New machine-learning method could supercharge EV battery development

Battery performance can make or break the EV experience, from driving range to charging time to the lifetime of the car. A team led by Stanford Professors Stefano Ermon and William Chueh has developed a machine-learning algorithm that could lead to longer-lasting, faster-charging batteries.

For decades, advances in EV batteries have been limited by 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. The Stanford researchers say their new method slashes these testing times by up to 98 percent. Although the group tested their method on battery charge speed, they said it can also be applied to numerous other parts of the battery development pipeline.

The study, published in Nature, was part of a larger collaboration among scientists from Stanford, MIT and the Toyota Research Institute that bridges foundational academic research and real-world industry applications. The goal: finding the best method for charging an EV battery in 10 minutes that maximizes the battery’s overall lifetime. The researchers wrote a program that, based on only a few charging cycles, predicted how batteries would respond to different charging approaches. The software also decided in real time what charging approaches to focus on or ignore. By reducing both the length and number of trials, the researchers cut the testing process from almost two years to 16 days. In addition to dramatically speeding up the testing process, the computer’s solution was also better—and more unusual—than what a battery scientist would likely have devised.

The study’s machine learning and data collection system will be made available for future battery scientists to use freely.

Source: Stanford University