Remaining Useful Life Prediction of Lithium-Ion Battery Based on CNN and BiLSTM Fusion

Aiming at the low accuracy and stability of the traditional prediction method of the remaining useful life(RUL) of lithium-ion batteries, in this study, the convolutional neural network(CNN)and bidirectional long short-term memory(BiL STM) neural network are integrated, and a method for predicting the RUL of lithium-ion batteries is designed. To make full use of the time-series characteristics of lithium-ion battery data, one-dimensional CNN(1D CNN) is used to extract the deep characteristics of battery capacity data, and the memory function of the BiL STM neural network is selected to retain important information in the data and predict the trend of the battery RUL change. Through the use of lithium-ion battery data from the National Aeronautics and Space Administration, the predictions are compared with the 1D CNN, LSTM, BiL STM, and 1D CNN-LSTM models. The experimental results show that 1D CNN-BiLSTM can more accurately predict the RUL of lithium-ion batteries and improve the stability of predicting the RUL of lithium-ion batteries.