Research On Induced Optimization Of Electric Vehicle Charging Service Considering User’s Multidimensional Preference

With the popularization and development of electric vehicles,electric vehicles have gradually become one of the main means of transportation for people to travel.How to choose the best charging station according to the user’s charging preference and avoid the situation of insufficient power or high charging cost during driving has become the main concern of users.In the case of considering users’ multi-dimensional charging preferences,it is the basis and guarantee for the larger-scale popularization of electric vehicles in the future to propose an effective charging induction method for electric vehicles and guide users to choose suitable charging stations for charging.The main research work carried out in this paper includes: From the perspective of users’ travel needs,based on the behavior data of users choosing charging stations,a preference model based on collaborative filtering algorithm is established to analyze users’ preferences for each charging station.Consider the energy consumption cost,time cost and charging cost of the user’s travel charging comprehensively,on this basis,add the user’s requirements for the charging time period,and take the cost of charging not within the time period as the penalty cost,which is included in the consideration of the objective function.Based on this,a multi-objective optimization model is established.The model consists of more than three optimization objectives,so this is a typical multiobjective optimization problem.In this paper,the NSGA-Ⅲ framework is used to design the algorithm,and the Pareto solution set is obtained by solving it.On this basis,according to the user’s charging preference to the charging station and the user’s personal charging preference,the optimal travel and charging scheme is determined with the lowest comprehensive cost as the criterion.In order to provide users with a more personalized charging induction strategy,combined with the actual charging situation,the impact of the time-of-use electricity price is further considered on the basis of the existing model,relax the full charge assumption at the same time,consider partial charging strategies,the optimal charging amount is integrated into the problem decision-making process to help users determine the required charging amount at different candidate charging stations,thereby obtaining the optimal travel and charging plan.This paper takes the minimum cost on the user side as the goal,considers the user’s multi-dimensional charging preference comprehensively,and finally provides a more comprehensive charging induction strategy for the user.In this paper,a simulation example is designed based on the data of some road networks and charging stations in Beijing.The experimental results show that the multiobjective optimization model built not only provides users with reliable charging induction decisions,but also integrates the user’s personalized charging preference characteristics,and the resulting travel and the charging scheme meets user expectations;on the basis of the existing model,the partial charging strategy in the case of time-ofuse electricity price is further considered.The results show that the energy consumption,time and penalty cost of the model considering the time-of-use electricity price have little change,while the fee cost is significantly reduced.Compared with the previous model,the average reduction is 96%,which proves that the proposed scheme can help users make better decisions and is of great significance for promoting the further development of electric vehicles in the future.