Xin Wang, Ph.D.
Department of Industrial and Systems Engineering, University of Wisconsin-Madison
Abstract: Incentivized Self-Rebalancing Fleet in Electric Vehicle Sharing
With the rising need for efficient and flexible short-distance urban transportation, more vehicle sharing companies are offering one-way car-sharing services. Electrified vehicle sharing systems are even more effective in terms of reducing fuel consumption and carbon emission. In this paper, we investigate a dynamic fleet management problem for an electric vehicle (EV) sharing system that faces time-varying random demand and electricity price. Demand is elastic in each time period, reacting to the announced price. To maximize the revenue, the EV fleet optimizes trip pricing and EV dispatching decisions dynamically. We develop a new value function approximation (VFA) with input convex neural networks (ICNNs) to generate high-quality solutions. Through a New York City case study, we compare it with standard dynamic programming methods and develop insights regarding the interaction between the EV fleet and the power grid.
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Meeting ID: 993 4735 4264
Department of Industrial & Systems Engineering