A hybrid mutational Northern Goshawk and elite opposition learning artificial rabbits optimizer for PEMFC parameter estimation
Abstract For the purpose of simulating, controlling, evaluating, managing and optimizing PEMFCs it is necessary to develop accurate mathematical models. The present study develops a mathematical model which uses empirical or semi-empirical equations to estimate unknown model parameters through optim...
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2024-11-01
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| Online Access: | https://doi.org/10.1038/s41598-024-80073-2 |
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| author | Pradeep Jangir Absalom E. Ezugwu Kashif Saleem Arpita Sunilkumar P. Agrawal Sundaram B. Pandya Anil Parmar G. Gulothungan Laith Abualigah |
| author_facet | Pradeep Jangir Absalom E. Ezugwu Kashif Saleem Arpita Sunilkumar P. Agrawal Sundaram B. Pandya Anil Parmar G. Gulothungan Laith Abualigah |
| author_sort | Pradeep Jangir |
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| description | Abstract For the purpose of simulating, controlling, evaluating, managing and optimizing PEMFCs it is necessary to develop accurate mathematical models. The present study develops a mathematical model which uses empirical or semi-empirical equations to estimate unknown model parameters through optimization techniques. This thesis calculates, analyzes and discusses the sum of squares error (SSE) between measured and estimated current and voltage values using parameters derived from multiple optimization techniques for six commercially available PEMFCs: BCS 500 W-PEMFC, 500 W SR-12 PEMFC, Nedstack PS6 PEMFC, H-12 PEMFC, HORIZON 500 W PEMFC and a 250 W-stack PEMFC. To minimize the SSE between measured and estimated current values under these new models we employ an advanced version of Artificial Rabbits Optimization called Mutational Northern goshawk and Elite opposition learning-based Artificial Rabbits Optimizer (MNEARO). Additionally SSE, Absolute Error (AE), and Mean Bias Error (MBE) are computed for different recent methods according to literature on voltage measurement. Other optimization algorithms including ARO, TLBO, DE and SSA are used for comparative analysis purposes. On top of that MNEARO outperforms others in terms of both computational cost as well as solution quality while experiments carried out using benchmark problems indicate its superiority over other meta-heuristics approaches. |
| format | Article |
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| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-458a5ca78d254ad6864e3bcd01c67f392025-08-20T02:33:05ZengNature PortfolioScientific Reports2045-23222024-11-0114112310.1038/s41598-024-80073-2A hybrid mutational Northern Goshawk and elite opposition learning artificial rabbits optimizer for PEMFC parameter estimationPradeep Jangir0Absalom E. Ezugwu1Kashif Saleem2Arpita3Sunilkumar P. Agrawal4Sundaram B. Pandya5Anil Parmar6G. Gulothungan7Laith Abualigah8University Centre for Research and Development, Chandigarh UniversityUnit for Data Science and Computing, North-West UniversityDepartment of Computer Science & Engineering, College of Applied Studies & Community Service, King Saud UniversityDepartment of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical SciencesDepartment of Electrical Engineering, Government Engineering CollegeDepartment of Electrical Engineering, Shri K.J. PolytechnicDepartment of Electrical Engineering, Shri K.J. PolytechnicDepartment of Electronics and Communication Engineering, SRM Institute of Science and TechnologyComputer Science Department, Al al-Bayt UniversityAbstract For the purpose of simulating, controlling, evaluating, managing and optimizing PEMFCs it is necessary to develop accurate mathematical models. The present study develops a mathematical model which uses empirical or semi-empirical equations to estimate unknown model parameters through optimization techniques. This thesis calculates, analyzes and discusses the sum of squares error (SSE) between measured and estimated current and voltage values using parameters derived from multiple optimization techniques for six commercially available PEMFCs: BCS 500 W-PEMFC, 500 W SR-12 PEMFC, Nedstack PS6 PEMFC, H-12 PEMFC, HORIZON 500 W PEMFC and a 250 W-stack PEMFC. To minimize the SSE between measured and estimated current values under these new models we employ an advanced version of Artificial Rabbits Optimization called Mutational Northern goshawk and Elite opposition learning-based Artificial Rabbits Optimizer (MNEARO). Additionally SSE, Absolute Error (AE), and Mean Bias Error (MBE) are computed for different recent methods according to literature on voltage measurement. Other optimization algorithms including ARO, TLBO, DE and SSA are used for comparative analysis purposes. On top of that MNEARO outperforms others in terms of both computational cost as well as solution quality while experiments carried out using benchmark problems indicate its superiority over other meta-heuristics approaches.https://doi.org/10.1038/s41598-024-80073-2Proton Exchange membrane fuel cell parameter identificationAdaptive rabbits optimizationMutation strategyMNEAROOptimization in Electrical Engineering |
| spellingShingle | Pradeep Jangir Absalom E. Ezugwu Kashif Saleem Arpita Sunilkumar P. Agrawal Sundaram B. Pandya Anil Parmar G. Gulothungan Laith Abualigah A hybrid mutational Northern Goshawk and elite opposition learning artificial rabbits optimizer for PEMFC parameter estimation Scientific Reports Proton Exchange membrane fuel cell parameter identification Adaptive rabbits optimization Mutation strategy MNEARO Optimization in Electrical Engineering |
| title | A hybrid mutational Northern Goshawk and elite opposition learning artificial rabbits optimizer for PEMFC parameter estimation |
| title_full | A hybrid mutational Northern Goshawk and elite opposition learning artificial rabbits optimizer for PEMFC parameter estimation |
| title_fullStr | A hybrid mutational Northern Goshawk and elite opposition learning artificial rabbits optimizer for PEMFC parameter estimation |
| title_full_unstemmed | A hybrid mutational Northern Goshawk and elite opposition learning artificial rabbits optimizer for PEMFC parameter estimation |
| title_short | A hybrid mutational Northern Goshawk and elite opposition learning artificial rabbits optimizer for PEMFC parameter estimation |
| title_sort | hybrid mutational northern goshawk and elite opposition learning artificial rabbits optimizer for pemfc parameter estimation |
| topic | Proton Exchange membrane fuel cell parameter identification Adaptive rabbits optimization Mutation strategy MNEARO Optimization in Electrical Engineering |
| url | https://doi.org/10.1038/s41598-024-80073-2 |
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