Scholarlink

This paper proposes a highly robust state-of-charge (SOC) estimation framework integrating temperature compensation, recursive least squares and extended Kalman filtering (EKF), which can effectively cope with the dynamic changes under complex environmental conditions. Through experimental verification in multi-temperature environments, this method demonstrates excellent measurement accuracy and system stability, significantly enhancing the reliability of SOC estimation and providing strong technical support for the optimization of battery management systems and the promotion and application of new energy technologies. In the future, this solution has broad application prospects and can meet the demands of future electric vehicles and energy storage systems for high-precision and robust SOC monitoring.

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

Temperature Compensation Extended Kalman Filter for Battery State of Charge Estimation via Recursive Least Squares Algorithm

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Yongjing Li,
  • Xiong Shu,
  • Kexiang Wei,
  • Wenxian Yang,
  • Bowen Yang,
  • Ming Zhang

摘要

This paper proposes a highly robust state-of-charge (SOC) estimation framework integrating temperature compensation, recursive least squares and extended Kalman filtering (EKF), which can effectively cope with the dynamic changes under complex environmental conditions. Through experimental verification in multi-temperature environments, this method demonstrates excellent measurement accuracy and system stability, significantly enhancing the reliability of SOC estimation and providing strong technical support for the optimization of battery management systems and the promotion and application of new energy technologies. In the future, this solution has broad application prospects and can meet the demands of future electric vehicles and energy storage systems for high-precision and robust SOC monitoring.