Improving Parameter Estimation of Fuel Cell Using Honey Badger Optimization Algorithm

Abstract

In this study, we proposed an alternative method to determine the parameter of the proton exchange membrane fuel cell (PEMFC) since there are multiple variable quantities with diverse nonlinear characteristics included in the PEMFC design, which is specified correctly to ensure effective modeling. The distinctive model of FCs is critical in determining the effectiveness of the cells’ inquiry. The design of FC has a significant influence on the simulation research of such methods, which have been used in a variety of applications. The developed method depends on using the honey badger algorithm (HBA) as a new identification approach for identifying the parameters of the PEMFC. In the presented method, the minimal value of the sum square error (SSE) is applied to determine the optimal fitness function. A set of experimental series has been conducted utilizing three datasets entitled 250-W stack, BCS 500-W, and NedStack PS6 to justify the usage of the HBA to determine the PEMFC’s parameters. The results of the competitive algorithms are assessed using SSE and standard deviation metrics after numerous independent runs. The findings revealed that the presented approach produced promising results and outperformed the other comparison approaches.