Model parameters estimation of a proton exchange membrane fuel cell using improved version of Archimedes optimization algorithm
Proton Exchange Membrane (PEM) fuel cell is one of the most popular fuel cells because of its higher efficiency among the other fuel cells. Because of the expensive materials in designing of this type of fuel cell, it should be first design and simulated in the best and optimum way to reduce the con...
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| Published in: | Energy reports Vol. 7; pp. 5700 - 5709 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.11.2021
Elsevier |
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| ISSN: | 2352-4847, 2352-4847 |
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| Abstract | Proton Exchange Membrane (PEM) fuel cell is one of the most popular fuel cells because of its higher efficiency among the other fuel cells. Because of the expensive materials in designing of this type of fuel cell, it should be first design and simulated in the best and optimum way to reduce the construction costs as much as possible. In the present study, a new model identification is proposed for optimal parameters identification of the PEM fuel cells. The major idea in this study is to provide a new optimal methodology to parameters estimation of the unknown variables in the PEM fuel cell model so that the absolute error (IAE) between the estimated data based on the proposed model and the real data has been minimized. The proposed method uses a new improved design of Archimedes Optimization Algorithm (IAOA) to this purpose. The designed model is then implemented on two practical case studies and the results are compared with some well-known methods. Final results shows that the proposed method with 0.10 and 0.14 error values for Nexa and NedStack PS6 models, respectively, provides the best solution among the other comparative methods. |
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| AbstractList | Proton Exchange Membrane (PEM) fuel cell is one of the most popular fuel cells because of its higher efficiency among the other fuel cells. Because of the expensive materials in designing of this type of fuel cell, it should be first design and simulated in the best and optimum way to reduce the construction costs as much as possible. In the present study, a new model identification is proposed for optimal parameters identification of the PEM fuel cells. The major idea in this study is to provide a new optimal methodology to parameters estimation of the unknown variables in the PEM fuel cell model so that the absolute error (IAE) between the estimated data based on the proposed model and the real data has been minimized. The proposed method uses a new improved design of Archimedes Optimization Algorithm (IAOA) to this purpose. The designed model is then implemented on two practical case studies and the results are compared with some well-known methods. Final results shows that the proposed method with 0.10 and 0.14 error values for Nexa and NedStack PS6 models, respectively, provides the best solution among the other comparative methods. |
| Author | Yao, Bin Hayati, Hosein |
| Author_xml | – sequence: 1 givenname: Bin surname: Yao fullname: Yao, Bin email: yaobinice@163.com organization: Department of Automotive Technology, Zhejiang Agricultural Business College, Shaoshing 312088, China – sequence: 2 givenname: Hosein surname: Hayati fullname: Hayati, Hosein organization: Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran |
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| CitedBy_id | crossref_primary_10_1016_j_energy_2023_130130 crossref_primary_10_1016_j_fuel_2022_127080 crossref_primary_10_1007_s11831_022_09876_8 crossref_primary_10_1109_ACCESS_2023_3236023 crossref_primary_10_1007_s00500_022_06886_3 crossref_primary_10_1016_j_energy_2024_130600 crossref_primary_10_3390_electronics11121808 crossref_primary_10_1007_s00202_024_02935_2 crossref_primary_10_1007_s12530_022_09425_5 crossref_primary_10_1007_s40996_024_01710_4 crossref_primary_10_32604_cmc_2022_025939 crossref_primary_10_1016_j_apenergy_2024_122767 crossref_primary_10_1093_ce_zkac010 crossref_primary_10_1016_j_tust_2023_105508 crossref_primary_10_3389_fenrg_2024_1384649 crossref_primary_10_3390_en14217115 |
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| Keywords | Modified Archimedes optimization algorithm Nexa PEMFC stack Parameter estimation Integral of the absolute Error Proton-exchange membrane fuel cells NedSstack PS6 PEMFC stack |
| Language | English |
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| Title | Model parameters estimation of a proton exchange membrane fuel cell using improved version of Archimedes optimization algorithm |
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