Optimal Model Parameter Identification of Solid Oxide Fuel Cell Using Honey Badger Algorithm

This article presents a model parameter identification approach for solid oxide fuel cell (SOFC) based on honey badger algorithm. Mathematical model of SOFC is observed as a complex and multivariate optimization problem. Metaheuristic optimization algorithms are seen as competent to solve multivariate and complex problems effectively and efficiently. To identify unknown model parameters accurately, an objective function based on error minimization is used in this article. The error function is described as the sum of squared error (SSE) between the evaluated and experimental voltage. It is proven that honey badger algorithm calculates the model parameter effectively with the value of SSE as 1.51E-05, 1.52E-05 and 1.04E-05 at three different temperatures, viz. 800°C, 900°C and 940°C. Also, the efficiency of the honey badger algorithm for parameter identification approach is compared with other competitive algorithms such as bald eagle search (BES), flower pollination (FPA), particle swarm optimization (PSO), and grey wolf optimization (GWO). An adequate closeness between the estimated and experimental values confirms the algorithm's accuracy. Furthermore, statistical study, including statistical indices like mean, standard deviation, minimum and maximum values, proves its superiority over other competitive algorithms. Also, a box plot study has been done to check the algorithm's robustness. Small interquartile range and median value proves that the honey badger algorithm outperforms other algorithms used and thus estimates the model parameters effectively.