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Demonstrating Faster Multi-Label Grey-Level Analysis for Crack Detection in Ex Situ and Operando Micro-CT Images of
Matthew P Jones1, Huw C W Parks1,2,3, Alice V Llewellyn1,3
1Electrochemical Innovation Laboratory, Department of Chemical Engineering, University College London, London, WC1E 6BT, UK.
Small Methods
|June 23, 2025
Summary
Cracking in Nickel Manganese Cobalt (NMC) battery particles is tracked using the new GREAT2 algorithm. This automated method significantly speeds up analysis of large datasets, enabling robust studies of battery degradation.
Area of Science:
- Materials Science
- Electrochemistry
- Data Science
Background:
- Cracking in Nickel Manganese Cobalt (NMC) battery particles is a key degradation mechanism.
- Cracked particles exhibit altered grey-level intensities in micro-computed tomography (micro-CT) images due to the partial volume effect.
- Previous analysis methods were limited by processing speed and dataset size.
Purpose of the Study:
- To develop an automated method for tracking grey-level changes in NMC particles within large micro-CT datasets.
- To significantly enhance the speed and scale of tomographic analysis for battery electrode cracking.
- To enable temporal analysis of particle degradation mechanisms during battery operation.
Main Methods:
- Extension of the GREAT algorithm to the new GREAT2 algorithm for faster processing.
- Automated tracking of grey-level intensity changes in individual NMC particles.
- Application to large micro-CT datasets (over 10,000 particles) and operando experiments.
- Development of the GRAPES Python toolkit for workflow implementation.
Main Results:
- GREAT2 processes over 10,000 particles in under a minute, a significant speed increase from the original GREAT algorithm.
- The method allows for statistically robust analysis of particle populations due to large sample sizes.
- Automated temporal tracking of grey-level changes provides insights into degradation mechanisms.
Conclusions:
- The GREAT2 algorithm and associated GRAPES toolkit substantially accelerate the tomographic study of cracking in battery electrodes.
- This advancement enables more comprehensive and statistically reliable investigations into battery material degradation.
- The developed methods facilitate a deeper understanding of degradation mechanisms in NMC batteries.
