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Researchers Can Predict Battery Lifetimes with ML


Further study in this area has the potential to guide the future of lithium-ion batteries, said Argonne National Laboratory computational scientist Noah Paulson. ​

Credit: Sealstep/Shutterstock

Researchers at the U.S. Department of Energy's Argonne National Laboratory used a machine learning (ML) algorithm to forecast the longevity of various battery chemistries.

The researchers used experimental data from a set of 300 batteries representing six distinct battery chemistries, including Argonne's nickel-manganese-cobalt-based cathode battery.

"One of the things we're able to do is to train the algorithm on a known chemistry and have it make predictions on an unknown chemistry," explained Argonne's Noah Paulson.

"Essentially, the algorithm may help point us in the direction of new and improved chemistries that offer longer lifetimes."

Paulson thinks the ML algorithm could expedite the development and testing of battery materials.

From Argonne National Laboratory
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Abstracts Copyright © 2022 SmithBucklin, Washington, DC, USA


 

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