# Cracking predictions of lithium-ion battery electrodes by X-ray computed tomography and modelling

arXiv: https://arxiv.org/abs/2202.09572 · HTML: https://mlsanity.com/paper/2202.09572/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2022.

## Built on by (most cited first, 3 of 3)

- [Towards Robust Surrogate Models: Benchmarking Machine Learning Approaches to Expediting Phase Field Simulations of Brittle Fracture](https://mlsanity.com/paper/2507.07237/) (2025)
- [Generating multi-scale NMC particles with radial grain architectures using spatial stochastics and GANs](https://mlsanity.com/paper/2407.05333/) (2024)
- [Data-efficient crack quantification in lithium-ion cathodes using foundation model transfer](https://mlsanity.com/paper/2608.27162/) (2026)

## Abstract (arXiv)

Fracture of lithium-ion battery electrodes is found to contribute to capacity fade and reduce the lifespan of a battery. Traditional fracture models for batteries are restricted to consideration of a single, idealised particle; here, advanced X-ray computed tomography (CT) imaging, an electro-chemo-mechanical model and a phase field fracture framework are combined to predict the void-driven fracture in the electrode particles of a realistic battery electrode microstructure. The electrode is shown to exhibit a highly heterogeneous electrochemical and fracture response that depends on the particle size and distance from the separator/current collector. The model enables prediction of increased cracking due to enlarged cycling voltage windows, cracking susceptibility as a function of electrode thickness, and damage sensitivity to discharge rate. This framework provides a platform that facilitates a deeper understanding of electrode fracture and enables the design of next-generation electrodes with higher capacities and improved degradation characteristics.