Tree Growth Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell Models

Author
Keywords
Abstract
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.
Year of Publication
2020
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
6
Start Page
101
Issue
Regular Issue
Number
2
Number of Pages
11
Date Published
06/2020
ISSN Number
1989-1660
Citation Key
URL
https://www.ijimai.org/journal/sites/default/files/2020-05/ijimai_6_2_11.pdf
DOI
10.9781/ijimai.2020.03.003
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