Open Access

ARTICLE

Enhancing Safety in Electric Vehicles: Multi-Tiered Fault Detection for Micro Short Circuits and Aging in Battery Modules

Yi-Feng Luo^1,*, Jyuan-Fong Yen², Wen-Cheng Su³

1 Graduate Institute of A.I. Cross-disciplinary Tech, National Taiwan University of Science and Technology, Taipei, 106335, Taiwan
2 Energy and Sustainability Tech, National Taiwan University of Science and Technology, Taipei, 106335, Taiwan
3 Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, 106335, Taiwan

* Corresponding Author: Yi-Feng Luo. Email:

(This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)

Computer Modeling in Engineering & Sciences 2025, 142(3), 3069-3087. https://doi.org/10.32604/cmes.2025.061180

Received 18 November 2024; Accepted 22 January 2025; Issue published 03 March 2025

Abstract

This article proposes a multi-tiered fault detection system for series-connected lithium-ion battery modules. Improper use of batteries can lead to electrolyte decomposition, resulting in the formation of lithium dendrites. These dendrites may pierce the separator, leading to the failure of the insulation layer between electrodes and causing micro short circuits. When a micro short circuit occurs, the electrolyte typically undergoes exothermic reactions, leading to thermal runaway and posing a safety risk to users. Relying solely on temperature-based judgment mechanisms within the battery management system often results in delayed intervention. To address this issue, the article develops a multi-tiered fault detection algorithm for series-connected lithium-ion batteries. This algorithm can effectively diagnose micro short circuits, aging, and normal batteries using minimal battery data, thereby improving diagnostic accuracy and enhancing the flexibility of fault detection. Simulations and experiments conducted under various levels of micro short circuits validate the effectiveness of the algorithm, demonstrating its ability to distinguish between short-circuited, aged, and normal batteries under different conditions. This technology can be applied to electric vehicles and energy storage systems, enabling early warnings to ensure safety and prevent thermal runaway.

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Keywords

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