Optimization Design Of Charging Services Based On Charging Device Overall Efficiency

In recent years, energy conservation, environment protection and haze pollution have become hot issues, it attracted much attention from the public. The promotion of new energy vehicles, such as EVs, is an effective method for energy conservation and emission reduction. The electrical vehicle industry develops rapidly with the help of government and large corporate firms.With the rapid development of EVs, there is a lack of charging equipment usage for the operators that running these charging stations. So they can’t do planning management effectively. The EV users choose the charge stations on their own terms, and it has got an effect the economical running. Therefore it is necessary to set new evaluation criterion for charging devices. For the EVs act as a high mobility and controllable load, we study on an effective regulation scheme to improve the overall efficiency of charging network. This paper analyzes development status and industrial policy of electrical vehicle and charging station at home and abroad. The limiting factors of charging station construction are given; meanwhile we draw a conclusion that the charging service network contributes to the promotion of EVs.With the investigation data of charging station operated in Shenzhen, a calculation method of overall charging equipment effectiveness based on time availability, the rate of power conversion and utilization is established. This method is applied for the running devices, and the usage of equipment in the charging station is evaluated accurately. From the point of time and energy consumption, the reason for unbalanced utilization and low efficiency of charging piles is analyzed. A perfect solution is come up at last.Finally, the optimization scheduling method of charging network is established to satisfy the users and obtain maximum charging efficiency according to the unbalanced time availability. The particle swarm optimization (PSO) is utilized to complete the simulation. By comparing the simulation results with real data, it balances the usage of charging station equipment, avoids leaving some charge stations unused caused by users choosing stations blindly and optionally, and verifies the correctness of scheduling optimization method.