Revolutionizing proton exchange membrane fuel cell modeling through hybrid aquila optimizer and arithmetic algorithm optimization
Abstract Parameter identification in a Proton Exchange Membrane Fuel Cell (PEMFC) entails the application of optimization algorithms to ascertain the optimal unknown variables essential for crafting an accurate model that predicts fuel-cell performance. These parameters are typically not included in...
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Nature Portfolio
2025-02-01
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| Online Access: | https://doi.org/10.1038/s41598-025-89631-8 |
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| author | Manish Kumar Singla S. A. Muhammed Ali Ramesh Kumar Pradeep Jangir Mohammad Khishe G. Gulothungan Haitham A. Mahmoud |
| author_facet | Manish Kumar Singla S. A. Muhammed Ali Ramesh Kumar Pradeep Jangir Mohammad Khishe G. Gulothungan Haitham A. Mahmoud |
| author_sort | Manish Kumar Singla |
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| description | Abstract Parameter identification in a Proton Exchange Membrane Fuel Cell (PEMFC) entails the application of optimization algorithms to ascertain the optimal unknown variables essential for crafting an accurate model that predicts fuel-cell performance. These parameters are typically not included in the manufacturer’s datasheet and must be identified to ensure precise modeling and forecasting of fuel cell behavior. This paper introduces a recently developed hybrid algorithm (Aquila Optimizer Arithmetic Algorithm Optimization (AOAAO)) that enhances the AO and AAO algorithm’s efficiency through a novel mutation strategy, aimed at determining seven unknown parameters of a PEMFC during the optimization process. These parameters function as decision variables, and the objective function aimed for minimization is the sum square error (SSE) between the predicted and actual measured cell voltages. AOAAO demonstrated superior performance across various metrics, achieving an SSE minimum in comparison to other compared algorithm. AOAAO’s robustness was validated through extensive testing with six commercially available PEMFCs, including BCS 500 W-PEM, 500 W SR-12PEM, Nedstack PS6 PEM, H-12 PEM, HORIZON 500 W PEM, and a 250 W-stack, across twelve case studies derived from various operational conditions detailed in manufacturers’ datasheets. For each datasheet, both Current–Voltage (I/V) and Power–Voltage (P/V) characteristics of the PEMFCs scenarios closely aligned with those observed in experimental data, affirming AOAAO’s superior accuracy, robustness, and time efficiency for real-time fuel cell modeling. In terms of computational efficiency, AOAAO runtime is significantly faster than all compared algorithms, demonstrating an efficiency improvement of approximately 98%. |
| format | Article |
| id | doaj-art-407770668f1b4e348791b3f3ca34e2e6 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-407770668f1b4e348791b3f3ca34e2e62025-08-20T02:43:10ZengNature PortfolioScientific Reports2045-23222025-02-0115113810.1038/s41598-025-89631-8Revolutionizing proton exchange membrane fuel cell modeling through hybrid aquila optimizer and arithmetic algorithm optimizationManish Kumar Singla0S. A. Muhammed Ali1Ramesh Kumar2Pradeep Jangir3Mohammad Khishe4G. Gulothungan5Haitham A. Mahmoud6Fuel Cell Institute, Universiti Kebangsaan MalaysiaFuel Cell Institute, Universiti Kebangsaan MalaysiaDepartment of Interdisciplinary Courses in Engineering, Chitkara University Institute of Engineering & Technology, Chitkara UniversityUniversity Centre for Research and Development, Chandigarh UniversityDepartment of Electrical Engineering, Imam Khomeini Naval Science University of NowshahrDepartment of Electronics and Communication Engineering, SRM Institute of Science and TechnologyIndustrial Engineering Department, College of Engineering, King Saud UniversityAbstract Parameter identification in a Proton Exchange Membrane Fuel Cell (PEMFC) entails the application of optimization algorithms to ascertain the optimal unknown variables essential for crafting an accurate model that predicts fuel-cell performance. These parameters are typically not included in the manufacturer’s datasheet and must be identified to ensure precise modeling and forecasting of fuel cell behavior. This paper introduces a recently developed hybrid algorithm (Aquila Optimizer Arithmetic Algorithm Optimization (AOAAO)) that enhances the AO and AAO algorithm’s efficiency through a novel mutation strategy, aimed at determining seven unknown parameters of a PEMFC during the optimization process. These parameters function as decision variables, and the objective function aimed for minimization is the sum square error (SSE) between the predicted and actual measured cell voltages. AOAAO demonstrated superior performance across various metrics, achieving an SSE minimum in comparison to other compared algorithm. AOAAO’s robustness was validated through extensive testing with six commercially available PEMFCs, including BCS 500 W-PEM, 500 W SR-12PEM, Nedstack PS6 PEM, H-12 PEM, HORIZON 500 W PEM, and a 250 W-stack, across twelve case studies derived from various operational conditions detailed in manufacturers’ datasheets. For each datasheet, both Current–Voltage (I/V) and Power–Voltage (P/V) characteristics of the PEMFCs scenarios closely aligned with those observed in experimental data, affirming AOAAO’s superior accuracy, robustness, and time efficiency for real-time fuel cell modeling. In terms of computational efficiency, AOAAO runtime is significantly faster than all compared algorithms, demonstrating an efficiency improvement of approximately 98%.https://doi.org/10.1038/s41598-025-89631-8Parameter estimationProton exchange membrane fuel cellHybrid algorithmMetaheuristicMachine learning-inspired optimization |
| spellingShingle | Manish Kumar Singla S. A. Muhammed Ali Ramesh Kumar Pradeep Jangir Mohammad Khishe G. Gulothungan Haitham A. Mahmoud Revolutionizing proton exchange membrane fuel cell modeling through hybrid aquila optimizer and arithmetic algorithm optimization Scientific Reports Parameter estimation Proton exchange membrane fuel cell Hybrid algorithm Metaheuristic Machine learning-inspired optimization |
| title | Revolutionizing proton exchange membrane fuel cell modeling through hybrid aquila optimizer and arithmetic algorithm optimization |
| title_full | Revolutionizing proton exchange membrane fuel cell modeling through hybrid aquila optimizer and arithmetic algorithm optimization |
| title_fullStr | Revolutionizing proton exchange membrane fuel cell modeling through hybrid aquila optimizer and arithmetic algorithm optimization |
| title_full_unstemmed | Revolutionizing proton exchange membrane fuel cell modeling through hybrid aquila optimizer and arithmetic algorithm optimization |
| title_short | Revolutionizing proton exchange membrane fuel cell modeling through hybrid aquila optimizer and arithmetic algorithm optimization |
| title_sort | revolutionizing proton exchange membrane fuel cell modeling through hybrid aquila optimizer and arithmetic algorithm optimization |
| topic | Parameter estimation Proton exchange membrane fuel cell Hybrid algorithm Metaheuristic Machine learning-inspired optimization |
| url | https://doi.org/10.1038/s41598-025-89631-8 |
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