锂离子电池SOH估算与RUL预测的新方法

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2018-05-01 DOI:10.1109/ICIEA.2018.8398166

Siwen Zhang

{"title":"锂离子电池SOH估算与RUL预测的新方法","authors":"Siwen Zhang","doi":"10.1109/ICIEA.2018.8398166","DOIUrl":null,"url":null,"abstract":"State of health (SOH) estimation and remaining useful lifetime (RUL) prediction are important for a battery management system (BMS). This paper presents a new method to estimate SOH by taking local voltage variation and capacity variation in charging or discharging process of the battery as SOH indexes, and realizes RUL prediction based on a particle filter. The effectiveness is validated using a NCM/LTO lithiumion battery pack.","PeriodicalId":140420,"journal":{"name":"2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A new method for lithium-ion battery's SOH estimation and RUL prediction\",\"authors\":\"Siwen Zhang\",\"doi\":\"10.1109/ICIEA.2018.8398166\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"State of health (SOH) estimation and remaining useful lifetime (RUL) prediction are important for a battery management system (BMS). This paper presents a new method to estimate SOH by taking local voltage variation and capacity variation in charging or discharging process of the battery as SOH indexes, and realizes RUL prediction based on a particle filter. The effectiveness is validated using a NCM/LTO lithiumion battery pack.\",\"PeriodicalId\":140420,\"journal\":{\"name\":\"2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIEA.2018.8398166\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIEA.2018.8398166","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 5

摘要

健康状态(SOH)估计和剩余使用寿命(RUL)预测是电池管理系统(BMS)的重要组成部分。提出了一种以电池充放电过程中的局部电压变化和容量变化作为SOH指标来估计SOH的新方法，并实现了基于粒子滤波的RUL预测。使用NCM/LTO锂离子电池组验证了其有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A new method for lithium-ion battery's SOH estimation and RUL prediction

State of health (SOH) estimation and remaining useful lifetime (RUL) prediction are important for a battery management system (BMS). This paper presents a new method to estimate SOH by taking local voltage variation and capacity variation in charging or discharging process of the battery as SOH indexes, and realizes RUL prediction based on a particle filter. The effectiveness is validated using a NCM/LTO lithiumion battery pack.

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

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)

自引率

0.00%

发文量