A data-driven modeling approach to quantify morphology effects on transport properties in nanostructured NMC particles

We present a data-driven modeling approach to quantify morphology effects on transport properties in nanostructured materials. Our approach is based on the combination of stochastic modeling of the 3D nanostructure and numerical modeling of effective transport properties, which is used to investigate process-structure–property relationships of hierarchically structured cathode materials for lithium-ion batteries. We focus on nanostructured LiNi_1/3Mn_1/3Co_1/3O₂ (NMC) particles, the nanoporous morphology of which has a crucial impact on their effective transport properties (i.e, effective ionic and electric conductivity) and thus on the performance of the cell. First, we develop a parametric stochastic model for the 3D morphology of the nanostructured NMC particles based on excursion sets of so-called χ²-fields. This model, which has only two parameters, is then fitted to FIB-SEM image data of the NMC particles manufactured with different calcination temperatures and different particle sizes. This way it is possible to generate digital twins of the NMC particles. In a second step, measured 3D image data and corresponding digital twins are used as input for the numerical simulation of effective transport properties. Based on the results obtained by these simulations, we can quantify process-structure–property relationships. Overall, we present a methodological framework that allows for an efficient optimization of the fabrication process of nanostructured NMC particles.