Adaptive Power Allocation with Real-Time Monitoring and Optimization for Fuel Cell/Supercapacitor Hybrid Energy Storage Systems

Qiuyu Li, Hengzhao Yang, Qian Xun
{"title":"Adaptive Power Allocation with Real-Time Monitoring and Optimization for Fuel Cell/Supercapacitor Hybrid Energy Storage Systems","authors":"Qiuyu Li, Hengzhao Yang, Qian Xun","doi":"10.1109/IECON49645.2022.9968352","DOIUrl":null,"url":null,"abstract":"Electric vehicles powered by hybrid energy storage systems composed of fuel cells and supercapacitors are of great interest. To further improve the efficiency of such hybrid systems, better energy management strategies need to be developed. This paper proposes an adaptive power allocation method with real-time monitoring and optimization for fuel cell/supercapacitor hybrid energy storage systems used in electric vehicles. This method utilizes a low-pass filter to distribute power between fuel cells and supercapacitors. The cut-off frequency of the filter is obtained by splitting the load current spectrum according to the supercapacitor state of charge (SOC). The DC-link voltage fluctuation and the supercapacitor SOC are monitored in a real-time fashion. Consequently, a real-time optimization scheme is developed to reduce the dependence of the proposed algorithm on its initial parameters and enhance the adaptivity of the proposed algorithm. To validate the effectiveness of the proposed method, a Simulink model is developed and two standard drive cycles (i.e., NYCC and US06) are selected. Simulation results show that the DC-link voltage fluctuation drops significantly and the supercapacitor SOC can be effectively controlled.","PeriodicalId":125740,"journal":{"name":"IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IECON49645.2022.9968352","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Electric vehicles powered by hybrid energy storage systems composed of fuel cells and supercapacitors are of great interest. To further improve the efficiency of such hybrid systems, better energy management strategies need to be developed. This paper proposes an adaptive power allocation method with real-time monitoring and optimization for fuel cell/supercapacitor hybrid energy storage systems used in electric vehicles. This method utilizes a low-pass filter to distribute power between fuel cells and supercapacitors. The cut-off frequency of the filter is obtained by splitting the load current spectrum according to the supercapacitor state of charge (SOC). The DC-link voltage fluctuation and the supercapacitor SOC are monitored in a real-time fashion. Consequently, a real-time optimization scheme is developed to reduce the dependence of the proposed algorithm on its initial parameters and enhance the adaptivity of the proposed algorithm. To validate the effectiveness of the proposed method, a Simulink model is developed and two standard drive cycles (i.e., NYCC and US06) are selected. Simulation results show that the DC-link voltage fluctuation drops significantly and the supercapacitor SOC can be effectively controlled.
基于实时监测和优化的燃料电池/超级电容器混合储能系统自适应功率分配
由燃料电池和超级电容器组成的混合储能系统驱动的电动汽车备受关注。为了进一步提高这种混合系统的效率,需要开发更好的能源管理策略。针对电动汽车燃料电池/超级电容器混合储能系统,提出了一种实时监测和优化的自适应功率分配方法。这种方法利用低通滤波器在燃料电池和超级电容器之间分配功率。该滤波器的截止频率是根据超级电容器的荷电状态(SOC)对负载电流谱进行拆分得到的。直流链路电压波动和超级电容SOC实时监测。为了降低算法对初始参数的依赖,提高算法的自适应能力,提出了一种实时优化方案。为了验证所提出方法的有效性,开发了一个Simulink模型,并选择了两个标准驱动循环(即NYCC和US06)。仿真结果表明,该方法可以有效地控制超级电容SOC,使直流电压波动显著降低。
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