{"title":"PHM of Li-ion Batteries","authors":"Saurabh Saxena, Yinjiao Xing, M. Pecht","doi":"10.1002/9781119515326.CH13","DOIUrl":null,"url":null,"abstract":"This chapter presents an overview of the prognostics and systems health management (PHM) techniques used for states estimation and remaining useful life (RUL) prediction of lithium‐ion (Li‐ion) batteries. Li‐ion batteries represent complex electrochemical‐mechanical systems in which various degradation mechanisms are present. State of charge (SOC) and state of health (SOH) provide the estimates of remaining charge and remaining usable capacity of a Li‐ion battery respectively. The chapter discusses methods for battery SOC estimation and also presents a few case studies on experimental data to elaborate these methods. It then presents a case study on SOH estimation and RUL prediction using a Bayesian framework. PHM‐based decision‐making framework for Li‐ion batteries can provide recommendations for mission planning and maintenance scheduling based on the prognostic information, and can control the battery usage in real‐time to optimize the battery life‐cycle performance.","PeriodicalId":163377,"journal":{"name":"Prognostics and Health Management of Electronics","volume":"86 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Prognostics and Health Management of Electronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/9781119515326.CH13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
This chapter presents an overview of the prognostics and systems health management (PHM) techniques used for states estimation and remaining useful life (RUL) prediction of lithium‐ion (Li‐ion) batteries. Li‐ion batteries represent complex electrochemical‐mechanical systems in which various degradation mechanisms are present. State of charge (SOC) and state of health (SOH) provide the estimates of remaining charge and remaining usable capacity of a Li‐ion battery respectively. The chapter discusses methods for battery SOC estimation and also presents a few case studies on experimental data to elaborate these methods. It then presents a case study on SOH estimation and RUL prediction using a Bayesian framework. PHM‐based decision‐making framework for Li‐ion batteries can provide recommendations for mission planning and maintenance scheduling based on the prognostic information, and can control the battery usage in real‐time to optimize the battery life‐cycle performance.