Researchers from Stanford University, MIT and the Toyota Research Institute in the US have used machine learning to cut the fast charging time for electric vehicle and energy storage batteries dramatically.
The group initially tested their method on battery charge speed, and said it can be applied to numerous other parts of the battery development pipeline and even to non-energy technologies.
“In battery testing, you have to try a massive number of things, because the performance you get will vary drastically,” said Stefano Ermon, an assistant professor of computer science. “With AI, we’re able to quickly identify the most promising approaches and cut out a lot of unnecessary experiments.”
The researchers wrote a machine learning framework 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 fast 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.
“We figured out how to greatly accelerate the testing process for extreme fast charging,” said Peter Attia, who co-led the study while he was a graduate student. “What’s really exciting, though, is the method. We can apply this approach to many other problems that, right now, are holding back battery development for months or years.”
Fast charging optimization uses many trial-and-error tests, something that is inefficient for humans, but the perfect problem for a machine. “Machine learning is trial-and-error, but in a smarter way,” said Aditya Grover, a graduate student in computer science who also co-led the study. “Computers are far better than us at figuring out when to explore – try new and different approaches – and when to exploit, or zero in, on the most promising ones.”
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.
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