Machine learning for full lifecycle management of lithium-ion batteries

Qiangxiang Zhai, Hongmin Jiang, Nengbing Long, Qiaoling Kang, Xianhe Meng, Mingjiong Zhou, Lijing Yan and Tingli Ma

Renewable and Sustainable Energy Reviews, 2024, vol. 202, issue C

Abstract: Developing advanced battery materials, monitoring and predicting the health status of batteries, and effectively managing retired batteries are crucial for accelerating the closure of the whole industrial chain of power lithium-ion batteries for electric vehicles. Machine learning technology plays a vital role in the research, production, service, and retirement of lithium-ion batteries due to its robust learning and predictive capabilities. While there have been detailed and valuable reviews on this topic, a comprehensive summary of machine learning progress from the perspective of full lifecycle management of lithium-ion batteries is still lacking. This review divides the full lifecycle of lithium-ion batteries into three stages: pre-prediction, mid-prediction, and late prediction phases, and summarizes recent advances in different machine learning methods categorized as materials screening, life prediction, and cascade utilization. It also emphasizes the implementation and evaluation of hybrid machine learning models. Finally, potential research opportunities and future directions are presented, mainly from two aspects of battery databases and the application of large-scale time-series models. This review provides valuable guidance and reference for researchers and practitioners to broaden the scope of machine learning for its application in lithium-ion batteries.

Keywords: Lithium-ion batteries; Machine learning; Material screening; Life prediction; Cascade utilization (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1364032124003733
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:rensus:v:202:y:2024:i:c:s1364032124003733

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/bibliographic
http://www.elsevier. ... 600126/bibliographic

DOI: 10.1016/j.rser.2024.114647

Access Statistics for this article

Renewable and Sustainable Energy Reviews is currently edited by L. Kazmerski

More articles in Renewable and Sustainable Energy Reviews from Elsevier
Bibliographic data for series maintained by Catherine Liu ().