Yuhao Zhang, Yunfei Han, Tao Cai, Jia Xie, Shijie Cheng
{"title":"锂离子电池剩余使用寿命预测的特征选择和数据驱动模型","authors":"Yuhao Zhang, Yunfei Han, Tao Cai, Jia Xie, Shijie Cheng","doi":"10.1049/esi2.12171","DOIUrl":null,"url":null,"abstract":"<p>To ensure long and reliable operation of lithium-ion battery storage workstations, accurate, fast, and stable lifetime prediction is crucial. However, due to the complex and interrelated ageing mechanisms of Li-ion batteries, using physical model-based methods for accurate description is challenging. Therefore, building data-driven models based on direct measurement data (voltage, current, capacity, etc.) during battery operation may be a more effective approach. This paper employs a time series analysis of discharge capacity/voltage curves to perform feature predication. The goal is to predict the state of health using a short-term model and the remaining useful life of batteries using a long-term iterative model. The validity of this method is verified using the open-source MIT battery dataset. Comparisons with models reported in the literature demonstrate that this method is generalisable and ensures accuracy across a wider range of predictions.</p>","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"6 S1","pages":"776-788"},"PeriodicalIF":1.6000,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.12171","citationCount":"0","resultStr":"{\"title\":\"Feature selection and data-driven model for predicting the remaining useful life of lithium-ion batteries\",\"authors\":\"Yuhao Zhang, Yunfei Han, Tao Cai, Jia Xie, Shijie Cheng\",\"doi\":\"10.1049/esi2.12171\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>To ensure long and reliable operation of lithium-ion battery storage workstations, accurate, fast, and stable lifetime prediction is crucial. However, due to the complex and interrelated ageing mechanisms of Li-ion batteries, using physical model-based methods for accurate description is challenging. Therefore, building data-driven models based on direct measurement data (voltage, current, capacity, etc.) during battery operation may be a more effective approach. This paper employs a time series analysis of discharge capacity/voltage curves to perform feature predication. The goal is to predict the state of health using a short-term model and the remaining useful life of batteries using a long-term iterative model. The validity of this method is verified using the open-source MIT battery dataset. Comparisons with models reported in the literature demonstrate that this method is generalisable and ensures accuracy across a wider range of predictions.</p>\",\"PeriodicalId\":33288,\"journal\":{\"name\":\"IET Energy Systems Integration\",\"volume\":\"6 S1\",\"pages\":\"776-788\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.12171\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Energy Systems Integration\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/esi2.12171\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Energy Systems Integration","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/esi2.12171","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Feature selection and data-driven model for predicting the remaining useful life of lithium-ion batteries
To ensure long and reliable operation of lithium-ion battery storage workstations, accurate, fast, and stable lifetime prediction is crucial. However, due to the complex and interrelated ageing mechanisms of Li-ion batteries, using physical model-based methods for accurate description is challenging. Therefore, building data-driven models based on direct measurement data (voltage, current, capacity, etc.) during battery operation may be a more effective approach. This paper employs a time series analysis of discharge capacity/voltage curves to perform feature predication. The goal is to predict the state of health using a short-term model and the remaining useful life of batteries using a long-term iterative model. The validity of this method is verified using the open-source MIT battery dataset. Comparisons with models reported in the literature demonstrate that this method is generalisable and ensures accuracy across a wider range of predictions.