Optimal parameter estimation of proton exchange membrane fuel cell using improved red fox optimizer for sustainable energy management

The normal electric grid loss becomes irregular as a result of climatic variations, demanding an effective technique for power. Fuel cell (FC) technologies have been developed to alleviate the shortcomings of conventional backup power alternatives. The automobiles that function with FC technologies also entered into the smartphone application. The FCs may be classified into numerous categories with respect to the type of electrodes employed. Among these, the proton exchange membrane fuel cell (PEMFC) is the most extensively deployed kind. Because of its high-power density at low temperatures and rapid responsiveness to electrodynamic processes, the PEMFC has piqued the interest of many research groups. Optimal modelling of PEMFC can increase the overall efficiency of the cell in diverse applications of smart microgrids. Since the extraction of optimum parameter values in the PEMFC is an optimization issue, it may be tackled by the construction of metaheuristic algorithms. For this purpose, this work provides an optimum parameter estimate of proton exchange membrane fuel cells utilizing the enhanced red fox optimizer (OPEMFC-IRFO) method for sustainable energy management. The fundamental objective of the OPEMFC-IRFO method is to estimate the optimal parameter values of the PEMFC systems. The OPEMFC-IRFO algorithm is essentially based on the notions of the stimulating behaviour of red foxes, and the performance of the OPEMFC-IRFO algorithm may be improved by the use of Levy flight. Besides, the OPEMFC-IRFO method develops an objective function for the reduction of the sum of square variation between measured and optimal estimated voltages. In addition, the unknown parameters involved in the PEMFC may be ideally calculated by the application of the OPEMFC-IRFO method. The performance validation of the OPEMFC-IRFO algorithm indicates the positive outcomes over its previous state-of-art methodologies.