Generating multi-scale NMC particles with radial grain architectures using spatial stochastics and GANs

TL;DR

This paper introduces a GAN-based model that generates 3D multi-scale NMC particles with radial grain architectures from 2D data, enabling cost-effective virtual characterization of battery cathodes.

Contribution

A novel stereological GAN approach for generating realistic 3D cathode particles from 2D data, bridging the gap between imaging limitations and material analysis.

Findings

Generated particles are statistically similar to experimental data.

The model enables rapid virtual testing of cathode materials.

A large dataset of simulated particles is publicly available.

Abstract

Understanding structure-property relationships of Li-ion battery cathodes is crucial for optimizing rate-performance and cycle-life resilience. However, correlating the morphology of cathode particles, such as in NMC811, and their inner grain architecture with electrode performance is challenging, particularly, due to the significant length-scale difference between grain and particle sizes. Experimentally, it is currently not feasible to image such a high number of particles with full granular detail to achieve representivity. A second challenge is that sufficiently high-resolution 3D imaging techniques remain expensive and are sparsely available at research institutions. To address these challenges, a stereological generative adversarial network (GAN)-based model fitting approach is presented that can generate representative 3D information from 2D data, enabling characterization of materials in 3D using cost-effective 2D data. Once calibrated, this multi-scale model is able to rapidly generate virtual cathode particles that are statistically similar to experimental data, and thus is suitable for virtual characterization and materials testing through numerical simulations. A large dataset of simulated particles with inner grain architecture has been made publicly available.