Lithium Metal Battery Characterization using X-ray Imaging and Machine Learning

In an ever-demanding world for zero emission clean energy sources, vehicle electrification will bring major contributions as each clean car that substitutes one based on fossil fuel could save 1.5 tons of carbon dioxide per year. To expand the e-vehicle fleet, new solutions to store energy must deliver lighter, longer ranges, and more powerful energy batteries, such as solid-state lithium metal batteries (LMB). Different from traditional lithium-ion, LMB uses solid electrodes and electrolytes, providing superior electrochemical performance and high energy density. Some of the challenges of this new technology are to predict the cycling stability and to prevent the formation of lithium dendrite growth. This harmful phenomenon may occur during LMB charge and discharge, when lithium can deposit irregularly, building up dendrites (lithium plating) that leads to failures, such as short-circuit. These morphologies are key to the LMB quality, and they can be captured and analyzed using X-ray microtomography (XRT) scans. This presentation will show a new set of machine learning algorithms, and multiscale representation of XRT from LMB samples, that enable the quantification of LMB defects, as well as new protocols to monitor the lifespan of a LMB and the evolution of them during cycling.