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As a particularly important part of the battery management system, the accurate estimation of the battery SOC (State Of Charge) has become the focus of lithium-ion battery research. In order to improve the SOC estimation accuracy under dynamic conditions, the equivalent model of lithium-ion batteries is analyzed, the second-order RC circuit is determined as the equivalent circuit model based on the AIC (Akaike Information) criterion, and the recursive least squares algorithm was used to identify the model parameters online, and in order to improve the identification accuracy, an improved least squares algorithm with dynamic forgetting factor was proposed, the forgetting factor is added to the recursive least squares algorithm, and the forgetting factor of the algorithm is dynamically adjusted in real time through the voltage result error. The recursive Least Squares algorithm and the Least Squares algorithm with dynamic forgetting factor are combined with the extended Kalman filtering (EKF) algorithm for SOC joint estimation respectively, compared the prediction results, the results showed that the joint estimation model containing the least squares with dynamic forgetting factor and EKF has higher accuracy and robustness.