Study on Charging Strategy of Light-duty BEV Driven by Data
With the increasing energy crisis and environmental pollution,electric vehicles have attracted wide attention and expanded rapidly in the market due to their low-carbon and environmentally friendly characteristics.However,many problems such as limited battery capacity,diversified travel demand and mismatch between service and demand of charging facilities have gradually become the bottleneck of the development of electric vehicles.Considering the daily travel needs of users and optimizing the driving range of electric vehicles is one of the feasible ways to solve the problems.A simulation method for the optimal mileage of battery electric vehicle(BEV)is proposed by modeling driving and charging behaviors.The driving and charging modes of BEV users are characterized by reconstructing daily travel chains based on actual data collected from Shanghai,China.Simultaneously,the interdependent behavior variables of daily travel and each travel are defined in the daily travel chain.In order to meet the fitness goal of the exercise field,a random simulation framework is established with Monte Carlo method.Finally,considering the heterogeneity of users,the optimal mileage with different charging scenarios is analyzed.The result shows that the daily travel chain can be reconstructed by using the behavioral variables of daily travel and each travel,and there is a correlation among the variables detected by the Copula function.Users with different daily travel needs have different optimal mileage,when choosing a BEV,users are recommended to consider that the number of vehicle kilometers per day is less than 34%of the battery endurance mileage.Increasing charging opportunities and charging power is more conducive to drivers with high daily travel demand.Under the premise of meeting travel demand,the beneficial effect of increasing fast charging power will gradually weaken.
automotive engineeringdriving and charging strategiesdata-driven simulationdaily itinerary chainBEV
NETL
NSTL
万方数据
