Real-time water content regulation in PEMFC shutdown via MPC with SH-AUKF-based state Feedback: Towards improved efficiency and reduced energy consumption

IF 17 1区 工程技术 Q1 ENERGY & FUELS
Yaowang Pei, Fengxiang Chen
{"title":"Real-time water content regulation in PEMFC shutdown via MPC with SH-AUKF-based state Feedback: Towards improved efficiency and reduced energy consumption","authors":"Yaowang Pei,&nbsp;Fengxiang Chen","doi":"10.1016/j.etran.2025.100461","DOIUrl":null,"url":null,"abstract":"<div><div>Effective regulation of membrane water content during shutdown is critical to ensuring the durability and performance recovery of proton exchange membrane fuel cells (PEMFCs). This study presents a model predictive control (MPC) strategy for purge-phase water removal, employing adaptive unscented Kalman filters (UKFs) for water content estimation. A reduced-order model is formulated to capture the essential purge dynamics while minimizing computational demands. Experimental validation is conducted using data from a 160 kW PEMFC system, incorporating purge voltage and high-frequency resistance (HFR) measurements. Based on the reduced-order model, three state observers—standard UKF, adaptive UKF (AUKF), and Sage-Husa-based AUKF (SH-AUKF), are designed and evaluated. Among them, the SH-AUKF provides the best trade-off between convergence speed and steady-state accuracy. It reconstructs internal states during the purge process from measurable signals and provides real-time feedback to the MPC controller. The MPC controller optimizes a dual-objective cost function that balances tracking accuracy and energy consumption, while enforcing constraints on purge flow magnitude and rate of change. With SH-AUKF state feedback, the MPC controller demonstrates excellent performance, maintaining a tracking error below 0.1, a response time under 12s, and an overshoot of 0.35 in a large-step test, compared to 0.57 with an augmented linear quadratic regulator (LQR). The controller's robustness is further validated under varying temperature and purge current conditions. Compared to fixed and intermittent flow strategies, the MPC-based approach significantly enhances purging efficiency and energy conservation, achieving the shortest purge duration of 11.53 s and the lowest energy consumption of 44.7 kJ. Relative to the constant excess oxygen ratio of 8 (OER = 8) strategy with similar energy use, the MPC-based method shortens purge duration by 11.56 s, indicating a 100 % improvement in time efficiency. Compared to the constant OER = 12 strategy, which achieves a similar purge duration, it lowers energy consumption by 5.5 %.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100461"},"PeriodicalIF":17.0000,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Etransportation","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590116825000682","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

Abstract

Effective regulation of membrane water content during shutdown is critical to ensuring the durability and performance recovery of proton exchange membrane fuel cells (PEMFCs). This study presents a model predictive control (MPC) strategy for purge-phase water removal, employing adaptive unscented Kalman filters (UKFs) for water content estimation. A reduced-order model is formulated to capture the essential purge dynamics while minimizing computational demands. Experimental validation is conducted using data from a 160 kW PEMFC system, incorporating purge voltage and high-frequency resistance (HFR) measurements. Based on the reduced-order model, three state observers—standard UKF, adaptive UKF (AUKF), and Sage-Husa-based AUKF (SH-AUKF), are designed and evaluated. Among them, the SH-AUKF provides the best trade-off between convergence speed and steady-state accuracy. It reconstructs internal states during the purge process from measurable signals and provides real-time feedback to the MPC controller. The MPC controller optimizes a dual-objective cost function that balances tracking accuracy and energy consumption, while enforcing constraints on purge flow magnitude and rate of change. With SH-AUKF state feedback, the MPC controller demonstrates excellent performance, maintaining a tracking error below 0.1, a response time under 12s, and an overshoot of 0.35 in a large-step test, compared to 0.57 with an augmented linear quadratic regulator (LQR). The controller's robustness is further validated under varying temperature and purge current conditions. Compared to fixed and intermittent flow strategies, the MPC-based approach significantly enhances purging efficiency and energy conservation, achieving the shortest purge duration of 11.53 s and the lowest energy consumption of 44.7 kJ. Relative to the constant excess oxygen ratio of 8 (OER = 8) strategy with similar energy use, the MPC-based method shortens purge duration by 11.56 s, indicating a 100 % improvement in time efficiency. Compared to the constant OER = 12 strategy, which achieves a similar purge duration, it lowers energy consumption by 5.5 %.

Abstract Image

通过基于sh - aukf状态反馈的MPC实时调节PEMFC关闭时的含水量:提高效率,降低能耗
关闭过程中膜含水量的有效调控对于保证质子交换膜燃料电池(pemfc)的耐久性和性能恢复至关重要。本研究提出了一种模型预测控制(MPC)策略,用于净化相水去除,采用自适应无气味卡尔曼滤波器(UKFs)进行含水量估计。制定了一个降阶模型来捕捉基本的吹扫动力学,同时最大限度地减少计算需求。实验验证使用来自160 kW PEMFC系统的数据,包括吹扫电压和高频电阻(HFR)测量。基于降阶模型,设计并评估了三种状态观测器——标准UKF、自适应UKF和基于sage - husa的AUKF。其中,SH-AUKF在收敛速度和稳态精度之间提供了最好的权衡。它从可测量的信号中重建吹扫过程中的内部状态,并向MPC控制器提供实时反馈。MPC控制器优化了双目标成本函数,平衡了跟踪精度和能耗,同时对吹扫流量大小和变化率进行了限制。使用SH-AUKF状态反馈,MPC控制器表现出优异的性能,在大步长测试中保持跟踪误差低于0.1,响应时间低于12s,超调值为0.35,而增广线性二次型调节器(LQR)的超调值为0.57。在不同温度和吹扫电流条件下,进一步验证了控制器的鲁棒性。与固定流动和间歇流动策略相比,基于mpc的方法显著提高了吹扫效率和节能,实现了最短的吹扫时间11.53 s和最低的能量消耗44.7 kJ。相对于相同能量使用的恒定过量氧比8 (OER = 8)策略,基于mpc的方法将吹扫持续时间缩短11.56 s,表明时间效率提高了100%。与恒定的OER = 12策略相比,它实现了类似的净化持续时间,它降低了5.5%的能量消耗。
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
Etransportation
Etransportation Engineering-Automotive Engineering
CiteScore
19.80
自引率
12.60%
发文量
57
审稿时长
39 days
期刊介绍: eTransportation is a scholarly journal that aims to advance knowledge in the field of electric transportation. It focuses on all modes of transportation that utilize electricity as their primary source of energy, including electric vehicles, trains, ships, and aircraft. The journal covers all stages of research, development, and testing of new technologies, systems, and devices related to electrical transportation. The journal welcomes the use of simulation and analysis tools at the system, transport, or device level. Its primary emphasis is on the study of the electrical and electronic aspects of transportation systems. However, it also considers research on mechanical parts or subsystems of vehicles if there is a clear interaction with electrical or electronic equipment. Please note that this journal excludes other aspects such as sociological, political, regulatory, or environmental factors from its scope.
×
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