Robust PEMFC MEA derived from model-based understanding of durability limitations for heavy duty applications

The R&D project PEMTASTIC aims to meet the key technical challenges to increase durability of MEAs for HD applications. These challenges are approached with a combination of model-based design and the development of a durable CCM using innovative materials tailored for heavy duty operation at high temperature (105°C). The quantitative targets correspond to a durability of 20,000 hours maintaining a state-of the art power density of 1.2 W/cm2@0.65 V at a Pt loading of 0.30 g/kW.

Truck mission profiles will be analyzed (Symbio) in order to define relevant FC operation protocols and stressors. Degradation tests will be carried out in differential cells and will be assisted by physical-chemical material characterization to assure well defined data required for parametrization of degradation models (CEA, DLR). A combination of micro- and mesoscale models as well as 1D and 2D cell models (ZHAW, DLR) will capture the impact of material parameters on performance and durability and will address all material and CCM parameters which will be iteratively adapted by industry partners. The materials which will be implemented and adapted are advanced corrosion resistant supports (Imerys) combined with a novel catalyst deposition technique (Heraeus) to mitigate for ECSA loss. Prototype Nafion ionomers and membranes with high conductivity in dry conditions will be used (Chemours). Eventually, an improved cathode catalyst layer will be designed considering Pt particle size distribution and superior catalyst ionomer interaction (IRD). The selection of a commercial GDL will consider accommodation of a wide range of operating conditions.

The final MEA and the concept of model-based MEA development will be validated in a short stack at TRL4 (Symbio). As additional outcomes, implications on system management and on the BoP components will be drawn, and the reduced computational demand for degradation modelling will facilitate fast health assessment and performance prediction.