Sujin Seol, Jungeun Lee, Jaewoo Yoon, Byeongwoo Kim
{"title":"Improving SOH estimation for lithium-ion batteries using TimeGAN","authors":"Sujin Seol, Jungeun Lee, Jaewoo Yoon, Byeongwoo Kim","doi":"10.1088/2632-2153/acfd08","DOIUrl":null,"url":null,"abstract":"Abstract Recently, the xEV market has been expanding by strengthening regulations on fossil fuel vehicles. It is essential to ensure the safety and reliability of batteries, one of the core components of xEVs. Furthermore, estimating the battery’s state of health (SOH) is critical. There are model-based and data-based methods for SOH estimation. Model-based methods have limitations in linearly modeling the nonlinear internal state changes of batteries. In data-based methods, high-quality datasets containing large quantities of data are crucial. Since obtaining battery datasets through measurement is difficult, this paper supplements insufficient battery datasets using time-series generative adversarial network and compares the improvement rate in SOH estimation accuracy through long short-term memory and gated recurrent unit based on recurrent neural networks. According to the results, the average root mean square error of battery SOH estimation improved by approximately 25%, and the learning stability improved by approximately 40%.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"6 1","pages":"0"},"PeriodicalIF":6.3000,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machine Learning Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/2632-2153/acfd08","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Recently, the xEV market has been expanding by strengthening regulations on fossil fuel vehicles. It is essential to ensure the safety and reliability of batteries, one of the core components of xEVs. Furthermore, estimating the battery’s state of health (SOH) is critical. There are model-based and data-based methods for SOH estimation. Model-based methods have limitations in linearly modeling the nonlinear internal state changes of batteries. In data-based methods, high-quality datasets containing large quantities of data are crucial. Since obtaining battery datasets through measurement is difficult, this paper supplements insufficient battery datasets using time-series generative adversarial network and compares the improvement rate in SOH estimation accuracy through long short-term memory and gated recurrent unit based on recurrent neural networks. According to the results, the average root mean square error of battery SOH estimation improved by approximately 25%, and the learning stability improved by approximately 40%.
期刊介绍:
Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.