Health and lifespan prediction considering degradation patterns of lithium-ion batteries based on transferable attention neural network

Aihua Tang, Yihan Jiang, Yuwei Nie, Quanqing Yu, Weixiang Shen and Michael G. Pecht

Energy, 2023, vol. 279, issue C

Abstract: With the continuous concern on the safety of battery systems, accurate and rapid assessment of battery degradation is essential for practical applications. In this paper, a transferable attention network model based on deep learning is developed to evaluate battery degradation, which can simultaneously predict state of health (SOH) and remaining useful life (RUL) for lithium-ion batteries. First, degradation patterns of the cells are identified by K-means clustering based on the difference of the cells at their early cycles. Secondly, the attention mechanisms are designed to suppress noises in extracted feature maps and trace the interaction between long- and short-term degradation modes. Thirdly, the common knowledge represented by the reference cells and the unique degradation features of the target cell are fused by transfer learning, then SOH and RUL prediction are realized through multi-task learning. Finally, a large-scale battery dataset containing different cycle conditions is used to verify the accuracy and generalization of the developed method. The results show that the developed method achieves accurate SOH and RUL prediction with the average root mean square error within 0.14% and six cycles, respectively.

Keywords: Deep learning; SOH; RUL; Lithium-ion batteries; Attention mechanism; Multi-task learning (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223015311
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:energy:v:279:y:2023:i:c:s0360544223015311

DOI: 10.1016/j.energy.2023.128137

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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