A novel surrogate model for channel geometry optimization of PEM fuel cell based on Bagging-SVM Ensemble Regression

Abstract

Channel structure plays an important role on the performance of proton exchange membrane fuel cell (PEMFC). In this study, the channel geometry of a PEMFC is optimized through genetic algorithm to obtain better performance. For the first time, a machine learning method called Bagging Ensemble Regression is employed as the surrogate model to calculate the fitness value of the algorithm, which accelerates the optimization process. First, a three-dimensional PEMFC simulation model is developed as the optimization prototype through CFD technology. Second, the Bagging ensemble model is trained through training data obtained from the CFD model. Then the Bagging ensemble model is integrated into the genetic algorithm to conduct the optimization process. Finally, the optimal model obtained is compared with the optimization prototype in terms of polarization curves, pressure drop, and reactant distribution, and the advantages of using Bagging ensemble model are discussed. Results show that the optimal model has a smaller pressure drop and a more uniform reactant distribution than the basic model at the expense of just a little power density. The presented surrogate model shows high prediction accuracy with only a small amount of training data, which is superior to the commonly used surrogate models.