State of charge estimation of a Lithium-ion battery for electric vehicle based on particle swarm optimization

N. Ismail, S. Toha
{"title":"State of charge estimation of a Lithium-ion battery for electric vehicle based on particle swarm optimization","authors":"N. Ismail, S. Toha","doi":"10.1109/ICSIMA.2013.6717978","DOIUrl":null,"url":null,"abstract":"Lithium-ion battery plays important roles in electric drive vehicles. It has several advantages among other battery technologies such as high energy density and specific energy. The primary concerns of Lithium-ion batteries are to maintain optimum battery performance and extend the battery's life. An accurate state of charge (SOC) estimation can improve the performance of Lithium-ion battery. In this paper, a method for SOC estimation for LiFePO4 using the particle swarm optimization (PSO) algorithm is presented. The results indicate the SOC estimation using PSO optimized algorithm has good performance. The simulation result has also been validated and complies within specific confidence level.","PeriodicalId":182424,"journal":{"name":"2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSIMA.2013.6717978","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12

Abstract

Lithium-ion battery plays important roles in electric drive vehicles. It has several advantages among other battery technologies such as high energy density and specific energy. The primary concerns of Lithium-ion batteries are to maintain optimum battery performance and extend the battery's life. An accurate state of charge (SOC) estimation can improve the performance of Lithium-ion battery. In this paper, a method for SOC estimation for LiFePO4 using the particle swarm optimization (PSO) algorithm is presented. The results indicate the SOC estimation using PSO optimized algorithm has good performance. The simulation result has also been validated and complies within specific confidence level.
基于粒子群优化的电动汽车锂离子电池充电状态估计
锂离子电池在电动汽车中起着重要的作用。与其他电池技术相比,它具有高能量密度和比能量等优点。锂离子电池的主要问题是保持最佳的电池性能和延长电池的寿命。准确的荷电状态估算可以提高锂离子电池的性能。本文提出了一种基于粒子群优化(PSO)算法的LiFePO4 SOC估计方法。结果表明,基于粒子群优化算法的SOC估计具有良好的性能。仿真结果也得到了验证,符合特定的置信水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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