A novel integration of scheduling and dynamic wireless charging planning models of battery electric buses

The development of electromobility along with recent dynamic wireless power transfer technology offers the potential to improve the carbon footprint of public transportation while offering quality services. Existing models for electric bus scheduling cannot adequately capture the dependence among electric bus (EB) battery size, dynamic wireless charging (DWC) infrastructure planning, and fleet size. This study aims to simultaneously optimize the integrated model of EB fleet size and DWC infrastructure planning based on a real-world case study. We have developed a novel Mixed Integer Linear Programming (MILP) model to find the optimal EB fleet size, battery capacity, location of transmitters, number of inverters, and total cable length, which adheres to the battery charging restrictions. We conduct a sensitivity analysis to understand the system behavior in response to system robustness, fleet size, battery cost, cable cost, and bus purchase cost. The results show that seven homogeneous EBs carrying a uniform battery size of 18.1 kWh can serve all bus routes with a total cost of $3,683,235 that includes bus purchase cost. The results show that consideration of bus purchase costs in the model can save 4.1% of the total system cost over a lifetime. •We developed a MILP model for dynamic wireless charging planning.•We simultaneously optimize EB fleet size and DWC infrastructure planning.•We optimize EB fleet size, battery capacity, transmitter locations, inverters, and cables.•We conduct a sensitivity analysis to understand the system behavior.•Lifetime cost was build using fleet size, battery cost, cable cost, and bus purchase cost.