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New machine learning method from Stanford, with Toyota researchers, could supercharge battery development for electric vehicles

Six people, a mix of professors, researchers, and graduate students, stand for a group photo
Image credit: Farrin Abbott
Feb 19 2020
Fellow, Research, Stanford, Students

Battery performance can make or break the electric vehicle experience, from driving range to charging time to the lifetime of the car. Now, artificial intelligence has made dreams like recharging an EV in the time it takes to stop at a gas station a more likely reality, and could help improve other aspects of battery technology.

For decades, advances in electric vehicle batteries have been limited by a major bottleneck: evaluation times. At every stage of the battery development process, new technologies must be tested for months or even years to determine how long they will last. But now, a team led by Stanford professors Stefano Ermon and William Chueh has developed a machine learning-based method that slashes these testing times by 98 percent. Although the group tested their method on battery charge speed, they said it can be applied to numerous other parts of the battery development pipeline and even to non-energy technologies.

The study co-leads include Peter Attia, a 2014 Stanford Graduate Fellow, and Aditya Grover, a 2019 Gerald J. Lieberman Fellow.

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