A SOC estimation method for a lithium battery that can track the abrupt state is proposed and applied to the SOC equalization of the multi-lithium battery pack. The strong tracking filter is introduced into the particle filter algorithm, and the current sampling results are incorporated into the prediction error update to get a new correction term, and then the correction term is used to correct the particle set of the particle filter, so as to quickly push the particles to the high likelihood region and restrain the particle degradation. The introduction of the fading factor can adjust the error covariance matrix in real-time so that the particle filter algorithm has both the strong robustness of the strong tracking filter and the tracking ability of the mutation state, which can effectively overcome the uncertainty of the model and further improve the estimation accuracy of the SOC. The proposed method was applied to much cell-active balance. The equilibrium strategy was designed based on the consistency criterion of battery SOC, and the adjacent cells with larger capacity gap were equilibrium first, then energy real-time bidirectional transmission is controlled, so the overall balancing speed was improved. Experimental results show that the average estimation error of the improved algorithm is within 0. 13%, and the standard deviation is 0. 12%. The improved algorithm is about 64%, 85%, and 75% higher than that of the traditional particle filter algorithm, the extended Kalman algorithm and the strong tracking algorithm, and the stability is further enhanced. The application in multi-battery active equalization can effectively reduce the inconsistency of battery pack capacity in the process of charge and discharge, and control the dispersion of the battery pack within 1%, which is beneficial to improve the utilization rate and service life of battery capacity.