An estimation method for the state-of-charge of lithium-ion battery based on PSO-LSTM

Abstract

The accuracy of state-of-charge (SOC) estimation will affect the performance of the battery management system. The higher the accuracy the better the performance. To improve the accuracy of SOC estimation, a particle swarm optimization (PSO) based method is proposed to optimize the long short term memory. First, a PSO-Long Short Term Memory (LSTM) estimation model is established by the PSO algorithm, thereby achieving optimal iteration parameters of the model. Then, the PSO-LSTM estimation model is simulated under different working conditions and temperatures. Finally, the voltage, current, and other discharge data of the lithium-ion battery are input into the PSO-LSTM neural network model to compare with the LSTM algorithm. The results show that the estimation accuracy of the optimized PSO-LSTM algorithm model and extended Kalman filter is 2.1% and 1.5%, respectively. The accuracy is improved.