The application of hybrid cuckoo search-grey wolf optimization algorithm in optimal parameters identification of solid oxide fuel cell

Abstract

In the present study, dependable and accurate variables of solid oxide fuel cell (SOFC) are identified using an accurate and remarkable convergence pace optimization algorithm, so-called cuckoo search grey wolf optimization (CSGWO). The mentioned hybrid algorithm is acquired from the integration of cuckoo search (CS) and grey wolf optimization (GWO) algorithms. To confirm the CSGWO performance, it is compared with well-known optimization algorithms. The proposed CSGWO hybrid algorithm is applied for a 5-kW physically-based dynamic tubular stack. Along with this, the modeling is performed for different temperatures and pressures. The results of the CSGWO integrated algorithm reveal the supremacy of the considered method compared to other algorithms with the lowest values of MSE and corroborate precision, robustness, and excellent convergence pace compared to different optimization algorithms. The statistical results revealed that the developed CSGWO algorithm has the lowest MSE values by 1.3%, 0.1%, 0.3%, 0.1%, and 0% for the operational pressures of 1, 2, 3, 4, and 5atm, respectively. All in all, the experimental records confirm that the CSGWO integrated algorithm could be introduced as a favorable substitute to the SOFC models’ parameter estimation.