Optimal parameter estimation strategy of PEM fuel cell using gradient-based optimizer

Abstract

The exact parameter estimation of the fuel cell model is considered a critical stage in delivering a consistent emulation for the fuel cell system characteristics. The aim of this is to suggeste a robust methodology based on the Gradient-based Optimizer (GBO) to identify the best parameters of PEM fuel cell (PEMFC). Three distinct types of PEM fuel cells: 250 W FC stack, BCS 500 W, and SR-12 500 W, were used to demonstrate the superiority of the GBO. To confirm the superiority of GBO, the results were compared with those obtained using different optimizers such as salp swarm algorithm (SSA), heap-based optimizer (HBO), differential evolution (DE), whale optimization algorithm (WOA), moth-flame optimization algorithm (MFO), sine cosine algorithm (SCA), and Harris's hawk optimizer (HHO). During the optimization process, the unknown parameters of PEM fuel cells are used as decision variables, whereas the objective function needs to be minimum is represented by the sum square error between the measured data and estimated data. In addition, the obtained results by GBO are compared with other methods achieved in the literature. The superiority of GBO in determining the optimal parameters of different PEM fuel cells is proved.