{"title":"On-board Health Prognosis of Lithium-Ion Battery Based on the Estimation of Internal Resistance Under Resistive and Inductive Loading Conditions","authors":"Pranjal Barman, Sushanta Bordoloi, C. Hazarika","doi":"10.1109/ICAECT54875.2022.9807976","DOIUrl":null,"url":null,"abstract":"In this work an effective health indicator to assess the useful battery life of lithium-ion battery under resistive and inductive loading conditions is presented. The health indicator in the form of battery internal resistance is derived from the experimentally obtained real time battery information in multiple charging-discharging cycles. Relying on the proposed health indicator, the state of health and end of life of the battery can be predicted at reasonable accuracy. The work also includes several sets of experimental data from the battery at different loading conditions within a particular range of operating temperature. A model-based prediction approach to forecast the battery health is derived from the dynamically changing internal resistance at different discharging instances. In this article, a simple and cost effective experimental set-up with necessary acquisition method is presented which extracts the battery information for proper analysis. The effectiveness and adaptability of the developed method is demonstrated in terms of experimental results, case studies and analysis.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAECT54875.2022.9807976","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this work an effective health indicator to assess the useful battery life of lithium-ion battery under resistive and inductive loading conditions is presented. The health indicator in the form of battery internal resistance is derived from the experimentally obtained real time battery information in multiple charging-discharging cycles. Relying on the proposed health indicator, the state of health and end of life of the battery can be predicted at reasonable accuracy. The work also includes several sets of experimental data from the battery at different loading conditions within a particular range of operating temperature. A model-based prediction approach to forecast the battery health is derived from the dynamically changing internal resistance at different discharging instances. In this article, a simple and cost effective experimental set-up with necessary acquisition method is presented which extracts the battery information for proper analysis. The effectiveness and adaptability of the developed method is demonstrated in terms of experimental results, case studies and analysis.