Remaining useful life prediction of lithium batteries based on jump connection multi-scale CNN

In order to better utilize the feature information obtained by all convolutional layers in convolutional neural networks (CNN), this paper proposes a lithium-ion battery remaining life prediction model based on jump connection multi-scale CNN. The proposed model takes the health factor of the battery as input, and uses the multi-scale CNN model based on jump connection to extract the local feature information and global feature information of the health factor of lithium-ion battery at different scales. Then, all the local feature information and global feature information are fused through the information fusion module, and finally the predicted remaining useful life(RUL)is output. The experimental results show that the proposed method can predict the RUL of lithium-ion batteries more accurately. Compared with prediction models such as VMD-COA-LSTM, BiGRU-TSAM, MSTformer and MSFMTP, the proposed method has better prediction accuracy and generalization ability.