数据-模型协同驱动的Robotaxi车队充电设施选址定容方法

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

摘要:

自动驾驶电动出租车Robotaxi车队的规模化商业运营需要完善的充电基础设施作为前提，但目前充电设施资源仍然存在容量不足、利用率低、布局不合理等问题。针对Robotaxi车队充电站选址定容问题，首先，以满足乘客出行需求为约束条件，提出基于出行网络行程衔接和Hopcroft-Karp算法的最小车队规模计算方法；在此基础上，通过蒙特卡洛仿真量化获取车队充电需求的时空分布；然后，考虑充电站建设投资、运营、维护的折旧成本以及车队空载行驶、排队充电和订单损失的机会成本，以综合成本最小化为目标，构建充电站选址定容优化模型，并针对模型求解提出一种基于遗传算子和自适应惯性权重的改进粒子群算法；最后，基于中国成都市的用户出行订单和地理数据验证了所提模型及算法的有效性。

关键词:自动驾驶出租车;充电设施选址;粒子群优化

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