EV Charging Path Distribution Solution Based on Intelligent Network Connection

Abstract

The long queuing time for electric vehicles to charge under intelligent network connection leads to low distribution efficiency. Therefore, this paper proposes a strategy to predict the probability of queues forming for electric vehicles arriving at charging stations under intelligent network 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 predicting the probability of other vehicles in the region going to the charging station. A distribution path optimization model based on intelligent network connection and queuing theory is proposed for electric logistics vehicles in charging mode, taking into account the time window constraint and the influence of charging vehicles when using intelligent network connection for path planning. The objective is to minimize the total cost, and the route for electric logistics vehicles is adjusted in real time. This is solved by an improved hybrid genetic-annealing algorithm. The experimental results show that this paper obtains real-time dynamic road information and charging information with the help of intelligent network connection. It predicts the queuing probability of electric vehicles by combining with queuing theory, which can help select a more suitable charging location and timing for electric logistics vehicles. This can effectively avoid peak periods and reduce waiting times. By comparing with other models, this paper’s model can save the distribution cost of electric vehicles.

Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand

“…For example, Yao et al [12] proposed a method based on bender decomposition to apply electric vehicle delivery to the VRP problem with dynamic demand. Based on the intelligent network connection and queuing theory, Wang et al [13] proposed a predictive charging queuing probability and a path optimization model to reduce the waiting time of electric logistics vehicles, select appropriate charging locations and reduce delivery costs. However, electric vehicles (road-based ground vehicles) often lead to delivery inefficiencies due to factors such as traffic congestion and road network constraints, making it difficult to reduce delivery costs.…”