Early prediction of battery lifetime for lithium-ion batteries based on a hybrid clustered CNN model

Jing Hou, Taian Su, Tian Gao, Yan Yang and Wei Xue

Energy, 2025, vol. 319, issue C

Abstract: Early prediction of lithium-ion battery lifetime is critical for energy storage equipment, because it can provide users with early warnings and alerts to avoid potential disasters. However, making an accurate early prediction is challenging due to the negligible capacity degradation and the scarcity of data in the early stages. To address this issue, a hybrid clustered CNN model is proposed for early prediction of the battery cycle life, which uses both manually extracted and machine learned features as input to strengthen the degradation characterization ability. Additionally, battery classification is innovatively utilized to boost prediction performance. After classifying the batteries into “short cycle life” and “long cycle life”, two improved CNN-based models are accordingly put forward for the two categories, resulting in a significant promotion in prediction accuracy. A well-known public dataset is employed for validation. Comparison results demonstrate that the proposed model exhibits superior prediction accuracy with a relatively small amount of training data. Meanwhile, an additional dataset is used to verify the repeatability and generalizability of the proposed approach. This study reveals a new perspective that the preliminary classification can contribute to the early prediction of battery lifetime.

Keywords: Classification; Cycle life prediction; SE-CNN; AL-CNN; Lithium-ion batteries (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225006346
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:319:y:2025:i:c:s0360544225006346

DOI: 10.1016/j.energy.2025.134992

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 ().