Advancing New Battery Design with Deep Learning

Electric cars are an integral part of our clean energy future – every time one replaces a gas-powered vehicle, it can save 1.5 tons of carbon dioxide per year. But to truly expand the population and reach of electric cars, new energy storage solutions must be developed to produce lighter vehicles with longer ranges and more powerful batteries.

A team of researchers from Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Irvine recently moved this effort forward with the development of deep-learning algorithms to automate the quality control and assessment of new battery designs.

The research team, led by Berkeley Lab’s Daniela Ushizima, a staff scientist in the Applied Mathematics and Computational Research Division and a Berkeley Institute for Data Science Research Affiliate, includes scientists from the National Fuel Cell Research Center (NFRC) at UC Irvine and collaborators from UC Berkeley’s Department of Electrical Engineering and Computer Sciences and School of Information. Together, they created these deep-learning algorithms to automate the inspection of batteries with data acquired using advanced instruments, including those at Berkeley Lab’s Advanced Light Source (ALS). By using X-ray tomography as the input data, as well as prototypes defined by battery experts, the research team developed automated methods to detect battery defects in rechargeable lithium metal batteries and measure their growth during battery cycling.

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