Trip Pricing Scheme for Electric Vehicle Sharing Network With Demand Prediction

With promising benefits such as emission reduction, traffic congestion alleviation and parking space saving, electric vehicle sharing systems have attracted increasing attentions. This paper proposes a Trip Pricing Scheme (TPS) for a large-scale EV-sharing network with Shared Electric Vehicle (SEV) demand prediction. In the proposed system, the SEV traffic demand is firstly predicted through a model which includes a cascade graph convolutional neural network and a long-short term memory neural network. Based on this, the TPS is modelled as a mixed-integer nonlinear programming problem, which aims at maximizing the total system profit from EV sharing business. The proposed TPS determines the optimal combination of the two price adjustment levels, which provides incentives to the spatial-temporal distribution of SEVs’ traffic flows to maximize the system’s profit. Numerical simulations are conducted to validate the effectiveness of the proposed method.