Coupling flow channel optimization and Bagging neural network to achieve performance prediction for proton exchange membrane fuel cells with varying imitated water-drop block channel

Abstract

The proton exchange membrane fuel cell (PEMFC) flow channel structure obviously affects the reaction gas distribution and electrochemical reactions. In this study, the imitated water-drop block heights and widths within the channel are optimized for better PEMFC performance. A machine learning-based Bagging neural network is applied for the first time to predict PEMFC output performance based on different block structure parameters. First, the proposed imitated water-drop block height and width are optimized by changing parameters. Then, a database is established. Finally, after the Bagging model is validated, the performance is compared with the back-propagation (BP) neural network. Results indicate that the mass transfer and the electrochemical reaction are improved under the optimal width and height of imitated water-drop block for PEMFC. The Bagging prediction model uses less training data to obtain high-precision prediction results in 10 s. The performance prediction model can effectively improve the efficiency of channel optimization.