Research Area: Metaheuristic Computing
In the present research, a new optimal approach is suggested for optimal parameters estimation of the mathematical model in the Solid Oxide Fuel Cell (SOFC) stacks. The main purpose is to propose a method for minimization of the Sum of Squared Error (SSE) of the polarization profile of the estimated model with the extracted data from the experimental simulations. To realize this aim, a new metaheuristic technique, namely the Adaptive African vulture optimization (AAVO) algorithm is offered. The method is then verified with a comparison of its results with various approaches introduced in the literature. Finally, to confirm the approach’s effectiveness, this is investigated by sensitivity analysis by various pressure and temperature changes, and its achievements are put in comparison to different approaches introduced in the literature to show the superiority of the proposed method. The final achievement designates that the suggested approach with a minimum value of 7.85e−4 with 6.14e−4 standard deviation SSE for the temperature variations and 1.28e−3 with 2.15e−5 standard deviation SSE for the pressure variations has the best results for optimal estimation of the unknown parameters in the SOFC stacks.
Keywords:
Solid Oxide Fuel Cell
Sum of squared error
Parameter identification
Polarization profile
Adaptive African vulture optimizer
Author(s) Name: Yanmei Wang, Siqing Li, Hongwei Sun, Changyong Huang, Naser Youssefi
Journal name: Energy Reports
Conferrence name:
Publisher name: ELSEVIER
DOI: https://doi.org/10.1016/j.egyr.2021.11.257
Volume Information: Volume 8, November 2022
