An Study On Charging Pile Sharing In Residential Areas Considering Traffic Prediction

At present,China is vigorously advocating for a prudent and low-carbon lifestyle by encouraging green consumption and developing a green market.In this context,new clean-fuel powered vehicles are becoming an increasingly attractive development.In particular,the popularization of electric vehicles is of great significance for energy conservation and reducing emissions.As such,a growing proportion of electric vehicle owners have chosen to install charging piles in private parking spaces for convenient recharging.However,because most residents keep schedules which require an early departure and late return,the benefits of private charging piles are not taken advantage of due to long idle times,which result in low overall utilization.To fully capitalize upon these idle resources and improve the economic benefits of private power outlets,a residential charging pile sharing scheme based on traffic prediction assistance is proposed here.To assess this proposal,its feasibility was explored from four perspectives:macro incentive policy;technical conditions;sharing economy;and operating mode.This was accompanied by a discussion of the problems which implementation of such a scheme would face,for which the possibility of integrating a traffic forecast auxiliary service was considered.With respect to the traffic forecast,the shortcomings of the Kalman filter algorithm in the prediction model were analyzed.To address the divergence of the Kalman filter algorithm,a data sample preprocessing method based on the extreme random tree algorithm was presented as a means of reducing the accumulation of mean square error and improve the accuracy of calculation.These two algorithms were then combined into a single model,which was simulated and verified.Subsequently,the training process characteristics of the basic prediction and combination models were compared and analyzed.Additionally,the user satisfaction,charging pile utilization rates,and shared charging benefits were investigated when under different charging path planning schemes and charging modes.Finally,applying game theory led to the construction of a game model of the sharing economy which included the sharing platform,the residents who provide charging piles,and the charging users.This model was simulated by the system dynamics analysis software,the results of which were analyzed with respect to the impact of traffic forecast and different pricing tariffs on the charging users and charging pile providers.