Energy Efficiency And Heterogeneous Analysis On Driving Distance Of Electric Vehicles

Electric vehicles play an important role in energy conservation and emission reduction,promoting renewable energy power generation and environmental protection.However,such disadvantages as short driving distance,long charging time and imperfect charging facilities hinder the promotion of electric vehicles.The travel energy efficiency of the actual use of electric vehicles needs to be improved.On the one hand,the battery management technology,charging and discharging technology and energy storage technology are not mature enough,which leads to the low energy efficiency of the trip.On the other hand,unreasonable driving behavior affects energy efficiency,especially leads to low efficiency of reverse energy recovery.Most of the existing research starts from the observable external environment and driving behavior,and hopes to explore ways to improve the accuracy of energy consumption prediction,or to use methods such as machine learning to predict dynamic energy consumption and residual driving distance.However,the quantitative analysis of energy efficiency loss in the actual use of electric vehicles has not received enough attention.This paper attempts to analyze the objective reasons(vehicle and battery technology)and subjective factors(driving skills and driving habits)that affect energy consumption,so as to explore the potential of electric vehicles driving distance improvement.Based on stochastic frontier model,this paper explores a more accurate model to describe the frontier of the driving distance.Taking the driving distance as the object of analysis,this paper analyzes the influence of external environmental factors on the frontier,and evaluates the gap between the actual driving distance and the maximum driving distance that can be achieved under ideal conditions.The results show that there is still about 20% room to improve the driving distance of electric vehicles at the present stage,and unobservable heterogeneity plays a positive role in improving the effect of model fitting and the estimation accuracy of inefficiencies.Finally,the influence of other factors on driving distance efficiency was explored by using the fixed effect model.Among them,the initial electric quantity influences the driver's state of mind.When the remaining electric quantity is high,the driver's range anxiety is not obvious,and when the remaining electric quantity is low,the driver's range anxiety is serious.In addition,the potential of improving mileage efficiency when going downhill is higher than that when going uphill.At this time,the efficiency of braking reverse energy recovery is low,which reflects the potential and demand for further improvement of driving behavior when braking.