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

American Physical Society (APS) March Meeting 2022


March 16, 2022
4:12pm to 4:24pm
Chicago, IL


BIDS Research Affiliate Daniela Ushizima (Lawrence Berkeley National Laboratory, UC Berkeley, UC San Francisco) will present this talk at the upcoming American Physical Society (APS) March Meeting 2022, as part of a session on Deep Learning Computer Vision

Lithium Metal Battery Characterization using X-ray Imaging and Machine Learning
Date: Wednesday, March 16, 2022
Time: 4:12 PM–4:24 PM
Location: McCormick Place W-192B

Abstract: 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. This work was supported by projects at LBNL funded by DOE ASCR and BES programs, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

Authors: Daniela Ushizima (Lawrence Berkeley National Laboratory, UC Berkeley, UC San Francisco), Ying Huang (National Fuel Cell Research Center, UC Irvine), Jerome Quenum (UC Berkeley, Lawrence Berkeley National Laboratory), David Perlmutter (Lawrence Berkeley National Laboratory), Dilworth Parkinson (Lawrence Berkeley National Laboratory), Iryna Zenyuk (National Fuel Cell Research Center, UC Irvine)


Daniela Ushizima

Staff Scientist, Applied Mathematics and Computational Research Division, Berkeley Lab

BIDS Faculty Affiliate Dani Ushizima is a Staff Scientist in the Machine Learning and Analytics Group in the Computational Research Division at Berkeley Lab, where she leads the Image Processing/Machine Vision team at CAMERA, and an Affiliate Faculty of the Bakar Computational Health Sciences Institute (BCHSI) at the University of California, San Francisco. She also leads the Center for Recognition and Inspection of Cells (CRIC), where her research focuses on imaging cancer cells for early-stage disease diagnosis. With 20 years of research and development experience in Computer Vision, Dani has focused primarily on quantitative microscopy and microstructure classification, from materials science to biomedical imaging.