Electrode Informatics Accelerated Optimization for Catalyst Layer Key Parameters in Direct Methanol Fuel Cells

Abstract

As the core component of direct methanol fuel cell, the catalyst layer plays the key role of material, proton and electron transport channels. However, due to the complexity of its system, optimizing its performance requires a large number of experiments and high costs. In this paper, finite element simulation combined with machine learning model is constructed to accelerate power density prediction and evaluate the influence of catalyst layer parameters on the maximum power density of direct methanol fuel cell. We built a fuel cell simulation model corresponding to different parameters, obtaining a database of more than 200 sets of 19 eigenvalues, and then used different machine learning models for training and prediction. Finally, three tree integration methods were selected to rank the importance of 19 characteristic parameters. In addition, we performed a high-throughput screening of 200,000 different parameter combinations based on Sequential Model-Based Algorithm Configuration. We selected the top 10 parameter combinations with high expected improvement score into numerical simulation model. The results show that a majority of the polarization curves obtained from the top combinations exceed the maximum power density of the original database. This method greatly saves the time of collecting fuel cell data for experiments and speeds up the parameter optimization process.