Author(s)

Shao Zijian

Corresponding Author

Shao Zijian

Abstract

Accurate State of Charge (SOC) estimation is a crucial function in lithium-ion battery management systems. The Kalman Filter (KF) family of algorithms, renowned for their self-correction capabilities and low computational complexity, are extensively employed in SOC estimation. This review systematically synthesizes the recent advancements in SOC estimation using the KF family of algorithms. This paper elucidate the underlying principles of SOC estimation via three key algorithms: Extended Kalman Filter (EKF), Sigma Point Kalman Filter (SPKF), and Cubature Kalman Filter (CKF). The review provides a comparative analysis of these algorithms, highlighting their respective strengths, limitations, and operational contexts. Furthermore, this paper offer a comprehensive overview of recent research focused on algorithmic enhancements. Additionally, this paper discuss the typical battery models and parameter identification processes utilized in SOC estimation with the KF algorithms. The review concludes with an in-depth analysis of error sources in SOC estimation, including voltage and current measurement inaccuracies, and derives insights that offer valuable guidance for future research endeavors.

Keywords

SOC estimation, Kalman filter, error analysis, equivalent-circuit model