An Efficient White Shark Optimizer for Enhancing the Performance of Proton Exchange Membrane Fuel Cells

This study investigates the substantial contribution of recent numerical optimization technique known as White Shark Optimizer (WSO) to evaluate performance proton exchange membrane fuel cell (PEMFC) design parameters that play a considerable role in boosting its effectiveness. A code was developed and implemented via MATLAB software achieve research goal. The proposed WSO employed identify unknown PEMFC equivalent circuit, considering experimental data. analyzed objective function root mean squared error (RMSE) between measured estimated terminal voltages. Additionally, compared with other intelligent approaches such salp swarm algorithm (SSA), Harris hawks (HHO), atom search (ASO), dung beetle (DBOA), stochastic paint optimizer (SPO), comprehensive learning Archimedes (HCLAOA). simulations revealed RMSE values varied lower higher 0.009095329 0.028663611, respectively. results indicated fitness value recorded considered 250 W stack 0.020057775. Moreover, minimum obtained using WSO, an operating temperature 353.15 K working anode cathode pressures are 3 bar 5 bar, offered best terms absolute errors optimizers, confirming robustness all cases.