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

Xiaohua Zhang^1,2, Haolin Li^1,3, Wenfeng Zhang^2,4

¹College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China.

|February 25, 2023

Summary

This summary is machine-generated.

This study introduces a complex-order beetle swarm optimization (CBSO) method to enhance lithium-ion battery (LIB) model accuracy. CBSO significantly improves parameter identification and prediction accuracy compared to traditional methods.

Keywords:

FO equivalent circuit beetle swarm optimization parameter identification

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Area of Science:

Electrochemistry
Computational Intelligence
Battery Technology

Background:

Accurate modeling of lithium-ion batteries (LIBs) is crucial for performance optimization and safety.
Traditional modeling approaches often struggle to capture the complex electrochemical dynamics of LIBs.
Electrochemical impedance spectroscopy (EIS) provides valuable data for battery model development.

Purpose of the Study:

To develop and validate a novel optimization algorithm for improving LIB model accuracy.
To enhance the parameter identification process for fractional-order equivalent circuit models of LIBs.
To demonstrate the superiority of the proposed method over existing techniques.

Main Methods:

Establishing a fractional-order equivalent circuit model for LIBs using EIS data.
Developing a complex-order beetle swarm optimization (CBSO) algorithm by incorporating complex-order operators and mutation into the traditional beetle swarm optimization (BSO).
Utilizing the CBSO algorithm for precise parameter identification of the LIB model.

Main Results:

The CBSO algorithm effectively identified the parameters of the fractional-order LIB model.
Simulation experiments confirmed the enhanced accuracy of the CBSO-based model.
The CBSO method demonstrated superior performance in minimizing root-mean-square error (RMSE) and maximum absolute error (MAE) compared to the fractional-order BSO.

Conclusions:

The proposed CBSO method offers a significant advancement in modeling lithium-ion batteries.
Accurate parameter identification using CBSO leads to improved model predictive capabilities.
This approach provides a robust tool for researchers and engineers working on LIBs.