Machine-learning software can predict the remaining useful lifetime of a lithium-ion battery by seeing how it reacts when a rapidly oscillating voltage is briefly applied across it, according to a study published in Nature Communications.
The constant cycle of discharging and charging Li-ion batteries gradually knackers their maximum capacity, though the degradation process is difficult to estimate. Having a system that can automatically figure out the number of charging cycles left in a battery before its maximum capacity drops too far, and the component is thus on the way out, would be nice.
And so, boffins at the University of Cambridge and Newcastle University in the UK have developed software capable of forecasting the “remaining useful lifetime” of Li-ion batteries using AI and a method known as electrochemical impedance spectroscopy (EIS).
EIS in this instance works by briefly applying an oscillating voltage across the battery, and measuring the current response. These readings – which reveal the battery's impedance characteristics, or its resistance to an alternating current – are fed into a trained model, which uses them to predict the remaining lifetime of the battery under test.
“Our system sends in an oscillatory signal into the battery, and measures its response,” Alpha Lee, first author of the paper and a research fellow at the University of Cambridge, told The Register. on Monday. “This impedance spectrum provides information about the different electrochemical processes that are happening within the battery.”
The approach relies on Gaussian process regression, a statistical algorithm that can be trained to learn what properties are most indicative of degradation in order to predict how many charging cycles are left before a battery’s capacity drops to 80 per cent of its initial capacity.
The model was trained using 20,000 EIS measurements from batteries spanning a range of health, to learn what impedance characteristics a battery is most likely to exhibit when it’s about to drop to an unacceptable level of capacity. After training, when shown EIS readings of an arbitrary battery, the model can predict the number of cycles that battery has left before its performance falls below that acceptable level.
“Our model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery,” the team's paper stated. The academics claimed that their model is more accurate than conventional methods.
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Lee hopes that the algorithm will be used in commercial Li-ion batteries in the future so as to generate a warning system that tells users when their batteries need to be replaced.
“This is particularly important for electric vehicles because battery failure between service stations could be a major inconvenience, and some failure modes could pose safely concerns," he said.
"The model is also important for battery recycling because it can rapidly assess how ‘healthy’ a battery is, informing the decision of whether to re-use it for less demanding applications or recycle it as scrap metal.
“A key advantage is that our method does not require any modifications to the battery chemistry, so it is a simple turnkey solution. We are looking to work with electric vehicle manufacturers, original equipment manufacturers in the consumer electronics space, as well as battery manufacturers.” ®