Introduction: GeoDict to Parametrize DFN-Models

P2D/DFN models can predict cell behavior quickly and efficiently. But there is a catch: their predictions are only as reliable as the input parameters they are based on. Many effective parameters are difficult to measure directly. Researchers therefore often estimate them or determine them through parameter fitting against experimental data.

The challenge: a good fit does not necessarily mean that the fitted parameters are physically correct. Different combinations of parameters can produce similar results. The model may reproduce an experiment well, but still provide limited insight into why an electrode behaves the way it does.

What if, instead, parameters such as tortuosity, effective conductivity, and diffusivity could be calculated directly from the electrode microstructure and thus gain physical meaning?

In this talk, we introduce how GeoDict can derive physically meaningful effective parameters from real 3D microstructures and use them to parametrize P2D/DFN models. This reduces guesswork and extensive empirical fitting while creating a reliable  link between electrode microstructure and cell performance.