An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine

Lei Yao, Zhanpeng Fang, Yanqiu Xiao, Junjian Hou and Zhijun Fu

Energy, 2021, vol. 214, issue C

Abstract: For the safe operation of the electric vehicle, it is critical to quickly detect the safety state and accurately identify the fault degree in battery packs. This article proposes a novel intelligent fault diagnosis method for Lithium-ion batteries based on the support vector machine, which can identify the fault state and degree timely and efficiently. Due to the noise signal’s existence, firstly, the discrete cosine filtering method is adopted, and the truncated frequency is optimized based on the characteristic of white noise to achieve reasonable denoising. Secondly, since the covariance matrix (CM) of filtered data is sensitive to the current fluctuation, a modified covariance matrix (MCM) is proposed to reduce the influence of current variation on the condition indicators. Thirdly, to ensure the accuracy and robustness of Support Vector Machine (SVM), the grid search method is proposed to optimize the kernel function parameter and penalty factor. Finally, the MCM and CM are respectively introduced into the model as the condition indicators, and the results show that the former has high accuracy and timeliness. In summary, the proposed intelligent fault diagnosis method is feasible. It provides the theoretical basis for future fault hierarchical management strategy of the battery system.

Keywords: Lithium-ion battery; Fault diagnosis; Grid search; Support vector machine; Modified covariance matrix (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (41)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:214:y:2021:i:c:s0360544220319733

DOI: 10.1016/j.energy.2020.118866

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