Abstract: Aiming at the shortcomings of unscented Kalman filter (UKF) algorithm for estimating lithium battery state of charge (SOC) due to low accuracy, poor stability, too many sigma points generated, and calculation difficulty, an algorithm of unscented Kalman filter based on adaptive spherical insensitive transformation (ASIT-UKF) was proposed, and the spherical insensitive transformation method was used to select weight coefficients and initialization line unary vector to select the generation of sigma points. Compared with the UKF algorithm, the ASIT-UKF algorithm was reduced nearly 50% fewer sigma points, so that the computational complexity of the algorithm was significantly reduced. At the same time, all the generated sigma points were normalized on the unit spherical surface to improve numerical stability. Considering the uncertainty of noise interference in the actual operation of lithium battery system, Sage-Husa adaptive filter was added to update and correct the interference of uncertain noise in real time, so as to improve the accuracy of online lithium battery SOC estimation. Finally, the standard deviation and mean deviation calculation formulas were introduced into the estimated performance indicators. Results show that the ASIT-UKF algorithm has superior performance in terms of accuracy, robustness and convergence.