Model Predictive Eco-Driving Control for Heavy-Duty Trucks Using Branch and Bound Optimization

Eco–driving (ED) can be used for fuel savings in existing vehicles, requiring only a few hardware modifications. For this technology to be successful in a dynamic environment, ED requires an online real–time implementable policy. In this work, a dedicated Branch and Bound (BnB) model predictive control (MPC) algorithm is proposed to solve the optimization part of an ED optimal control problem. The developed MPC solution for ED is based on a prediction model that includes velocity dynamics as a function of distance and a finite number of driving modes and gear positions. The MPC optimization problem minimizes a cost function with two terms: one penalizing the fuel consumption and one penalizing the trip duration. The authors exploit contextual elements and use a warm–started solution to make the BnB solver run in real–time. The results are evaluated in numerical simulations on two routes in Israel and France and the long haul cycle of the Vehicle Energy consumption Calculation Tool (VECTO). In comparison with a human driver and a Pontryagin’s Minimum Principle (PMP) solution, 25.8% and 12.9% fuel savings, respectively, are achieved on average.

• Record URL:
  • https://doi.org/10.1109/TITS.2023.3309467
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Wingelaar, Bart
  • Gonçalves da Silva, Gustavo R
  • Lazar, Mircea
• Publication Date: 2023-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 15178-15189
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 12
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Branch and bound algorithms; Ecodriving; Fuel conservation
• Identifier Terms: Model Predictive Control
• Subject Areas: Data and Information Technology; Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01913308
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 29 2024 10:02AM