{"title":"Analysis of Online Parameter Estimation for Electrochemical Li-ion Battery Models via Reduced Sensitivity Equations","authors":"Z. Gima, Dylan Kato, Reinhardt Klein, S. Moura","doi":"10.23919/acc45564.2020.9147260","DOIUrl":null,"url":null,"abstract":"This paper focuses on the problem of online parameter estimation in an electrochemical Li-ion battery model. Online parameter estimation is necessary to account for model mismatch, environmental disturbances, and cycle-induced aging in Li-ion battery models. Sensitivity analysis can improve parameter estimation by identifying which data the parameters are most sensitive to. However, computing parameter sensitivity in full-order electrochemical models is typically intractable for online applications. Using a reduced-order model can lower the computational burden and, as we demonstrate, approximates well the sensitivity of the higher-order model. To provide further insight into the parameter estimation challenge, we analyze the effect that identifying parameters according to voltage RMSE data has on internal state errors. We perform a simulation study which demonstrates that parameter estimation approaches based on this paradigm are not sufficient for safe battery operation or other control objectives that require accurate estimates of these states.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 American Control Conference (ACC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/acc45564.2020.9147260","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
This paper focuses on the problem of online parameter estimation in an electrochemical Li-ion battery model. Online parameter estimation is necessary to account for model mismatch, environmental disturbances, and cycle-induced aging in Li-ion battery models. Sensitivity analysis can improve parameter estimation by identifying which data the parameters are most sensitive to. However, computing parameter sensitivity in full-order electrochemical models is typically intractable for online applications. Using a reduced-order model can lower the computational burden and, as we demonstrate, approximates well the sensitivity of the higher-order model. To provide further insight into the parameter estimation challenge, we analyze the effect that identifying parameters according to voltage RMSE data has on internal state errors. We perform a simulation study which demonstrates that parameter estimation approaches based on this paradigm are not sufficient for safe battery operation or other control objectives that require accurate estimates of these states.