Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization
An optimal parameter estimation methodology of solid oxide fuel cell (SOFC) using modern optimization is proposed in this paper. An equilibrium optimizer (EO) has been used to identify the unidentified parameters of the SOFC equivalent circuit with the assistance of experimental results. This is pre...
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MDPI AG
2021-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/9/1066 |
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| author | Hesham Alhumade Ahmed Fathy Abdulrahim Al-Zahrani Muhyaddin Jamal Rawa Hegazy Rezk |
| author_facet | Hesham Alhumade Ahmed Fathy Abdulrahim Al-Zahrani Muhyaddin Jamal Rawa Hegazy Rezk |
| author_sort | Hesham Alhumade |
| collection | DOAJ |
| description | An optimal parameter estimation methodology of solid oxide fuel cell (SOFC) using modern optimization is proposed in this paper. An equilibrium optimizer (EO) has been used to identify the unidentified parameters of the SOFC equivalent circuit with the assistance of experimental results. This is presented via formulating the modeling process as an optimization problem considering the sum mean squared error (SMSE) between the observed and computed voltages as the target. Two modes of the SOFC-based model are investigated under variable operating conditions, namely, the steady-state and the dynamic-state based models. The proposed EO results are compared to those obtained via the Archimedes optimization algorithm (AOA), Heap-based optimizer (HBO), Seagull Optimization Algorithm (SOA), Student Psychology Based Optimization Algorithm (SPBO), Marine predator algorithm (MPA), Manta ray foraging optimization (MRFO), and comprehensive learning dynamic multi-swarm marine predators algorithm. The minimum fitness function at the steady-state model is obtained via the proposed EO with value of 1.5527 × 10<sup>−6</sup> at 1173 K. In the dynamic based model, the minimum SMSE is 1.0406. The obtained results confirmed the reliability and superiority of the proposed EO in constructing a reliable model of SOFC. |
| first_indexed | 2024-03-10T11:34:18Z |
| format | Article |
| id | doaj.art-0d484dca120d4feda7808510012b1f66 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T11:34:18Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj.art-0d484dca120d4feda7808510012b1f662023-11-21T18:57:20ZengMDPI AGMathematics2227-73902021-05-0199106610.3390/math9091066Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern OptimizationHesham Alhumade0Ahmed Fathy1Abdulrahim Al-Zahrani2Muhyaddin Jamal Rawa3Hegazy Rezk4Department of Chemical and Materials Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi ArabiaElectrical Engineering Department, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi ArabiaDepartment of Chemical and Materials Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi ArabiaCenter of Research Excellence in Renewable Energy and Power systems, King Abdulaziz University, Jeddah 21589, Saudi ArabiaCollege of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj 11911, Saudi ArabiaAn optimal parameter estimation methodology of solid oxide fuel cell (SOFC) using modern optimization is proposed in this paper. An equilibrium optimizer (EO) has been used to identify the unidentified parameters of the SOFC equivalent circuit with the assistance of experimental results. This is presented via formulating the modeling process as an optimization problem considering the sum mean squared error (SMSE) between the observed and computed voltages as the target. Two modes of the SOFC-based model are investigated under variable operating conditions, namely, the steady-state and the dynamic-state based models. The proposed EO results are compared to those obtained via the Archimedes optimization algorithm (AOA), Heap-based optimizer (HBO), Seagull Optimization Algorithm (SOA), Student Psychology Based Optimization Algorithm (SPBO), Marine predator algorithm (MPA), Manta ray foraging optimization (MRFO), and comprehensive learning dynamic multi-swarm marine predators algorithm. The minimum fitness function at the steady-state model is obtained via the proposed EO with value of 1.5527 × 10<sup>−6</sup> at 1173 K. In the dynamic based model, the minimum SMSE is 1.0406. The obtained results confirmed the reliability and superiority of the proposed EO in constructing a reliable model of SOFC.https://www.mdpi.com/2227-7390/9/9/1066solid oxide fuel cellparameter identificationoptimization |
| spellingShingle | Hesham Alhumade Ahmed Fathy Abdulrahim Al-Zahrani Muhyaddin Jamal Rawa Hegazy Rezk Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization Mathematics solid oxide fuel cell parameter identification optimization |
| title | Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization |
| title_full | Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization |
| title_fullStr | Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization |
| title_full_unstemmed | Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization |
| title_short | Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization |
| title_sort | optimal parameter estimation methodology of solid oxide fuel cell using modern optimization |
| topic | solid oxide fuel cell parameter identification optimization |
| url | https://www.mdpi.com/2227-7390/9/9/1066 |
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