The state of charge 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 volumetric Kalman filter algorithm. The second-order DP equivalent circuit model of lithium batteries is used for offline identification of state parameters, and a high-precision volumetric Kalman filter algorithm is used to estimate a single SOC. An adaptive factor is introduced to estimate real-time noise. On the basis of obtaining SOC, the internal resistance of lithium batteries is estimated in real time, and the dual adaptive volumetric Kalman filter algorithm is used to estimate SOC. In order to fully verify that the conclusions of this paper meet the requirements of actual working conditions, this paper conducted dynamic stress testing, federal city driving, urban driving cycle and suburban driving cycle simulation experiments. The SOC errors of the first three working conditions obtained by the algorithm are within 0.5%, and the error of the suburban driving cycle working condition is within 1%, and it has strong robustness, proving that the algorithm is valid..