Optimal parameter identification of SOFC model using modified gray wolf optimization algorithm

Industrial and commercial use of fuel cells as a source of clean energy production are two important goals of researchers in the field of energy today. Therefore, researchers in this science are always looking for new methods for industrial production and at a reasonable price for fuel cells. This s...

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Vydáno v:Energy (Oxford) Ročník 240; s. 122800
Hlavní autoři: Wang, Jian, Xu, Yi-Peng, She, Chen, Xu, Ping, Bagal, Hamid Asadi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 01.02.2022
Elsevier BV
Témata:
Algorithms
Clean energy
Efficiency
electric potential difference
Fuel cells
Fuel technology
Gray wolf optimization algorithm
Industrial production
Optimization
Parameter estimation
Parameter identification
Parameter modification
prices
Production methods
Solid oxide fuel cell
Solid oxide fuel cells
System identification
System reliability
temperature
Parameter estimation
Gray wolf optimization algorithm
System identification
Solid oxide fuel cell
Optimization
ISSN:0360-5442, 1873-6785
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Shrnutí:Industrial and commercial use of fuel cells as a source of clean energy production are two important goals of researchers in the field of energy today. Therefore, researchers in this science are always looking for new methods for industrial production and at a reasonable price for fuel cells. This study presents a new well-organized methodology for model identification of a Solid Oxide Fuel Cell (SOFC) stack by providing optimal selection of the unknown variables in the model. The main objective here is to minimize the sum of squared error value between the designed model output voltage and the experimental data. Here, to provide an optimization process, a modified version of gray wolf optimization (MGWO) algorithm has been utilized. This algorithm is then utilized to improve the algorithm efficiency and to get better results. To show the system reliability, two scenarios based on temperature and pressure variations have been utilized. The technique has been finally compared with several other techniques to verify its prominence. With considering the achieved results, it can be observed that the sum of square error for different temperature values based on the proposed method is too small, such that for the temperatures with values 553.4 °C, 652.3 °C, 669.8 °C, 754.6 °C, and 800 °C the SSE value is 5.27 e−4, 2.66 e−4, 3.91 e−6, 4.19 e−3, 2.07 e−4, respectively. Furthermore, the pressure value variations from 1 atm to 5 atm with 1.44 e−3, 3.20 e−3, 5.84 e−3, 3.36 e−3, 2.67 e−3, respectively indicate its higher efficiency toward the other studied methods. Final results designate that the proposed technique delivers outstanding efficiency toward the compared methods. •New well-organized methodology for model identification of a Solid Oxide Fuel Cell (SOFC) stack.•Minimizing error between the designed model output voltage and the experimental data.•A modified version of gray wolf optimization (MGWO) is used for identification.•The technique has been finally compared with several other techniques to verify its prominence.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2021.122800