Analytical model of Direct Methanol Fuel Cell

It has become an important issue to search for alternative energy source due to energy crisis and pollution of the natural environment in recent years. Direct methanol fuel cell (DMFC) is one of the attractive energy supplies. There are some technical challenges for DMFC such as low efficiency due to methanol crossover, low methanol oxidation rate, low power density, and the need for excessive water and heat management, etc. For a better description of the cell operation and optimization of performance, it would be important to develop an accurate and quick mathematical model for DMFC.
In this research, a 2D analytical mathematical model of a direct methanol fuel cell was developed to describe not only electrochemical reactions on the anode and cathode electrodes, but also transport phenomena within the fuel cell, operating isothermally at steady state. One could use this model to understand the cell performance such as polarization curve, efficiency, power density and concentration profile. Further, we could predict cell performance and understand how to deal water management when methanol input concentration changes.
Compared with other models in the literature, our model allows for prediction of the open circuit voltage of the DMFC. This model contains forty three parameters; most of them are decided by cell structure and operation condition. Only seven parameters(transfer coefficient of electron、resistance of material interface、porosity、thickness of catalyst layer and diffusion coefficient of oxygen) are obtained by regressing to experiment data. The theoretical prediction was in good agreement with experiments from three different fuel cells (including different cell structure, operating condition). This indicates that this model is robust and reliable.
With this model, one could better understand electrochemical reactions and mass transport phenomena in fuel cell, including methanol crossover and reactant transport in membrane electrode assembly (MEA), and how these phenomena affect cell performance. The knowledge provided from such an analytical model may help one search for the key factors to improve DMFC performance.