Affiliation:
1. Faculty of Computers and Information, Minia University, Minia 61519, Egypt
2. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
3. Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 43518, Egypt
Abstract
<abstract>
<p>The process of identifying the optimal unknown variables for the creation of a precision fuel-cell performance forecasting model using optimization techniques is known as parameter identification of the proton exchange membrane fuel cell (PEMFC). Recognizing these factors is crucial for accurately forecasting and assessing the fuel cell's performance, as they may not always be included in the manufacturer's datasheet. Six optimization algorithms—the Walrus Optimizer (WO), the Tunicate Swarm Algorithm (TSA), the Harris Hawks Optimizer (HHO), the Heap Based Optimizer (HBO), the Chimp Optimization Algorithm (ChOA), and the Osprey Optimization Algorithm (OOA) were used to compute six unknown variables of a PEMFC. Also, the proposed WO method was compared with other published works' methods such as the Equilibrium Optimizer (EO), Manta Rays Foraging Optimizer (MRFO), Neural Network Algorithm (NNA), Artificial Ecosystem Optimizer (AEO), Slap Swarm Optimizer (SSO), and Vortex Search Approach with Differential Evolution (VSDE). Minimizing the sum squares error (SSE) between the estimated and measured cell voltages requires treating these six parameters as choice variables during optimization. The WO algorithm yielded an SSE of 1.945415603, followed by HBO, HHO, TSA, ChOA, and OOA. Given that WO accurately forecasted the fuel cell's performance, it is appropriate for the development of digital twins for fuel cell applications and control systems for the automobile industry. Furthermore, it was shown that the WO convergence speed was faster than the other approaches studied.</p>
</abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)