  Battery cycle life prediction using early degradation data has many potential
applications throughout the battery product life cycle. For that reason,
various data-driven methods have been proposed for point prediction of battery
cycle life with minimum knowledge of the battery degradation mechanisms.
However, managing the rapidly increasing amounts of batteries at end-of-life
with lower economic and technical risk requires prediction of cycle life with
quantified uncertainty, which is still lacking. The interpretability (i.e., the
reason for high prediction accuracy) of these advanced data-driven methods is
also worthy of investigation. Here, a Quantile Regression Forest (QRF) model,
having the advantage of not assuming any specific distribution of cycle life,
is introduced to make cycle life range prediction with uncertainty quantified
as the width of the prediction interval, in addition to point predictions with
high accuracy. The hyperparameters of the QRF model are optimized with a
proposed alpha-logistic-weighted criterion so that the coverage probabilities
associated with the prediction intervals are calibrated. The interpretability
of the final QRF model is explored with two global model-agnostic methods,
namely permutation importance and partial dependence plot.
