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<p>Accurate estimation of the state of charge (SOC) in lithium-ion batteries is critical for the safe and efficient operation of battery management systems (BMS). In real-world conditions, BMS may work in complex and changeable working conditions,&#xa0;which leads to measurement data being affected by non-Gaussian noise (or outliers). This paper proposes an algorithm for SOC estimation that combines an Artificial Protozoa Optimizer (APO) and an improved adaptive genetic particle filter (IAGPF) based on generalized correntropy (GC) to address this challenge. By establishing a second-order fractional-order model (FOM) for lithium-ion battery and conducting simulation experiments. Experimental results show that the proposed algorithm outperforms conventional methods, such as the fractional-order particle filter (FOPF) and fractional-order improved genetic particle filter (FOIGPF), across various non-Gaussian environments and operational scenarios. The algorithm achieves a mean absolute error (MAE) and root mean square error (RMSE) both below 1%, demonstrating its robustness and high estimation accuracy.</p>

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An algorithm based on Artificial Protozoa Optimizer and generalized correntropy-improved adaptive genetic particle filter for the state of charge estimation of lithium-ion batteries

  • 期刊论文

  • 发表日期：

    2026年6月27日

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  • Jinyang Lin,
  • Guizhao Li,
  • Tihao Gao,
  • Shuhua Lu

摘要

Accurate estimation of the state of charge (SOC) in lithium-ion batteries is critical for the safe and efficient operation of battery management systems (BMS). In real-world conditions, BMS may work in complex and changeable working conditions, which leads to measurement data being affected by non-Gaussian noise (or outliers). This paper proposes an algorithm for SOC estimation that combines an Artificial Protozoa Optimizer (APO) and an improved adaptive genetic particle filter (IAGPF) based on generalized correntropy (GC) to address this challenge. By establishing a second-order fractional-order model (FOM) for lithium-ion battery and conducting simulation experiments. Experimental results show that the proposed algorithm outperforms conventional methods, such as the fractional-order particle filter (FOPF) and fractional-order improved genetic particle filter (FOIGPF), across various non-Gaussian environments and operational scenarios. The algorithm achieves a mean absolute error (MAE) and root mean square error (RMSE) both below 1%, demonstrating its robustness and high estimation accuracy.