Since the accuracy of extended Kalman filter (EKF) algorithm in State of Charge (SOC) estimation is significantly reduced due to the voltage plateau of LiFePO₄ battery, this paper proposes an SOC estimation method based on the improved EKF algorithm (IEKF). This method designs a weight function based on the slope of the open-circuit voltage (OCV) curve to adjust the Kalman gain, which solves the problem of inaccurate SOC estimation caused by the small change of OCV when the LiFePO₄ battery is in the voltage plateau region. The experimental results show that, the proposed IEKF algorithm effectively improves the accuracy and reliability of SOC estimation within a wide voltage platform range, and exhibits strong potential for engineering applications.

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

State of Charge Estimation of LiFePO₄ Batteries Based on Improved EKF Algorithm

  • Fu Li,
  • Yunze Jiang,
  • Xuelian Wang,
  • Jinrong Xu,
  • Jiajian Hu

摘要

Since the accuracy of extended Kalman filter (EKF) algorithm in State of Charge (SOC) estimation is significantly reduced due to the voltage plateau of LiFePO₄ battery, this paper proposes an SOC estimation method based on the improved EKF algorithm (IEKF). This method designs a weight function based on the slope of the open-circuit voltage (OCV) curve to adjust the Kalman gain, which solves the problem of inaccurate SOC estimation caused by the small change of OCV when the LiFePO₄ battery is in the voltage plateau region. The experimental results show that, the proposed IEKF algorithm effectively improves the accuracy and reliability of SOC estimation within a wide voltage platform range, and exhibits strong potential for engineering applications.