Multi-objective decoupling control of thermal management system for PEM fuel cell

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-11-15 DOI:10.1016/j.egyai.2024.100447
Jun-Hong Chen , Pu He , Ze-Hong He , Jia-Le Song , Xian-Hao Liu , Yun-Tian Xiao , Ming-Yang Wang , Lu-Zheng Yang , Yu-Tong Mu , Wen-Quan Tao
{"title":"Multi-objective decoupling control of thermal management system for PEM fuel cell","authors":"Jun-Hong Chen ,&nbsp;Pu He ,&nbsp;Ze-Hong He ,&nbsp;Jia-Le Song ,&nbsp;Xian-Hao Liu ,&nbsp;Yun-Tian Xiao ,&nbsp;Ming-Yang Wang ,&nbsp;Lu-Zheng Yang ,&nbsp;Yu-Tong Mu ,&nbsp;Wen-Quan Tao","doi":"10.1016/j.egyai.2024.100447","DOIUrl":null,"url":null,"abstract":"<div><div>Operating temperature is an important factor that affects the efficiency, durability, and safety of proton exchange membrane fuel cells (PEMFC). Thus, a thermal management system is necessary for controlling the appropriate temperature. In this paper, a novel thermal management system based on two-stage utilization of cooling air is first established, whose core characteristic is utilizing the temperature difference between the cooling air leaving the main radiator and the auxiliary radiator. The novel thermal management system can reduce the parasitic power of the fan by 59.27 % and improve the temperature control effect to a certain extent. The traditional feedforward decoupling control based on system identification is first adopted to control the temperature and surpasses dual-PID on all the 5 indexes, which are Integral Absolute Error Criterion (IAE) of temperature difference, IAE of inlet coolant temperature, parasitic power of fan, average overshoot of temperature difference and average overshoot of inlet coolant temperature. The multi-objective decoupling control based on multi-objective optimization is then proposed to further improve the temperature control effect on the basis of traditional feedforward decoupling control. The above 5 indexes are chosen as the optimization objectives, the decoupling coefficients are chosen as the decision variables, and the Pareto set is obtained by NSGAⅡ and NSGAⅢ. The results show that the proposed multi-objective decoupling control has the main advantages as follows: (1) It can provide comprehensive optimization options for different design preferences; (2) It can significantly optimize a certain objective while other objectives are not too extreme; (3) It has the ability to surpass traditional feedforward decoupling control on all the 5 indexes; (4) It does not rely on the system identification.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100447"},"PeriodicalIF":9.6000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy and AI","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666546824001137","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Operating temperature is an important factor that affects the efficiency, durability, and safety of proton exchange membrane fuel cells (PEMFC). Thus, a thermal management system is necessary for controlling the appropriate temperature. In this paper, a novel thermal management system based on two-stage utilization of cooling air is first established, whose core characteristic is utilizing the temperature difference between the cooling air leaving the main radiator and the auxiliary radiator. The novel thermal management system can reduce the parasitic power of the fan by 59.27 % and improve the temperature control effect to a certain extent. The traditional feedforward decoupling control based on system identification is first adopted to control the temperature and surpasses dual-PID on all the 5 indexes, which are Integral Absolute Error Criterion (IAE) of temperature difference, IAE of inlet coolant temperature, parasitic power of fan, average overshoot of temperature difference and average overshoot of inlet coolant temperature. The multi-objective decoupling control based on multi-objective optimization is then proposed to further improve the temperature control effect on the basis of traditional feedforward decoupling control. The above 5 indexes are chosen as the optimization objectives, the decoupling coefficients are chosen as the decision variables, and the Pareto set is obtained by NSGAⅡ and NSGAⅢ. The results show that the proposed multi-objective decoupling control has the main advantages as follows: (1) It can provide comprehensive optimization options for different design preferences; (2) It can significantly optimize a certain objective while other objectives are not too extreme; (3) It has the ability to surpass traditional feedforward decoupling control on all the 5 indexes; (4) It does not rely on the system identification.

Abstract Image

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
PEM 燃料电池热管理系统的多目标解耦控制
工作温度是影响质子交换膜燃料电池(PEMFC)效率、耐用性和安全性的重要因素。因此,需要一个热管理系统来控制适当的温度。本文首先建立了一种基于冷却空气两级利用的新型热管理系统,其核心特点是利用离开主散热器的冷却空气与辅助散热器之间的温差。新型热管理系统可降低风扇的寄生功率 59.27%,并在一定程度上改善了温度控制效果。首先采用基于系统辨识的传统前馈解耦控制来控制温度,并在温差积分绝对误差准则(IAE)、入口冷却剂温度积分绝对误差准则(IAE)、风扇寄生功率、温差平均过冲和入口冷却剂温度平均过冲这 5 项指标上全面超越了双 PID。然后提出基于多目标优化的多目标解耦控制,在传统前馈解耦控制的基础上进一步提高温度控制效果。选取上述 5 个指标作为优化目标,选取解耦系数作为决策变量,利用 NSGAⅡ 和 NSGAⅢ求出帕累托集。结果表明,所提出的多目标解耦控制具有以下主要优点:(1)能针对不同的设计偏好提供全面的优化方案;(2)能显著优化某一目标,而其他目标不会过于极端;(3)在所有 5 项指标上都具有超越传统前馈解耦控制的能力;(4)不依赖系统识别。
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
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
期刊最新文献
Neural network potential-based molecular investigation of thermal decomposition mechanisms of ethylene and ammonia Machine learning for battery quality classification and lifetime prediction using formation data Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU Real-world validation of safe reinforcement learning, model predictive control and decision tree-based home energy management systems Decentralized coordination of distributed energy resources through local energy markets and deep reinforcement learning
×
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