A two-step quantitative diagnosis method for battery internal short circuit faults

Jinlei Sun, Siwen Chen, Shiyou Xing, Yilong Guo, Shuhang Wang, Ruoyu Wang, Yuhao Wu and Xiaogang Wu

Energy, 2025, vol. 335, issue C

Abstract: Battery internal short circuit (ISC) fault is one of the most critical faults that threatens the safety operation of large-scale battery energy storage systems (BESSs), making the ISC fault diagnosis a significant concern. This paper proposes a two-step quantitative diagnosis method for battery ISC faults. For the fault qualitative diagnosis, electrochemical impedance spectroscopy (EIS) is employed to qualitatively distinguish between aging and ISC faults. For the ISC fault quantitative diagnosis, a combination of the Extended Kalman Filtering (EKF) and the Least Squares Algorithm with Forgetting Factor (FFRLS) is proposed to quantitatively estimate the equivalent ISC resistance, thereby determining the ISC fault stage. Experimental results indicate that the proposed method can effectively distinguish between aging and ISC fault using EIS curve analysis. And the ISC fault is further evaluated with ISC resistance under Urban Dynamometer Driving Schedule (UDDS) and Federal Urban Driving Schedule (FUDS) operating conditions. The estimation accuracy of ISC resistance for the two operation conditions can reach up to 95.08 % and 98.48 % respectively.

Keywords: Battery internal short circuit; Electrochemical impedance spectroscopy; Extended kalman filtering; Recursive least squares with forgetting factor; Quantitative fault diagnosis (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:335:y:2025:i:c:s0360544225038836

DOI: 10.1016/j.energy.2025.138241

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