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<p>To enhance the accuracy and robustness of state of charge (SOC) estimation for lithium-ion batteries, this study proposes an Adaptive Strong Tracking Filter (ASTF) algorithm based on the Extended Kalman Filter (EKF). By introducing a fading factor, the ASTF dynamically adjusts the gain matrix, improving adaptability to model uncertainties and enabling real-time updates of the covariance matrix. A Simulink-based battery simulation model is developed, and comparative experiments are performed under the UDDS driving cycle using both EKF and ASTF. Results show that ASTF significantly outperforms EKF, achieving an 89.22% reduction in estimation error and a 73.56% improvement in stability. Furthermore, compared to existing STF-based algorithms, ASTF reduces the maximum SOC estimation error by 68.7% and 50.6% relative to AT-EKF and ST-H∞, respectively. It also lowers the average error by 86.4% and 13.3% compared to ST-AFEKF and ST-H∞, while enhancing stability by 65.8% over ST-AFEKF. These results demonstrate that ASTF offers improved estimation accuracy and robustness, validating its effectiveness under dynamic and uncertain conditions.</p>

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A state of charge (SOC) estimation method for lithium-ion batteries based on an adaptive strong-tracking Kalman filter

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月7日

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  • Jinhan Li,
  • Shulin Liu

摘要

To enhance the accuracy and robustness of state of charge (SOC) estimation for lithium-ion batteries, this study proposes an Adaptive Strong Tracking Filter (ASTF) algorithm based on the Extended Kalman Filter (EKF). By introducing a fading factor, the ASTF dynamically adjusts the gain matrix, improving adaptability to model uncertainties and enabling real-time updates of the covariance matrix. A Simulink-based battery simulation model is developed, and comparative experiments are performed under the UDDS driving cycle using both EKF and ASTF. Results show that ASTF significantly outperforms EKF, achieving an 89.22% reduction in estimation error and a 73.56% improvement in stability. Furthermore, compared to existing STF-based algorithms, ASTF reduces the maximum SOC estimation error by 68.7% and 50.6% relative to AT-EKF and ST-H∞, respectively. It also lowers the average error by 86.4% and 13.3% compared to ST-AFEKF and ST-H∞, while enhancing stability by 65.8% over ST-AFEKF. These results demonstrate that ASTF offers improved estimation accuracy and robustness, validating its effectiveness under dynamic and uncertain conditions.