A quasi affine transformation evolution algorithm with evolution matrix selection operation for parameter estimation of proton exchange membrane fuel cells
Abstract Electrochemical energy conversion technologies include proton exchange membrane fuel cells (PEMFCs) where proton interchange is an alternative to diesel distributed generation, and PEMFCs are considered as a promising backup power source and a tool to regulate power consumption. Some of the...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-83538-6 |
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| author | Mohammad Aljaidi Pradeep Jangir Sunilkumar P. Agrawal Sundaram B. Pandya Anil Parmar Samar Hussni Anbarkhan Laith Abualigah |
| author_facet | Mohammad Aljaidi Pradeep Jangir Sunilkumar P. Agrawal Sundaram B. Pandya Anil Parmar Samar Hussni Anbarkhan Laith Abualigah |
| author_sort | Mohammad Aljaidi |
| collection | DOAJ |
| description | Abstract Electrochemical energy conversion technologies include proton exchange membrane fuel cells (PEMFCs) where proton interchange is an alternative to diesel distributed generation, and PEMFCs are considered as a promising backup power source and a tool to regulate power consumption. Some of the major benefits of these PEMFCs especially in power system applications include low emission of carbon, fast load following capability, no noise and high start-up reliability. It is challenging to find the best PEMFC parameters because the model is complex and the problem is nonlinear; not all optimization algorithms can solve this problem. This paper presents a new approach that applies QUasi-Affine TRansformation Evolution algorithm with a new adaptation of Evolution Matrix and Selection operation (QUATRE-EMS) to determine optimal values of uncertain parameters in PEMFC stack references. The objective function of the optimization problem is defined as the sum of squared errors of the actual and predicted voltage data. The effectiveness of the proposed QUATRE-EMS algorithm is also checked through statistical analysis and the QUATRE-EMS variant is compared with other variants of DE optimization algorithms which are recently proposed in the state-of-the-art literature such as LSHADE, MadDE, CS-DE, LPalmDE, EDEV, jSO, SHADE, ISDE, and JADE. Results show that the QUATRE-EMS algorithm reduces SSE significantly, with an average SSE of 0.078492, which is 15% less than the best performing existing algorithms. QUATRE-EMS also achieved the lowest average values of absolute error, relative error and mean bias error among different PEMFC stack references, with accuracy improved by up to 20%. It was also computationally more efficient, cutting runtime in half compared to other methods. The results of these findings confirm the effectiveness and practicability of the QUATRE-EMS algorithm for improving the accuracy of BCS500W, NedStackPS6, SR12, H12, HORIZON, and Standard 250W PEMFC stack references. |
| format | Article |
| id | doaj-art-a1a47bb9886c46db8948618b9e1c6dfe |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-a1a47bb9886c46db8948618b9e1c6dfe2025-01-12T12:23:11ZengNature PortfolioScientific Reports2045-23222025-01-0115113710.1038/s41598-024-83538-6A quasi affine transformation evolution algorithm with evolution matrix selection operation for parameter estimation of proton exchange membrane fuel cellsMohammad Aljaidi0Pradeep Jangir1Sunilkumar P. Agrawal2Sundaram B. Pandya3Anil Parmar4Samar Hussni Anbarkhan5Laith Abualigah6Department of Computer Science, Faculty of Information Technology, Zarqa 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. PolytechnicInformation Systems Department, Northern Border UniversityComputer Science Department, Al Al-Bayt UniversityAbstract Electrochemical energy conversion technologies include proton exchange membrane fuel cells (PEMFCs) where proton interchange is an alternative to diesel distributed generation, and PEMFCs are considered as a promising backup power source and a tool to regulate power consumption. Some of the major benefits of these PEMFCs especially in power system applications include low emission of carbon, fast load following capability, no noise and high start-up reliability. It is challenging to find the best PEMFC parameters because the model is complex and the problem is nonlinear; not all optimization algorithms can solve this problem. This paper presents a new approach that applies QUasi-Affine TRansformation Evolution algorithm with a new adaptation of Evolution Matrix and Selection operation (QUATRE-EMS) to determine optimal values of uncertain parameters in PEMFC stack references. The objective function of the optimization problem is defined as the sum of squared errors of the actual and predicted voltage data. The effectiveness of the proposed QUATRE-EMS algorithm is also checked through statistical analysis and the QUATRE-EMS variant is compared with other variants of DE optimization algorithms which are recently proposed in the state-of-the-art literature such as LSHADE, MadDE, CS-DE, LPalmDE, EDEV, jSO, SHADE, ISDE, and JADE. Results show that the QUATRE-EMS algorithm reduces SSE significantly, with an average SSE of 0.078492, which is 15% less than the best performing existing algorithms. QUATRE-EMS also achieved the lowest average values of absolute error, relative error and mean bias error among different PEMFC stack references, with accuracy improved by up to 20%. It was also computationally more efficient, cutting runtime in half compared to other methods. The results of these findings confirm the effectiveness and practicability of the QUATRE-EMS algorithm for improving the accuracy of BCS500W, NedStackPS6, SR12, H12, HORIZON, and Standard 250W PEMFC stack references.https://doi.org/10.1038/s41598-024-83538-6Parameter estimationProton exchange membrane fuel cell (PEMFC)Differential evolutionQUATRE |
| spellingShingle | Mohammad Aljaidi Pradeep Jangir Sunilkumar P. Agrawal Sundaram B. Pandya Anil Parmar Samar Hussni Anbarkhan Laith Abualigah A quasi affine transformation evolution algorithm with evolution matrix selection operation for parameter estimation of proton exchange membrane fuel cells Scientific Reports Parameter estimation Proton exchange membrane fuel cell (PEMFC) Differential evolution QUATRE |
| title | A quasi affine transformation evolution algorithm with evolution matrix selection operation for parameter estimation of proton exchange membrane fuel cells |
| title_full | A quasi affine transformation evolution algorithm with evolution matrix selection operation for parameter estimation of proton exchange membrane fuel cells |
| title_fullStr | A quasi affine transformation evolution algorithm with evolution matrix selection operation for parameter estimation of proton exchange membrane fuel cells |
| title_full_unstemmed | A quasi affine transformation evolution algorithm with evolution matrix selection operation for parameter estimation of proton exchange membrane fuel cells |
| title_short | A quasi affine transformation evolution algorithm with evolution matrix selection operation for parameter estimation of proton exchange membrane fuel cells |
| title_sort | quasi affine transformation evolution algorithm with evolution matrix selection operation for parameter estimation of proton exchange membrane fuel cells |
| topic | Parameter estimation Proton exchange membrane fuel cell (PEMFC) Differential evolution QUATRE |
| url | https://doi.org/10.1038/s41598-024-83538-6 |
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