Researchers at MIT, Stanford and Toyota Research Institute say they can accurately predict the cycle life of lithium-ion batteries using early cycle data and machine learning. New collaborative research published today in Nature Energy shows that combining 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% 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% 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, said to be the largest of its kind, publicly available.
“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.”
The work was carried out at the Center for Data-Driven Design of Batteries, an academic-industrial collaboration that integrates theory, experiments and data science. The Stanford researchers, led by William Chueh, assistant professor in Materials Science & Engineering, conducted the battery experiments. MIT’s team, led by Richard Braatz, professor in Chemical Engineering, performed the machine learning work. Kristen Severson is co-lead author of the research. She completed her Ph.D. in chemical engineering at MIT last spring.
Study co-authors Muratahan Aykol and Patrick Herring brought TRI’s experience with big data to the project and their own expertise on battery development to enable effective management and seamless flow of battery data, which was essential for this collaboration to create accurate machine-learning models for the early-prediction of battery failure.
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. In this project, the batteries lasted anywhere from 150 to 2300 cycles. That variance was partly the result of testing different methods of fast charging, but also due to the normal differences that emerge in commercially produced devices that depend on molecular interfaces.