Accurate state of health estimation for lithium-ion batteries under random charging scenarios

Jiangwei Shen, Wensai Ma, Xing Shu, Shiquan Shen, Zheng Chen and Yonggang Liu

Energy, 2023, vol. 279, issue C

Abstract: Accurate state of health (SOH) knowledge is critical for reliable operations of lithium-ion batteries. However, short-term random charging operations of lithium-ion batteries are not conducive to reliable SOH estimation. To solve it, a charging voltage prediction and machine learning based estimation are employed to supply precise estimation. Firstly, the correlated feature variables in terms of SOH are determined by analyzing the raw charging voltage distribution. Then, the wide range charging voltage is predicted via the constructed polynomials and short-term measures. Next, the extreme learning machine algorithm is employed to achieve online SOH estimation. Finally, the feasibility of the proposed voltage estimation method is verified at different aging cycles and different charging intervals, and the reliability of SOH estimation is investigated in the full lifetime range and at different state of charge (SOC) charging intervals. The experimental results manifest that the proposed method can supply reliable SOH estimation with the error of less than 2.02% based on only the short-term random charging scenarios, furnishing reliable and safe operation guideline for lithium-ion battery systems.

Keywords: State of health; Voltage estimation; Feature variables; Extreme learning machine; Lithium-ion battery (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422301486X
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:s036054422301486x

DOI: 10.1016/j.energy.2023.128092

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 ().