Article · IEEE Transactions on Intelligent Transportation Systems · June 16, 2021

A Reinforcement Learning Approach for Rebalancing Electric Vehicle Sharing Systems

Aigerim Bogyrbayeva, Sungwook Jang, Ankit Shah, Young Jae Jang, Changhyun Kwon

Abstract

This paper proposes a reinforcement learning approach for nightly offline rebalancing operations in free-floating electric vehicle sharing systems (FFEVSS). Due to sparse demand in a network, FFEVSS requires relocation of electrical vehicles (EVs) to charging stations and demander nodes, which is typically done by a group of drivers. A shuttle is used to pick up and drop off drivers throughout the network. The objective of this study is to solve the shuttle routing problem to finish the rebalancing work in minimal time. We consider a reinforcement learning framework for the problem, in which a central controller determines the routing policies of a fleet of multiple shuttles. We deploy a policy gradient method for training recurrent neural networks and compare the obtained policy results with heuristic solutions. Our numerical studies show that unlike the existing solutions in the literature, the proposed methods allow solving the general version of the problem with no restrictions on the urban EV network structure and charging requirements of EVs. Moreover, the learned policies offer a wide range of flexibility, resulting in a significant reduction in the time needed to rebalance the network.

Transportation and Mobility InnovationsElectric Vehicles and InfrastructureVehicle Routing Optimization Methodsshared electric vehiclesvehicle rebalancingreinforcement learning

A Reinforcement Learning Approach for Rebalancing Electric Vehicle Sharing Systems

Abstract

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