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Related Experiment Video

Updated: Jun 18, 2026

Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques

Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques

Published on: November 11, 2013

Computer-Vision-Based Approach to Classify and Quantify Flaws in Li-Ion Electrodes.

Sohrab R Daemi¹, Chun Tan¹, Thomas G Tranter¹

¹Electrochemical Innovation Lab, Department of Chemical Engineering, University College London, London, WC1E 7JE, UK.

|September 11, 2022

Summary

This summary is machine-generated.

This study introduces a novel AI approach using convolutional neural networks to analyze battery electrode microstructures from X-ray computed tomography (X-CT) data. This method automates the identification and classification of flawed particles, aiding battery degradation studies.

Keywords:

computer vision convolutional networks lithium-ion batteries mask R-CNN nano X-ray tomography

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Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

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Published on: March 7, 2022

Area of Science:

Materials Science
Electrochemistry
Artificial Intelligence

Background:

X-ray computed tomography (X-CT) is crucial for non-destructive battery electrode microstructure analysis.
Manual data analysis of X-CT data is time-consuming and limits understanding of battery degradation.
Automated analysis is needed to extract meaningful metrics for battery performance.

Purpose of the Study:

To develop an automated pipeline for analyzing battery electrode microstructures using X-CT.
To identify and classify particles with internal flaws or cracks.
To quantify microstructural evolution and its relation to battery cycle life.

Main Methods:

Combined two convolutional neural networks (CNNs) for particle segmentation and flaw classification.
Applied the pipeline to nano-CT datasets of LiNiMnCoO2 (NMC) electrodes.
Validated segmentation metrics against traditional methods and used a pre-trained classifier for flaw detection.

Main Results:

Successfully segmented individual particles and classified flawed ones in NMC electrodes.
Quantified microstructural changes in uncycled and cycled NMC811 electrodes.
Demonstrated proof-of-concept for segmenting individual flaws within particles.

Conclusions:

The developed AI pipeline automates microstructure analysis of battery electrodes.
This approach accelerates the extraction of critical metrics for understanding battery degradation mechanisms.
The method shows significant potential for advancing battery research and development.