算法优化规则控制策略的阈值参数。最后,将所设计的控制策略在多种国际标准工况下进行仿真对比,结果表明,较纯发动机运行而言,普通规则控制策略可以平均节省14.9%的燃油,而基于智能优化规则的控制策略可以平均节省22%的燃油。In order to improve fuel economy,an energy management strategy based on intelligent optimization rules is proposed under the premise of satisfying driving performance and vehicle power demands for parallel hybrid electric vehicles.Firstly,considering the optimal engine operating area and battery state of charge,the appropriate engine optimalworking area torque and batterys tate of charge threshold are selected based on certainhistorical experience to design a rule-based energy management control strategy.Then,considering the uncertainty of some parameter thresholds in the rule control strategy,an intelligent algorithm,particle swarm optimization,is used to optimize the threshold parameters of the rule-based control strategy.Finally,the proposed control strategy is simulated and compared under a variety of standard operating conditions.The results show that,compared with pure engine operation,the ordinary rule control strategy can save averagely 14.9%fuel,while the intelligent optimization rule-based control strategy can saveaveragely 22%fuel.

关 键 词：混合动力汽车 能量管理策略 规则控制 粒子群算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]