Researchers have designed a new way to monitor batteries by sending electrical pulses into them and measuring the response. The measurements are then processed by a machine learning algorithm to predict the battery’s health and useful lifespan. 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 big problems limiting widespread adoption of electric vehicles and also affects the safety of mobile phones. 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 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.
The researchers designed a way to monitor a battery by sending electrical pulses into it and measuring its response. A machine learning model is then used to discover specific features in the electrical response that are the telltale sign of battery aging. The researchers performed more than 20,000 experimental measurements to train the model. Importantly, the model learns how to distinguish important signals from irrelevant noise. The method is noninvasive and is a simple add-on to any existing battery systems.
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 aging, which in turn allows them to design specific experiments to probe why and how batteries degrade.
The machine learning platform is being used to understand degradation in different battery chemistries. Optimal battery charging protocols are being developed, powered by machine learning, to enable fast charging and minimize degradation.