Deep Optimization of Catalyst Layer Composition via Data-Driven Machine Learning Approach

Proton exchange membrane fuel cell (PEMFC) provides a promising future low carbon automotive powertrain solution. The catalyst layer (CL) is its core component which directly influences the output performance. PEMFC performance can be greatly improved by the effective optimization of CL composition. This work demonstrates a deep optimization of CL composition for improving the PEMFC performance, including the platinum (Pt) loading, Pt percentage of carbon-supported Pt and ionomer to carbon ratio of the anode and the cathode,. The simulation results by a PEMFC three-dimensional (3D) computation fluid dynamics (CFD) model coupled with the CL agglomerate model is used to train the artificial neural network (ANN) which can efficiently predict the current density under different CL composition. Squared correlation coefficient (R-square) and mean percentage error in the training set and validation set are 0.9867, 0.2635% and 0.9543, 1.1275%, respectively. It illustrates that the well-trained ANN has a comparable accuracy with the physical model. Then, the ANN is utilized as the fitness function in the genetic algorithm (GA) to search the optimal CL composition for maximizing the current density. For verification, the optimal solution of CL composition is returned to the physical model and the comparison between the ANN predicted current density and the physical model simulated current density is provided. The percentage error is only 2.418% which can illustrate the validity of this work.