The EV Coordinated Charging Strategy Based On Users’ Driving Behavior

In view of the global energy and environmental problems,electric vehicle(EV)has been developed vigorously in the world because of its advantage of low pollution and high energy efficiency.However,as a type of power load,EV charging may have a significant impact on the normal operation of the distribution network because EV charging is random and intermittent.It is necessary to make a coordinated charging strategy for EV charging.The thesis mainly studies the coordinated charging strategy based on the EV users driving behavior.The main works and specific researches are summarized as follows:Firstly,different types of electric vehicles are introduced briefly.The characteristics,advantages and disadvantages of each type of electric vehicles are analyzed.Taking the plug-in hybrid electric vehicles(PHEV)as an example,the paper analyses the energy consumption of electric vehicles.The factors like average speed,ambient temperature and power of air conditioner have obvious effects on the energy consumption of electric vehicles.The driving range prediction is based on the energy consumption of electric vehicles.Secondly,a way to predict the driving range of PHEV based on users’ driving behavior,which is used to judge if the electric vehicles need to be charged in coordinated charging strategy,is proposed per the results of the energy consumption analysis of PHEV.The users’ driving behavior is shown by the different operating condition of PHEV.Except for the previous factors,some extra characteristic parameters are used to express the users’ driving behavior accurately.The way to predict the driving range of PHEV with the principal component analysis and fuzzy clustering algorithm and it is verified by the actual road test data of PHEV.Thirdly,for the Li-ion battery,how the factors such as charging and discharging voltage,the ambient temperature,the charging rate,SOC range affect the capacity loss of the battery.A Li-ion battery degradation model in electric vehicles which is proposed based the previous factors is analyzed.The model is used to calculated the charging quantity,where the capacity loss is least when charging,in coordinated charging strategy.The Li-ion battery data from NASA is used to verify the accuracy of the model.At last,an EV coordinated charging strategy based on users’ driving behavior is proposed.The strategy is divided into the user side and the network side.User side is used to judge if the EV needs to be charged and to calculate the charging quantity that the capacity loss is least based on the study in chapter 3 and chapter 4 while the network side guides the EV to charge properly to get the goal that the EV will be charged earliest and the peak-to-peak of the daily load curve will be mitigated with the genetic algorithm.The case study shows that the proposed EV coordinated charging strategy can effectively reduce the capacity loss of power battery in EV as well as the peak-to-peak of the daily load curve in distribution network throughout the day.