电工技术学报  2017, Vol. 32 Issue (11): 198-207    DOI:
电工理论与新技术 |
充电模态下电动汽车动力电池模型辨识
刘伟龙1, 2, 王丽芳1, 廖承林1, 王立业1
1.中国科学院电力电子与电力传动重点实验室(电工研究所) 北京 100190;
2.中国科学院大学 北京 100049
Parameters Identification Method of Battery Model for Electric Vehicles under the Charging Mode
Liu Weilong1, 2, Wang Lifang1, Liao Chenglin1, Wang Liye1
1.Key Laboratory of Power Electronics and Electric Drives Institute of Electrical Engineering Chinese Academy of Science Beijing 100190 China;
2. University of Chinese Academy of Sciences Beijing 100049 China
全文: PDF (3191 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 电池模型及参数辨识是电动汽车动力电池进行充、放电优化控制的基础,同时模型参数受充、放电工况的影响。为对充电模态下的电动汽车动力电池进行建模与参数辨识,对动力电池建模方法、模型参数辨识算法展开研究,建立基于电极阻抗谱理论的可变阶次电池等效电路模型,提出基于遗忘因子扩展递推最小二乘算法(FFRELS)的模型参数辨识算法,构建基于贝叶斯信息准则(BIC)的电池模型最优阶次选择算法,创建基于晶格气体模型(LGM)的电池开路电压模型,对电池模型参数辨识算法进行修正,实现了充电模态下的电池模型参数辨识与最优阶次选择。仿真结果证明了该方法的有效性。
服务
把本文推荐给朋友
加入我的书架
加入引用管理器
E-mail Alert
RSS
作者相关文章
刘伟龙
王丽芳
廖承林
王立业
关键词 电池模型参数辨识遗忘因子贝叶斯信息准则晶格气体模型    
Abstract:The battery model and parameters identification are the base of the charge and discharge optimal control of electric vehicle traction batteries. And the parameters of the battery model are affected by the working condition of the traction battery. In order to model the traction battery and identify the model parameters, modeling algorithm of traction battery and parameters identification method were studied in this paper. A variable order equivalent circuit model was established, which is based on the electrode impedance spectrum theory. A parameter identification algorithm was proposed, which is based on the forgetting factor recursive extended least square (FFRELS). A selection algorithm for the optimal order of the battery model was built, which is based on the Bayesian information criterions (BIC). A battery open circuit voltage model that was used for calibration of the proposed parameter identification algorithm was created, which is based on the lattice gas model (LGM). In the end, the battery model parameters identification algorithm and the optimal order selection under the charging mode was achieved. Validation results show that the proposed modeling and parameters identification algorithm is efficient.
Key wordsBattery model    parameters identification    forgetting factor    Bayesian information criterions    lattice gas model   
收稿日期: 2016-03-05      出版日期: 2017-06-20
PACS: TM912.8  
基金资助:国家重点研发计划项目(2016YFB0101801,2016YFB0101800)和国家电网公司科技项目“电动汽车基础设施运行安全与互联网互通技术”资助
通讯作者: 刘伟龙 男,1988年生,博士研究生,研究方向为电动汽车电池管理技术。E-mail:liuweilong@mail.iee.ac.cn   
作者简介: 王丽芳 女,1971年生,博士,研究员,研究方向为电动汽车电池管理技术和电动汽车无线充电技术等。E-mail:wlf@mail.iee.ac.cn
引用本文:   
刘伟龙, 王丽芳, 廖承林, 王立业. 充电模态下电动汽车动力电池模型辨识[J]. 电工技术学报, 2017, 32(11): 198-207. Liu Weilong, Wang Lifang, Liao Chenglin, Wang Liye. Parameters Identification Method of Battery Model for Electric Vehicles under the Charging Mode. Transactions of China Electrotechnical Society, 2017, 32(11): 198-207.
链接本文:  
https://dgjsxb.ces-transaction.com/CN/Y2017/V32/I11/198