Mobility-Aware Charging Scheduling for Shared On-Demand Electric Vehicle Fleet Using Deep Reinforcement Learning

With the emerging concept of sharing-economy, shared electric vehicles (EVs) are playing a more and more important role in future mobility-on-demand traffic system. This article considers joint charging scheduling, order dispatching, and vehicle rebalancing for large-scale shared EV fleet operator. To maximize the welfare of fleet operator, we model the joint decision making as a partially observable Markov decision process (POMDP) and apply deep reinforcement learning (DRL) combined with binary linear programming (BLP) to develop a near-optimal solution. The neural network is used to evaluate the state value of EVs at different times, locations, and states of charge. Based on the state value, dynamic electricity prices and order information, the online scheduling is modeled as a BLP problem where the decision variables representing whether an EV will 1) take an order, 2) rebalance to a position, or 3) charge. We also propose a constrained rebalancing method to improve the exploration efficiency of training. Moreover, we provide a tabular method with proved convergence as a fallback option to demonstrate the near-optimal characteristics of the proposed approach. Simulation experiments with real-world data from Haikou City verify the effectiveness of the proposed method.

Automated summary

This article discusses the joint charging scheduling, order dispatching, and vehicle rebalancing for large-scale shared EV fleet operators, and proposes a solution based on deep reinforcement learning and binary linear programming. By evaluating the state value of EVs using neural networks, the online scheduling model is established and a constrained rebalancing method is introduced to enhance training efficiency. This approach is further validated through simulation experiments using real-world data from Haikou City.