Abstract: Aiming at the problem that the noise covariance of Sage-Husa adaptive traceless Kalman filtering algorithm is negatively determined during the iterative computation of adaptive filtering which leads to the interruption of the algorithm, an improved Sage-Husa adaptive traceless Kalman filtering algorithm is proposed. By selecting the Thevenin circuit model as the equivalent circuit model of the lithium battery, and using the recursive least squares method to identify the parameters of the equivalent circuit model of the lithium battery, and then setting the process noise covariance array in the time-varying noise statistical estimator to a constant value, and at the same time, calculating the absolute value of the measurement noise covariance array to improve the algorithm, and then improving the accuracy and performance of the Sage-Husa adaptive trace-free Kalman filtering algorithm, we propose to improve the Sage-Husa adaptive trace-free Kalman filtering algorithm. Finally, the accuracy and convergence of the improved Sage-Husa adaptive traceless Kalman filtering algorithm are simulated and verified. The simulation results show that the improved algorithm can not only carry out complete iterative estimation under different working conditions and different initial values, but also has higher SOC estimation accuracy compared with the trace-free Kalman filter algorithm under the same conditions; at the same time, the improved algorithm can quickly converge to the true value under different initial value conditions, and it has faster convergence speed.