{"title":"纯电动汽车锂离子电池剩余寿命预测方法研究","authors":"Zhiwen An","doi":"10.1504/ijmpt.2021.10039891","DOIUrl":null,"url":null,"abstract":"To overcome the complexity of the lithium-ion battery inside the chemical reaction resulting in a low battery life remaining prediction accuracy, the paper proposes a new electric vehicle lithium ion battery remaining life prediction method based on a correlation vector machine. According to the operating characteristics of lithium-ion batteries in electric vehicles, this method selects health factors that affect battery life, and selects related factors. According to the marginal likelihood function, the factor weights are integrated to obtain the health factor sequence target. Relevance vector machine is used to optimise and evaluate the characteristics of health factors, and complete the prediction of electric vehicle lithium-ion battery capacity and remaining battery life. Comparative experiments show that the prediction effect and stability of the method in this paper are better, and the minimum prediction error is only 0.013.","PeriodicalId":14167,"journal":{"name":"International Journal of Materials & Product Technology","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Research on residual life prediction method of lithium ion battery for pure electric vehicle\",\"authors\":\"Zhiwen An\",\"doi\":\"10.1504/ijmpt.2021.10039891\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To overcome the complexity of the lithium-ion battery inside the chemical reaction resulting in a low battery life remaining prediction accuracy, the paper proposes a new electric vehicle lithium ion battery remaining life prediction method based on a correlation vector machine. According to the operating characteristics of lithium-ion batteries in electric vehicles, this method selects health factors that affect battery life, and selects related factors. According to the marginal likelihood function, the factor weights are integrated to obtain the health factor sequence target. Relevance vector machine is used to optimise and evaluate the characteristics of health factors, and complete the prediction of electric vehicle lithium-ion battery capacity and remaining battery life. Comparative experiments show that the prediction effect and stability of the method in this paper are better, and the minimum prediction error is only 0.013.\",\"PeriodicalId\":14167,\"journal\":{\"name\":\"International Journal of Materials & Product Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2021-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Materials & Product Technology\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1504/ijmpt.2021.10039891\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Materials & Product Technology","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1504/ijmpt.2021.10039891","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Research on residual life prediction method of lithium ion battery for pure electric vehicle
To overcome the complexity of the lithium-ion battery inside the chemical reaction resulting in a low battery life remaining prediction accuracy, the paper proposes a new electric vehicle lithium ion battery remaining life prediction method based on a correlation vector machine. According to the operating characteristics of lithium-ion batteries in electric vehicles, this method selects health factors that affect battery life, and selects related factors. According to the marginal likelihood function, the factor weights are integrated to obtain the health factor sequence target. Relevance vector machine is used to optimise and evaluate the characteristics of health factors, and complete the prediction of electric vehicle lithium-ion battery capacity and remaining battery life. Comparative experiments show that the prediction effect and stability of the method in this paper are better, and the minimum prediction error is only 0.013.
期刊介绍:
The IJMPT is a refereed and authoritative publication which provides a forum for the exchange of information and ideas between materials academics and engineers working in university research departments and research institutes, and manufacturing, marketing and process managers, designers, technologists and research and development engineers working in industry.