  Accurately predicting the future health of batteries is necessary to ensure
reliable operation, minimise maintenance costs, and calculate the value of
energy storage investments. The complex nature of degradation renders
data-driven approaches a promising alternative to mechanistic modelling. This
study predicts the changes in battery capacity over time using a Bayesian
non-parametric approach based on Gaussian process regression. These changes can
be integrated against an arbitrary input sequence to predict capacity fade in a
variety of usage scenarios, forming a generalised health model. The approach
naturally incorporates varying current, voltage and temperature inputs, crucial
for enabling real world application. A key innovation is the feature selection
step, where arbitrary length current, voltage and temperature measurement
vectors are mapped to fixed size feature vectors, enabling them to be
efficiently used as exogenous variables. The approach is demonstrated on the
open-source NASA Randomised Battery Usage Dataset, with data of 26 cells aged
under randomized operational conditions. Using half of the cells for training,
and half for validation, the method is shown to accurately predict non-linear
capacity fade, with a best case normalised root mean square error of 4.3%,
including accurate estimation of prediction uncertainty.
