{"title":"Battery Remaining Useful Life Prediction Based on a Combination of ARMA and Degradation Model","authors":"R. Jiao, X. Ma, L. Li, J. Xiao","doi":"10.1109/ICRMS55680.2022.9944596","DOIUrl":null,"url":null,"abstract":"The remaining useful life (RUL) of batteries is an important and helpful reference for battery management system. Since autoregressive moving average (ARMA) model is a relatively mature time series analysis method for prognostics, the long-term prediction results are not reliable due to dynamic noise and constantly cumulative system errors. In order to improve the accuracy of long-term prediction for battery RUL, a method combining ARMA and exponential degradation model is proposed in this paper. A case study using battery dataset from CALCE is performed to demonstrate the effectiveness of the proposed method, and the results show that the proposed method gives better prediction accuracy.","PeriodicalId":421500,"journal":{"name":"2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRMS55680.2022.9944596","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
The remaining useful life (RUL) of batteries is an important and helpful reference for battery management system. Since autoregressive moving average (ARMA) model is a relatively mature time series analysis method for prognostics, the long-term prediction results are not reliable due to dynamic noise and constantly cumulative system errors. In order to improve the accuracy of long-term prediction for battery RUL, a method combining ARMA and exponential degradation model is proposed in this paper. A case study using battery dataset from CALCE is performed to demonstrate the effectiveness of the proposed method, and the results show that the proposed method gives better prediction accuracy.