Lithium Plating Dynamics Detected in Solid-State Batteries Using X-ray CT and Machine Learning

Researchers from the University of California, San Diego, have developed a machine learning-based system that detects the evolution of lithium components inside a battery during operation, according to a study published in the npj Computational Materials journal. The team used automatised image segmentation to split micro-CT battery scans into components including dendrites (lithium structures growing on electrode surfaces) and deposited lithium. The system measured changes in each component’s volume and the spatial correlation between different elements. The researchers anticipate potential applications in automating quality control checks on energy devices.
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