Work

Remaining useful life prediction of lithium batteries based on multiscale feature extraction and multiscale feature aggregation

Year: 2025

Type: article

Abstract: The estimation of the Remaining Useful Life (RUL) of lithium batteries is a highly effective method of monitoring their health status. The present paper proposes a novel approach for predicting the remaining useful life (RUL) of lithium batteries. The proposed Multiscale Feature Extraction Module (MFEM) and Learnable Multiscale Aggregation Module (LMAM) to forecast RUL based on the capacity degradation trend of lithium batteries. Unlike the single-scale modelling of previous approaches, the model primarily utilizes the feature extraction module’s ability to extract RUL-specific information at different scales. This information is then coded to integrate long-term trend data while detecting subtle fluctuations in the short-term. The efficacy of the proposed model is validated through experimental comparisons based on two publicly available datasets, NASA and CALCE. The results demonstrate the effectiveness of the prediction model presented in this paper. RE, MAE and RMSE, respectively. Moreover, a comparative analysis was conducted between the newly proposed model and previous classical prediction models. This analysis revealed consistent improvements across all comparison metrics.

Source: Journal of Energy Storage

DOI: 10.1016/j.est.2025.118296

Authors: Jinxiao Geng, Le Tian, Maozu Guo

Institution: Beijing University of Civil Engineering and Architecture

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $3,560