A cooperative energy efficient truck platoon lane-changing model preventing platoon decoupling in a mixed traffic environment

Truck platooning has gained increasing attention due to the benefits in energy and operation efficiency in freight transportation. One significant challenge for deploying truck platoons is the safe and efficient interaction with surrounding traffic, especially at freeway discontinuities where mandatory lane changes usually lead to the decoupling of truck platoons. This study proposes a cooperative truck platoon lane-changing model (CTPLC) to prevent the decoupling of truck platoons in a mixed traffic environment. Specifically, a two-step control strategy is presented, where vehicles in the target lane firstly cooperatively adjust speeds to create an appropriate gap for a truck platoon, and then trucks within the truck platoon conduct lane change sequentially. The cooperative speed profiles are generated by solving an optimization problem considering the lane-changing influence and energy consumption. Based on that, a two-dimensional nonlinear model predictive control (MPC) algorithm is employed to generate vehicular acceleration and steering angle for each truck. A series of numerical simulation experiments were conducted to validate the proposed strategy. As shown by the results, the authors' proposed method truck platoon could conduct a lane change in a traffic-efficient and safe manner, and meanwhile, their method was more energy-efficient than a benchmark strategy.

• Record URL:
  • https://doi.org/10.1080/15472450.2022.2119386
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/15472450
• Supplemental Notes:
  • © 2022 Taylor & Francis Group, LLC 2022. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Li, Meng
  • Li, Zhibin
  • Zhou, Yang
  • Wu, Jiaming
• Publication Date: 2024-3

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 174-188
• Serial:
  • Journal of Intelligent Transportation Systems
  • Volume: 28
  • Issue Number: 2
  • Publisher: Taylor & Francis
  • ISSN: 1547-2450
  • EISSN: 1547-2442
  • Serial URL: http://www.tandfonline.com/loi/gits20

Subject/Index Terms

• TRT Terms: Algorithms; Autonomous vehicles; Connected vehicles; Energy consumption; Lane changing; Traffic platooning; Trucks
• Identifier Terms: Model Predictive Control
• Subject Areas: Data and Information Technology; Energy; Freight Transportation; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01909816
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 26 2024 8:53AM