The findings, detailed in the journal Nature Communications, could aid in the development of safer and more reliable batteries for electric vehicles and consumer electronics as predicting the state of health and the remaining useful lifespan of lithium-ion batteries is one of the big problems limiting widespread adoption of electric vehicles.
It is also a familiar annoyance to mobile phone users.
“Safety and reliability are the most important design criteria as we develop batteries that can pack a lot of energy in a small space,” said Alpha Lee from University of Cambridge who co-led the research.
The researchers designed a new way to monitor batteries by sending electrical pulses into them and measuring the response.
The measurements were then processed by a machine learning algorithm to predict the battery’s health and useful lifespan.
The researchers performed over 20,000 experimental measurements to train the model.
Their method is non-invasive and is a simple add-on to any existing battery system.
The researchers also showed that the machine learning model can be interpreted to give hints about the physical mechanism of degradation.
The model can inform which electrical signals are most correlated with ageing, which in turn allows them to design specific experiments to probe why and how batteries degrade.
“By improving the software that monitors charging and discharging, and using data-driven software to control the charging process, I believe we can power a big improvement in battery performance,” Lee said.