Big data-driven prognostics and health management of lithium-ion batteries:A review

IF 16.3 1区工程技术 Q1 ENERGY & FUELS

Renewable and Sustainable Energy Reviews Pub Date : 2025-02-27 DOI:10.1016/j.rser.2025.115522

Kui Chen, Yang Luo, Zhou Long, Yang Li, Guangbo Nie, Kai Liu, Dongli Xin, Guoqiang Gao, Guangning Wu

{"title":"Big data-driven prognostics and health management of lithium-ion batteries:A review","authors":"Kui Chen, Yang Luo, Zhou Long, Yang Li, Guangbo Nie, Kai Liu, Dongli Xin, Guoqiang Gao, Guangning Wu","doi":"10.1016/j.rser.2025.115522","DOIUrl":null,"url":null,"abstract":"<div><div>As the preferred green energy storage solution for the transition to renewable and sustainable energy sources, the prognostics and health management (PHM) of lithium-ion batteries play a crucial role in enhancing energy utilization efficiency, optimizing battery maintenance, and accurately detecting health degradation while predicting remaining useful life (RUL). With the rapid advancement of artificial intelligence(AI) and big data technologies, data-driven approaches have gained widespread adoption in the field of battery PHM due to their high accuracy, simplicity, and efficiency. This review provides a comprehensive analysis of the fundamental steps involved in data-driven battery PHM systems, including an in-depth examination of key aspects such as data acquisition, feature parameter construction, and diagnostic methods. The review further highlights prominent research trends rooted in data-driven approaches. Moreover, this study aims to propose novel methodologies and insights that describe the system behaviors of battery aging at both physical and mathematical scales. Ultimately, this work introduces new perspectives and techniques for battery PHM, expanding its applicability and offering valuable guidance for the on-board implementation of PHM systems.</div></div>","PeriodicalId":418,"journal":{"name":"Renewable and Sustainable Energy Reviews","volume":"214 ","pages":"Article 115522"},"PeriodicalIF":16.3000,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Renewable and Sustainable Energy Reviews","FirstCategoryId":"1","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364032125001959","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}

引用次数: 0

Abstract

As the preferred green energy storage solution for the transition to renewable and sustainable energy sources, the prognostics and health management (PHM) of lithium-ion batteries play a crucial role in enhancing energy utilization efficiency, optimizing battery maintenance, and accurately detecting health degradation while predicting remaining useful life (RUL). With the rapid advancement of artificial intelligence(AI) and big data technologies, data-driven approaches have gained widespread adoption in the field of battery PHM due to their high accuracy, simplicity, and efficiency. This review provides a comprehensive analysis of the fundamental steps involved in data-driven battery PHM systems, including an in-depth examination of key aspects such as data acquisition, feature parameter construction, and diagnostic methods. The review further highlights prominent research trends rooted in data-driven approaches. Moreover, this study aims to propose novel methodologies and insights that describe the system behaviors of battery aging at both physical and mathematical scales. Ultimately, this work introduces new perspectives and techniques for battery PHM, expanding its applicability and offering valuable guidance for the on-board implementation of PHM systems.

Abstract Image

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Renewable and Sustainable Energy Reviews 工程技术-能源与燃料

CiteScore

31.20

自引率

5.70%

发文量

1055

审稿时长

62 days

期刊介绍： The mission of Renewable and Sustainable Energy Reviews is to disseminate the most compelling and pertinent critical insights in renewable and sustainable energy, fostering collaboration among the research community, private sector, and policy and decision makers. The journal aims to exchange challenges, solutions, innovative concepts, and technologies, contributing to sustainable development, the transition to a low-carbon future, and the attainment of emissions targets outlined by the United Nations Framework Convention on Climate Change. Renewable and Sustainable Energy Reviews publishes a diverse range of content, including review papers, original research, case studies, and analyses of new technologies, all featuring a substantial review component such as critique, comparison, or analysis. Introducing a distinctive paper type, Expert Insights, the journal presents commissioned mini-reviews authored by field leaders, addressing topics of significant interest. Case studies undergo consideration only if they showcase the work's applicability to other regions or contribute valuable insights to the broader field of renewable and sustainable energy. Notably, a bibliographic or literature review lacking critical analysis is deemed unsuitable for publication.