Scholarlink

<p>Lithium iron phosphate (LFP) batteries have been widely applied in electric vehicles and energy storage systems owing to their advantages of low cost, high safety, and long service life. However, accurate state-of-charge (SOC) estimation for LFP batteries remains challenging due to their extremely flat voltage plateau. To improve SOC estimation accuracy of LFP batteries over a wide temperature range, this paper proposes an adaptive variable-weight fusion estimation algorithm based on Extended Kalman Filter – Least Squares Boosting (EKF–LSBoost). First, a battery test platform is established, a second-order RC equivalent circuit model of the LFP battery is constructed, and online parameter identification is performed using a Variable Forgetting Factor Recursive Least Squares (VFFRLS) method. On this basis, an EKF–LSBoost fusion algorithm capable of stable SOC estimation over a wide temperature range is developed. The proposed fusion algorithm is validated under UDDS and FUDS dynamic operating conditions using a hardware-in-the-loop (HIL) experimental platform. The results demonstrate that, within a wide temperature range of 0&#xa0;°C to 55&#xa0;°C, the mean absolute error (MAE) and root mean square error (RMSE) of SOC estimation are reduced by more than 30% on average compared with single-algorithm approaches, significantly enhancing estimation accuracy and robustness. These findings provide a reliable reference for high-accuracy SOC estimation of lithium iron phosphate batteries under wide temperature conditions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Research on SOC fusion estimation of lithium iron phosphate batteries over a wide temperature range

  • 期刊论文

  • 发表日期：

    2026年4月23日

查看全文
  • Kou Farong,
  • Fu Xing,
  • Yang Tianxiang,
  • Duan Xuwei

摘要

Lithium iron phosphate (LFP) batteries have been widely applied in electric vehicles and energy storage systems owing to their advantages of low cost, high safety, and long service life. However, accurate state-of-charge (SOC) estimation for LFP batteries remains challenging due to their extremely flat voltage plateau. To improve SOC estimation accuracy of LFP batteries over a wide temperature range, this paper proposes an adaptive variable-weight fusion estimation algorithm based on Extended Kalman Filter – Least Squares Boosting (EKF–LSBoost). First, a battery test platform is established, a second-order RC equivalent circuit model of the LFP battery is constructed, and online parameter identification is performed using a Variable Forgetting Factor Recursive Least Squares (VFFRLS) method. On this basis, an EKF–LSBoost fusion algorithm capable of stable SOC estimation over a wide temperature range is developed. The proposed fusion algorithm is validated under UDDS and FUDS dynamic operating conditions using a hardware-in-the-loop (HIL) experimental platform. The results demonstrate that, within a wide temperature range of 0 °C to 55 °C, the mean absolute error (MAE) and root mean square error (RMSE) of SOC estimation are reduced by more than 30% on average compared with single-algorithm approaches, significantly enhancing estimation accuracy and robustness. These findings provide a reliable reference for high-accuracy SOC estimation of lithium iron phosphate batteries under wide temperature conditions.