Analytical Modeling and Experimental Study of Thermal Conductivity of Catalyst Layer of Polymer Electrolyte Membrane (PEM) Fuel Cells

In a PEM fuel cell, waste heat generation occurs due to: 1) reversible heat of the electrochemical reaction in catalyst layer, 2) irreversible heat due to losses caused by over-potential in catalyst layer, 3) latent heat due to phase change in cathode catalyst layer, and 4) joule heating in all of the fuel cell components including catalyst layer. The mentioned heat sources induce local temperature variations inside fuel cells, which, in turn, highly affect their drying, flooding, and degradation. Accordingly, detailed knowledge about the in situ temperature distribution in PEM fuel cells is essential for efficiently managing water and heat in these systems as well as for minimizing their degradation. A temperature distribution can be obtained through comprehensive structural models for thermal conductivity of different components. The thermal conductivity of PEM fuel cell gas diffusion layers (GDL) is well understood, and some experimental data on thermal conductivity of other components have been provided in literature. However, the thermal conductivity of the catalyst layer, where most of the heat generation modes occur, is still unknown. Accordingly, this work is concerned with structural modeling of this property through a unit cell approach. A detailed geometrical model is developed based on the real microstructural properties of the catalyst layer, and unit cells of various scales are developed. Then, the thermal conductivities of various unit cells are modeled in a mechanistic manner and are interconnected to and integrated with each other to yield the effective thermal conductivity of the whole catalyst layer. In addition to modeling, catalyst layers with different compositions are produced via precise micro-fabrication techniques, their thermal conductivity is tested by transient plane source and guarded heat flux methods, and the experimental values are used to tune and validate the model.