Research On Joint Scheduling Optimization Of Path Planning & Charging And Discharging Strategies For Electric Vehicles Based On V2G Technology

With the increasingly serious global energy crisis and environmental pollution,electric vehicles have attracted wide attention as a clean,efficient,and environmentally friendly means of transportation.However,as the number of electric vehicles continues to increase,their charging behavior has gradually had a greater impact on users’ daily travel and route planning.If there is a lack of scientific and systematic path planning and charging strategy scheduling for electric vehicles,it will lead to worsened traffic congestion near charging stations,increased waiting times for charging,and ultimately an increase in charging costs.Therefore,this paper aims to optimize the total charging cost of electric vehicles and improve the utilization of green energy by studying the joint optimization problem of electric vehicle path planning and charging strategy.This paper addresses the path planning and charging/discharging scheduling problems for electric vehicles.Based on vehicle-to-grid technology and green energy charging stations,a problem model is first established including a traffic road network,charging stations,and an electric vehicle model.Then,an optimization method for electric vehicle path selection and charging/discharging scheduling based on the k-shortest path algorithm and particle swarm optimization algorithm is proposed.Vehicle owners’ time cost and charging cost are unified as a single objective optimization model by using weighted summation to solve the problem.Finally,simulation experiments verify that the proposed charging/discharging scheduling optimization method can adjust path selection and charging/discharging strategies according to vehicle owners’ different time sensitivity,effectively reducing total cost while improving green energy consumption.To address the issues of k-shortest path algorithms that do not consider charging stations in the found paths and scheduling strategies that do not account for driver preferences,this paper proposes a driver preference-aware path planning method.This method provides personalized optional path sets for vehicles,and utilizes a particle swarm optimization algorithm to jointly optimize path selection and charging/discharging scheduling strategies.Experimental simulations confirm that the proposed joint optimization approach can better satisfy driver preferences and demands while minimizing overall costs.