Modeling, optimization, and thermal management strategies of hydrogen fuel cell systems
The transition to sustainable energy has intensified the focus on Proton Exchange Membrane Fuel Cells (PEMFCs) due to their high efficiency, zero emissions, and flexibility for both mobile and stationary uses. However, widespread adoption is limited by technical challenges, including efficiency loss...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025019954 |
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| author | Abubakar Unguwanrimi Yakubu Liu Qingsheng Meng Kai Chen Jinwei Omer Abbaker Ahmed Mohammed Jiahao Zhao Qi Jiang Xuanhong Ye Junyi Liu Qinglong Yu Muhammad Aurangzeb Shusheng Xiong |
| author_facet | Abubakar Unguwanrimi Yakubu Liu Qingsheng Meng Kai Chen Jinwei Omer Abbaker Ahmed Mohammed Jiahao Zhao Qi Jiang Xuanhong Ye Junyi Liu Qinglong Yu Muhammad Aurangzeb Shusheng Xiong |
| author_sort | Abubakar Unguwanrimi Yakubu |
| collection | DOAJ |
| description | The transition to sustainable energy has intensified the focus on Proton Exchange Membrane Fuel Cells (PEMFCs) due to their high efficiency, zero emissions, and flexibility for both mobile and stationary uses. However, widespread adoption is limited by technical challenges, including efficiency losses from activation and mass transport overpotentials, thermal instability under transient loads, and high system and hydrogen production costs. Traditional static modeling approaches struggle to capture the real-time, coupled dynamics of PEMFCs in practical scenarios. This review synthesizes over one hundred studies published between 2015 and 2025, highlighting advancements in steady-state and dynamic modeling, AI-driven multi-objective optimization, and integrated thermal management. Optimization algorithms such as PSO, WOA, MIGA, and NSGA-II have shown promising results, including up to 15 % reduction in hydrogen consumption and 20 to 30 % improvement in thermal uniformity. The review also explores hybrid physical-AI models, CFD-based surrogate models, and predictive machine-learning methods like LSTM and CNN. Emphasis is placed on AI-enhanced energy management systems (EMS) capable of real-time control by integrating stress, degradation, and load conditions. Economic modeling for green hydrogen production is also included. The paper concludes by offering a unified framework and identifying current limitations, paving the way for scalable, intelligent PEMFC deployment in real-world energy systems. |
| format | Article |
| id | doaj-art-29f8d3ed8dae4e61a77e1a43984bbf0b |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-29f8d3ed8dae4e61a77e1a43984bbf0b2025-08-20T03:32:46ZengElsevierResults in Engineering2590-12302025-09-012710592410.1016/j.rineng.2025.105924Modeling, optimization, and thermal management strategies of hydrogen fuel cell systemsAbubakar Unguwanrimi Yakubu0Liu Qingsheng1Meng Kai2Chen Jinwei3Omer Abbaker Ahmed Mohammed4Jiahao Zhao5Qi Jiang6Xuanhong Ye7Junyi Liu8Qinglong Yu9Muhammad Aurangzeb10Shusheng Xiong11College of Energy Engineering, Zhejiang University, Hangzhou 310027, China; College of Agriculture and Environmental Science, Kaduna State University, Kaduna 800283, Nigeria; Longquan Industrial Innovation Research Institute, Longquan 323700, China; Provincial Key Laboratory of New Energy Vehicles Thermal Management, Longquan 323700, ChinaJiashan Power Supply Company, State Grid Zhejiang Electric Power Co., LTD, ChinaJiashan Power Supply Company, State Grid Zhejiang Electric Power Co., LTD, ChinaJiashan Power Supply Company, State Grid Zhejiang Electric Power Co., LTD, ChinaJiashan Power Supply Company, State Grid Zhejiang Electric Power Co., LTD, China; Longquan Industrial Innovation Research Institute, Longquan 323700, China; Department of Electrical and Electronics Engineering, Faculty of Engineering Sciences, University of Nyala, Nyala, SudanCollege of Energy Engineering, Zhejiang University, Hangzhou 310027, China; Longquan Industrial Innovation Research Institute, Longquan 323700, ChinaCollege of Energy Engineering, Zhejiang University, Hangzhou 310027, China; Longquan Industrial Innovation Research Institute, Longquan 323700, ChinaCollege of Energy Engineering, Zhejiang University, Hangzhou 310027, China; Longquan Industrial Innovation Research Institute, Longquan 323700, ChinaCollege of Energy Engineering, Zhejiang University, Hangzhou 310027, China; Longquan Industrial Innovation Research Institute, Longquan 323700, ChinaCollege of Energy Engineering, Zhejiang University, Hangzhou 310027, China; Longquan Industrial Innovation Research Institute, Longquan 323700, ChinaLongquan Industrial Innovation Research Institute, Longquan 323700, China; Provincial Key Laboratory of New Energy Vehicles Thermal Management, Longquan 323700, ChinaCollege of Energy Engineering, Zhejiang University, Hangzhou 310027, China; Longquan Industrial Innovation Research Institute, Longquan 323700, China; Provincial Key Laboratory of New Energy Vehicles Thermal Management, Longquan 323700, China; Corresponding author at: College of Energy Engineering, Zhejiang University, Hangzhou 310027, China.The transition to sustainable energy has intensified the focus on Proton Exchange Membrane Fuel Cells (PEMFCs) due to their high efficiency, zero emissions, and flexibility for both mobile and stationary uses. However, widespread adoption is limited by technical challenges, including efficiency losses from activation and mass transport overpotentials, thermal instability under transient loads, and high system and hydrogen production costs. Traditional static modeling approaches struggle to capture the real-time, coupled dynamics of PEMFCs in practical scenarios. This review synthesizes over one hundred studies published between 2015 and 2025, highlighting advancements in steady-state and dynamic modeling, AI-driven multi-objective optimization, and integrated thermal management. Optimization algorithms such as PSO, WOA, MIGA, and NSGA-II have shown promising results, including up to 15 % reduction in hydrogen consumption and 20 to 30 % improvement in thermal uniformity. The review also explores hybrid physical-AI models, CFD-based surrogate models, and predictive machine-learning methods like LSTM and CNN. Emphasis is placed on AI-enhanced energy management systems (EMS) capable of real-time control by integrating stress, degradation, and load conditions. Economic modeling for green hydrogen production is also included. The paper concludes by offering a unified framework and identifying current limitations, paving the way for scalable, intelligent PEMFC deployment in real-world energy systems.http://www.sciencedirect.com/science/article/pii/S2590123025019954Hydrogen fuel cellsMulti-objective optimizationThermal managementEnergy management systemsAI and ML in fuel cells |
| spellingShingle | Abubakar Unguwanrimi Yakubu Liu Qingsheng Meng Kai Chen Jinwei Omer Abbaker Ahmed Mohammed Jiahao Zhao Qi Jiang Xuanhong Ye Junyi Liu Qinglong Yu Muhammad Aurangzeb Shusheng Xiong Modeling, optimization, and thermal management strategies of hydrogen fuel cell systems Results in Engineering Hydrogen fuel cells Multi-objective optimization Thermal management Energy management systems AI and ML in fuel cells |
| title | Modeling, optimization, and thermal management strategies of hydrogen fuel cell systems |
| title_full | Modeling, optimization, and thermal management strategies of hydrogen fuel cell systems |
| title_fullStr | Modeling, optimization, and thermal management strategies of hydrogen fuel cell systems |
| title_full_unstemmed | Modeling, optimization, and thermal management strategies of hydrogen fuel cell systems |
| title_short | Modeling, optimization, and thermal management strategies of hydrogen fuel cell systems |
| title_sort | modeling optimization and thermal management strategies of hydrogen fuel cell systems |
| topic | Hydrogen fuel cells Multi-objective optimization Thermal management Energy management systems AI and ML in fuel cells |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025019954 |
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