Parameter Identification for Lithium-Ion Battery Based on Hybrid Genetic–Fractional Beetle Swarm Optimization Method

Peng Guo, Xiaobo Wu, António M. Lopes (), Anyu Cheng, Yang Xu and Liping Chen
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Peng Guo: College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Xiaobo Wu: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
António M. Lopes: LAETA/INEGI, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Anyu Cheng: College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Yang Xu: College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Liping Chen: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China

Mathematics, 2022, vol. 10, issue 17, 1-11

Abstract: This paper proposes a fractional order (FO) impedance model for lithium-ion batteries and a method for model parameter identification. The model is established based on electrochemical impedance spectroscopy (EIS). A new hybrid genetic–fractional beetle swarm optimization (HGA-FBSO) scheme is derived for parameter identification, which combines the advantages of genetic algorithms (GA) and beetle swarm optimization (BSO). The approach leads to an equivalent circuit model being able to describe accurately the dynamic behavior of the lithium-ion battery. Experimental results illustrate the effectiveness of the proposed method, yielding voltage estimation root-mean-squared error (RMSE) of 10.5 mV and mean absolute error (MAE) of 0.6058%. This corresponds to accuracy improvements of 32.26% and 7.89% for the RMSE, and 43.83% and 13.67% for the MAE, when comparing the results of the new approach to those obtained with the GA and the FBSO methods, respectively.

Keywords: FO equivalent circuit; parameter identification; genetic algorithm; beetle swarm optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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