Advanced battery management system using MATLAB/Simulink

B. Kumar, Neeta Khare, P. K. Chaturvedi
{"title":"Advanced battery management system using MATLAB/Simulink","authors":"B. Kumar, Neeta Khare, P. K. Chaturvedi","doi":"10.1109/INTLEC.2015.7572447","DOIUrl":null,"url":null,"abstract":"A battery management system (BMS) is a system that manages a rechargeable battery (cell or battery pack), by protecting the battery to operate beyond its safe limits and monitoring its state of charge (SoC) & state of health (SoH). BMS has been the essential integral part of hybrid electrical vehicles (HEVs) & electrical vehicles (EVs). BMS provides safety to the system and user with run time monitoring of battery for any critical hazarder conditions. In the present work, design & simulation of BMS for EVs is presented. The entire model of BMS & all other functional blocks of BMS are implemented in Simulink toolbox of MATLAB R2012a. The BMS presented in this research paper includes Neural Network Controller (NNC), Fuzzy Logic Controller (FLC) & Statistical Model. The battery parameters required to design and simulate the BMS are extracted from the experimental results and incorporated in the model. The Neuro-Fuzzy approach is used to model the electrochemical behavior of the Lead-acid battery (selected for case study) then used to estimate the SoC. The Statistical model is used to address battery's SoH. Battery cycle test results have been used for initial model design, Neural Network training and later; it is transferred to the design & simulation of BMS using Simulink. The simulation results are validated by experimental results and MATLAB/Simulink simulation. This model provides more than 97% accuracy in SoC and reasonably accurate SoH.","PeriodicalId":211948,"journal":{"name":"2015 IEEE International Telecommunications Energy Conference (INTELEC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Telecommunications Energy Conference (INTELEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INTLEC.2015.7572447","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

Abstract

A battery management system (BMS) is a system that manages a rechargeable battery (cell or battery pack), by protecting the battery to operate beyond its safe limits and monitoring its state of charge (SoC) & state of health (SoH). BMS has been the essential integral part of hybrid electrical vehicles (HEVs) & electrical vehicles (EVs). BMS provides safety to the system and user with run time monitoring of battery for any critical hazarder conditions. In the present work, design & simulation of BMS for EVs is presented. The entire model of BMS & all other functional blocks of BMS are implemented in Simulink toolbox of MATLAB R2012a. The BMS presented in this research paper includes Neural Network Controller (NNC), Fuzzy Logic Controller (FLC) & Statistical Model. The battery parameters required to design and simulate the BMS are extracted from the experimental results and incorporated in the model. The Neuro-Fuzzy approach is used to model the electrochemical behavior of the Lead-acid battery (selected for case study) then used to estimate the SoC. The Statistical model is used to address battery's SoH. Battery cycle test results have been used for initial model design, Neural Network training and later; it is transferred to the design & simulation of BMS using Simulink. The simulation results are validated by experimental results and MATLAB/Simulink simulation. This model provides more than 97% accuracy in SoC and reasonably accurate SoH.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
先进的电池管理系统使用MATLAB/Simulink
电池管理系统(BMS)是一种管理可充电电池(电池或电池组)的系统,通过保护电池超出其安全限制并监控其充电状态(SoC)和健康状态(SoH)。BMS已成为混合动力汽车(hev)和电动汽车(ev)必不可少的组成部分。BMS为系统和用户提供安全的电池运行时间监控任何关键的危险条件。本文介绍了电动汽车BMS系统的设计与仿真。在MATLAB R2012a的Simulink工具箱中实现了BMS的整个模型和BMS的所有功能模块。本文提出的BMS包括神经网络控制器(NNC)、模糊逻辑控制器(FLC)和统计模型。从实验结果中提取设计和仿真BMS所需的电池参数,并将其纳入模型。神经模糊方法用于模拟铅酸电池的电化学行为(选择用于案例研究),然后用于估计SoC。采用统计模型求解电池的SoH问题。电池循环试验结果用于初始模型设计、神经网络训练等后续工作;并运用Simulink进行了BMS的设计与仿真。通过实验结果和MATLAB/Simulink仿真验证了仿真结果。该模型提供超过97%的SoC精度和相当准确的SoH。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
An Energy Optimization Model (EOM) to reduce mobile service providers network costs: A multi-objective optimization approach Peak-cut control of smart energy BTS: Power control technology for reducing the power consumed by base stations Performance characteristic of interleaved LLC resonant converter with phase shift modulation Research and application of green power system for new data centers The cost benefits of deploying line powering systems from a centralized location over an existing a copper twisted-pair outside plant to energize remote equipment in the distributed telecom network
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1