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作 者:李奇[1] 王晓锋 孟翔[1] 张国瑞[1] 陈维荣[1] LI Qi;WANG Xiaofeng;MENG Xiang;ZHANG Guorui;CHEN Weirong(School of Electrical Engineering,Southwest Jiaotong University,Chengdu 610031,Sichuan Province,China;School of Graduate school of Tangshan,Southwest Jiaotong University,Tangshan 063000,Hebei Province,China)
机构地区:[1]西南交通大学电气工程学院,四川省成都市610031 [2]西南交通大学唐山研究生院,河北省唐山市063000
出 处:《中国电机工程学报》2020年第21期6991-7001,共11页Proceedings of the CSEE
基 金:国家自然科学基金项目(51977181);四川省科技计划项目(19YYJC0698);霍英东教育基金会高等院校青年教师基金项目(171104)。
摘 要:为了有效改善燃料电池混合动力系统的能耗,减少燃料电池性能衰减,保持辅助动力源的荷电状态(state of charge,SOC),提出一种基于遗忘因子递推最小二乘算法(forgetting factor recursive least square,FFRLS)的在线辨识方法和极小值原理的综合能量管理方法。该方法能根据在线辨识的结果和直流母线需求功率,完成对主动力源及辅助动力源的功率分配工作,并与基于离线辨识的算法结果以及等效氢耗最小能量管理方法(equivalent consumption minimization strategy,ECMS)进行对比分析。结果表明,该方法对等效氢耗的优化比离线以及ECMS的效果分别提升了6.33%和4.35%,对燃料电池性能衰减则分别优化了4.72%和6.98%,并能更好地维持辅助动力源的SOC。In order to effectively improve the energy consumption of the fuel cell hybrid power system, reduce fuel cell performance weaken, and maintain the state of charge(SOC) of the auxiliary power source, this paper proposed an online identification method based on forgetting factor recursive least squares(FFRLS) and an integrated energy management strategy based on minimum principle. This method can transform multiple targets into single targets. This method can complete the power allocation between the main power source and the auxiliary power source based on the online identification results and the DC bus demand power, and compare to the offline identification algorithm results and the results of the equivalent consumption minimization strategy(ECMS). The results show that this method optimizes the energy consumption ratio offline and ECMS results by 6.33% and 4.35%, respectively, and optimizes the fuel cell performance weaken by 4.72% and 6.98%, respectively, and can better maintain the SOC of auxiliary power sources.
关 键 词:燃料电池混合动力系统 遗忘因子递推最小二乘算法在线辨识 多目标优化 能量管理
分 类 号:TM61[电气工程—电力系统及自动化]
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