Self-adaptive henry gas solubility optimizer for identification of solid oxide fuel cell

Fuel cell is the best suggestion to replace internal combustion engines. Fuel cell systems have no pollution and no moving parts. The efficiency of fuel cells is more than three times that of internal combustion engines. Modeling the behavior of solid oxide fuel cells has special complications, and determining its performance according to its structural characteristics is one of the required parameters to further understand the behavior of solid oxide fuel cells. In this study, a new methodology is presented for optimal parameters estimation of the solid oxide fuel cell (SOFC) model. This paper's major goal was to provide a novel, efficient method for estimating the SOFC model's unknown parameters. To achieve this, the sum of squared errors between the output voltage of the proposed model and the experimental voltage measurements should be as little as possible. To reduce the error value, this study developed a better metaheuristic algorithm dubbed the Self-adaptive Henry Gas Solubility Optimizer. The developed method was then used with a 96-cell SOFC stack, and the sensitivity analysis was carried out while using various optimization algorithms at various temperatures and pressures. When 150 data points from a temperature sensitivity analysis at five temperatures, including 625 & DEG;C, 675 & DEG;C, 725 & DEG;C, and 775 & DEG;C under constant pressure, values of 3 atm, were taken into consideration, the smallest error was 9.41 e-5 for 575 & DEG;C. For pressure variations between 1 and 5 atm at constant temperatures of 775 & DEG;C, the lowest inaccuracy was 8.21 e-3 for 1 atm. Simulation results show that the proposed approach is more effective than the other techniques as an identifying tool.