A generalizable, data-driven online approach to forecast capacity degradation trajectory of lithium batteries

Estimating battery degradation is vital not only to monitor battery's state-of-health but also to accelerate research on new battery chemistries. Herein, we present a data-driven approach to forecast the capacity fading trajectory of lab-assembled lithium batteries. Features with physical meanings in addition to predictive abilities are extracted from discharge voltage curves, enabling online prediction for a single cell with only its historical data. The robustness and generalizability allow for the demonstration on a compromised quality dataset consisting of batteries varying in battery architectures and cycling conditions, with superior accuracy for end of life and degradation trajectory prediction with average errors of 8.2% and 2.8%, respectively. Apart from the impressive prediction accuracy, the as-extracted features also provide physical insights, the incorporation of which into material design or battery operation conditions further enlightens the development of better batteries. We highlight the effectiveness of time-series-based techniques in forecasting battery cycling performance, as well as the huge potential of data-driven methods in unveiling hidden correlations in complicated energy chemistries such as lithium metal batteries.