Improved Parameter Identification for Lithium-Ion Batteries Based on Complex-Order Beetle Swarm Optimization Algorithm

被引：7

作者：

Zhang, Xiaohua ^{[1
,2
]}

Li, Haolin ^{[1
,3
]}

Zhang, Wenfeng ^{[2
,4
]}

Lopes, Antonio M. ^{[5
]}

Wu, Xiaobo ^{[6
]}

Chen, Liping ^{[6
]}

机构：

[1] Zhongkai Univ Agr & Engn, Coll Automat, Guangzhou 510225, Peoples R China

[2] Guangdong Hong Kong Macao Greater Bay Area Agr Pro, Guangzhou 510225, Peoples R China

[3] Zhongkai Univ Agr & Engn, Coll Sch Mech & Elect Engn, Guangzhou 510225, Peoples R China

[4] Guangdong Agr Prod Cold Chain Transportat & Logist, Guangzhou 510225, Peoples R China

[5] Univ Porto, Fac Engn, LAETA, INEGI, Rua Dr Roberto Frias, P-4200465 Porto, Portugal

[6] Hefei Univ Technol, Sch Elect Engn & Automat, Hefei 230009, Peoples R China

来源：

MICROMACHINES | 2023年 / 14卷 / 02期

关键词：

FO equivalent circuit; parameter identification; beetle swarm optimization; OF-CHARGE ESTIMATION; STATE;

D O I：

10.3390/mi14020413

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

With the aim of increasing the model accuracy of lithium-ion batteries (LIBs), this paper presents a complex-order beetle swarm optimization (CBSO) method, which employs complex-order (CO) operator concepts and mutation into the traditional beetle swarm optimization (BSO). Firstly, a fractional-order equivalent circuit model of LIBs is established based on electrochemical impedance spectroscopy (EIS). Secondly, the CBSO is used for model parameters' identification, and the model accuracy is verified by simulation experiments. The root-mean-square error (RMSE) and maximum absolute error (MAE) optimization metrics show that the model accuracy with CBSO is superior when compared with the fractional-order BSO.

引用

页数：10

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