纯电动汽车锂离子电池剩余寿命预测方法研究

IF 0.5 4区 材料科学 Q4 MATERIALS SCIENCE, MULTIDISCIPLINARY
Zhiwen An
{"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":" ","pages":""},"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\":\" \",\"pages\":\"\"},\"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}
引用次数: 5

摘要

为了克服锂离子电池内部化学反应的复杂性导致电池剩余寿命预测精度低的问题,本文提出了一种新的基于相关向量机的电动汽车锂离子电池剩余寿命预测方法。该方法根据电动汽车中锂离子电池的工作特点,选取影响电池寿命的健康因素,并选取相关因素。根据边际似然函数,对各因子权重进行积分,得到健康因子序列目标。利用相关向量机对健康因素特征进行优化评估,完成对电动汽车锂离子电池容量和剩余电池寿命的预测。对比实验表明,本文方法的预测效果和稳定性较好,最小预测误差仅为0.013。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
International Journal of Materials & Product Technology
International Journal of Materials & Product Technology 工程技术-材料科学:综合
CiteScore
0.80
自引率
0.00%
发文量
61
审稿时长
8 months
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信