It is one of the important tasks of battery management system (BMS) to realize battery charge state (SOC) estimation. The identification of battery model parameters is the precondition of SOC estimation for lithium-ion batteries, which is also the key factor determining the estimation accuracy of SOC. This paper took 18650 lithium-ion battery as the research object, and used the recursive least square method with time-varying forgetting factor (TVFFRLS) to identify the battery parameters online, so as to realize the automatic optimization of forgetting factor adaptation and improve the stability of parameter online identification. On this basis, the adaptive cubature Kalman filter (ACKF) was used to realize the estimation of SOC of lithium-ion batteries, and the covariance of process noise and measurement noise was updated in real time.The algorithm was verified under various working conditions. The results show that the algorithm has good noise suppression performance and can realize the estimation of SOC. The maximum estimation error of SOC is no more than 1.5%, and ACKF algorithm has strong robustness.