A remaining useful life prediction method based on CNN-LSTM

Currently, lithium batteries are widely used in industry such as electric vehicles. It's important to know the RUL (Remaining useful life). In this paper, we apply a frame of deep learning technology in order to predict the RUL of batteries. CNN method is provided to exact features, while LSTM method is to do the prediction. The NASA lithium batteries data are used for verifying the proposed algorithm. The result shows that the RUL prediction method of lithium batteries is accurate.