  This paper presents a combination of machine learning techniques to enable
prompt evaluation of retired electric vehicle batteries as to either retain
those batteries for a second-life application and extend their operation beyond
the original and first intent or send them to recycle facilities. The proposed
algorithm generates features from available battery current and voltage
measurements with simple statistics, selects and ranks the features using
correlation analysis, and employs Gaussian Process Regression enhanced with
bagging. This approach is validated over publicly available aging datasets of
more than 200 cells with slow and fast charging, with different cathode
chemistries, and for diverse operating conditions. Promising results are
observed based on multiple training-test partitions, wherein the mean of Root
Mean Squared Percent Error and Mean Percent Error performance errors are found
to be less than 1.48% and 1.29%, respectively, in the worst-case scenarios.
