Electric Vehicle Route Optimization And Application Research Based On Charging And Swapping Options

As energy shortages and the greenhouse effect become more and more serious,my country clearly proposes to achieve carbon peaking before 2030.From the concept to triggering energy transformation,the dual-carbon policy is the inevitable progress of human economic and social development and the requirement to achieve high-quality development.New energy logistics vehicles,mainly electric vehicles,are rapidly replacing traditional fuel vehicles.In this context,many logistics companies,including J Company,have begun to use electric vehicles to deliver goods.However,under the current technical conditions,electric vehicles are limited by the cruising range due to reasons such as small battery capacity and high load,and need to continuously replenish energy during the delivery process.At present,there are two types of energy replenishment methods: charging and battery exchange.Charging takes a long time but the price is low,and battery exchange takes a short time but is expensive.When serving different customers,different power replenishment methods will affect the arrival of subsequent customers.Time may cause different time window costs.Therefore,prior to delivery,optimizing the order of customer delivery,the insertion position of the power supply station,and the way of power supply can effectively reduce costs.To this end,this paper first constructs an electric vehicle route optimization model based on charging and swapping options.The model aims to minimize the total cost of vehicle usage cost,route travel cost,time window penalty cost,and power supply cost,and considers the vehicle’s load and battery life as well as the customer’s time window constraints.When solving the problem,a threedimensional coding method is designed according to the characteristics of the power station,and the customer sequence and the power station sequence are coded and mapped to each other to form a path.The selection strategy of the genetic algorithm is improved and the local search operator is added,and the improved genetic algorithm is used as the framework to carry out two-stage optimization solution,that is,the first stage optimizes the customer sequence and the power station to form a path solution that satisfies the power constraint,and the second stage optimizes each The power stations in each path are optimized to optimize the charging and swapping modes to form the final path solution.A comparative analysis with related charging and swapping algorithms shows the superiority of the proposed algorithm.Further,in view of the complex environment of the power station on the current distribution route,the impact of designing different power station environments on the distribution cost is analyzed.The comparison and analysis of five different power station environments,including integrated service stations and integrated service stations plus single charge and single exchange,shows that the distribution cost of power stations with both charging and swapping functions on the distribution route is the lowest,which provides a great deal of support for the construction of power stations on the distribution route.guide.Secondly,the optimization model is verified by taking company J,whose power station environment on the distribution route is both charging and swapping functions,as an example.Firstly,it analyzes the current distribution status and existing problems of J company,and then uses the above path optimization solution method to solve the problem,analyzes the results,and puts forward relevant distribution suggestions.