Prediction of fuel cell performance based on Bagging neural network ensemble model

Abstract

The influence of the inlet and outlet heights of the flow channel on the performance of a proton exchange membrane fuel cell was studied with a three-dimensional fuel cell mathematical model. The numerical results were used as the basic training data of a three-layer back-propagation artificial neural network (ANN) model in which the inlet and outlet heights of the flow channel as well as the cell voltage were set to be the input variables while cell current density is the output variable. Then, applying the Bagging ensemble learning method to integrate the neural network model, a fuel cell performance prediction method is constructed. It was shown that as compared with the ANN model, the Bagging neural network model exhibited higher prediction accuracy and less requirements of model training data. It also can be applied for rapid prediction of fuel cell performance in a wide range of conditions beyond the training data, showing the robustness of the proposed Bagging neural network ensemble model.