Integration of DEM-based microstructure generation with FEM and RNM simulations for evaluating ionic conductivity in composite cathodes of all-solid-state batteries

Cathode microstructure critically governs ionic transport in composite cathodes for all-solid-state batteries (ASSBs). In this study, we present a simulation framework that integrates Discrete Element Method (DEM)-based microstructure generation with Finite Element Method (FEM) and Resistor Network Modeling (RNM) to analyze the effective ionic conductivity σ eff,ion within the solid electrolyte (SE) phase. The framework enables quantitative evaluation of SE network connectivity and transport pathways across realistic 3D microstructures. Both FEM and RNM models were validated against experimental impedance data for composite cathodes with varying active material fractions ( f CAM = 70 – 80 wt.% ), demonstrating strong agreement and consistent trends. RNM reproduced FEM results with significantly reduced computational cost, making it suitable for high-throughput parametric studies. Systematic investigations revealed that decreasing the mean SE particle size ( D ¯ SE ) enhances σ eff,ion by increasing SE utilization ( θ SE ) and coordination number ( Z SE−SE ), despite higher individual contact resistances ( R ̄ SE−SE ). Increasing compaction pressure further improves transport by densifying particle contacts, though gains diminish beyond 400 MPa. These results establish the DEM–FEM–RNM framework as an efficient and predictive tool for linking processing, microstructure, and ionic transport in ASSB cathodes, providing a foundation for microstructure-informed electrode design and future electrochemical performance modeling. • DEM, FEM, and RNM combined to model SE transport in cathodes. • FEM and RNM validated using experimental conductivity data. • RNM is accurate, efficient, and pressure/convergence sensitive. • SE size and pressure impact percolation and contact quality. • Network metrics reveal structure–property relationships.