A State-of-Charge Estimation Method based on Bidirectional LSTM Networks for Lithium-ion Batteries

State-of-charge (SOC) estimation is the key to the BMS of lithium-ion batteries. At present, data-driven methods are more popularly used to estimate the SOC. Among them, the deep learning technology especially recurrent neural networks (RNNs) performed very well. In this paper, we proposed a bidirectional long short-term memory (Bi-LSTM) neural network to perform SOC estimation. The bidirectional LSTM layer is employed to catch the forward and backward temporal dependencies of battery sequential states, and shows better performance in SOC estimation of LiBs. With a public dataset of vehicle driving cycles, we separately train our proposed network at three different constant temperatures (0 °C, 10 °C, and 25 °C), and the evaluated MAEs are 0.498%, 0.411 %, 0.738%. Besides, the Bi-LSTM network trained with overall datasets at three temperatures achieves a mean absolute error (MAE) of 0.616% and maximum absolute error (MaxE) of 3.809%. It shows that the proposed method is robust and accurate in SOC estimation.