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

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.

Automated summary

To improve the model accuracy of lithium-ion batteries (LIBs), this paper introduces a complex-order beetle swarm optimization (CBSO) method, which combines complex-order (CO) operator concepts and mutation into the traditional beetle swarm optimization (BSO). The study establishes a fractional-order equivalent circuit model of LIBs based on electrochemical impedance spectroscopy (EIS), and uses CBSO for model parameters' identification and simulation experiments to verify the model accuracy. The optimization metrics of root-mean-square error (RMSE) and maximum absolute error (MAE) demonstrate the superiority of CBSO over fractional-order BSO in terms of model accuracy.