  Recent data-driven approaches have shown great potential in early prediction
of battery cycle life by utilizing features from the discharge voltage curve.
However, these studies caution that data-driven approaches must be combined
with specific design of experiments in order to limit the range of aging
conditions, since the expected life of Li-ion batteries is a complex function
of various aging factors. In this work, we investigate the performance of the
data-driven approach for battery lifetime prognostics with Li-ion batteries
cycled under a variety of aging conditions, in order to determine when the
data-driven approach can successfully be applied. Results show a correlation
between the variance of the discharge capacity difference and the end-of-life
for cells aged under a wide range of charge/discharge C-rates and operating
temperatures. This holds despite the different conditions being used not only
to cycle the batteries but also to obtain the features: the features are
calculated directly from cycling data without separate slow characterization
cycles at a controlled temperature. However, the correlation weakens
considerably when the voltage data window for feature extraction is reduced, or
when features from the charge voltage curve instead of discharge are used. As
deep constant-current discharges rarely happen in practice, this imposes new
challenges for applying this method in a real-world system.
