The researchers, from Cambridge and Newcastle Universities, have said the new model could aid in the development of safer and more reliable batteries for electric vehicles and consumer electronics.

The method monitors batteries by sending electrical pulses into them and measuring the response. These measurements are then processed by a machine-learning algorithm to predict the battery’s health and useful lifespan. According to the team, the method is non-invasive and is a simple add-on to any existing battery system.

Predicting the state of health and the remaining useful lifespan of lithium-ion batteries is one of the most challenging issues limiting widespread adoption of electric vehicles: it’s also a “familiar annoyance” to mobile phone users, experts have said.

Over time, battery performance degrades via 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 severely shorten a battery’s performance and lifespan.

Current methods for predicting battery health are based on tracking the current and voltage during battery charging and discharging. This, however, 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. “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.”

The researchers performed over 20,000 experimental measurements to train the model, the largest dataset of its kind. Importantly, the model learns how to distinguish important signals from irrelevant noise.

The researchers also showed that the machine learning model can be interpreted to give hints about the physical mechanism of degradation. It 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.

“Machine learning complements and augments physical understanding,” said Dr Yunwei Zhang, also from the Cavendish Laboratory. “The interpretable signals identified by our machine learning model are a starting point for future theoretical and experimental studies.”

The researchers are now using their machine learning platform to understand degradation in different battery chemistries. They are also developing optimal battery charging protocols, powering by machine learning, to enable fast charging and minimise degradation.

The researchers, from Cambridge and Newcastle Universities, have said the new model could aid in the development of safer and more reliable batteries for electric vehicles and consumer electronics.

The method monitors batteries by sending electrical pulses into them and measuring the response. These measurements are then processed by a machine-learning algorithm to predict the battery’s health and useful lifespan. According to the team, the method is non-invasive and is a simple add-on to any existing battery system.

Predicting the state of health and the remaining useful lifespan of lithium-ion batteries is one of the most challenging issues limiting widespread adoption of electric vehicles: it’s also a “familiar annoyance” to mobile phone users, experts have said.

Over time, battery performance degrades via 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 severely shorten a battery’s performance and lifespan.

Current methods for predicting battery health are based on tracking the current and voltage during battery charging and discharging. This, however, 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. “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.”

The researchers performed over 20,000 experimental measurements to train the model, the largest dataset of its kind. Importantly, the model learns how to distinguish important signals from irrelevant noise.

The researchers also showed that the machine learning model can be interpreted to give hints about the physical mechanism of degradation. It 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.

“Machine learning complements and augments physical understanding,” said Dr Yunwei Zhang, also from the Cavendish Laboratory. “The interpretable signals identified by our machine learning model are a starting point for future theoretical and experimental studies.”

The researchers are now using their machine learning platform to understand degradation in different battery chemistries. They are also developing optimal battery charging protocols, powering by machine learning, to enable fast charging and minimise degradation.