  Lithium-ion cells may experience rapid degradation in later life, especially
with more extreme usage protocols. The onset of rapid degradation is called the
'knee point', and forecasting it is important for the safe and economically
viable use for batteries. We propose a data-driven method that uses automated
feature selection to produce inputs for a Gaussian process regression model
that estimates changes in battery health, from which the entire capacity fade
trajectory, knee point and end of life may be predicted. The feature selection
procedure flexibly adapts to varying inputs and prioritises those that impact
degradation. For the datasets considered, it was found that calendar time and
time spent in specific voltage regions had a strong impact on degradation rate.
The approach produced median root mean square errors on capacity estimates
under 1%, and also produced median knee point and end of life prediction errors
of 2.6% and 1.3% respectively.
