State of health estimation for lithium-ion batteries on few-shot learning

Shuxin Zhang, Zhitao Liu and Hongye Su

Energy, 2023, vol. 268, issue C

Abstract: State of health (SOH) is a critical indicator for implementing detection, diagnostics and prognostics on lithium-ion batteries. However, considering the difficulty of data collection and additional cost for gathering comprehensive field data in practical application, only limited data can be available for model establishment. In order to handle this insufficient data scenario, a novel Bayesian deep neural network has been established and validated on few-shot learning. Moreover, from the perspective of feature extraction, degradation patterns extracted from temporal cyclic discharge profiles are utilized for reflecting degradation mode and operation state, while the Gramian angular field is proposed for data distribution learning and information enhancement. Different percentages of data are conducted on model training to compare the comprehensive performance on various features and state-of-art methods with the proposed method on few-shot learning. Ultimately, experimental results prove better adaptability, generalization and effectiveness of the proposed method on lithium-ion battery SOH estimation regardless of data size.

Keywords: Lithium-ion battery; State of health estimation; Few-shot learning; Bayesian deep neural network; Degradation pattern; Gramian angular field (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223001202
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:268:y:2023:i:c:s0360544223001202

DOI: 10.1016/j.energy.2023.126726

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 ().