Precise PEM fuel cell parameter extraction based on a self-consistent model and SCCSA optimization algorithm

2021 ◽  
Vol 229 ◽  
pp. 113777
Author(s):  
Mehrzad Alizadeh ◽  
Farschad Torabi
Keyword(s):  
Fuel Cell ◽  
Optimization Algorithm ◽  
Cell Parameter ◽  
Pem Fuel Cell ◽  
Parameter Extraction ◽  
Consistent Model ◽  
Self Consistent

scholarly journals Comparative study of PEM fuel cell parameter extraction using Genetic Algorithm

2015 ◽  
Vol 6 (4) ◽  
pp. 1187-1194 ◽  
Author(s):  
N. Rajasekar ◽  
Basil Jacob ◽  
Karthik Balasubramanian ◽  
K. Priya ◽  
K. Sangeetha ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fuel Cell ◽  
Comparative Study ◽  
Cell Parameter ◽  
Pem Fuel Cell ◽  
Parameter Extraction

scholarly journals A Novel Opposition-Based Arithmetic Optimization Algorithm for Parameter Extraction of PEM Fuel Cell

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2834
Author(s):  
Abhishek Sharma ◽  
Rizwan Ahamad Khan ◽  
Abhinav Sharma ◽  
Diwakar Kashyap ◽  
Shailendra Rajput
Keyword(s):  
Fuel Cell ◽  
Optimization Algorithm ◽  
Proton Exchange Membrane ◽  
Model Identification ◽  
Pem Fuel Cell ◽  
Parameter Extraction ◽  
Proton Exchange ◽  
System Efficiency ◽  
The Cost ◽  
Exchange Membrane

The model-identification and parameter extraction are a well-defined method for modeling and development purposes of a proton exchange membrane fuel cell (PEMFC) to improve the performance. This paper introduces a novel opposition-based arithmetic optimization algorithm (OBAOA) for identifying the unspecified parameters of PEMFCs. The cost function is defined as the sum of the square deviations between the experimentally measured values and the optimal achieved values from the algorithm. Ballard Mark V PEM fuel cell is employed and analyzed to demonstrate the capability of the proposed algorithm. To demonstrate system efficiency, simulation results are compared to those of other optimizers under the same conditions. Furthermore, the proposed algorithm is validated through benchmark functions. The final results revealed that the proposed opposition-based arithmetic optimization algorithm can accurately retrieve the parameters of a PEMFC model.

Precise modeling of PEM fuel cell using improved chaotic MayFly optimization algorithm

2021 ◽  
Author(s):  
Mohamed A. M. Shaheen ◽  
Hany M. Hasanien ◽  
M. S. El Moursi ◽  
Attia A. El‐Fergany
Keyword(s):  
Fuel Cell ◽  
Optimization Algorithm ◽  
Pem Fuel Cell

Solar Cell Parameter Extraction by Using Harris Hawks Optimization Algorithm

2020 ◽  
pp. 349-379
Author(s):  
Ashutosh Sharma ◽  
Akash Saxena ◽  
Shalini Shekhawat ◽  
Rajesh Kumar ◽  
Akhilesh Mathur
Keyword(s):  
Solar Cell ◽  
Optimization Algorithm ◽  
Cell Parameter ◽  
Parameter Extraction

scholarly journals Optimal Estimation of Proton Exchange Membrane Fuel Cells Parameter Based on Coyote Optimization Algorithm

2021 ◽  
Vol 11 (5) ◽  
pp. 2052
Author(s):  
Amlak Abaza ◽  
Ragab A. El-Sehiemy ◽  
Karar Mahmoud ◽  
Matti Lehtonen ◽  
Mohamed M. F. Darwish
Keyword(s):  
Fuel Cells ◽  
Fuel Cell ◽  
Optimization Algorithm ◽  
Distribution Systems ◽  
Proton Exchange Membrane ◽  
Pem Fuel Cell ◽  
Pem Fuel Cells ◽  
Proton Exchange ◽  
Operating Conditions ◽  
Exchange Membrane

In recent years, the penetration of fuel cells in distribution systems is significantly increased worldwide. The fuel cell is considered an electrochemical energy conversion component. It has the ability to convert chemical to electrical energies as well as heat. The proton exchange membrane (PEM) fuel cell uses hydrogen and oxygen as fuel. It is a low-temperature type that uses a noble metal catalyst, such as platinum, at reaction sites. The optimal modeling of PEM fuel cells improves the cell performance in different applications of the smart microgrid. Extracting the optimal parameters of the model can be achieved using an efficient optimization technique. In this line, this paper proposes a novel swarm-based algorithm called coyote optimization algorithm (COA) for finding the optimal parameter of PEM fuel cell as well as PEM stack. The sum of square deviation between measured voltages and the optimal estimated voltages obtained from the COA algorithm is minimized. Two practical PEM fuel cells including 250 W stack and Ned Stack PS6 are modeled to validate the capability of the proposed algorithm under different operating conditions. The effectiveness of the proposed COA is demonstrated through the comparison with four optimizers considering the same conditions. The final estimated results and statistical analysis show a significant accuracy of the proposed method. These results emphasize the ability of COA to estimate the parameters of the PEM fuel cell model more precisely.

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