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

A data-driven approach based on time-series-based machines learning techniques was developed to forecast the capacity degradation trajectory of lithium batteries, which only adopt historic data for the prediction on an individual battery. Sensible prognosis on compromised-quality, lab-made batteries was achieved with superior prediction accuracy and mechanistic insights into battery degradation chemistries.