In an advance that could accelerate battery development and improve manufacturing, scientists have found how to accurately predict the useful lifespan of lithium-ion batteries, used in devices from mobile phones to electric cars.
If manufacturers of cell-phone batteries could tell which cells will last at least two years, then they could sell only those to phone makers and send the rest to makers of less demanding devices. New research shows how manufacturers could do this. The technique could be used not only to sort manufactured cells but to help new battery designs reach the market more quickly.
Combining comprehensive experimental data and artificial intelligence revealed the key for accurately predicting the useful life of lithium-ion batteries before their capacities start to wane, scientists at Stanford University, the Massachusetts Institute of Technology and the Toyota Research Institute discovered. After the researchers trained their 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 9 percent of the number of cycles the cells actually lasted. 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.
Published March 25 in Nature Energy, this machine learning method could accelerate research and development of new battery designs and reduce the time and cost of production, among other applications. The researchers have made the dataset - the largest of its kind - publicly available.
"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."
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 and engineering, conducted the battery experiments. MIT's team, led by Richard Braatz, professor in chemical engineering, performed the machine learning work. Kristen Severson, co-lead author of the research, completed her doctorate in chemical engineering at MIT last spring.
