The state of charge of lithium-ion battery is one of the key parameters of the battery management system,and in order to solve the problem of low estimation accuracy of a single LSTM network,this paper proposes a long-term and short-term neural network optimized by quantum particle swarm optimization,and introduces the quantum particle swarm optimization algorithm to optimize the key parameters of the LSTM neural network model,so as to improve the estimation performance of the network on SoC.In addition,the INR-18650 battery dataset was used to test the proposed model,which included three different temperatures(0℃,25℃,45℃)and four working conditions,including dynamic stress test DST,federal city driving timetable FUDS,US06 highway driving timetable and Beijing dynamic stress test BJDST.Finally,the performance of the model is verified under each working condition and compared with other optimization algorithms,and the verification results show that the proposed method can improve the SoC estimation results of the model at all temperatures,and the mean absolute error(MAE)and root mean square error(RMSE)are less than 1%and the root mean square error(RMSE)are all less than 1.1%under the four working conditions at different temperatures,and the maximum error is within 5%.