A novel state of health estimation method for lithium-ion batteries based on constant-voltage charging partial data and convolutional neural network

Si-Zhe Chen, Zikang Liang, Haoliang Yuan, Ling Yang, Fangyuan Xu and Yuanliang Fan

Energy, 2023, vol. 283, issue C

Abstract: State-of-health (SOH) estimation is critical for the reliable operation of lithium-ion batteries. Existing methods for manually extracting health features from constant-current charging, constant-voltage (CV) charging, and relaxation phases are limited for practical applications. This study proposes a SOH estimation method for lithium-ion batteries based on partial CV charging phase data and a convolutional neural network (CNN). By analysing the current profile trend with battery aging in the CV charging phase, we propose the estimation of the SOH using partial CV charging data. We suggest adding differential current data as input to extract battery aging information from limited data. Additionally, we design a CNN-based SOH estimation framework that can automatically extract features from current and differential current data in the early stage of CV charging phase, thereby avoiding complex feature engineering. In addition, we devise a transfer learning strategy to improve the model's generalization. A battery dataset comprising three materials is used to validate the proposed method. The results show that the proposed method requires only the first 1000 s of data from the CV charging phase to achieve a highly accurate estimation of the SOH, demonstrating the method's practicality and its excellent generalization for batteries composed of different materials.

Keywords: Lithium-ion battery; State of health; Constant-voltage (CV) charging phase; One dimensional convolutional neural network; Transfer 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 (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223024970
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:283:y:2023:i:c:s0360544223024970

DOI: 10.1016/j.energy.2023.129103

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 ().