Exergy analysis of a fuel cell power system and optimizing it with Fractional-order Coyote Optimization Algorithm

Abstract This study presents an exergy assessment methodology for a power production system defined by a high-temperature proton exchange membrane fuel cell. The evaluated structure has an organic Rankine cycle for recovering the lost heat. This study provides an optimum balanced model by optimizing the variables of the system. Here, a new improved metaheuristic, called Fractional-order Coyote Optimization Algorithm is proposed to the studied system to provide results with higher accuracy and precision. Three cost functions have been utilized for optimization: irreversibility, work, and exergy. Simulation results of the proposed method are then implemented in a case study and its results are validated by comparing with experimental data, the original COA, and the Genetic Algorithm (GA) from the literature. Final achievements indicate that the proposed algorithm gives the highest confirmation by the experimental data.