{"title":"Battery Digital Twins","authors":"M. Olteanu, D. Petreus","doi":"10.1109/ISSE54558.2022.9812769","DOIUrl":null,"url":null,"abstract":"It is no longer a secret that we are experiencing a revolution in electric cars. However, one problem remains unresolved: how do we manage lithium-ion batteries more efficiently? Battery life depends on the materials the batteries are made of, the design of the system, and the conditions under which it operates. All these factors make efficient battery power management a real challenge. Even so, to the growing understanding of how a battery degrades over time, due to the existence of new tools for modeling and providing a diagnosis, the idea of merging the knowledge that we have already with artificial intelligence, to create a digital “twin” of the battery. This paper aims to present some preliminary results of a complex system that uses artificial intelligence to simulate and model the real behavior of batteries.","PeriodicalId":413385,"journal":{"name":"2022 45th International Spring Seminar on Electronics Technology (ISSE)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 45th International Spring Seminar on Electronics Technology (ISSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSE54558.2022.9812769","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
It is no longer a secret that we are experiencing a revolution in electric cars. However, one problem remains unresolved: how do we manage lithium-ion batteries more efficiently? Battery life depends on the materials the batteries are made of, the design of the system, and the conditions under which it operates. All these factors make efficient battery power management a real challenge. Even so, to the growing understanding of how a battery degrades over time, due to the existence of new tools for modeling and providing a diagnosis, the idea of merging the knowledge that we have already with artificial intelligence, to create a digital “twin” of the battery. This paper aims to present some preliminary results of a complex system that uses artificial intelligence to simulate and model the real behavior of batteries.