Optimization Algorithms: Optimal Parameters Computation for Modeling the Polarization Curves of a PEFC Considering the Effect of the Relative Humidity

Abstract

The development of green energy conversion devices has been promising to face climate change and global warming challenges over the last few years. Energy applications require a confident performance prediction, especially in polymer electrolyte fuel cell (PEFC), to guarantee optimal operation. Several researchers have employed optimization algorithms (OAs) to identify operating parameters to improve the PEFC performance. In the current study, several nature-based OAs have been performed to compute the optimal parameters used to describe the polarization curves in a PEFC. Different relative humidity (RH) values, one of the most influential variables on PEFC performance, have been considered. To develop this study, experimental data have been collected from a lab-scale fuel cell test system establishing different RH percentages, from 18 to 100%. OAs like neural network algorithm (NNA), improved grey-wolf optimizer (I-GWO), ant lion optimizer (ALO), bird swarm algorithm (BSA), and multi-verse optimization (MVO) were evaluated and compared using statistical parameters as training error and time. Results gave enough information to conclude that NNA had better performance and showed better results over other highlighted OAs. Finally, it was found that sparsity and noise are more present at lower relative humidity values. At low RH, a PEFC operates under critical conditions, affecting the fitting on OAs.

Optimized PID and NN-based Speed Control of a Load-coupled DC Motor

“…Generally, any OF is subjected to a group of inequality constraints that helps to adjust the search space over which the OA tries to find the optimal solution [10]. In this work, the constraints correspond to the range of values for the PID controller parameters, i.e., K p , K i , and K d .…”

“…PSO is a method that is highly preferred and is driven by swarm behavior. [10] With its simplicity, low cost of calculation, and strong performance, it is very promising for adjusting PID control parameters, [9]. This study models a two-mass drive system with the goal of implementing motor and load speed control to create a reliable control while reducing torsional vibrations.…”