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<p>In this paper, a novel method that combines the forgetting factor adaptive moment estimation (FF-Adam) algorithm with the extended Kalman filter(EKF) is introduced for the simultaneous estimation of model parameters and the state of charge (SOC) in lithium batteries. The FF-Adam algorithm is employed for rapid and stable parameter estimation, while the EKF performs robust SOC estimation by effectively handling system noise. By integrating the fast convergence of FF-Adam with the adaptive filtering capability of the EKF, the proposed method overcomes the limitations of conventional single-method approaches. This integration yields markedly superior performance, demonstrating significant enhancements in estimation accuracy, convergence rate, and overall stability against various disturbances. Finally, comprehensive simulations are provided to verify the efficiency of the proposed algorithm.</p>

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SOC estimation for lithium batteries using forgetting factor adaptive moment estimation algorithm combined with extended Kalman filter

  • 期刊论文

  • 发表日期：

    2026年5月11日

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  • Ye Zhao,
  • Jing Chen,
  • Yawen Mao,
  • Ke Tian

摘要

In this paper, a novel method that combines the forgetting factor adaptive moment estimation (FF-Adam) algorithm with the extended Kalman filter(EKF) is introduced for the simultaneous estimation of model parameters and the state of charge (SOC) in lithium batteries. The FF-Adam algorithm is employed for rapid and stable parameter estimation, while the EKF performs robust SOC estimation by effectively handling system noise. By integrating the fast convergence of FF-Adam with the adaptive filtering capability of the EKF, the proposed method overcomes the limitations of conventional single-method approaches. This integration yields markedly superior performance, demonstrating significant enhancements in estimation accuracy, convergence rate, and overall stability against various disturbances. Finally, comprehensive simulations are provided to verify the efficiency of the proposed algorithm.