{"title":"估算锂离子电池SoH的新方法","authors":"Palash Jain, Sudipto Saha, V. Sankaranarayanan","doi":"10.1109/IEMRE52042.2021.9386881","DOIUrl":null,"url":null,"abstract":"State of Health (SoH) estimation is one of the most important functions of the Battery Management System as it ensures safe and reliable operation of Lithium-Ion battery. SoH estimation is done by developing a regression model between SoH and Health parameter, by studying the charging dataset of the battery throughout it’s working life (upto 60 % SoH). The health parameter is calculated as the charging time between two selected voltage ranges instead of taking the entire voltage range. The selection of the voltage range for SoH prediction is done based on the goodness of fit of the regression model. The training dataset consists of health parameter evaluated at regular intervals of SoH. The regression line is tested with a wide range of test data and the accuracy is found to be over 99%.","PeriodicalId":202287,"journal":{"name":"2021 Innovations in Energy Management and Renewable Resources(52042)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Novel method to Estimate SoH of Lithium-Ion Batteries\",\"authors\":\"Palash Jain, Sudipto Saha, V. Sankaranarayanan\",\"doi\":\"10.1109/IEMRE52042.2021.9386881\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"State of Health (SoH) estimation is one of the most important functions of the Battery Management System as it ensures safe and reliable operation of Lithium-Ion battery. SoH estimation is done by developing a regression model between SoH and Health parameter, by studying the charging dataset of the battery throughout it’s working life (upto 60 % SoH). The health parameter is calculated as the charging time between two selected voltage ranges instead of taking the entire voltage range. The selection of the voltage range for SoH prediction is done based on the goodness of fit of the regression model. The training dataset consists of health parameter evaluated at regular intervals of SoH. The regression line is tested with a wide range of test data and the accuracy is found to be over 99%.\",\"PeriodicalId\":202287,\"journal\":{\"name\":\"2021 Innovations in Energy Management and Renewable Resources(52042)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 Innovations in Energy Management and Renewable Resources(52042)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEMRE52042.2021.9386881\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Innovations in Energy Management and Renewable Resources(52042)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMRE52042.2021.9386881","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Novel method to Estimate SoH of Lithium-Ion Batteries
State of Health (SoH) estimation is one of the most important functions of the Battery Management System as it ensures safe and reliable operation of Lithium-Ion battery. SoH estimation is done by developing a regression model between SoH and Health parameter, by studying the charging dataset of the battery throughout it’s working life (upto 60 % SoH). The health parameter is calculated as the charging time between two selected voltage ranges instead of taking the entire voltage range. The selection of the voltage range for SoH prediction is done based on the goodness of fit of the regression model. The training dataset consists of health parameter evaluated at regular intervals of SoH. The regression line is tested with a wide range of test data and the accuracy is found to be over 99%.
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