Research On Electric Vehicle Charging Recommendation Model Based On Improved Moth Flame Optimization

With the increasing environmental pollution and energy crisis,people pay more attention to electric vehicles.As an important way to deal with these problems,electric vehicles can not only reduce environmental pollution and improve energy efficiency,but also realize smart networking and low-carbon transition.At present,the discrete and disorderly charging behavior of electric vehicles leads to long charging time and low charging efficiency of electric vehicles,causing serious range anxiety of users,which greatly reduces the charging experience of users.Therefore,it is of great practical significance to provide real-time online charging recommendation services for EV charging requests and guide EV users to charge in an orderly manner,which can alleviate users’ range anxiety and promote the rapid development of the industry.Aiming at the problem of EV charging recommendation,this paper puts forward solutions and optimization strategies,and uses Eclipse and MATLAB to conduct simulation experiments on the algorithm proposed in this paper.The main research contents are as follows:1.In this paper,the characteristics and charging behavior of electric vehicles are analyzed,and the objective function of the total charging time of electric vehicles is established by fully considering the arrival time,waiting time in the station,actual charging time and departure time of electric vehicles.Based on the road traffic flow and the available information of charging stations,an electric vehicle charging recommendation model was constructed.The Fastest Charging Recommendation Algorithm(FCRA)was proposed,and the charging piles are preallocated to the vehicles with the goal of the shortest charging time.It is compared with the Shortest Path Recommendation Algorithm(SPRA)to prove its effectiveness.2.This paper proposes an algorithm based on Moth Flame Optimization(MFO)suitable for solving the charging recommendation problem of large-scale electric vehicles.The algorithm reduces the total charging time of the global vehicle through continuous iteration.In the simulation experiment,the classical MFO charging recommendation algorithm was compared with four widely used optimization algorithms under different vehicle numbers,and the optimization ability of the algorithm was analyzed from four indexes,namely fitness value,running time,charging station load and average charging time.3.In this paper,Levy Flight strategy is introduced into the MFO algorithm to expand the search scope of the algorithm and increase the diversity of the population.An EV charging recommendation algorithm combining Levy Moth Flame Optimization(LMFO)was proposed.The experimental results show that the improved LMFO charging recommendation algorithm not only further reduces the average charging time of vehicles,but also has strong stability.