Lithium-ion battery remaining useful life prediction based on interpretable deep learning and network parameter optimization

Bo Zhao, Weige Zhang, Yanru Zhang, Caiping Zhang, Chi Zhang and Junwei Zhang

Applied Energy, 2025, vol. 379, issue C, No S0306261924020968

Abstract: As intelligent computation power in embedded systems has rapidly developed in recent years, the health state monitoring and remaining useful life prediction of batteries based on deep learning can gradually be deployed and applied in the onboard management system. However, there are still problems with large amounts of data calculation, high model complexity, and poor interpretability. Therefore, this paper proposes a remaining life prediction method for batteries combined with interpretable deep learning and network optimization. First, based on the fused deep learning model, the interpretable algorithm is used to explain the degree of attention of the model to different features and quantify the contribution of each part in input data, thereby identifying important aging features and removing useless data. Then, structured pruning is adopted to remove redundant network parameters under the constraints of ensuring prediction accuracy. The structure generally realizes model interpretation and full process optimization from battery aging data to network parameters. According to the validation of the selected dataset, compared with the original model, the model optimized by the method proposed in this paper has an average prediction accuracy increase of 0.19 % and an average speed increase of 46.88 %. It greatly saves computational resource consumption and improves model operation efficiency while ensuring prediction accuracy. In addition, the explanation and analysis of crucial feature areas in battery aging data provide a reference for effective health management.

Keywords: Lithium-ion battery; Health management; Remaining useful life; Interpretable deep learning; Structured pruning (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924020968
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:379:y:2025:i:c:s0306261924020968

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.124713

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().