Researchers in the UK have designed a machine learning method that can predict battery health with 10 times the accuracy of industry standard approaches and made the code freely available.
The researchers from Cambridge and Newcastle Universities, working on a Faraday Institute project, used a new way of monitoring batteries by sending electrical pulses into them and measuring the response. The measurements are then processed by a machine learning algorithm to predict battery health and useful lifespan. Their method is non-invasive and is a simple add-on to any existing battery system. The results are reported in the journal Nature Communications.
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. Battery performance degrades as a result of a complex network of subtle chemical processes. Individually, each of these processes doesn't have much of an effect on battery performance, but collectively they can shorten a battery's performance and lifespan signficantly.
Current methods for predicting battery health are based on tracking the current and voltage during battery charging and discharging. This misses important features that indicate battery health. Tracking the many processes that are happening within the battery requires new ways of probing batteries in action, as well as new algorithms that can detect subtle signals as they are charged and discharged.
"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 Dr Alpha Lee from Cambridge's Cavendish Laboratory, who co-led the research. "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."