4.6 Article

Predictive Cruise Control Under Cloud Control System for Urban Bus Considering Queue Dissipation Time

期刊

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
卷 8, 期 4, 页码 2639-2649

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIV.2023.3235352

关键词

Cloud computing; Optimization; Control systems; Automobiles; Vehicle dynamics; Real-time systems; Queueing analysis; Predictive cruise control; cloud control system; queue dissipation time estimation; dynamic programming; receding optimization

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Based on a vehicle-cloud hierarchical architecture, this paper proposes a predictive cruise control for urban buses, which estimates the dissipation time of the intersection queue to predict changes in traffic state. The proposed method saves 44.94%-56.74% of energy consumption and at least 26.8s of waiting time compared to human drivers, and 22.72%-41.27% of energy consumption compared to vehicle with the Intelligent Vehicle Infrastructure Cooperative Systems.
The driving conditions of urban consecutive signalized intersections are one of the main research scenarios for vehicle speed trajectory optimization, and typical for bus driving, where frequent acceleration and deceleration before and after the intersection can intensify the energy consumption of the bus. Prior research has predictive cruise controlled under Intelligent Transportation System, which is not feasible to directly communicate with controllers' units of Intelligent Connected Vehicles. Besides, the effect of queue dissipation is a topic that has received less attention in recent related work. Therefore, this paper proposes a vehicle-cloud hierarchical architecture based on Cloud Control System at first, under which a predictive cruise control for urban buses is deployed. Given the impact of intersection queue length and dissipation time on vehicle driving, a queue dissipation time estimation model based on shockwave theory is proposed to predict changes in intersection traffic state. The queue dissipation time equivalent to the extension of the red-light window is reflected in the constraints of the Receding Distance Horizon Dynamic Programming (RDHDP) algorithm for solving the optimal control problem. Eventually, comparison simulations, a segment of realistic trip between adjacent stops, are presented. The results show that the proposed method saves 44.94%-56.74% of energy consumption and at least 26.8s of waiting time compared to human drivers, and 22.72%-41.27% of energy consumption compared to vehicle with the Intelligent Vehicle Infrastructure Cooperative Systems.

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