Abstract: Polymer Electrolyte Membrane Fuel Cell (PEMFC) is
such a time-vary nonlinear dynamic system. The traditional linear
modeling approach is hard to estimate structure correctly of PEMFC
system. From this reason, this paper presents a nonlinear modeling of
the PEMFC using Neural Network Auto-regressive model with
eXogenous inputs (NNARX) approach. The multilayer perception
(MLP) network is applied to evaluate the structure of the NNARX
model of PEMFC. The validity and accuracy of NNARX model are
tested by one step ahead relating output voltage to input current from
measured experimental of PEMFC. The results show that the obtained
nonlinear NNARX model can efficiently approximate the dynamic
mode of the PEMFC and model output and system measured output
consistently.
Abstract: The purpose of this paper is applied Taguchi method on the optimization for PEMFC performance, and a representative Computational Fluid Dynamics (CFD) model is selectively performed for statistical analysis. The studied factors in this paper are pressure of fuel cell, operating temperature, the relative humidity of anode and cathode, porosity of gas diffusion electrode (GDE) and conductivity of GDE. The optimal combination for maximum power density is gained by using a three-level statistical method. The results confirmed that the robustness of the optimum design parameters influencing the performance of fuel cell are founded by pressure of fuel cell, 3atm; operating temperature, 353K; the relative humidity of anode, 50%; conductivity of GDE, 1000 S/m, but the relative humidity of cathode and porosity of GDE are pooled as error due to a small sum of squares. The present simulation results give designers the ideas ratify the effectiveness of the proposed robust design methodology for the performance of fuel cell.