In this paper, the influences of gas pressure and flow rate parameters on fuel cell performance are studied. The fuel cell is operated at various pressures and flow rates that are regulated by mass flow controllers placed upstream of the stack. In this study, the four types of control factors considered are pressures of the fuel and oxidant and the flow rates of the fuel and oxidant to select the optimized conditions for fuel cell operation. Each factor has two levels, leading to a full factorial design requiring 24 experiments leading to 16 experiments and fractional factorial experiments, 24-1, leading to eight experiments. The experimental data collected were analyzed by statistical sensitivity analysis by checking the effect of one variable parameter on the other. The mixed interaction between the factors was also considered along with the main interaction to explain the model developed using the design of experiments. From the analysis, maximum fuel cell performance was found to be hydrogen flow rate, oxygen flow rate and the interaction between the hydrogen pressure and oxygen flow rate compared to all other factors and their interactions. These fractional factorial experiments, presently applied to fuel cell systems, can be extended to other ranges and factors with various levels, with a goal to minimize the variation caused by various factors that influence the fuel cell performance, but with less number of trials compared to full factorial experiments.

Polymer Electrolyte Membrane Fuel Cell (PEMFC) is the most promising system among different kinds of fuel cells due to their various advantages, such as easy startup, room temperature operation, no liquid electrolyte and high current density. To achieve high current density, the optimal operating conditions need to be identified for fuel cell systems, in addition to design parameters such as membrane, catalyst particle size, quantity and nature of gas diffusion layers. There are many variables in the operation of fuel cell systems, viz., fuel cell temperature, reactant pressure, reactant flow, relative humidity and load. These parameters are related among themselves to nonlinear relations, leading to an impact on the fuel cell voltage (Larmine and Dicks, 2000; Scholta et al., 2004; Frano, 2005; and Haolin et al., 2007). This is concerned with minimizing the effect of uncertainty or variation in design parameters on a design without eliminating the source of the uncertainty or variation. The robust design is less sensitive to variation in uncontrollable design parameters than the traditional optimal designs. Robust design has found many successful applications in engineering and is presently expanded to fuel cell systems with a goal to minimize the variation caused by uncontrollable noise factors, such as ambient temperature, operating environment and natural phenomena that are difficult to control (Bendell et al., 1989; Parkinson, 1995; Fujimoto, 2003; and Esue et al., 2006). The robust design can also help minimize the cause by control factors such as pressure, flow rate and humidity. Improvement of power and stabilization of cell performance under different operating conditions are important for developing a practical PEMFC system. In general, these desirable parameter combinations were decided by one-factor experiments, and the analysis is done by fixing the other parameters, where the influence of other parameters is not considered. In addition, a large number of experiments are needed to analyze the performances of a given fuel cell system to identify the parameters of a physical model. In order to evaluate the impacts of the physical control parameters on the fuel cell operation, the Design of Experiment (DOE) method, developed by Fisher (Montgomerry, 1983; and Zivorad, 2004), is being used, in particular, to reduce the number of tests when many parameters were considered. Recently, this method is being used by many researchers in fuel cell technology for the development of fuel cells, materials for fuel cells and optimal solution for operating conditions, which determine the most significant parameters (Rahman et al., 1998; Shigyo and Nishiguchi, 2006; Wahdame et al., 2006; and Rajalakshmi et al., 2009). Grujicic et al. (2004) studied the cathode and optimized the distributor geometry by Analysis of Variance (ANOVA) method. The effect of material and manufacturing variations on membrane electrode assembly pressure distribution has been analyzed by Vlahinos et al. (2003). The experimental analysis of combined heat and power performance of a PEMFC stack of 800 W capacity has been studied by factorial design method and showed that cathode stoichiometry has a positive effect on electrical power and a negative effect on thermal power (Torchio et al., 2005). In another detailed study by Guvelioglu and Stenger (2006), the main and interaction effects of PEMFC design parameters have been studied with five factors such as channel width, Gas Diffusion Layers (GDL) thickness, GDL conductivity and GDL porosity. They found that the strongest interaction occurs between the channel size and the GDL conductivity, while the weakest interaction effects are observed between the GDL thickness and the porosity.

Chemical Engineering Journal, Polymer Electrolyte Membrane Fuel Cell, PEMFC, Fuel Cell Systems, Gas Diffusion Layers, Statistical Techniques, Graphical Representations, Factorial Method, Fractional Factorial Designs, Fractional Factorial Design Methods, Full Factorial Design, Electrochemical Reactions, Electronic Thermal Mass, Humidifier Systems, Cell Management.