Md Shahriar Nazim , Arbil Chakma , Md. Ibne Joha, Syed Samiul Alam, Md Minhazur Rahman, Miftahul Khoir Shilahul Umam, Yeong Min Jang
{"title":"Artificial intelligence for estimating State of Health and Remaining Useful Life of EV batteries: A systematic review","authors":"Md Shahriar Nazim , Arbil Chakma , Md. Ibne Joha, Syed Samiul Alam, Md Minhazur Rahman, Miftahul Khoir Shilahul Umam, Yeong Min Jang","doi":"10.1016/j.icte.2025.05.013","DOIUrl":null,"url":null,"abstract":"<div><div>Lithium-ion batteries are critical to electric vehicles (EVs) but degrade over time, requiring accurate State of Health (SOH) and Remaining Useful Life (RUL) estimation. This review examines recent AI-based methods, especially Convolutional and Recurrent Neural Networks, for their effectiveness in prediction. It discusses key optimization strategies such as feature selection, parameter tuning, and transfer learning. Public datasets (NASA, CALCE, Oxford) are evaluated for benchmarking. The paper also assesses model complexity, performance metrics, and deployment challenges. Finally, it outlines future directions for improving battery management systems, supporting more efficient, reliable, and scalable integration into real-world EV applications.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"11 4","pages":"Pages 769-789"},"PeriodicalIF":4.2000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICT Express","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S240595952500075X","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Lithium-ion batteries are critical to electric vehicles (EVs) but degrade over time, requiring accurate State of Health (SOH) and Remaining Useful Life (RUL) estimation. This review examines recent AI-based methods, especially Convolutional and Recurrent Neural Networks, for their effectiveness in prediction. It discusses key optimization strategies such as feature selection, parameter tuning, and transfer learning. Public datasets (NASA, CALCE, Oxford) are evaluated for benchmarking. The paper also assesses model complexity, performance metrics, and deployment challenges. Finally, it outlines future directions for improving battery management systems, supporting more efficient, reliable, and scalable integration into real-world EV applications.
期刊介绍:
The ICT Express journal published by the Korean Institute of Communications and Information Sciences (KICS) is an international, peer-reviewed research publication covering all aspects of information and communication technology. The journal aims to publish research that helps advance the theoretical and practical understanding of ICT convergence, platform technologies, communication networks, and device technologies. The technology advancement in information and communication technology (ICT) sector enables portable devices to be always connected while supporting high data rate, resulting in the recent popularity of smartphones that have a considerable impact in economic and social development.