Cost-effective electric bus resource assignment based on optimized charging and decision robustness

This paper explores a cost-efficient resource assignment for urban battery-electric-buses (BEBs) considering decision robustness based on the optimized charging plan. A network flow model was proposed to minimize the operation cost with mixed fleet, nonlinear charging and the constraint of charging station capacity. A sensitivity analysis was implemented using samples generated from clustered real-world data to simulate traffic-dependent bus travel time and energy consumption. The result shows that charging at night or with short duration in the daytime significantly increases the operation cost. Sufficient charging resources improve the robustness of the charging plan, while economic benefits merely increase when the charging station capacity reaches a certain threshold. Bus operators can reduce the investment in chargers to ensure both economic benefits and operation stability. When bus routes are longer with higher energy consumption, buses with medium or high battery capacity become more demanding, while the requirement on charging resources remains stable