Modeling of a HTPEM fuel cell using Adaptive Neuro-Fuzzy Inference Systems

In this work an Adaptive Neuro-Fuzzy Inference System (ANFIS) model of the voltage of a fuel cell is developed. The inputs of this model are the fuel cell temperature, current density and the carbon monoxide concentration of the anode supply gas. First an identification experiment which spans the expected operating range of the fuel cell is performed in a test station. The data from this experiment is then used to train ANFIS models with 2, 3, 4 and 5 membership functions. The performance of these models is then compared and it is found that using 3 membership functions provides the best compromise between performance and fast model evaluation. This model has a mean absolute error of 0.70%. It is concluded that the developed ANFIS model is suitable for optimization of fuel cell systems and as the steady state component in larger dynamic system models. Copyright (C) 2015, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.