Research Of Charging Strategy Of Electric Vehicle Based On Participatory Sensing

With the rapid development of economic society,energy demand continues to grow,nervous energy and environmental pollution problem is becoming more and more highlighted.According to industry experts,10%?20%of the China passenger car sales will come from the pure electric vehicles,plug-in hybrid cars and other new energy vehicles in 2020,including urban traffic will be given priority to with electric cars,electric cars will become the future development of the mainstream,and become the guarantee of energy saving and emission reduction.However scale operation of the electric car charging is random,if it is not controlled access to the grid,not only affect the power quality,load balance and power capacity,which affects the stable,but also can increase the cost of power grid construction,makes the equipment utilization rate to drop,increases the power grid operation pressure.Therefore,on the basis of the electric vehicle charging characteristics of the electric vehicle charging behavior of users in order to manage and control,will be conducive to the safe and stable operation of power system.This thesis has done the following research on this topic:First this thesis designed the model of charging strategy of electric vehicle based on crowd sensing.According to the algorithm,this thesis studied electric vehicle dynamic route choice based on LBS data processing system and crowd sensing,and designed weighted Dijkstra algorithm based on crowd sensing which has considered the shortest time and the shortest distance,built the objective function of charging strategy of electric vehicle based on crowd sensing,and built the electric vehicle dynamic route choice model,the electric vehicle charging queuing model.For electric vehicle charging strategy based on crowd sensing of the objective function,contains three constraints:charging power,battery capacity constraints,choice of path constraint.And then combined with the proposed technology of electric vehicle charging strategy based on crowd sensing model using the genetic algorithm to simulate the model.Case study was undertaken in a city center 33 nodes logistics distribution system within 10×10km zone,under the time-sharing electricity price mechanism,analyzed and compared the electric vehicle user's travel costs and the impact on the power distribution system between the charging strategy of electric vehicle based on crowd sensing and the traditional chaotic charging strategy.Results show that charging strategy of electric vehicle based on crowd sensing,not only can reduce the electric vehicles' driving time and the economic cost effectively,but also play a role of peak load shifting,make the power load curve more smooth.Thesis analyzed the mathematical modeling of electric vehicle charging strategy by using the complementary characteristics between electric vehicles and the distributed energy.This thesis provided reference and significant guidance which can reduce the electric vehicle users'travel costs,extend the sell electricity market power system,reduce the peak valley load of electric power system,maintain the stability of power system and effective operation,expand new energy into the net,and increase the efficiency of electric power equipment load.