Parameter Estimation of PEM Fuel-Cell Using Brownian Butterfly Algorithm

The growing demand for electrical-energy (EE) has driven the development of varied power generation methods to meet global energy needs. The traditional Energy Generation Scheme (EGS) has greater impact on various environmental concerns and hence cleaner and sustainable energy sources are invented. Fuel-cell (FC) is one of the modern EGS, which converts the chemical-energy (CE) into EE with better efficiency. Further, the performance of the FC depends mainly on its parameters and hence it is necessary to identify the finest parameters. This research aims to develop a procedure to optimize the parameters of Nedstack-PS6; a commercially available Proton Exchange Membrane (PEM) FC (PEM-FC). The Nedstack-PS6 is a portable EE-source and its efficiency can be improved by assigning the finest possible value to its parameters. This research proposes a Brownian-Butterfly-Algorithm (BBA) based optimization process using a chosen cost-function for the chosen PEM-FC model. The BBA based search is implemented to solve a 7-dimensional optimization task using a bounded-search methodology and the achieved result with the BBA is compared against few chosen results by the optimization-algorithms. The experimental results confirms that the proposed scheme works well on the chosen PEM-FC model and help to identify the optimal values for all the 7-parametters.