Battery safety issue detection in real-world electric vehicles by integrated modeling and voltage abnormality

Da Li, Lei Zhang, Zhaosheng Zhang, Peng Liu, Junjun Deng, Qiushi Wang and Zhenpo Wang

Energy, 2023, vol. 284, issue C

Abstract: Detecting battery safety issues is essential to ensure safe and reliable operation of electric vehicles (EVs). This paper proposes an enabling battery safety issue detection method for real-world EVs through integrated battery modeling and voltage abnormality detection. Firstly, a battery voltage abnormality degree that is adaptive to different battery types and working conditions is defined. Then an integrated battery model is developed by combining an electrochemical model, an equivalent circuit model (ECM), and a data-driven model to evaluate the normal voltage. To ensure normality of input current, a current processing model is presented. The performance of the proposed scheme is examined under random loading profiles using operating data collected from real-world EVs. The results show that the integrated battery model can precisely predict normal battery terminal voltage, with mean-squared-errors of 1.034e−4 V2, 7.221e−5 V2, and 4.612e−5 V2 for driving, quick charging, and slow charging, respectively. The accuracy in classifying faulty and normal batteries is verified based on the operating data collected from 20 EVs.

Keywords: Electric vehicles; Lithium-ion batteries; Battery safety; Electrochemical model; Equivalent circuit model; Radial basis function neural network (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:284:y:2023:i:c:s0360544223018327

DOI: 10.1016/j.energy.2023.128438

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