{"title":"新一代电动汽车电池能量管理系统:人工智能概述","authors":"Nayan Kumar , Prabhansu","doi":"10.1016/j.fub.2025.100087","DOIUrl":null,"url":null,"abstract":"<div><div>This article proposes a comprehensive overview of the potential of artificial intelligence (AI) and its subsets-machine learning (ML) and deep learning (DL) in next-generation battery energy management systems (BEMS) for electric vehicles (EVs). Next-generation BEMS has gained close attention from professionals in the energy sectors due to monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data analytics. The discussion also highlights the challenges and opportunities associated with AI-based BEMS, considering efficiency, energy management, reliability, control, and life factors. Finally, the article discusses several other potential disruptive impacts of AI-enabled BEMSs for next-generation EVs. The article also highlights key challenges and critically analyzes recent research efforts and open gaps in BEMS.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"7 ","pages":"Article 100087"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Next-generation battery energy management systems in electric vehicles: An overview of artificial intelligence\",\"authors\":\"Nayan Kumar , Prabhansu\",\"doi\":\"10.1016/j.fub.2025.100087\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This article proposes a comprehensive overview of the potential of artificial intelligence (AI) and its subsets-machine learning (ML) and deep learning (DL) in next-generation battery energy management systems (BEMS) for electric vehicles (EVs). Next-generation BEMS has gained close attention from professionals in the energy sectors due to monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data analytics. The discussion also highlights the challenges and opportunities associated with AI-based BEMS, considering efficiency, energy management, reliability, control, and life factors. Finally, the article discusses several other potential disruptive impacts of AI-enabled BEMSs for next-generation EVs. The article also highlights key challenges and critically analyzes recent research efforts and open gaps in BEMS.</div></div>\",\"PeriodicalId\":100560,\"journal\":{\"name\":\"Future Batteries\",\"volume\":\"7 \",\"pages\":\"Article 100087\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Future Batteries\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2950264025000668\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Batteries","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2950264025000668","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Next-generation battery energy management systems in electric vehicles: An overview of artificial intelligence
This article proposes a comprehensive overview of the potential of artificial intelligence (AI) and its subsets-machine learning (ML) and deep learning (DL) in next-generation battery energy management systems (BEMS) for electric vehicles (EVs). Next-generation BEMS has gained close attention from professionals in the energy sectors due to monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data analytics. The discussion also highlights the challenges and opportunities associated with AI-based BEMS, considering efficiency, energy management, reliability, control, and life factors. Finally, the article discusses several other potential disruptive impacts of AI-enabled BEMSs for next-generation EVs. The article also highlights key challenges and critically analyzes recent research efforts and open gaps in BEMS.