Machine learning slashes battery fast charging scheme development time: Page 2 of 2

March 20, 2020 //By Nick Flaherty
From left, Stanford Professor William Chueh, Toyota Research Institute scientist Muratahan Aykol, Stanford PhD student Aditya Grover, Stanford PhD alumnus Peter Attia, Stanford Professor Stefano Ermon and TRI scientist Patrick Herring. (Image credit: Farrin Abbott)
Machine learning has cut the time taken to develop a fast charging scheme for electric vehicle batteries from two years to 16 days.

In a previous study, the researchers found that instead of charging and recharging every battery until it failed – the usual way of testing a battery’s lifetime –they could predict how long a battery would last after only its first 100 charging cycles. This is because the machine learning system, after being trained on a few batteries cycled to failure, could find patterns in the early data that determined how long the cell would last.

Machine learning then reduced the number of methods they had to test. Instead of testing every possible charging method equally, or relying on intuition, the computer learned from its experiences to quickly find the best protocols to test. By testing fewer methods for fewer cycles, the team quickly found an optimal ultra-fast-charging protocol for their battery. In addition to dramatically speeding up the testing process, the solution was also better, and much more unusual, than what a battery scientist would likely have devised. “It gave us this surprisingly simple charging protocol – something we didn’t expect,” said Ermon. “That’s the difference between a human and a machine: The machine is not biased by human intuition, which is powerful but sometimes misleading.”

The researchers said their approach could accelerate nearly every piece of the battery development pipeline: from designing the chemistry of a battery to determining its size and shape, to finding better systems for manufacturing and storage. This would have broad implications not only for electric vehicles but for other types of energy storage. “This is a new way of doing battery development,” said Patrick Herring, co-author of the study and a scientist at the Toyota Research Institute. “Having data that you can share among a large number of people in academia and industry, and that is automatically analyzed, enables much faster innovation.”

The study’s machine learning and data collection system will be made available for future battery scientists to freely use, he added. By using this system to optimize other parts of the process with machine learning, battery development – and the arrival of newer, better technologies – could accelerate by an order of magnitude or more, he said.

www.stanford.edu

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