Researchers Use AI to Predict Life of Batteries

LOS ALTOS, Calif. — Wouldn’t it be nice if battery manufacturers could tell which of their batteries will last at least two years and sell those to mobile phone makers, and which will last for 10 years or more and sell those to electric vehicle manufacturers? New collaborative research published in Nature Energy shows how they could start doing that.

Scientists at the Massachusetts Institute of Technology, Stanford University and the Toyota Research Institute discovered that combining comprehensive experimental data and artificial intelligence revealed the key for accurately predicting the useful life of lithium-ion batteries before their capacities started to wane.

After the researchers trained their machine learning model with a few hundred million data points, 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 9 percent of the actual cycle life. Separately, the algorithm categorized batteries as either long or short life expectancy based on just the first five charge/discharge cycles. Here, the predictions were correct 95 percent of the time.

This machine learning method could accelerate the research and development of new battery designs, and reduce the time and cost of production, among other applications. The researchers have made the data —the largest of its kind—publicly available.

One focus in the project was to find a better way to charge batteries in 10 minutes, a feature that could accelerate the mass adoption of electric vehicles.

En route to optimizing fast charging, the researchers wanted to find out whether if it was necessary to run their batteries into the ground. Can the answer to a battery question be found in the information from just the early cycles?

“Advances in computational power and data generation have recently enabled machine learning to accelerate progress for a variety of tasks. These include prediction of material properties,” said Dr. Richard Braatz of MIT. “Our results here show how we can predict the behavior of complex systems far into the future.”

Generally, the capacity of a lithium-ion battery is stable for a while. Then it takes a sharp turn downward. The plummet point varies widely, as most 21st century consumers know.

“The standard way to test new battery designs is to charge and discharge the cells until they die. 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.”