Abstract
For the next generation of proton exchange membrane (PEM) fuel cells, the conventional electrode with uniform distribution of functional components is urged to be replaced by functional graded electrode for the prominent performance, efficiency and avoid exacerbated catalyst cost. Due to the complex and non-linear behaviours of PEM fuel cell system, rapid and effective computational model and optimisation algorithm are required to handle such a complex relationship between electrode design parameters and cell performance. In this work, a multi-physics model with multi-directionally graded electrode is developed, in which a deep machine learning approach is embedded, to create a surrogate model for multi-objective optimisation empowered by non-dominated sort genetic algorithm (NSGA-II). A robust prediction deep neural network model with the mean square error lower than 0.01 is obtained from training and then coupling with NSGA-II to evaluate and optimise the fuel cell performances and cost. Remarkably, the Pareto front is successfully defining the trade-off relationship between the objectives where it aids to identify an optimum point where it satisfies the cost effectiveness while maintaining relatively high cell performances. Our work presents a promising strategy to optimise the fuel cell system with underlying interaction and allow rapid and accurate prediction and optimisation.