An optimal parameters estimation for the proton exchange membrane fuel cells based on amended deer hunting optimization algorithm

Abstract

The present research proposes a new optimal dynamic modeling of proton exchange membrane fuel cells (PEMFCs). The main idea is on the optimal selection of the uncertain parameters in the PEMFC dynamic model. To do so, the main objective is considered to minimize the sum of the square error between the model voltage and the experimental voltage data. The minimization technique is based on using an amended version of the Deer Hunting Optimization Algorithm (ADHO) algorithm. For authentication of the presented method, two different case studies including 250 W PEMFC and Nexa PEMFC stacks have been utilized and the results have been compared with the experimental data and some other state-of--of-the--the-art methods, including (N + λ) - ES algorithm, Improved Grass Fibrous Root Optimization (IGFRO) Algorithm, and Adaptive Sparrow Search Algorithm (ASSA) to specify the advantage of the suggested method toward the other comparative methods. Simulation results indicate a promising confirmation between the suggested ADHO algorithm and the empirical data.