Generating multi-scale NMC particles with radial grain architectures using spatial stochastics and GANs
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.
Keywords
Cite
@article{arxiv.2407.05333,
title = {Generating multi-scale NMC particles with radial grain architectures using spatial stochastics and GANs},
author = {Lukas Fuchs and Orkun Furat and Donal P. Finegan and Jeffery Allen and Francois L. E. Usseglio-Viretta and Bertan Ozdogru and Peter J. Weddle and Kandler Smith and Volker Schmidt},
journal= {arXiv preprint arXiv:2407.05333},
year = {2024}
}