Parameter sensitivity analysis and calibration of a discrete element model for optimizing all-solid-state-battery cathode microstructures

This study develops and evaluates a Discrete Element Method (DEM) model designed to simulate the produced cathode composite microstructures during uniaxial cold-pressing of All-Solid-State-Battery (ASSB) electrodes. The research primarily investigates how variations in model input parameters – such as particle size distribution, interparticle friction, and mechanical properties – affect critical performance indicators, including cathode active material (CAM) utilization (${\theta }_{\text{CAM}}$), packing behavior, porosity ($ϵ$), and solid electrolyte (SE) utilization (${\theta }_{\text{SE}}$), contributing to network formation and ionic percolation within DEM-generated composite cathodes. A comprehensive parameter sensitivity analysis highlights that frictional interactions are the most influential factors governing particle connectivity, percolation, and porosity, especially at higher CAM loadings ($f_{\text{CAM}}$). In addition to simulations, experimental measurements were performed to validate the DEM model and further analyze the cathode behavior under varying conditions. Both experimental and simulation results consistently demonstrate that increasing uniaxial pressure enhances discharge capacity by improving particle connectivity. However, while the DEM model exhibits high accuracy at lower $f_{\text{CAM}}$, it shows notable discrepancies and increased sensitivity to parameter variations at higher loadings, indicating the need for further refinements to achieve better alignment with experimental observations. This research provides essential insights into optimizing ASSB cathode designs, offering a robust framework for refining DEM models to predict and enhance battery performance under various conditions.