  It is of extreme importance to monitor and manage the battery health to
enhance the performance and decrease the maintenance cost of operating electric
vehicles. This paper concerns the machine-learning-enabled state-of-health
(SoH) prognosis for Li-ion batteries in electric trucks, where they are used as
energy sources. The paper proposes methods to calculate SoH and cycle life for
the battery packs. We propose autoregressive integrated modeling average
(ARIMA) and supervised learning (bagging with decision tree as the base
estimator; BAG) for forecasting the battery SoH in order to maximize the
battery availability for forklift operations. As the use of data-driven methods
for battery prognostics is increasing, we demonstrate the capabilities of ARIMA
and under circumstances when there is little prior information available about
the batteries. For this work, we had a unique data set of 31 lithium-ion
battery packs from forklifts in commercial operations. On the one hand, results
indicate that the developed ARIMA model provided relevant tools to analyze the
data from several batteries. On the other hand, BAG model results suggest that
the developed supervised learning model using decision trees as base estimator
yields better forecast accuracy in the presence of large variation in data for
one battery.
