Energy Reports (Nov 2021)
The application of metaheuristics in optimal parameter identification of solid oxide fuel cell
Abstract
This study suggests a new optimized model for optimum identification of the unknown variables in a Solid Oxide (SO) fuel cell. The method is conditioned by designing an improved version of metaheuristics to improve the effectiveness of the original method and using it by the Sum of Squared Error (SSE) minimization between the model and the experimental data including output voltage. The metaheuristic is based on a new improved model of a new metaheuristic, called the Red Fox Optimization Algorithm (IRFO). To assess the method’s accuracy and its consistency, it is validated by various pressure and temperature functional conditions. The results showed that the error value of the produced power by different temperatures is lower than 0.0073 kW and the error value of the output voltage by different temperatures is lower than 0.16 V which is a promising result for stack identification. Also, the achievements of the algorithm are compared with several approaches from the literature. The final results show a satisfying verification between the empirical and the optimized technique
