Artificial Intelligence Predicts Battery Life

Researchers at Stanford University, the Massachusetts Institute of Technology and the Toyota Research Institute at the Center for Data-Driven Design of Batteries have found out how the useful life of lithium-ion batteries can be precisely determined.

“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 Peter Attia, Stanford doctoral candidate in materials science and engineering. “It’s an expensive bottleneck in battery research.”

Because battery manufacturers must guarantee the predicted life of their products, they rely on a secure method to predict the battery life. More than one hundred million battery life data were entered into a learning model, which used the entered data to create an algorithm for the individual charging cycles. With a hit rate of 95%, the algorithm calculates a correct prediction based on only 5 charge/discharge cycles. The researchers have made the algorithm available to the public and thus made an important contribution to massively reducing production time and costs. William Chueh, Assistant Professor of Materials Science and Engineering at Stanford University, led the experiments and the Massachusetts Institute of Technology research team led the learning work under the direction of Richard Braatz, Professor of Chemical Engineering. Nicholas Perkins, Peter Attia and Norman Jin are also among the researchers who have found the key to accurately predicting the useful life of lithium-ion batteries.


Source: Stanford University |
Image Source: Pixabay |

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