  This work presents a novel hierarchical approach to increase Battery Electric
Buses (BEBs) utilization in transit fleets. The proposed approach relies on
three key components. A learning-based BEB digital twin cloud platform is used
to accurately predict BEB charge consumption on a per vehicle, per driver, and
per route basis, and accurately predict the time-to-charge BEB batteries to any
level. These predictions are then used by a Predictive Block Assignment module
to maximize the BEB fleet utilization. This module computes the optimal BEB
daily assignment and charge management strategy. A Depot Parking and Charging
Queue Management module is used to autonomously park and charge the vehicles
based on their charging demands. The paper discusses the technical approach and
benefits of each level in architecture and concludes with a realistic
simulations study. The study shows that if our approach is employed, BEB fleet
utilization can increase by 50% compared to state-of-the-art methods.
