Abstract

Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles. In this talk, I will introduce our recent work on how to use AI techniques to improve battery health and safety. We built an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS)—a real-time, non-invasive and information-rich measurement that is hitherto underused in battery diagnosis—with Gaussian process machine learning. Over 20,000 EIS spectra of commercial Li-ion batteries were collected to train the model, the largest data of this kind. The model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation from irrelevant noise. Our model accurately predicts the remaining useful life and can be interpreted to give hints about the physical mechanism of degradation. Our results demonstrate the value of EIS signals in battery management systems.