Route Planning for Electric Vehicle Delivery Considering Charging Queuing Delay

The load and transmission in electric vehicle delivery make the power consumption nonlinear. Meanwhile, charging and queuing delays will affect the efficiency of delivery. To address this issue, a delivery route planning method for optimizing the selection of charging stations and charging time by applying a dynamic energy consumption model is studied. By adopting the electric vehicle dynamic energy consumption rate (ECR) model, a nonlinear energy consumption function relationship related to the load is established. Meanwhile, for the charging process of electric vehicles in queues, based on the queuing theory model, the functional relationships between the arrival rate of electric vehicles, service rate, charging station capacity and charging queuing delay (CQD) are analyzed. Then, based on the above analysis of ECR energy consumption and CQD latency, a route planning model aiming to minimize the total travel time is established. The model considers the access constraints, electric vehicle load constraints, and battery charge constraints to ensure its feasibility and accuracy in scenarios involving multiple vehicles, multiple tasks, and multiple charging stations. To efficiently solve the model, an optimization algorithm based on deep reinforcement learning (DRL) is designed. Specifically, for the problem of queueing and charging timing decisions, a dynamic decision-making algorithm using real-time information from charging stations is developed to reduce the difficulty of learning process of the DRL and improve the computational efficiency. Finally, the effectiveness of the proposed method is verified through multi-scale simulation experiments. The experimental results show that this method effectively optimizes the charging queuing time, reducing the average total driving time per vehicle by 0.14 hours; compared with various typical intelligent optimization algorithms, the comparison results show that the proposed method achieves an average reduction of 0.52 hours in travel time per vehicle and improves computational efficiency by 75.4%.