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Traditional fuzzy control methods for energy management in EV hybrid energy storage systems rely on expert experience to set membership functions and rule libraries. This reliance results in high subjectivity and poor parameter matching, ultimately leading to insufficient control stability. In this paper, an improved particle swarm optimization (PSO) algorithm is proposed to enhance the energy recovery efficiency and overall control strategy of the EV energy system. Simulation results verify the effectiveness of the proposed method.

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A Novel Energy Management Strategy for EV Hybrid Energy Storage System Based on Improved PSO

  • 会议论文

  • 发表日期：

    2026年

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  • Keling Song,
  • Yongliang Ni,
  • Conghui Lu,
  • Xinyuan Zheng,
  • Zhi Qiao,
  • Dazhi Wang

摘要

Traditional fuzzy control methods for energy management in EV hybrid energy storage systems rely on expert experience to set membership functions and rule libraries. This reliance results in high subjectivity and poor parameter matching, ultimately leading to insufficient control stability. In this paper, an improved particle swarm optimization (PSO) algorithm is proposed to enhance the energy recovery efficiency and overall control strategy of the EV energy system. Simulation results verify the effectiveness of the proposed method.