用非线性卡尔曼滤波估计锂离子电池组的充电状态

IF 2.6 4区 化学 Q3 CHEMISTRY, PHYSICAL
Ionics Pub Date : 2025-05-31 DOI:10.1007/s11581-025-06420-z
Shivanshu Kumar, Saikat Mondal, Amalendu Bikash Choudhury, Himadri Sekhar Bhattacharyya, Chandan Kumar Chanda
{"title":"用非线性卡尔曼滤波估计锂离子电池组的充电状态","authors":"Shivanshu Kumar,&nbsp;Saikat Mondal,&nbsp;Amalendu Bikash Choudhury,&nbsp;Himadri Sekhar Bhattacharyya,&nbsp;Chandan Kumar Chanda","doi":"10.1007/s11581-025-06420-z","DOIUrl":null,"url":null,"abstract":"<div><p>Estimating the state of charge (SOC) of a lithium-ion battery (LiB) pack is challenging due to the inherent variability across individual battery cells. This study uses a hardware configuration comprising a 13s10p battery pack, a switched-mode power supply (SMPS), a brushless direct current motor (BLDC) as a load, and a charger to charge and discharge the battery pack for gathering the real-time data. The data is subsequently fed into the simulation model, which estimate the SOC for a 2 RC model at temperatures 288 K, 298 K, and 318 K. Several nonlinear Kalman filter (KF) techniques, such as the extended Kalman filter method (EKF), the unscented Kalman filter method (UKF), extended Kalman-Bucy filter method (EKBF), and the unscented Kalman-Bucy filter method (UKBF), are utilized in estimating SOC. The UKBF and EKBF provide the most accurate estimation for SOC, with an overall root mean square error (RMSE) of less than 1% and 1.5%, respectively, while the mean absolute percentage error (MAPE) is below 1.5% and 3% for the 2 RC model across all temperatures.</p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"31 8","pages":"7953 - 7968"},"PeriodicalIF":2.6000,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimation of state of charge for a lithium-ion battery pack using nonlinear Kalman filters\",\"authors\":\"Shivanshu Kumar,&nbsp;Saikat Mondal,&nbsp;Amalendu Bikash Choudhury,&nbsp;Himadri Sekhar Bhattacharyya,&nbsp;Chandan Kumar Chanda\",\"doi\":\"10.1007/s11581-025-06420-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Estimating the state of charge (SOC) of a lithium-ion battery (LiB) pack is challenging due to the inherent variability across individual battery cells. This study uses a hardware configuration comprising a 13s10p battery pack, a switched-mode power supply (SMPS), a brushless direct current motor (BLDC) as a load, and a charger to charge and discharge the battery pack for gathering the real-time data. The data is subsequently fed into the simulation model, which estimate the SOC for a 2 RC model at temperatures 288 K, 298 K, and 318 K. Several nonlinear Kalman filter (KF) techniques, such as the extended Kalman filter method (EKF), the unscented Kalman filter method (UKF), extended Kalman-Bucy filter method (EKBF), and the unscented Kalman-Bucy filter method (UKBF), are utilized in estimating SOC. The UKBF and EKBF provide the most accurate estimation for SOC, with an overall root mean square error (RMSE) of less than 1% and 1.5%, respectively, while the mean absolute percentage error (MAPE) is below 1.5% and 3% for the 2 RC model across all temperatures.</p></div>\",\"PeriodicalId\":599,\"journal\":{\"name\":\"Ionics\",\"volume\":\"31 8\",\"pages\":\"7953 - 7968\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ionics\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11581-025-06420-z\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ionics","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s11581-025-06420-z","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0

摘要

锂离子电池(LiB)电池组的充电状态(SOC)评估是一项挑战,因为各个电池单元之间存在固有的可变性。本研究使用的硬件配置包括一个13s10p的电池组,一个开关电源(SMPS),一个无刷直流电机(BLDC)作为负载,以及一个充电器对电池组进行充放电以收集实时数据。这些数据随后被输入到模拟模型中,该模型在288 K, 298 K和318 K的温度下估计2 RC模型的SOC。几种非线性卡尔曼滤波技术,如扩展卡尔曼滤波方法(EKF)、无气味卡尔曼滤波方法(UKF)、扩展卡尔曼-布西滤波方法(EKBF)和无气味卡尔曼-布西滤波方法(UKBF),被用于SOC的估计。UKBF和EKBF提供了最准确的SOC估计,总体均方根误差(RMSE)分别小于1%和1.5%,而2 RC模型在所有温度下的平均绝对百分比误差(MAPE)低于1.5%和3%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Estimation of state of charge for a lithium-ion battery pack using nonlinear Kalman filters

Estimation of state of charge for a lithium-ion battery pack using nonlinear Kalman filters

Estimating the state of charge (SOC) of a lithium-ion battery (LiB) pack is challenging due to the inherent variability across individual battery cells. This study uses a hardware configuration comprising a 13s10p battery pack, a switched-mode power supply (SMPS), a brushless direct current motor (BLDC) as a load, and a charger to charge and discharge the battery pack for gathering the real-time data. The data is subsequently fed into the simulation model, which estimate the SOC for a 2 RC model at temperatures 288 K, 298 K, and 318 K. Several nonlinear Kalman filter (KF) techniques, such as the extended Kalman filter method (EKF), the unscented Kalman filter method (UKF), extended Kalman-Bucy filter method (EKBF), and the unscented Kalman-Bucy filter method (UKBF), are utilized in estimating SOC. The UKBF and EKBF provide the most accurate estimation for SOC, with an overall root mean square error (RMSE) of less than 1% and 1.5%, respectively, while the mean absolute percentage error (MAPE) is below 1.5% and 3% for the 2 RC model across all temperatures.

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
Ionics
Ionics 化学-电化学
CiteScore
5.30
自引率
7.10%
发文量
427
审稿时长
2.2 months
期刊介绍: Ionics is publishing original results in the fields of science and technology of ionic motion. This includes theoretical, experimental and practical work on electrolytes, electrode, ionic/electronic interfaces, ionic transport aspects of corrosion, galvanic cells, e.g. for thermodynamic and kinetic studies, batteries, fuel cells, sensors and electrochromics. Fast solid ionic conductors are presently providing new opportunities in view of several advantages, in addition to conventional liquid electrolytes.
×
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:604180095
Book学术官方微信