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A Distributed and Hierarchical Optimal Control Method for Intelligent Connected Vehicles in Multi-Intersection Road Networks
Content Provider | MDPI |
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Author | Yu, Jie Jiang, Fachao Kong, Weiwei Luo, Yugong |
Copyright Year | 2022 |
Description | Intelligent connected vehicles (ICVs) technologies will bring significant changes to future transportation, and urban intersections will be an important scenario for the application of ICVs. There exists one significant challenge to address for the control of ICVs in unsignalized, multi-intersection road networks, that is, how to realize the comprehensive optimization of traffic efficiency and energy saving. To solve this problem, the distributed and hierarchical optimal control architecture is first established in this paper, consisting of a cloud decision layer and a vehicle control layer. For the cloud decision layer, the distributed model predictive control (DMPC) method is utilized for distributed optimization control of multi-intersection road network systems, to achieve optimization in terms of traffic efficiency. For the vehicle control layer, based on the reference speed optimized from the cloud decision layer, the DMPC method is further utilized for distributed optimal control of each vehicle platoon, to achieve optimization in terms of energy saving. Finally, the comparative simulation tests are carried out based on MATLAB and SUMO. The feasibility and effectiveness of the proposed method were verified, and the improvement of traffic efficiency and energy saving was achieved. |
Starting Page | 34 |
e-ISSN | 20326653 |
DOI | 10.3390/wevj13020034 |
Journal | World Electric Vehicle Journal |
Issue Number | 2 |
Volume Number | 13 |
Language | English |
Publisher | MDPI |
Publisher Date | 2022-02-04 |
Access Restriction | Open |
Subject Keyword | World Electric Vehicle Journal Transportation Science and Technology Distributed and Hierarchical Optimal Control Method Optimization Control Unsignalized Multi-intersection Road Network Intelligent Connected Vehicles |
Content Type | Text |
Resource Type | Article |