Charging Facility Layout and Planning Approach for Robotaxi Fleets Collaboratively Driven by Data and Model

Abstract:

The large-scale commercial application of electric Robotaxi fleets requires well-developed charging infrastructure as a prerequisite. However， there are still issues with insufficient quantity， low utilization rate， and inappropriate distribution of charging facilities. In addressing the site selection and capacity planning of charging stations for electric Robotaxi fleets， firstly， a minimum fleet size calculation method based on the trip network articulation and Hopcroft-Karp algorithm was proposed to meet passenger temp-spatial travel demands. Based on this， the spatio-temporal distribution of charging demands of Robotaxi fleets was quantified through Monte Carlo simulation. Then， considering the construction and operation costs of charging stations， grid loss costs， no-load driving， queuing for charging and loss of order opportunity costs of Robotaxi fleets， an optimization model for the site selection and capacity planning of charging stations for electric Robotaxi fleets was constructed with the objective of minimizing overall costs， and an improved particle swarm optimization algorithm based on genetic operators and adaptive inertia weight was proposed. Finally， the effectiveness of the proposed model and algorithm was validated using real user travel orders and geographic data from the city of Chengdu， China.

Key words:Robotaxi;charging facility siting;particle swarm optimization