Previous work primarily focused on forecasting future battery performance in the laboratory setting, where cells are charged and discharged in the same way over the entirety of their lifetimes, thus the impact of variable cell usage on future performance can be ignored (see Figure ). These studies applied machine learning on features extracted from the charging or discharging curve to predict discharge capacity , remaining useful life , and abrupt capacity decays . Innovations in extracting features from charge/discharge curves and machine learning approaches for modelling time-series data have enabled significant improvements in the accuracy of predictions. Going beyond charging and discharging curves, approaches such as electrochemical impedance spectroscopy (EIS) and acoustic time-of-flight analysis have been used for degradation forecasting.