Accurate SoC is an important guarantee for the safe and efficient operation of lithium batteries. Aiming at the problem that the traditional unscented Kalman filter (UKF) has poor tracking ability for the abrupt state of nonlinear systems, which in turn leads to the low accuracy of SoC estimation, a new adaptive fading unscented Kalman filter was proposed for SoC estimation in this paper. First, the UKF error covariance matrix is weighted by designing a novel fading factor, and the design of the AFUKF is completed based on the novel fading factor, which reduces the influence of stale measurements on the estimation results, improves the estimation accuracy and tracking ability of the traditional UKF. Second, based on the test data of the self-built experimental platform, it is verified that the AFUKF proposed in this paper, in the presence of the initial error, compared with the traditional UKF, the mean absolute error (MAE) and root-mean-square error (RMSE) under the ECE condition are decreased by 47.95% and 33.92%, respectively, the MAE and RMSE under the DST condition are decreased by 36.40% and 27.73%, respectively. Compared with the similar improved AUKF, the MAE and EMSE decreased by 43.36% and 33.51% for the ECE condition, 39.01% and 25.63% for the DST condition, respectively. The modeling results show that, AFUKF has higher accuracy and better robustness under initial SoC errors than the traditional UKF as well as the improved AUKF of the same type.