High Precision Parameter Identification of Lithium-Ion Battery Model Based on Feedback Particle Swarm Optimization Algorithm
Huang Kai1, Guo Yongfang2, Li Zhigang1
1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin 300130 China; 2. School of Artificial Intelligence Hebei University of Technology Tianjin 300130 China
Abstract：The parameter precision of lithium-ion battery model is an important factor affecting the model to simulate the static and dynamic characteristics of the battery. In recent years, particle swarm optimization (PSO) is often applied to identify the model parameter. However, PSO and its improved algorithm could encounter such problem that, the position of a particle is updating while the local optimal position of the particle and the global optimal position of all the particles stop updating, resulting in the optimization algorithm can’t obtain more precision results. In view of such problem, this paper presents an improved feedback PSO (FPSO), the position of the particle can be adjusted according to the feedback information of the particle to continue to update the local position of the particle to improve the optimization precision. Typical benchmark functions are used to validate the performance of FPSO. On the other hand, the FPSO of the paper is applied to identify the parameter of the lithium-ion battery model, and the experimental results show that, comparing with the models based on Linear PSO, Adaptive weight PSO, and Least Square (LS) parameter identification, the model using FPSO of the paper can achieve high precision.
黄凯, 郭永芳, 李志刚. 基于信息反馈粒子群的高精度锂离子电池模型参数辨识[J]. 电工技术学报, 2019, 34(zk1): 378-387.
Huang Kai, Guo Yongfang, Li Zhigang. High Precision Parameter Identification of Lithium-Ion Battery Model Based on Feedback Particle Swarm Optimization Algorithm. Transactions of China Electrotechnical Society, 2019, 34(zk1): 378-387.
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2019, Vol. 34 
