According to the application requirements of SOC in battery management system, an ARWLS-AEKF joint algorithm was proposed to estimate the SOC of lithium ion battery. Based on the second-order R-C network model, the method introduced adaptive genetic factors through the weighted adaptive algorithm, to optimized the parameter identification method,. And combined with the adaptive extended Kalman Filter (AEKF) algorithm for online identification, to complete the estimate of SOC. The simulation results and experimental data show that the error of ARWLS-AEKF algorithm is within 2% under LA_92, UDDS and HWFET conditions, MAE is 0.45%, 0.74% and 0.87%, RMSE is 0.54%, 0.71% and 0.42%, respectively. ARWLS-AEKF algorithm has higher accuracy and stronger disturbance resistance to noise than off-line EKF method.