Abstract: Absrtact: Aiming at the low accuracy of the state of charge（SOC） estimation of lithium batteries， a neural network model based on improved LSTM algorithm is proposed to obtain the mapping relationship between voltage and current input and SOC output. By extending the Kalman filter to filter the noise of the output estimate， the stability of the model is enhanced. In the process of neural network modeling， the improved particle swarm optimization algorithm is used to optimize the number of neurons， learning rate， step size and other super parameters， which further improves the efficiency and accuracy of lithium battery SOC estimation. Finally， the DST condition data in the university of Maryland CALCE dataset is used for model training， and the FUDS and US06 condition data-sets are used for comparative experiments on the improved LSTM algorithm， CNN-LSTM、GRU algorithm and CatBoost algorithm. The experimental results show that the improved LSTM estimation model has high stability and accuracy， which verifies the feasibility of the improved scheme.

Key words: Key words: lithium battery, SOC estimation, LSTM, particle swarm optimization, EKF filter