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

A Dynamic Battery State-of-Health Forecasting Model for Electric Trucks: Li-Ion Batteries Case-Study