The key to improve the accuracy of parameter identification and state of charge algorithm is to improve the estimation of the state-of-charge (SOC). In this paper, we construct a second-order RC equivalent circuit model, adopt the least squares algorithm to achieve parameter identification, and use the untraceable Kalman filtering algorithm (UKF) to estimate the SOC of the lithium batteries. In the phase of parameter identification, the least squares algorithm can quickly make the model best fit to the data points, and the identification results are obtained. In the SOC estimation stage, the untraceable Kalman algorithm (UKF) approximates the state distribution by selecting the Sigma points and directly handles the state estimation of the nonlinear system, which overcomes the shortcomings of the traditional Kalman filtering algorithm in dealing with the nonlinear system, and enhances the robustness to the model error. Simulations show that the approach can achieve a better balance between model accuracy and parameter stability and reliability, balance the computational complexity and estimation accuracy, and improve the linearisation error problem.