Bald Eagle Search Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell

A precise and reliable proton exchange membrane fuel cell (PEMFC) parameter identification performs an essential function in simulation analysis, optimal control, and performance research of actual PEMFC systems. Unfortunately, achieving an accurate, efficient, and stable parameter identification can sometimes be problematic for traditional optimization methods, owing to its strong coupling, inherent nonlinear, and multi-variable characteristics. Therefore, an advanced bald eagle search (BES) algorithm is designed to dependably identify the unknown parameters of the electrochemical PEMFC model in this work. For evaluating and analyzing the overall optimization performance of the BES comprehensively, it is compared with the genetic algorithm (GA) based on MATLAB under three cases. According to the simulation results, the optimum root mean square error (RMSE) achieved by BES is 96.27% less than that achieved by GA in parameter identification, which fully indicates that the precision, accuracy, and stability of the optimization results can be remarkably improved via the application of BES.

Automated summary

Accurate and reliable parameter identification for proton exchange membrane fuel cells (PEMFC) is crucial for simulation analysis, optimal control, and performance research. Traditional optimization methods face difficulties in accurately and efficiently identifying parameters due to the strong coupling, inherent nonlinear, and multi-variable characteristics. In this study, an advanced bald eagle search (BES) algorithm is proposed to reliably identify the unknown parameters of the electrochemical PEMFC model. Results show that BES outperforms the genetic algorithm (GA) in parameter identification, achieving a 96.27% reduction in root mean square error (RMSE).