{"title":"数字孪生在电动汽车电池组产品生命周期管理中的应用","authors":"Suriyan Anandavel, Wei Li, Akhil Garg, Liang Gao","doi":"10.1049/cim2.12028","DOIUrl":null,"url":null,"abstract":"<p>Lithium-ion batteries have become a core component of electric vehicles (EVs) because of their high energy density. However, several issues in lithium-ion batteries usage, such as safety, durability, charging time, and driving range, limit the development of EVs. Meanwhile, with the emergence of Industry 4.0, the digital twins technology has received widespread attention in the manufacturing industry because it provides real-time monitoring and intelligent management of the production process. The authors propose a framework based on digital twins, which can be used for real-time monitoring, intelligent management, and autonomous control of battery packs. The framework covers all aspects of a battery pack's lifecycle, including design, manufacturing, operation monitoring, and second use options. Such a framework can solve some critical issues inhibiting the usage of batteries. A case study of the application of the proposed digital twins-based framework to electric vehicle battery systems has been conducted. The results show that deploying digital twins into the battery packs of EVs will improve the safety and service life of the battery packs.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"3 4","pages":"356-366"},"PeriodicalIF":2.5000,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12028","citationCount":"7","resultStr":"{\"title\":\"Application of digital twins to the product lifecycle management of battery packs of electric vehicles\",\"authors\":\"Suriyan Anandavel, Wei Li, Akhil Garg, Liang Gao\",\"doi\":\"10.1049/cim2.12028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Lithium-ion batteries have become a core component of electric vehicles (EVs) because of their high energy density. However, several issues in lithium-ion batteries usage, such as safety, durability, charging time, and driving range, limit the development of EVs. Meanwhile, with the emergence of Industry 4.0, the digital twins technology has received widespread attention in the manufacturing industry because it provides real-time monitoring and intelligent management of the production process. The authors propose a framework based on digital twins, which can be used for real-time monitoring, intelligent management, and autonomous control of battery packs. The framework covers all aspects of a battery pack's lifecycle, including design, manufacturing, operation monitoring, and second use options. Such a framework can solve some critical issues inhibiting the usage of batteries. A case study of the application of the proposed digital twins-based framework to electric vehicle battery systems has been conducted. The results show that deploying digital twins into the battery packs of EVs will improve the safety and service life of the battery packs.</p>\",\"PeriodicalId\":33286,\"journal\":{\"name\":\"IET Collaborative Intelligent Manufacturing\",\"volume\":\"3 4\",\"pages\":\"356-366\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2021-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12028\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Collaborative Intelligent Manufacturing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/cim2.12028\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Collaborative Intelligent Manufacturing","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cim2.12028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
Application of digital twins to the product lifecycle management of battery packs of electric vehicles
Lithium-ion batteries have become a core component of electric vehicles (EVs) because of their high energy density. However, several issues in lithium-ion batteries usage, such as safety, durability, charging time, and driving range, limit the development of EVs. Meanwhile, with the emergence of Industry 4.0, the digital twins technology has received widespread attention in the manufacturing industry because it provides real-time monitoring and intelligent management of the production process. The authors propose a framework based on digital twins, which can be used for real-time monitoring, intelligent management, and autonomous control of battery packs. The framework covers all aspects of a battery pack's lifecycle, including design, manufacturing, operation monitoring, and second use options. Such a framework can solve some critical issues inhibiting the usage of batteries. A case study of the application of the proposed digital twins-based framework to electric vehicle battery systems has been conducted. The results show that deploying digital twins into the battery packs of EVs will improve the safety and service life of the battery packs.
期刊介绍:
IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly.
The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).