Study On The Dynamic Estimation And Influence Factors Of Driving Range Of Battery Electric Vehicles

With the development of battery electric vehicle technology,more and more products enter the market.However,due to the existence of "range anxiety",the promotion of battery electric vehicles become difficult.It is of great significance to study the method of the driving range estimation of battery electric vehicles,to provide users proper charging plan and release their range anxiety.However,most of the existing methods are based on the analysis of typical driving cycles and the range estimation error is quite large when they are used in real road driving.In this paper,based on the study of typical working conditions,the driving range and its influencing factors are studied based on the actual driving conditions of Wuhan urban roads.The main contents are as follows:Firstly,the accurate estimation of battery(State of Charge)SOC is addressed because it is the necessary condition for the estimation of the driving range.The SOC estimation model based on EKF is established in Simulink.The charge and discharge experiments of polymer lithium battery were carried out and the model parameters were identified.Then,the SOC estimation model was verified by experiments.The results show that the accuracy of the model is more than 97%.The model and converges quickly in spite of big deviation of initial value.Last,according to the estimation principle,the mathematical formula of the driving range in different working conditions is deduced.Based on the vehicle dynamic system model and SOC estimation model,a battery electric vehicle energy consumption model is established,which lays the foundation for the later research.Secondly,two methods are studied to estimate the driving range.Iterative method is studied at first.This method is based on the relationship between energy consumption and range,as well as the idea of mathematical iteration.However,due to the low energy consumption per unit range,the initial estimation error is larger.Then,based on the acquired road driving data,the K-means clustering method is studied to estimate the driving range.First of all,a large number of Wuhan city traffic data is divided into 10127 short strokes.The short strokes are divided into five categories by K-means clustering.The average energy consumption per unit range of each class of short strokes are calculated by the vehicle model.Using the method of characteristic parameter identification to estimate the remaining range,and the estimated results are filtered.Last,the fitting method of urban road conditions in Wuhan is used to verify the proposed method.The results show that the mean absolute error is 0.612 km,and the average relative error is 1.78%.Finally,the influence factors of driving range are analyzed.The results show that the energy consumption of body electric appliances(air conditioning)will reduce the driving range by nearly 30%.The driving range of ECE cycle is 15.68% higher than that of UDDS.Good driving behavior and braking energy recovery are the most effective ways to increase the driving range.Due to the complexity of the driving environment,it is not easy to estimate the driving range accurately.In this paper,combined with the actual conditions of Wuhan City,the real driving condition data based K-means clustering method is used to estimate the remaining driving range.The results show that the estimation accuracy is well.