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

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Guo, P (Author) Other; Wu, XB (Author) Other; António Mendes Lopes (Author) FEUP; Cheng, AY (Author) Other; Xu, Y (Author) Other; Chen, LP (Author) Other

Journal

Title: Mathematics

Vol. 10

Final page: 3056

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Other information

Authenticus ID: P-00X-3TF

DOI: 10.3390/math10173056

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11