Research On Influencing Factors And Prediction Of The Remaining Driving Range For Electric Vehicles

When an electric vehicle is driving,an accurate remaining driving range prediction can be helpful for customs to adjust driving plans rationally and efficiently mitigate their 'range anxiety',which will enhance the customs' confidence in electric vehicles.Therefore,it is of great importance and practical value to study the remaining driving range prediction.This dissertation takes battery electric vehicles as the research object,and studies the influencing factors and prediction of the remaining driving range.And the main work is summarized as follows.(1)Analysis of factors influencing the remaining driving range of electric vehicles.The main factors influencing the remaining driving range are analyzed qualitatively,and then are categorized into two aspects:battery remaining available energy,which is considered as a direct extension of battery SOC in this dissertation,and vehicle energy consumption.Relevant data that is obtained from ADVISOR 2002 are used to analyze quantitatively the effect of battery SOC and vehicle energy consumption on the remaining driving range,respectively.(2)Research on battery SOC estimation for electric vehicles.Seeking a tradeoff between accuracy and computational complexity of the equivalent circuit models,the second-order RC equivalent circuit model is adopted.Lithium battery data is obtained from ADVISOR 2002,using data fitting method to obtain the relationship between open circuit voltage and SOC,and identifying the resistance and capacitance parameters with the forgetting factor recursive least squares method.Three typical standard conditions are used to verify the accuracy of the model and parameter identification results.Based on the established lithium battery discrete state space model and the identified model parameters,proportional integrated Kalman filter is adopted to estimate lithium battery SOC.The comparative simulation results are presented to show that the proposed estimation algorithm not only has good steady-state estimation accuracy,but also has good robust performance against changeable running conditions.(3)Research on remaining driving range prediction combining battery SOC estimation with vehicle energy consumption prediction.A history-based estimation method is adopted to predict the vehicle future energy consumption,namely,the vehicle future energy consumption is assumed to be the same as the energy consumption in the past.And then a remaining driving range prediction model is designed,which is based on battery SOC estimation and vehicle energy consumption prediction.Simulation results are presented to verify the good estimation performance and the effectiveness of the designed model under different operating conditions.