SOC estimation for lithium battery based on adaptive extended Kalman filter

State of Charge（SOC） is a crucial indicator for evaluating battery performance. Accurate estimation of SOC is essential for maximizing battery capacity and performance. Currently, there are many methods for measuring SOC, and they are also relatively mature. However, there is still room for research and exploration to seek more effective and accurate estimation methods. This article proposes an approach for SOC estimation,combining a recursive least squares（FFRLS） parameter identification algorithm with a forgetting factor and an online SOC estimation method based on adaptive extended Kalman filter（AEKF）. The FFRLS algorithm is employed to identify battery parameters in real-time, based on the second-order RC equivalent circuit model. Using the identified parameters, the AEKF algorithm dynamically adjusts the system noise parameters to achieve more precise SOC estimation results. The proposed SOC estimation method is validated through verification under HPPC and UDDS operating conditions, with an algorithm error of approximately 2%, proving the accuracy and robustness of the proposed method.