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    • Revista IJIMAI
    • 2020
    • vol. 6, nº 2, june 2020
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    • Revista IJIMAI
    • 2020
    • vol. 6, nº 2, june 2020
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    Tree Growth Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell Models

    Autor: 
    Kamel, Salah
    ;
    Jurado, Francisco
    ;
    Sultan, Hamdy
    ;
    Menesy, Ahmed
    Fecha: 
    06/2020
    Palabra clave: 
    parameter estimation; proton exchange membrane fuel cell; tree growth algorithm; total of the squared deviations (TSD); IJIMAI
    Revista / editorial: 
    International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/12731
    DOI: 
    https://doi.org/10.9781/ijimai.2020.03.003
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/2759
    Open Access
    Resumen:
    Demonstrating an accurate mathematical model is a mandatory issue for realistic simulation, optimization and performance evaluation of proton exchange membrane fuel cells (PEMFCs). The main goal of this study is to demonstrate a precise mathematical model of PEMFCs through estimating the optimal values of the unknown parameters of these cells. In this paper, an efficient optimization technique, namely, Tree Growth Algorithm (TGA) is applied for extracting the optimal parameters of different PEMFC stacks. The total of the squared deviations (TSD) between the experimentally measured data and the estimated ones is adopted as the objective function. The effectiveness of the developed parameter identification algorithm is validated through four case studies of commercial PEMFC stacks under various operating conditions. Moreover, comprehensive comparisons with other optimization algorithms under the same study cases are demonstrated. Statistical analysis is presented to evaluate the accuracy and reliability of the developed algorithm in solving the studied optimization problem.
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