Cracking predictions of lithium-ion battery electrodes by X-ray computed tomography and modelling
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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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Feb 2022.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.
- 2025Towards Robust Surrogate Models: Benchmarking Machine Learning Approaches to Expediting Phase Field Simulations of Brittle Fracturecited by 1
- 2024Generating multi-scale NMC particles with radial grain architectures using spatial stochastics and GANscited by 0
- 2026Data-efficient crack quantification in lithium-ion cathodes using foundation model transfercited by 0
- 2026Data-efficient crack quantification in lithium-ion cathodes using foundation model transfer27 Aug 2026
- 2025Towards Robust Surrogate Models: Benchmarking Machine Learning Approaches to Expediting Phase Field Simulations of Brittle Fracture9 Jul 2025
- 2024Generating multi-scale NMC particles with radial grain architectures using spatial stochastics and GANs7 Jul 2024