Research On Electric Vehicle Charging Scheduling Based On Improved Ant Colony Algorithm

As an important means to deal with the energy crisis and environmental pollution,electric vehicles have been gradually promoted all over the world with the advantages of high energy efficiency and low emission.However,it has the problem of shortening the driving range due to insufficient power.In addition,urban traffic congestion leads to time-varying speed of vehicles,resulting in reduced battery life and reduced endurance capacity.Therefore,it is of great practical significance to reasonably plan the charging scheduling route and improve the endurance capacity of electric vehicles.Aiming at the problem that users need to go to the charging station to charge due to insufficient power during driving,and then start from the charging station to the destination again,this paper proposes an electric vehicle charging scheduling optimization method based on improved ant colony algorithm.Through the joint optimization of charging path selection and charging station selection,based on the traffic flow of time-varying speed and time slice,by considering the real-time battery remaining power and traffic conditions,Build a charging scheduling path planning scheme that can meet the current remaining battery power to reach the charging station and use the least time when the electric vehicle is driving,and study the traffic flow of the road condition stability time slice and the traffic flow of the road condition change time slice respectively.The research contents are as follows:(1)Based on the existing literature and theory,this paper makes a detailed analysis of the electric vehicle charging scheduling problem and expounds the purpose of charging scheduling,then introduces the swarm intelligence algorithm,and analyzes and studies the ant colony algorithm,which lays a theoretical foundation for the establishment of the dual objective model and the design of the solution algorithm under the traffic flow of time-varying speed and time slice.(2)The ant colony algorithm is improved in four aspects,and an ant colony optimization algorithm with adaptive dynamic search is proposed.Firstly,the greedy algorithm is used to construct the suboptimal path,improve the pheromone on the passing section,and realize the initial distribution of pheromone in different sections;Secondly,when selecting the next node,the congestion factor is introduced as the reference basis;thirdly,dynamically adjust the volatilization factor to increase the global search ability and convergence speed of ant colony algorithm;fourthly,the mutation operation is performed on the optimal solution of each iteration to prevent the algorithm from falling into the local optimal solution;then,the adaptive dynamic search ant colony optimization algorithm and the other two ant colony algorithms are simulated in different benchmark functions to verify that the improved ant colony algorithm has better convergence speed.(3)In order to verify the ability of the improved ant colony algorithm to solve the electric vehicle charging scheduling model,this paper establishes a double objective optimization function from the perspectives of energy consumption and time consumption,that is,while meeting the current remaining battery power can reach the charging station,the electric vehicle has the least time consumption when driving,and different algorithm solving models are set for various scenes of time slices of different time-varying speeds and traffic conditions.(4)In the simulation experiment,different colors are used to mark the congestion of each road.Firstly,the improved ant colony algorithm is compared with the greedy algorithm under the same traffic conditions;Secondly,under the regular road network model,the improved ant colony algorithm is simulated by setting different starting points and changing speed and traffic conditions at different times to verify the stability of the improved ant colony algorithm;By setting the number of different charging stations,the simulation experiments of the improved ant colony algorithm,greedy algorithm and basic ant colony algorithm also verify that the improved ant colony algorithm is stable for the dual-objective charging scheduling optimization problem.