A robust hybrid whale optimization algorithm with single candidate optimizer for efficient parameter identification of hydrogen based PEM fuel cells

The need for hydrogen proton electrolyte membrane fuel cells (PEMFCs) as a tool to replace fossil fuel energy generation systems has increased significantly in recent years due to their ability to provide clean electricity efficiently. Many researchers have dedicated considerable effort to elucidating the operation of these generators using advanced and precise modeling and simulation tools. This article proposes a new hybrid optimization method based on the Whale Optimization Algorithm (WOA) and Single Candidate Optimizer (SCO) to find the authentic model parameters of the PEMFCs. The proposed WOA-SCO technique is developed to combine the WOA excellent search abilities on the promising areas with the efficient population diversity over the space of the SCO. The performance of the WOA-SCO is first tested using 10 commonly used benchmark functions. Besides, a comparison is undertaken between some state-of-the-art methods, such as WOA, SCO, Salp Swarm Optimizer (SSO), Elk Herd Optimizer (EHO), Cuckoo Search with Explosion Operator (CS-EO), hybrid Jaya-NM, and the Fractional-Order WOA algorithm. Thereafter, the efficacy of the technique in estimating the optimal parameters is investigated using Heliocentris- 50 W and Nexa 1200W PEMFCs. Eventually, the proposed WOA-SCO approach showed a competitive performance in solving complex functions, where the method was able to find the exact solution in some studied cases. Additionally, the method proved superiority in defining accurately PEMFC model parameters, offering further accuracy, considerable convergence rate, and stable performance.