Thermal runaway fault prediction in air-cooled lithium-ion battery modules using machine learning through temperature sensors placement optimization

Rojo Kurian Daniels, Vikas Kumar, Satyendra Singh Chouhan and Aneesh Prabhakar

Applied Energy, 2024, vol. 355, issue C, No S0306261923017166

Abstract: The rise of severe accidents caused due to thermal runaway (TR) and its propagation in lithium-ion battery (LiB) modules is one of the most challenging factors that decelerate the rapid expansion of the electric vehicle (EV) industry. Timely detection of the TR undergoing cells in the module is crucial as the heat generated during TR is adequate to trigger the TR of the surrounding cells. In this study, an accurate machine learning (ML) based faulty cell position prediction model is developed for the air-cooled cylindrical LiB modules with the cells in aligned, staggered, and cross arrangements. The CFD model used for data generation is validated with the in-house experiments on an aligned surrogate 32-cell module for multiple failure positions. Further, to predict the TR cell position in the battery module, the random forest classification (RFC) model is developed based on the temperature distribution data obtained from the optimized temperature sensors derived for the two types of initial temperature sensor distributions (single and multiple-planes) using a heat map approach. The model developed is tested for varying design and operating conditions, and the prediction results, along with the error metrics and the prediction timings, are compared. It is revealed that except for the cross-cell arrangement in the single-plane temperature sensors distribution scenario, the RFC model produces higher accuracy when tested on the optimized temperature sensor layouts for the multiple-plane sensor distribution. The results of this study can allow early failure detection in battery modules, resulting in increased safety and cost savings.

Keywords: Air-cooled liB; Thermal runaway; Machine learning; Temperature sensors; Optimization; Random forest classification (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.apenergy.2023.122352

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