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March 21, 2026Journal of Renewable and Sustainable Energy1 citations

Equalization control of marine power lithium battery systems based on dynamic topology reconfiguration

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RTRuoli TangZGZhaoquan GuPZPeng Zhang

Key Points

  • To develop an efficient equalization control method for power lithium battery systems to enhance operational safety and economy.
  • Developed a dynamic topology reconfiguration (DTR) based equalization control framework for battery systems.
  • Constructed a numerical model for capacity decay of battery cells according to different topological configurations.
  • Analyzed power demand data from a real all-electric ship for model validation.
  • Utilized a promising regions evolutionary algorithm to optimize the DTR-ECM model.
  • The proposed method effectively equalizes the state of health of the batteries.
  • Maximized battery capacity utilization was achieved.
  • Demonstrated feasibility of differential discharge in the control strategy.
  • Showed superiority of the promising regions evolutionary algorithm in complex optimization problems.

Abstract

Due to the inconsistency of power lithium battery cells (P-LiB), the efficient equalization control method (ECM) for battery systems is required to achieve operational safety and economy. In this study, a dynamic topology reconfiguration (DTR)-based DTR-ECM for P-LiB systems is proposed to optimize the dynamic operation performance of the battery pack. First, based on the DTR of the battery system, an equalization control framework comprising four mechanisms and four statuses is constructed. Then, a DTR-ECM model for the battery system is developed. Second, a numerical model for capacity decay of battery cells under different topological configurations is developed by analyzing the power demand data of a real all-electric ship. Finally, the developed DTR-ECM model is effectively optimized by using promising regions evolutionary algorithm (PREA), verifying the feasibility of differential discharge in the proposed control strategy and assessing the scalability and robustness of different algorithms in larger-scale optimization applications. Experimental results show that the developed method can effectively equalize battery state of health and achieve the maximization of battery capacity utilization while also proving the superiority of the PREA in optimizing complex multi-objective optimization problems.

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Cite This Study

Tang et al. (2026) studied this question.

synapsesocial.com/papers/69be361e6e48c4981c674ccdhttps://doi.org/10.1063/5.0277690
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