Research Article
Data-Driven Intelligent Management of Energy Constrained Autonomous Vehicles in Smart Cities
@INPROCEEDINGS{10.1007/978-3-030-73423-7_9, author={Yingzhu Ren and Qimei Cui and Xiyu Zhao and Yingze Wang and Xueqing Huang and Wei Ni}, title={Data-Driven Intelligent Management of Energy Constrained Autonomous Vehicles in Smart Cities}, proceedings={Cognitive Radio-Oriented Wireless Networks. 15th EAI International Conference, CrownCom 2020, Rome, Italy, November 25-26, 2020, Proceedings}, proceedings_a={CROWNCOM}, year={2021}, month={3}, keywords={Electric autonomous vehicle (EAV) Intelligent scheduling Network calculus (NC) Energy consumption}, doi={10.1007/978-3-030-73423-7_9} }- Yingzhu Ren
Qimei Cui
Xiyu Zhao
Yingze Wang
Xueqing Huang
Wei Ni
Year: 2021
Data-Driven Intelligent Management of Energy Constrained Autonomous Vehicles in Smart Cities
CROWNCOM
Springer
DOI: 10.1007/978-3-030-73423-7_9
Abstract
Intelligent transportation is an important component of future smart cities, and electric autonomous vehicles (EAVs) are envisioned to be the main form of transportation because EAVs can save energy, protect the environment, and improve service efficiency. With limited vehicle-specific energy storage capacity and overall constraint in the smart grid’s electric load, we propose a novel intelligent management scheme to jointly schedule the travel and charging activities of the EAV fleet in one geographical area. This scheme not only schedules EAVs to meet the passengers’ requests but also explores the matching problem between the energy requirement of EAVs and the deployment of charging piles in smart cities. We minimize the total cruise energy consumption of EAVs under the condition of limited energy supply while guaranteeing the quality-of-service (QoS). Network Calculus (NC) is extended to model the electric traffic flow in this paper. With the real-world electric taxi data in Beijing, simulation results demonstrate that the proposed scheme can achieve substantial energy reduction and remarkable improvements in both the order completion rate and utilization rate of the charging stations.
