AI-based technique predicts useful life of batteries

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."