AI-based technique predicts useful life of batteries

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Researchers at Stanford University, MIT and the Toyota Research Institute have developed a technique that can be used to accurately predict the useful life of lithium-ion batteries.

battery life
New batteries can be sorted by predicted cycle life accurately with new technique based on five test charge/discharge cycles. Image: Younghee Lee/CUBE3D Graphic

The technique – which could accelerate research and development of new battery designs and reduce the time and cost of production was developed by training a machine learning model with a few hundred million data points of batteries charging and discharging. The algorithm predicted how many more cycles each battery would last, based on voltage declines and a few other factors among the early cycles.

The predictions were within nine per cent of the number of cycles the cells actually lasted. The algorithm also categorised batteries as either long or short life expectancy based on just the first five charge/discharge cycles. Here, the predictions were correct 95 per cent of the time.

"The standard way to test new battery designs is to charge and discharge the cells until they fail. Since batteries have a long lifetime, this process can take many months and even years," said co-lead author of a paper in Nature Energy Peter Attia, Stanford doctoral candidate in materials science and engineering. "It's an expensive bottleneck in battery research."

"For all of the time and money that gets spent on battery development, progress is still measured in decades," said study co-author Patrick Herring, a scientist at the Toyota Research Institute. "In this work, we are reducing one of the most time-consuming steps - battery testing - by an order of magnitude."

The new method has many potential applications, Attia said. For example, it can shorten the time for validating new types of batteries, which is especially important given rapid advances in materials. With the sorting technique, electric vehicle batteries determined to have short lifespans - too short for cars - could be used instead to power street lights or back up data centres. Recyclers could find cells from used EV battery packs with enough capacity left for a second life.

Another possibility is optimising battery manufacturing. "The last step in manufacturing batteries is called 'formation,' which can take days to weeks," Attia said. "Using our approach could shorten that significantly and lower the production cost."

The researchers are now using their model to optimise ways of charging batteries in just 10 minutes, which they say will cut the process by more than a factor of 10.

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