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

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Zhang, XH (Author) Other; Li, HL (Author) Other; Zhang, WF (Author) Other; António Mendes Lopes (Author) FEUP; Wu, XB (Author) Other; Chen, LP (Author) Other

Journal

Title: Micromachines

Vol. 118

Final page: 413

ISSN: 2072-666X

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00X-WA4

DOI: 10.3390/mi14020413

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10