A hybrid model combining a support vector machine with an empirical equation for predicting polarization curves of PEM fuel cells

•A hybrid model for more accurate prediction of the polarization curves of PEM fuel cells.•The hybrid model combines a support vector machine (SVM) with an empirical equation.•The predictive performance of the hybrid model was compared with that of a SVM model.•The prediction errors of the hybrid model are only 14-21% of those of the SVM model.

A hybrid model was proposed by combining a support vector machine (SVM) model with an empirical equation for more accurate prediction of the polarization curves of a PEM (polymer electrolyte membrane) fuel cell under various operating conditions. Operational data were obtained from designed experiments for a PEM fuel cell for training, testing, and validating the hybrid model, and a model training procedure was presented for determining the model coefficients and hyper-parameters of the hybrid model. The predictive performance of the hybrid model was compared with that of a SVM model. The SVM model showed somewhat poor performance, especially yielding large prediction errors in the high voltage ranges of the polarization curves as reported in the literature. In contrast, the hybrid model exhibited almost perfect matches between the predicted and measured polarization curves, resulting in significantly lower root-mean-square errors of 1.7-4.4 mV which correspond to only 14-21% of those obtained from the SVM model.