考虑充电排队时延的电车配送路径规划方法OACSCD

The load and transmission in electric vehicle delivery make the power consumption nonlinear.Mean-while,charging and queuing delays will affect the efficiency of delivery.To address this issue,a delivery route plan-ning method for optimizing the selection of charging stations and charging time by applying a dynamic energy con-sumption model is studied.By adopting the electric vehicle dynamic energy consumption rate(ECR)model,a non-linear energy consumption function relationship related to the load is established.Meanwhile,for the charging pro-cess of electric vehicles in queues,based on the queuing theory model,the functional relationships between the ar-rival rate of electric vehicles,service rate,charging station capacity and charging queuing delay(CQD)are ana-lyzed.Then,based on the above analysis of ECR energy consumption and CQD latency,a route planning model aim-ing 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 vehi-cles,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 de-cisions,a dynamic decision-making algorithm using real-time information from charging stations is developed to re-duce the difficulty of learning process of the DRL and improve the computational efficiency.Finally,the effective-ness 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 computa-tional efficiency by 75.4%.