Amidst the global shift towards the electrification of mass transportation, the effective long-and short-term planning of electric buses (e-buses) is gaining precedence due to challenges such as limited driving range, charging infrastructure requirements, charging costs, and battery lifetime. In this work, we propose a three-module modeling and optimization framework for strategic, tactical, and operational planning of multi-depot e-bus shuttle fleets by developing a series of mixed-integer programming models. The vehicle scheduling module determines e-bus deployment and trip assignments, ensuring feasible service while leveling energy consumption and mitigating battery degradation across the fleet. The charger deployment and charging planning module determines the number of chargers to deploy at depots and e-bus charging schedules to minimize life cycle costs. This module integrates an e-bus charging model that accounts for limited charger availability and practical considerations such as minimum charging duration, charger recovery period, and out-of-office hours, along with a neural network-based battery degradation model to minimize degradation costs and enable uniform battery aging. Finally, the online charging scheduling module updates the charging schedules to handle uncertainties in trip energy consumption. Case studies on a university campus shuttle e-bus network demonstrate a life cycle cost reduction of up to 38.2%, including savings on charger procurement, electricity, and battery degradation. Moreover, the proposed framework facilitates up to a 90.2% decrease in degradation costs and up to a 92.2% reduction in aging non-uniformity, maintaining cost optimality under uncertainties with a deviation of less than 1.5% compared to an oracle model in randomly generated scenarios.
