The state of charge (SOC) of lithium batteries is the most important state parameter for the safe operation of lithium batteries. In order to improve the estimation accuracy of lithium battery SOC, this paper proposes a dual adaptive cubature Kalman Filtering (Dual adaptive cubature Kalman Filtering) algorithm. The second-order DP equivalent circuit model of the lithium battery is used for offline identification of state parameters, a highly accurate cubature Kalman filter algorithm is used to estimate a single SOC, and an adaptive factor is introduced to estimate real-time noise. On the basis of obtaining the SOC The internal resistance of lithium battery is estimated in real time, and the SOC is estimated using dual adaptive cubature Kalman filtering algorithm. In order to prove that its conclusion meets the requirements of actual working conditions, this article conducted a dynamic stress test (DST) and a federal urban driving (Federal Urban Driving Scheduled) simulation experiment. The error of SOC obtained through the algorithm is within 0.5%, and It has strong robustness and proves that its algorithm is established