Performance modeling of flame-assisted fuel cells based on a swirl burner

Aiming at the problems of a narrow operating range and complex modeling of Flame-assisted Fuel Cells (FFCs), an FFC system based on a swirl burner is proposed, and neural network algorithms are used to construct the prediction model for the polarization curve of the FFC system. First, the output voltage and power values of the FFC system are measured under different working conditions, and various experimental parameters are collected to form a dataset; second, the correlation analysis method is used to screen out the parameters that are highly correlated with the output voltage as the input variables of the neural network; finally, the prediction model of the polarization curve is constructed, and back propagation (BP), long short term memory, and 1D-CNN algorithms are chosen to examine the applicability of various neural networks for the FFC system. The experimental and polarization characteristic curve prediction results show that the FFC system can obtain a maximum output voltage of 10.6 V and power of 7.71 W. The average relative errors of the three algorithms are 5.23%, 4.08%, and 6.19%, respectively, with the BP neural network algorithm showing the best generalization ability. The study provides support for the application of the FFC system in aerospace and other fields.