Research On Optimization Of Vehicle Scheduling Based On Power Consumption

With the continuous progress of society and economy,environmental pollution and energy crisis are becoming increasingly severe.Facing the dual challenges of both,how to reduce energy consumption and exhaust emissions and establish a new transportation system that is efficient,energy-saving and environmentally friendly has become an urgent need for domestic and foreign transportation companies.New problems solved.New energy vehicles provide new ideas for alleviating environmental pollution and energy crisis with their unique advantages of low pollution and low energy consumption.At the same time,the state has issued a series of policies to vigorously support the development of the new energy automobile industry.On the other hand,in the face of increasing delivery costs and customers’ "high-frequency,small-batch" delivery needs,as well as responding to the national "blue sky defense" policy,"green logistics" has gradually received more and more attention from express companies.Express companies have begun to gradually try to use pure electric vehicles as distribution vehicles to replace traditional fuel vehicles to complete the distribution tasks from distribution centers to customers.Therefore,it is an inevitable trend to vigorously develop pure electric vehicles.However,when most companies use pure electric vehicles to complete their distribution tasks,they seldom consider the charging status of the vehicles in transit,which makes the distribution vehicles vulnerable to driving mileage restrictions,resulting in an increase in the number of distributions,which in turn increases the cost of distribution.Therefore,it is particularly important to consider power consumption for the vehicle scheduling problem of pure electric vehicles,and due to the characteristics of pure electric vehicles,it is necessary to comprehensively consider the constraints of vehicle load,driving range,and charging demand,which makes the vehicle scheduling problem more complicated.In view of this,this article has done the following work on the research of vehicle scheduling optimization based on power consumption:(1)Summarize the theory of logistics distribution and vehicle scheduling.Based on the analysis of the current research situation of pure electric vehicles,clarify the feasibility of pure electric vehicles in urban logistics distribution,introduce the concept of vehicle scheduling and the concept of pure electric vehicle scheduling,and focus on the analysis and research of pure electric vehicle scheduling considering several aspects.(2)Analyze the relevant theories and energy consumption laws of pure electric vehicles.Introduce the basic structure of pure electric vehicles,explain the advantages and disadvantages of electric vehicles,analyze the main influencing factors of power consumption,and then construct the power consumption model according to the transformation relationship between mechanical energy,electrical energy and battery energy during the driving process of the vehicle.Above,focus on the impact of vehicle start-stop and load dynamic changes on power consumption,build a power consumption model based on vehicle start-stop,and analyze the power consumption law under this model.(3)Constructing and solving the optimization model of vehicle scheduling based on power consumption.First,on the basis of the power consumption model,consider various constraints,etc.to build a vehicle scheduling optimization model based on the uniform power consumption model with the goal of the lowest total cost of the distribution center and design the algorithm to solve the problem.The uniform consumption model of the electric power fails to fully reflect the actual situation,so it fully considers the impact of the three factors of vehicle travel distance,vehicle start-braking,and vehicle load change on power consumption,and builds a power-based dynamic with the goal of minimizing the total cost of the distribution center Consumed vehicle scheduling optimization model,the genetic algorithm used in the previous article is improved,and finally the case is analyzed to verify the correctness of the model and algorithm.