Work

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

Year: 2025

Type: article

Abstract: 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 connected 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 is output. The experimental results show that the proposed method can predict the remaining useful life of lithium-ion batteries more accurately. Compared with the classical CNN method, Bi-LSTM method, EMD-LSTM method and VMD-GRU method, the root mean square error (ERMSE) of the proposed method is reduced by 75.7%, 78.3%, 83.8% and 77.8%, respectively. Mean absolute error (EMAE) decreased by 80.7%, 80.9%, 86.8%, 82.3%, and mean absolute percentage error (EMAPE) decreased by 81.0%, 82.2%, 87.0%, 83.1%, respectively. The model determination coefficient (R2) increased by 17.4%, 23.2%, 44.5% and 25.8%, respectively.

Source: Scientific Reports

DOI: 10.1038/s41598-025-08619-6

Authors: Lin Sun, Xiaojie Huang, Jing Liu, Jing Song, Shimiao Wu

Institutions: Wenhua College, National University of Defense Technology, Wuhan University of Science and Technology

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,690