Stochastic 3D modeling of nanostructured NVP/C active material particles for sodium-ion batteries

TL;DR

This paper introduces a data-driven stochastic modeling approach combining advanced imaging and experimental characterization to simulate and optimize the 3D nanostructure of NVP/C particles in sodium-ion batteries.

Contribution

It develops a calibrated Pluri-Gaussian stochastic model for NVP/C nanostructure, enabling predictive simulations for morphology optimization.

Findings

Validated the stochastic model against imaging data.

Simulated effects of varying carbon content on nanostructure.

Demonstrated potential for morphology optimization in battery materials.

Abstract

A data-driven modeling approach is presented to quantify the influence of morphology on effective properties in nanostructured sodium vanadium phosphate $\mathrm{Na}_3\mathrm{V}_2(\mathrm{PO}_4)_3$/ carbon composites (NVP/C), which are used as cathode material in sodium-ion batteries. This approach is based on the combination of advanced imaging techniques, experimental nanostructure characterization and stochastic modeling of the 3D nanostructure consisting of NVP, carbon and pores. By 3D imaging and subsequent post-processing involving image segmentation, the spatial distribution of NVP is resolved in 3D, and the spatial distribution of carbon and pores is resolved in 2D. Based on this information, a parametric stochastic model, specifically a Pluri-Gaussian model, is calibrated to the 3D morphology of the nanostructured NVP/C particles. Model validation is performed by comparing the nanostructure of simulated NVP/C composites with image data in terms of morphological descriptors which have not been used for model calibration. Finally, the stochastic model is used for predictive simulation to quantify the effect of varying the amount of carbon while keeping the amount of NVP constant. The presented methodology opens new possibilities for a ressource-efficient optimization of the morphology of NVP/C particles by modeling and simulation.