Fuel Cell Performance Variables

Some PEM fuel cell performance data were obtained using an electrical resistor to provide a variable load. Two digital multimeters and a shunt resistor were used to measure the voltage and current, so we could calculate the power produced. 

Bearing in mind that phenomena occurring in nature are too complex to be completely described by mathematical equations, the required details to be described by the model must be goal-driven, i.e. the complexity of the model, and the related results, must be strictly connected to the main goal of the analysis itself. When, for example, the main purpose of the model is to provide the fuel cell performance, in order to analyze the whole system in which it is embedded, the spatial variation in the physical and chemical variables (such as gas concentration, temperature, pressure and current density, for example) are not relevant however the performances, in terms of efficiency, electrical and thermal power and input requirements are important [1-4],... 

Sulfur oxides (SOJ, especially SO2, are emitted from the combustion of coal and oil, which contain variable proportions of sulfur (0.5 to 10% for coal and 0.5 to 3% for oil). They can also be found in urban areas with heavy traffic, and areas with active agricultural activities. SO (SO2 and SO3), acidic in nature, can cause a decrease in pH inside the MEA, resulting in free acids in the MEA and causing potential shifts. SO can also adsorb on the Ft surface, blocking the active catalyst sites that would otherwise be used for oxygen adsorption and reduction, and leading to fuel cell performance degradation. 

It is no longer economical to operate a fuel cell or fuel cell system. Power is the third central equation for fuel cell performance. General expressions can be derived for fuel cell performance involving the variables E, J, mF, pressure, and fuel flow rate to explore the full envelope of fuel cell operation. 

The fuel cell performance curve can also be developed from first principles and mechanistic modeling combined with empirical relations. Since there are a number of variables in a given design of a fuel cell, a semiempirical approach seems to be more appropriate. Springer et al. (1993, 1996), Amphlett et al. (1995a, b, 1996) and Mann et al. (2000) have carried out such semiempirical models for PEMFC. 

The current density and impurities also affect the fuel cell performance. In the initial stages, activation polarisation decreases the cell voltage. When current densities increase, then the concentration losses are predominant and a sharp decrease in cell performance is observed. During normal fuel cell application, ohmic losses are observed due to internal resistances of the fuel cell. This section deals with the effect of different variables on fuel cell performance and efficiency. The different fuel cells and their performances are discussed one by one. 

In conclusion, PEM is a core part of PEMFCs. The membrane resistance and water content of the membrane is crucial to the fuel cell performance. This model describes the dynamic process of the membrane variables (e.g., the flux density of HjO, the flux density of H+ and water content). 

Fig. 3 Catalyst layer evaluation flow-chart. Material selection, process variables significantly influence fuel cell performance.

The transient response of DMFC is inherently slower and consequently the performance is worse than that of the hydrogen fuel cell, since the electrochemical oxidation kinetics of methanol are inherently slower due to intermediates formed during methanol oxidation [3]. Since the methanol solution should penetrate a diffusion layer toward the anode catalyst layer for oxidation, it is inevitable for the DMFC to experience the hi mass transport resistance. The carbon dioxide produced as the result of the oxidation reaction of methanol could also partly block the narrow flow path to be more difScult for the methanol to diflhise toward the catalyst. All these resistances and limitations can alter the cell characteristics and the power output when the cell is operated under variable load conditions. Especially when the DMFC stack is considered, the fluid dynamics inside the fuel cell stack is more complicated and so the transient stack performance could be more dependent of the variable load conditions. 

The performance of fuel cells is affected by operating variables (e.g., temperature, pressure, gas composition, reactant utilizations, current density) and other factors (impurities, cell life) that influence the ideal cell potential and the magnitude of the voltage losses described above. Any number of operating points can be selected for application of a fuel cell in a practical system, as illustrated by Figure 2-4. 

In another study, Chen and Zhao [55] demonstrated that by using a Ni-Cr alloy metal foam as the cathode DL (and current collector), instead of a CFP or CC, the performance of a DMFC can be enhanced significantly due to the improvement of the mass transfer of oxygen and overall water removal on the cathode side. Fly and Brady [56] designed a fuel cell stack in which the distribution layers were made out of metal foams (open cell foams). In addition, more than one foam (with different porosity) could be sandwiched together in order to form a DL with variable porosity. 

When, on the other hand, the model is used as a tool for designing or improving a specific component of the fuel cell, it is important that the model is capable of providing very detailed information on the performance-related variables in that specific component. Examples of such analyses are copious in the literature (e.g. [4-8]), and most of them are developed at single cell level, with particular emphasis on one particular component or cell characteristic. Chan et al. [4], for example, applied an SOFC single cell model for analyzing the effect of the electrodes and... 

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