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With the growing deployment of lithium-ion battery packs in electric vehicles and energy storage systems, effective equalization control has become essential to ensure safety, consistency, and service life. This study proposes a multi-indicator reconfigurable equalization strategy that integrates voltage and State of Charge (SOC) information to overcome the limitations of conventional voltage-based balancing methods. A system-theoretic control framework is developed, combining segmented decision logic and dynamic current adjustment to achieve adaptive balancing under varying operating conditions. The proposed control algorithm is implemented and validated on a Hardware-in-the-Loop (HIL) platform comprising a battery simulator, equalizer, and controller. Experimental results under diverse imbalance scenarios demonstrate that the hybrid strategy effectively suppresses pseudo-equalization during voltage plateau phases and improves balancing precision by up to 5.5% compared with conventional multi-level voltage deviation control. The results confirm that incorporating energy-state indicators enables more accurate and adaptive balancing control. This work provides practical insights for the design of intelligent Battery Management Systems (BMS) and offers a feasible pathway toward enhancing the efficiency and reliability of next-generation energy storage systems.

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Research on a Multi-indicator Reconfigurable Equalization Strategy for Lithium-Ion Batteries Based on Hardware-in-the-Loop

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Shirui Zhang,
  • Yumiao Jin,
  • Yuhao Pan,
  • Jinhao Meng,
  • Zhengxiang Song,
  • Kena Chen

摘要

With the growing deployment of lithium-ion battery packs in electric vehicles and energy storage systems, effective equalization control has become essential to ensure safety, consistency, and service life. This study proposes a multi-indicator reconfigurable equalization strategy that integrates voltage and State of Charge (SOC) information to overcome the limitations of conventional voltage-based balancing methods. A system-theoretic control framework is developed, combining segmented decision logic and dynamic current adjustment to achieve adaptive balancing under varying operating conditions. The proposed control algorithm is implemented and validated on a Hardware-in-the-Loop (HIL) platform comprising a battery simulator, equalizer, and controller. Experimental results under diverse imbalance scenarios demonstrate that the hybrid strategy effectively suppresses pseudo-equalization during voltage plateau phases and improves balancing precision by up to 5.5% compared with conventional multi-level voltage deviation control. The results confirm that incorporating energy-state indicators enables more accurate and adaptive balancing control. This work provides practical insights for the design of intelligent Battery Management Systems (BMS) and offers a feasible pathway toward enhancing the efficiency and reliability of next-generation energy storage systems.