Seven-parameter PEMFC model optimization using an battlefield optimization algorithm
Precise modeling of Proton Exchange Membrane Fuel Cells (PEMFCs) requires accurate identification of key parameters, which are often unavailable from manufacturers but crucial for predicting fuel cell performance. The system relies on seven key parameters to determine activation and ohmic and concen...
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| Format: | Article |
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Elsevier
2025-10-01
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| Series: | Electrochemistry Communications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1388248125001730 |
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| author | Manish Kumar Singla S.A. Muhammed Ali Jyoti Gupta Pradeep Jangir Arpita Ramesh Kumar Reena Jangid Mohammad Khishe |
| author_facet | Manish Kumar Singla S.A. Muhammed Ali Jyoti Gupta Pradeep Jangir Arpita Ramesh Kumar Reena Jangid Mohammad Khishe |
| author_sort | Manish Kumar Singla |
| collection | DOAJ |
| description | Precise modeling of Proton Exchange Membrane Fuel Cells (PEMFCs) requires accurate identification of key parameters, which are often unavailable from manufacturers but crucial for predicting fuel cell performance. The system relies on seven key parameters to determine activation and ohmic and concentration overpotential values through ξ1, ξ2, ξ3, ξ4, λ, Rc, and β. The Battlefield Optimization Algorithm (BfOA) represents a new optimization method that finds these seven essential PEMFC parameters effectively. Using Sum Squared Error (SSE) to minimize the difference between estimated and actual cell voltages, BfOA outperformed other optimization algorithms in determining parameters for six PEMFC models under varying operating conditions. The optimized parameters enabled accurate prediction of I-V and PV curves, closely matching experimental data. BfOA's efficiency and robustness make it well-.suited for real-time fuel cell modeling. Its effectiveness as a method for precise PEMFC device analysis within electronic component simulators is demonstrated. Future development will explore BfOA's compatibility with other fuel cell technologies, incorporate real-time data capabilities, and implement the algorithm in embedded systems for real-time PEMFC monitoring and control. |
| format | Article |
| id | doaj-art-d33b9e2dd833489dbd8fd5abfc4c83dd |
| institution | Kabale University |
| issn | 1388-2481 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Electrochemistry Communications |
| spelling | doaj-art-d33b9e2dd833489dbd8fd5abfc4c83dd2025-08-23T04:47:43ZengElsevierElectrochemistry Communications1388-24812025-10-0117910803310.1016/j.elecom.2025.108033Seven-parameter PEMFC model optimization using an battlefield optimization algorithmManish Kumar Singla0S.A. Muhammed Ali1Jyoti Gupta2Pradeep Jangir3 Arpita4Ramesh Kumar5Reena Jangid6Mohammad Khishe7Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602 105, India; Jadara University Research Center, Jadara University, PO Box 733, Irbid, Jordan; Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602 105, IndiaFuel Cell Institute, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, MalaysiaSchool of Engineering and Technology, K. R. Mangalam University, Haryana, Gurgaon 122003, IndiaDepartment of Electronics and Communication Engineering, Chandigarh University, Mohali 140413, India; Innovation Center for Artificial Intelligence Applications, Yuan Ze University, Taoyuan 320315, TaiwanDepartment of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602 105, IndiaDepartment of Interdisciplinary Courses in Engineering, Chitkara University Institute of Engineering & Technology, Chitkara University, Punjab, IndiaDepartment of CSE, Graphic Era Hill University, Dehradun 248002, Uttarakhand, India; Department of CSE, Graphic Era Deemed To Be University, Dehradun 248002, Uttarakhand, IndiaApplied Science Research Centre, Applied Science Private University, 11937, Amman, Jordan; Department of Electrical Engineering, Imam Khomeini Naval Science University of Nowshahr, Nowshahr, Iran; Corresponding author at: Department of Electrical Engineering, Imam Khomeini Naval Science University of Nowshahr, Nowshahr, Iran.Precise modeling of Proton Exchange Membrane Fuel Cells (PEMFCs) requires accurate identification of key parameters, which are often unavailable from manufacturers but crucial for predicting fuel cell performance. The system relies on seven key parameters to determine activation and ohmic and concentration overpotential values through ξ1, ξ2, ξ3, ξ4, λ, Rc, and β. The Battlefield Optimization Algorithm (BfOA) represents a new optimization method that finds these seven essential PEMFC parameters effectively. Using Sum Squared Error (SSE) to minimize the difference between estimated and actual cell voltages, BfOA outperformed other optimization algorithms in determining parameters for six PEMFC models under varying operating conditions. The optimized parameters enabled accurate prediction of I-V and PV curves, closely matching experimental data. BfOA's efficiency and robustness make it well-.suited for real-time fuel cell modeling. Its effectiveness as a method for precise PEMFC device analysis within electronic component simulators is demonstrated. Future development will explore BfOA's compatibility with other fuel cell technologies, incorporate real-time data capabilities, and implement the algorithm in embedded systems for real-time PEMFC monitoring and control.http://www.sciencedirect.com/science/article/pii/S1388248125001730Machine learningOptimizationFuel cellParameter estimationRun timeFriedman ranking test |
| spellingShingle | Manish Kumar Singla S.A. Muhammed Ali Jyoti Gupta Pradeep Jangir Arpita Ramesh Kumar Reena Jangid Mohammad Khishe Seven-parameter PEMFC model optimization using an battlefield optimization algorithm Electrochemistry Communications Machine learning Optimization Fuel cell Parameter estimation Run time Friedman ranking test |
| title | Seven-parameter PEMFC model optimization using an battlefield optimization algorithm |
| title_full | Seven-parameter PEMFC model optimization using an battlefield optimization algorithm |
| title_fullStr | Seven-parameter PEMFC model optimization using an battlefield optimization algorithm |
| title_full_unstemmed | Seven-parameter PEMFC model optimization using an battlefield optimization algorithm |
| title_short | Seven-parameter PEMFC model optimization using an battlefield optimization algorithm |
| title_sort | seven parameter pemfc model optimization using an battlefield optimization algorithm |
| topic | Machine learning Optimization Fuel cell Parameter estimation Run time Friedman ranking test |
| url | http://www.sciencedirect.com/science/article/pii/S1388248125001730 |
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