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<p>With the rapid proliferation of electric vehicles (EVs) and energy storage systems (ESSs), the importance of lithium-ion batteries is growing. However, battery packs comprised of lithium-ion battery experience cell-to-cell imbalances during operation, which can lead to performance degradation, shortened lifespan, and safety issues. To prevent this, a battery management system (BMS) monitors voltage, current, and temperature and performs cell balancing to ensure safety. However, identifying faulty cells, the root cause of imbalances, previously required additional sensors or expensive equipment, leading to increased costs and system complexity, limiting practicality. In this study, we propose and experimentally validate a software-based diagnostic method that utilizes balancing data collected during the BMS passive balancing process to detect faulty cells without the need for additional sensors or expensive equipment. Experimental results show that faulty cells require significantly more balancing counts than normal cells, confirming that the proposed technique is a practical and cost-effective solution that improves the reliability of large-scale battery packs and enables preventive maintenance without additional costs.</p>

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Faulty Cell Detection Based on Balancing Data Among Lithium-Ion Battery Cells

  • 期刊论文

  • 发表日期：

    2026年2月11日

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  • Soonwon Baek,
  • Woongchul Choi

摘要

With the rapid proliferation of electric vehicles (EVs) and energy storage systems (ESSs), the importance of lithium-ion batteries is growing. However, battery packs comprised of lithium-ion battery experience cell-to-cell imbalances during operation, which can lead to performance degradation, shortened lifespan, and safety issues. To prevent this, a battery management system (BMS) monitors voltage, current, and temperature and performs cell balancing to ensure safety. However, identifying faulty cells, the root cause of imbalances, previously required additional sensors or expensive equipment, leading to increased costs and system complexity, limiting practicality. In this study, we propose and experimentally validate a software-based diagnostic method that utilizes balancing data collected during the BMS passive balancing process to detect faulty cells without the need for additional sensors or expensive equipment. Experimental results show that faulty cells require significantly more balancing counts than normal cells, confirming that the proposed technique is a practical and cost-effective solution that improves the reliability of large-scale battery packs and enables preventive maintenance without additional costs.