基于无气味卡尔曼滤波的电动汽车磷酸铁锂电池组容量估计

Lei Zhao, Guoqing Xu, Weimin Li, Taimoor Zahid, Zhibin Song
{"title":"基于无气味卡尔曼滤波的电动汽车磷酸铁锂电池组容量估计","authors":"Lei Zhao, Guoqing Xu, Weimin Li, Taimoor Zahid, Zhibin Song","doi":"10.1109/ICINFA.2013.6720314","DOIUrl":null,"url":null,"abstract":"As is known to all, an accurate on-line estimation of the battery capacity is important for forecasting the EV driving range. But because of the different driving environment and the property of the battery, it is hard to estimate the capacity of the battery pack. This paper presents an unscented Kalman filtering method to estimate the state of charge of LiFePO4 battery pack. Five comparison experiments with different open circuit voltage curves shows that the unscented Kalman filter has a better performance than extended kalman filter.","PeriodicalId":250844,"journal":{"name":"2013 IEEE International Conference on Information and Automation (ICIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"LiFePO4 battery pack capacity estimation for electric vehicles based on unscented Kalman filter\",\"authors\":\"Lei Zhao, Guoqing Xu, Weimin Li, Taimoor Zahid, Zhibin Song\",\"doi\":\"10.1109/ICINFA.2013.6720314\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As is known to all, an accurate on-line estimation of the battery capacity is important for forecasting the EV driving range. But because of the different driving environment and the property of the battery, it is hard to estimate the capacity of the battery pack. This paper presents an unscented Kalman filtering method to estimate the state of charge of LiFePO4 battery pack. Five comparison experiments with different open circuit voltage curves shows that the unscented Kalman filter has a better performance than extended kalman filter.\",\"PeriodicalId\":250844,\"journal\":{\"name\":\"2013 IEEE International Conference on Information and Automation (ICIA)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Conference on Information and Automation (ICIA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICINFA.2013.6720314\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Information and Automation (ICIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINFA.2013.6720314","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

众所周知,准确的在线估计电池容量对于预测电动汽车续驶里程至关重要。但由于行驶环境和电池性能的不同,很难估计电池组的容量。提出了一种无气味卡尔曼滤波方法来估计磷酸铁锂电池组的充电状态。5个不同开路电压曲线的对比实验表明,无气味卡尔曼滤波器比扩展卡尔曼滤波器具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LiFePO4 battery pack capacity estimation for electric vehicles based on unscented Kalman filter
As is known to all, an accurate on-line estimation of the battery capacity is important for forecasting the EV driving range. But because of the different driving environment and the property of the battery, it is hard to estimate the capacity of the battery pack. This paper presents an unscented Kalman filtering method to estimate the state of charge of LiFePO4 battery pack. Five comparison experiments with different open circuit voltage curves shows that the unscented Kalman filter has a better performance than extended kalman filter.
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
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