{"title":"可再生能源中锂离子电池二次寿命评估测试方法的发展","authors":"Mussab Najeeb, U. Schwalbe","doi":"10.2991/ahe.k.220301.018","DOIUrl":null,"url":null,"abstract":"Lithium-ion batteries can still be used in many applications after removal from their first use in electric vehicles, e.g. as a storage media in photovoltaic systems and for grid support. Therefore, there is a great need to develop reliable methodologies and tools to characterize the expected performance of lithium-ion batteries after their first life in electric vehicles to enable the economical and sustainable re-use of the large amount of lithium-ion batteries, which will be available in the near future. In this paper, we will develop a robust, fast, and non-destructive measurement procedure using artificial intelligence to estimate their state of health. Keywords— Lithium-ion batteries, Second life of batteries, Battery modelling, Artificial neural networks, State of charge, State of health, Electrical vehicle, Energy storage systems","PeriodicalId":177278,"journal":{"name":"Atlantis Highlights in Engineering","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Development of a Test Method to Evaluate Lithium-Ion Batteries for Second Life in Renewable Energy Applications\",\"authors\":\"Mussab Najeeb, U. Schwalbe\",\"doi\":\"10.2991/ahe.k.220301.018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Lithium-ion batteries can still be used in many applications after removal from their first use in electric vehicles, e.g. as a storage media in photovoltaic systems and for grid support. Therefore, there is a great need to develop reliable methodologies and tools to characterize the expected performance of lithium-ion batteries after their first life in electric vehicles to enable the economical and sustainable re-use of the large amount of lithium-ion batteries, which will be available in the near future. In this paper, we will develop a robust, fast, and non-destructive measurement procedure using artificial intelligence to estimate their state of health. Keywords— Lithium-ion batteries, Second life of batteries, Battery modelling, Artificial neural networks, State of charge, State of health, Electrical vehicle, Energy storage systems\",\"PeriodicalId\":177278,\"journal\":{\"name\":\"Atlantis Highlights in Engineering\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Atlantis Highlights in Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2991/ahe.k.220301.018\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atlantis Highlights in Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2991/ahe.k.220301.018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development of a Test Method to Evaluate Lithium-Ion Batteries for Second Life in Renewable Energy Applications
Lithium-ion batteries can still be used in many applications after removal from their first use in electric vehicles, e.g. as a storage media in photovoltaic systems and for grid support. Therefore, there is a great need to develop reliable methodologies and tools to characterize the expected performance of lithium-ion batteries after their first life in electric vehicles to enable the economical and sustainable re-use of the large amount of lithium-ion batteries, which will be available in the near future. In this paper, we will develop a robust, fast, and non-destructive measurement procedure using artificial intelligence to estimate their state of health. Keywords— Lithium-ion batteries, Second life of batteries, Battery modelling, Artificial neural networks, State of charge, State of health, Electrical vehicle, Energy storage systems