Artificial Neural Networks as Efficient Models of Proton Exchange Membrane Fuel Cells

Abstract Utilization of machine learning methodologies, particularly artificial neural networks (ANNs), presents a good approach to accurately model physical systems. Such predictive simulations offer the ability to predict system performance across diverse operational conditions without the need of use mathematical descriptions. This approach contrast with traditional, time-consuming and unstable approaches reliant on partial differential equations. In this study, ANN methodology is used to obtain the characteristics of proton exchange membrane fuel cells (PEMFCs). PEMFC are low temperature devices which convert chemical energy into electricity. This devices are promising applications in the automotive sector. Utilizing data gained from computational fluid dynamics simulations of PEMFCs, was explored various data collection techniques and network architectures to check their impact on predictive fidelity. Conclusions show that the ANN-based framework enables rapid prediction of current-voltage characteristics, achieving accuracy levels surpassing 90%. Practical of machine learning model implications were discussed, accenting its utility in optimizing PEMFC operational parameters and its potential integration within digital twin frameworks as a data-driven surrogate model. This study underscores the efficiency of machine learning techniques in advancing the comprehension and optimization of complex physical systems such as PEM-FCs, thereby paving the way for their use in engineering and energy sectors.