Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes

Area of Science:

• Materials Science
• Electrochemistry
• Computational Imaging

Background:

• Accurate 3D electrode models are crucial for understanding lithium-ion battery performance.
• Standard segmentation methods struggle with low-contrast volumetric images of battery electrodes.

Purpose of the Study:

• To develop a deep learning methodology for reliable segmentation of battery electrode microstructures.
• To enhance segmentation accuracy using synthetic training data.
• To analyze microstructural evolution during battery operation.

Main Methods:

• Implementation of the 3D U-Net architecture for image segmentation.
• Generation of realistic synthetic electrode structures and their tomographic reconstructions for training.
• Application of the method to X-ray tomographic microscopy images of graphite-silicon composite electrodes.

Main Results:

• The deep learning method achieved accurate segmentation of electrode components (active particles, binder, pores).
• Performance was validated using standard segmentation metrics.
• The approach enabled statistically meaningful analysis of microstructural changes.

Conclusions:

• Deep learning, augmented by synthetic data, provides a robust solution for segmenting challenging battery electrode images.
• This methodology facilitates detailed analysis of electrode microstructural evolution, aiding battery performance improvement.