SOC Prediction of Lithium-Ion Battery Based on FSSA-LSTM

SOC reflects the residual storage volume of the battery and is an essential benchmark for preventing overcharging of the battery and predicting the remaining range of the vehicle. Thus, the accurate SOC estimation is critical for batteries. This paper proposes a methodology to predict SOC using FSSA to optimize the LSTM, and this method not only takes advantage of the LSTM in time series prediction, but also adopt the FSSA algorithm wisely which has strong search capability. Data under FUDS conditions are selected for training, and the performance of the different models are compared according to different evaluation indicators. The experimental data demonstrates that the predictive precision of this model is superior to the LSTM, PSO-LSTM and SSA-LSTM models, which demonstrates its robustness. Meanwhile, the experiments show that the FSSA-LSTM has 46.23% less computation time, and 40.73% less in MSE.