Study On Energy Management System Of Electric Vehicles Under Service Network Environment

With the increasing pressure on the global energy and environmental problem,electric vehicles,by advantages of zero emissions,energy conservation and environmental protection,highly intelligent,and low noise,are becoming into the development direction of the contemporary car.In the field of electric vehicles,because electricity is the main energy source of electric vehicles,energy management system of electric vehicles under the service network environment is becoming the focus of the field.Energy management system of electric vehicles under the service network environment is made up of battery system and service network management center.Battery management system mainly realizes the SOC estimation and service network management center achieves space scheduling of electric vehicle charging orderly through the information of SOC and spatial location of electric vehicles.Battery management system is a tool,which monitor the battery working state at real-time and make battery work safely.To realize the accurate estimation of the battery SOC is of great significance to improve the efficiency and prolong circle life of battery and ensure safe and economic operation of electric vehicles.Scheduling orderly of electric vehicles charging is beneficial to optimize distribution of charging load and achieve rational distribution of electricity facilities.At the same time,it is of great significance to improve the utilization rate of electricity facilities,reduce the adverse effects of the charging load on the power grid and improve the efficiency of grid operation.This paper analyzes the overall architecture of energy management system of electric vehicles under the service network environment.Battery management system is designed and SOC estimation method based on Ah metrology and multi-objective spatial scheduling policy of charging orderly are proposed,which provide effective solution for large-scale application of electric vehicles.The working process of the battery presents a highly nonlinear because of influence of many factors,such as temperature,charging and discharging efficiency,aging factors and so on,which makes SOC estimation difficult and SOC estimation error large.To solve these problems,an improved Ah metrology was proposed through modifying aging factor,charging and discharging efficiency and temperature in order to improve the SOC estimation accuracy The simulation results show that compared with traditional Ah metrology,the modified Ah metrology improves SOC estimation accuracy and reduces the estimation error.At the same time,it verifies the effectiveness of the method.Due to the random position of electric vehicles charging,disordered charging of electric vehicle users will not only increase users' electric cost,but also result in adverse impact on power management and operation of the power grid.Therefore,in order to reduce adverse impact on the power grid,a spatial scheduling policy of charging orderly for electric vehicles was put forward to implement orderly charging on the basis of flexible and adjustable charging load.The objective function,which represents maximum utilization of electric facilities in the filling station,charging load distribution according to need,the low cost of charging for electricity for electric vehicles and the shortest distance and time for electric vehicles are set up in this paper.At the same time,constraint conditions including distribution of electric vehicles,charging distance,filling in power station and electric safely of battery charging are proposed.Particle swarm optimization was adopted to resolve the solution of the objective function.According to the constraint problem,the double fitness value method is put forward.The simulation results show that this method can optimize the distribution of electric position and verify the validity of method based on meeting the needs of electric vehicle users and power plants economic operation.