A novel remaining useful life prediction method based on CNN-Attention combined with SMA-GPR

Aina Tian, Haijun Yu, Zhaoyu Hu, Yuqin Wang, Tiezhou Wu and Jiuchun Jiang

Energy, 2025, vol. 321, issue C

Abstract: Accurately predicting the future capacity and remaining useful life (RUL) of lithium-ion batteries is crucial for ensuring their safety and reducing the maintenance costs of related equipment. However, the aging data of lithium-ion batteries (LIBs) exhibit significant nonlinearity and are also affected by uncertainties such as capacity regeneration. To address this issue, this paper proposes an RUL prediction method based on a Convolutional Neural Networks-Attention Mechanism (CNN-Attention) combined with a Slime Mold Algorithm - Gaussian Process Regression (SMA-GPR) model. Firstly, SVMD is applied to extract capacity regeneration features and capacity decay features. Next, to solve the data dependency of single-model prediction, the SMA-GPR model is applied to improve the CNN-Attention prediction, thus solving the generalization problem of and obtaining an accurate RUL. Next, to verify the accuracy and robustness of the proposed method, the experiments involved long-term aging tests under multiple scenarios including three types of LiFeO4(LFP)batteries including 142Ah square cells、280Ah square cells and 10Ah pouch cells, and varying conditions including temperature, charge-discharge rates and pre-tightening forces. The prediction error based on experimental data applying the three batteries is within 1 %.

Keywords: Lithium-ion batteries; Successive variational modal decomposition; Slime mold algorithm; RUL prediction (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225008758
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:321:y:2025:i:c:s0360544225008758

DOI: 10.1016/j.energy.2025.135233

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 ().