基于智能网联的电动车充电路径规划研究

he long queuing time of electric vehicles in charging under smart grid connection leads to low distribution efficiency, therefore, this paper proposes a strategy to predict the queuing probability of electric vehicles arriving at charging stations under smart grid connection. Both the dynamic demand of customers and the characteristics of the alternating influence of charging vehicles should be considered when studying such problems. Based on the above problem characteristics, a real-time dynamic charging selection strategy is developed by introducing prediction probability, and a distribution path optimization model based on smart netlink and queuing theory for electric logistics vehicles in charging mode is proposed, considering the time window constraint and the influence of charging vehicles when using smart netlink for path planning, minimizing the total cost as the optimization objective, adjusting the route for electric logistics vehicles in real time, and using an improved genetic- annealing hybrid algorithm is solved. The experimental results show that this paper obtains real-time dynamic road information and charging information with the help of intelligent netlink, and predicts the queuing probability of electric vehicles by combining with queuing theory, which can select more suitable charging location and timing for electric logistics vehicles, effectively avoid peak periods and reduce waiting time; by comparing with other models, this paper\'s model can save the distribution cost of electric vehicles.