The aim of the project "WirLebenSOFC" is to investigate degradation mechanisms in solid oxide fuel cell (SOFC) systems for the conversion of green hydrogen into electricity, to develop models for lifetime prediction and to derive countermeasures reducing the impact of critical degradation phenomena. In order to achieve this goal, (i) machine learning algorithms are used to systematically analyze the impact of different operating parameters and (ii) experimentally based physicochemical models are applied, which allow a deeper understanding of degradation processes in the electrodes and components of the fuel cells.
At the KIT, the focus is on experimentally based lifetime models that allow predicting the temporal change of physicochemical processes taking place in the cell. Aging will be described as a function of cell structure (materials, microstructures, layer thicknesses) and operating conditions (temperatures, gases, electrical load). Extensive electrochemical measurements (long-term cell tests) and pre- & post-test analyses (high-resolution electron microscopy, 3D reconstruction of porous electrodes) are required for model development and parameterization. The experimental and simulative results obtained at KIT are incorporated into the development of hybrid models.