On neural network modeling to maximize the power output of PEMFCs

Abstract

Optimum operating conditions of a fuel cell will provide its maximum efficiency and the operating cost will be minimized. Thus, operation optimization of the fuel cell is essential. Neural networks can simulate systems without using simplifying assumptions. Therefore, the neural network can be used to simulate complex systems. This paper investigates the effects of important parameters, i.e., temperature, relative humidity in the cathode and anode, stoichiometry on the cathode and anode sides, on the po larization curve of a PEMFC (Proton Exchange Membrane Fuel Cell) having MPL (Micro Porous Layer) by ANN (artificial neural network). For this purpose, an analytical model validated using laboratory data is applied for prediction of the operating conditions providing maximum (and/or minimum) output power of a PEM fuel cell for arbitrary values of the current. The mean absolute relative error was calculated to 1.95%, indicating that the network results represented the laboratory data very accurately. The results show 23.6% and 28.9% increase of the power by the model and the network, respectively, when comparing the maximum and minimum power outputs.