Year: 2026
Type: preprint
Abstract: The energy density and rate capability of lithium-ion batteries can be improved through the design of bilayer and compositionally graded architectures for thick electrodes. For this, manufacturing steps such as drying and calendering are key considerations, since they determine the final electrode microstructure and therefore affect its electrochemical performance. Here, we extend our previously developed processcoupled coarse-grained molecular dynamics and discrete element method manufacturing computational workflow from single-layer LiNi0.33Co0.33Mn0.33O2 cathodes to bilayer cathode architectures. The model accounts for active material particles with realistic shapes obtained from X-ray micro-computed tomography. We use bilayer cathodes with different layer formulations as study cases. The Our simulation results show that the initial through-thickness heterogeneity can be preserved after slurry drying but is progressively reshaped during calendering. The layer-averaged porosity contrast can be weakened or even reversed under compression. This indicates a transition from porosity heterogeneity arising from slurry drying to residual porosity governed more by formulation-dependent particle packing. Meanwhile, the interfacial mixing region between the two layers broadens and the effective tortuosity factor increases sharply in the low-porosity regime. These results demonstrate that bilayer electrode design should be treated as a process-history-dependent engineering problem rather than as a static layer stacking one.
Source: ChemRxiv
Institutions: Centre National de la Recherche Scientifique, Réseau sur le Stockage Electrochimique de l'énergie, Laboratoire de Réactivité et Chimie des Solides, Université de Picardie Jules Verne, Zentrum für Sonnenenergie- und Wasserstoff-Forschung Baden-Württemberg, Institut Universitaire de France, Alistore
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7213974259
