Enhancing Solid Oxide Fuel Cell Efficiency Through Advanced Model Identification Using Differential Evolutionary Mutation Fennec Fox Algorithm
Abstract Fuel cells (FCs) are increasingly attracting attention for their efficient conversion of chemical energy into electricity without the need for combustion. Their high efficiency and versatility make them a promising technology across various applications. Researchers are actively exploring w...
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| Format: | Article |
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Springer
2025-02-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-025-00759-x |
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| author | Manish Kumar Singla Jyoti Gupta Ramesh Kumar Pradeep Jangir Mohamed Louzazni Nimay Chandra Giri Ahmed Jamal Abdullah Al-Gburi E. I.-Sayed M. EI-Kenawy Amal H. Alharbi |
| author_facet | Manish Kumar Singla Jyoti Gupta Ramesh Kumar Pradeep Jangir Mohamed Louzazni Nimay Chandra Giri Ahmed Jamal Abdullah Al-Gburi E. I.-Sayed M. EI-Kenawy Amal H. Alharbi |
| author_sort | Manish Kumar Singla |
| collection | DOAJ |
| description | Abstract Fuel cells (FCs) are increasingly attracting attention for their efficient conversion of chemical energy into electricity without the need for combustion. Their high efficiency and versatility make them a promising technology across various applications. Researchers are actively exploring ways to optimize FC systems to meet specific energy needs. Among the different types of fuel cells, solid oxide fuel cells (SOFCs) stand out as a promising clean energy technology that generates electricity through electrochemical reactions. However, accurately modeling SOFCs, which is essential for reducing design costs, presents a challenge due to their complex and nonlinear characteristics. An ideal model should be adaptable to varying operating pressures and temperatures. This research introduces a novel approach for optimal SOFC model identification using a differential evolutionary mutation Fennec fox algorithm (DEMFFA). A real-world case study demonstrates the superior effectiveness of DEMFFA compared to existing methods. Additionally, a sensitivity analysis evaluates the influence of temperature and pressure on the model, with results indicating that the proposed method achieves higher efficiency than other approaches. The sum of the square error of the proposed algorithm is 1.18E-11 followed by the parent algorithm, Fennec fox algorithm (FFA) (1.24E-09), and some of the compared algorithms. The computational time of the proposed algorithm is 1.001 s, followed by the parent algorithm FFA (1.199 s) and some of the compared algorithms. DEMFFA offers significant potential, enhancing renewable energy, minimizing SOFC's environmental impact, and improving real-world applications like distributed power generation and hydrogen integration. |
| format | Article |
| id | doaj-art-6bb54cbfb37b459cb5111a9b52d6f64b |
| institution | OA Journals |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-6bb54cbfb37b459cb5111a9b52d6f64b2025-08-20T02:01:39ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-02-0118113110.1007/s44196-025-00759-xEnhancing Solid Oxide Fuel Cell Efficiency Through Advanced Model Identification Using Differential Evolutionary Mutation Fennec Fox AlgorithmManish Kumar Singla0Jyoti Gupta1Ramesh Kumar2Pradeep Jangir3Mohamed Louzazni4Nimay Chandra Giri5Ahmed Jamal Abdullah Al-Gburi6E. I.-Sayed M. EI-Kenawy7Amal H. Alharbi8Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical SciencesSchool of Engineering and Technology, K. R. Mangalam UniversityDepartment of Interdisciplinary Courses in Engineering, Chitkara University Institute of Engineering and Technology, Chitkara UniversityUniversity Centre for Research and Development, Chandigarh UniversityScience Engineer Laboratory for Energy, National School of Applied Sciences, Chouaib Doukkali University of El JadidaDepartment of Electronics and Communication Engineering, Centurion University of Technology and ManagementCenter for Telecommunication Research and Innovation (CeTRI), Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer, Jalan Hang Tuah JayaSchool of ICT, Faculty of Engineering, Design and Information and Communication Technology (EDICT), Bahrain PolytechnicDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman UniversityAbstract Fuel cells (FCs) are increasingly attracting attention for their efficient conversion of chemical energy into electricity without the need for combustion. Their high efficiency and versatility make them a promising technology across various applications. Researchers are actively exploring ways to optimize FC systems to meet specific energy needs. Among the different types of fuel cells, solid oxide fuel cells (SOFCs) stand out as a promising clean energy technology that generates electricity through electrochemical reactions. However, accurately modeling SOFCs, which is essential for reducing design costs, presents a challenge due to their complex and nonlinear characteristics. An ideal model should be adaptable to varying operating pressures and temperatures. This research introduces a novel approach for optimal SOFC model identification using a differential evolutionary mutation Fennec fox algorithm (DEMFFA). A real-world case study demonstrates the superior effectiveness of DEMFFA compared to existing methods. Additionally, a sensitivity analysis evaluates the influence of temperature and pressure on the model, with results indicating that the proposed method achieves higher efficiency than other approaches. The sum of the square error of the proposed algorithm is 1.18E-11 followed by the parent algorithm, Fennec fox algorithm (FFA) (1.24E-09), and some of the compared algorithms. The computational time of the proposed algorithm is 1.001 s, followed by the parent algorithm FFA (1.199 s) and some of the compared algorithms. DEMFFA offers significant potential, enhancing renewable energy, minimizing SOFC's environmental impact, and improving real-world applications like distributed power generation and hydrogen integration.https://doi.org/10.1007/s44196-025-00759-xMathematical modelingOptimizationFuel cellsStatistical testsRenewable energy |
| spellingShingle | Manish Kumar Singla Jyoti Gupta Ramesh Kumar Pradeep Jangir Mohamed Louzazni Nimay Chandra Giri Ahmed Jamal Abdullah Al-Gburi E. I.-Sayed M. EI-Kenawy Amal H. Alharbi Enhancing Solid Oxide Fuel Cell Efficiency Through Advanced Model Identification Using Differential Evolutionary Mutation Fennec Fox Algorithm International Journal of Computational Intelligence Systems Mathematical modeling Optimization Fuel cells Statistical tests Renewable energy |
| title | Enhancing Solid Oxide Fuel Cell Efficiency Through Advanced Model Identification Using Differential Evolutionary Mutation Fennec Fox Algorithm |
| title_full | Enhancing Solid Oxide Fuel Cell Efficiency Through Advanced Model Identification Using Differential Evolutionary Mutation Fennec Fox Algorithm |
| title_fullStr | Enhancing Solid Oxide Fuel Cell Efficiency Through Advanced Model Identification Using Differential Evolutionary Mutation Fennec Fox Algorithm |
| title_full_unstemmed | Enhancing Solid Oxide Fuel Cell Efficiency Through Advanced Model Identification Using Differential Evolutionary Mutation Fennec Fox Algorithm |
| title_short | Enhancing Solid Oxide Fuel Cell Efficiency Through Advanced Model Identification Using Differential Evolutionary Mutation Fennec Fox Algorithm |
| title_sort | enhancing solid oxide fuel cell efficiency through advanced model identification using differential evolutionary mutation fennec fox algorithm |
| topic | Mathematical modeling Optimization Fuel cells Statistical tests Renewable energy |
| url | https://doi.org/10.1007/s44196-025-00759-x |
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