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汽车安全与节能学报

汽车安全与节能学报 ›› 2023, Vol. 14 ›› Issue (5): 591-599.DOI: 10.3969/j.issn.1674-8484.2023.05.008

• 智能驾驶与智慧交通 • 上一篇    下一篇

云支持的分层式车辆队列预测性巡航控制

王宙1,3(), 褚端峰2, 高博麟3,*(), 梅润1,3, 钟薇4   

  1. 1.武汉理工大学 机电工程学院，武汉 430070，中国
    2.武汉理工大学 智能交通系统研究中心，武汉 430070，中国
    3.清华大学 车辆与运载学院，北京 100084，中国
    4.国家智能网联汽车创新中心，北京 102607，中国
  • 收稿日期:2023-03-21 修回日期:2023-08-01 出版日期:2023-10-31 发布日期:2023-10-31
  • 通讯作者: *高博麟，副研究员。E-mail：gaobolin@tsinghua.edu.cn。
  • 作者简介:王宙（1997—），男（汉），海南，硕士研究生。E-mail: wz2020@whut.edu.cn。
  • 基金资助:
    国家重点研发项目(2021YFB2501000);国家自然科学基金面上项目(52172393);潍柴技术研发项目(20202000342)

Cloud-based control for hierarchical vehicle platoon predictive cruise control

WANG Zhou1,3(), CHU Duanfeng2, GAO Bolin3,*(), MEI Run1,3, ZHONG Wei4   

  1. 1. School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan 430070, China.
    2. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430070, China.
    3. School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
    4. National Innovation Center of Intelligent and Connected Vehicles, Beijing 102607, China
  • Received:2023-03-21 Revised:2023-08-01 Online:2023-10-31 Published:2023-10-31

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摘要：

为了提升队列行驶的经济性，提出了一种高速公路场景下的云支持的队列预测性巡航控制方法（CPPCC），并进行真实道路和车辆数据模型的仿真实验。该方法采用了分层式结构，上层为云端的队列速度规划层，下层为队列稳定控制层。云端的速度规划层，考虑了道路坡度的滚动域的动态规划（RDP）算法，实现队列行驶的经济性目标。下层的车端队列稳定控制层，搭载了分布式模型预测控制器（DMPC），来跟踪云端发送速度，同时考虑了队列的稳定控制。结果表明：与传统的前车与领航车跟随的定速巡航队列（PLF-CC）方法相比，在行驶时间减小0.24%的前提下，本文所提出的方法节省6.04%的能源。

关键词: 智能车辆, 云支持的队列预测性巡航控制（CPPCC）, 滚动动态规划（RDP）, 分布式模型预测控制（DMPC）, 前车与领航车跟随的定速巡航队列（PLF-CC）

Abstract:

A cloud-based platoon predictive cruise control method (CPPCC) was proposed for highway scenarios to improve the economy of platoon driving and simulation experiments with real road and vehicle data models were conducted. The method employed a hierarchical structure, with the upper layer being the cloud-based platoon speed planning layer and the lower layer being the platoon stabilization control layer. The speed planning layer in the cloud considered the receding dynamic planning (RDP) algorithm of the road slope to achieve the economic goal of platoon driving. The lower vehicle-side platoon stability control layer was equipped with a distributed model predictive controller (DMPC) to track the speed send from the cloud, while considering the platoon stability control. The results show that the proposed method saves 6.04% of energy with 0.24% improvement in the overall operating efficiency compared to the predecessor-leader following’s cruise control platoon (PLF-CC) method.

Key words: smart vehicles, cloud-based platoon predictive cruise control (CPPCC), receding dynamic programming (RDP), distributed model predictive controller (DMPC), predecessor-leader following’s cruise control platoon (PLF-CC)

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