Research On The Path And Scheduling Of Electric Vehicle Service

With the rapid development of the electric vehicle industry,the influence of electric vehicles in the power market is growing.However,due to the limitation of battery technology and charge and discharge technology at this stage,electric vehicles have constraints in large-scale applications.Therefore,how to formulate a reasonable and orderly electric vehicle routing and scheduling optimization program has become an important issue at this stage.Due to the low battery capacity of electric vehicles and the low penetration rate of charging facilities,the range of electric vehicles is low and the utilization rate of vehicles is seriously insufficient.This aspect increases the company's operating costs.On the other hand,it also affects the company's service quality.Therefore,how to increase the cruising range of electric vehicles and improve the utilization rate of vehicles under the existing conditions is very important.In order to achieve the above goals,we have conducted research in the field of logistics and distribution and medical services,analyzed the relevant characteristics of electric vehicles in the power market,and obtained the routing and scheduling optimization program for electric vehicles.In the field of logistics and distribution,first of all,by analyzing the characteristics of electric vehicle in actual operation,taking into account as many practical factors as possible,a power consumption model and a charging model of electric vehicle have been established successively.Then,combined with the customer time window,an electric vehicle routing optimization model with the goal of the sum of the power consumption of electric vehicle and the weight of the total advance or delay time was established.Finally,the grey wolf optimizer algorithm and brain storm optimization algorithm are introduced.The simulation examples of the three charging situations are simulated through the Matlab simulation system.The experimental results verify the feasibility and correctness of the EVRPTW model.At the same time,it also verifies the superiority of the grey wolf optimizer algorithm in solving such problems.That is to say,this optimized scheduling scheme can increase the utilization rate of electric vehicle in the distribution center,increase the distribution range,and save the cost of the enterprise.On the other hand,the flexible charging method can save the distribution time,meet customer requirements,and improve service quality.In the field of medical services,first of all,it focuses on the actual influencing factors of drivers and patients during the driving of electric vehicles,and focuses on analyzing the relationship between care workers and patients in terms of medical cost and service time.Then,combined with the electric vehicle's electricity consumption model and time model,home health care routing and scheduling optimization model aimed at minimizing the sum of electric vehicle use cost,medical service cost and penalty cost was established.Finally,the particle swarm optimization algorithm is introduced,and the simulation experiments of four Solomon derivative examples are carried out through the Matlab simulation system.Experimental results verify the feasibility and effectiveness of this model.At the same time,it also verifies the superiority of particle swarm optimization in solving such problems.That is to say,this optimized dispatching scheme improves the utilization rate of personnel and vehicles in the medical service center,saves the cost of enterprises,and improves the quality of service.