The utilization of adaptive African vulture optimizer for optimal parameter identification of SOFC

Abstract

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.