Deep Learning–based Eco-driving System for Battery Electric Vehicles

Eco-driving strategies based on connected and automated vehicles (CAV) technology, such as Eco-Approach and Departure (EAD), have attracted significant worldwide interest due to their potential to save energy and reduce tail-pipe emissions. In this project, the research team developed and tested a deep learning–based trajectory-planning algorithm (DLTPA) for EAD. The DLTPA has two processes: offline (training) and online (implementation), and it is composed of two major modules: 1) a solution feasibility checker that identifies whether there is a feasible trajectory subject to all the system constraints, e.g., maximum acceleration or deceleration; and 2) a regressor to predict the speed of the next time-step. Preliminary simulation with microscopic traffic modeling software PTV VISSIM showed that the proposed DLTPA can achieve the optimal solution in terms of energy savings and a greater balance of energy savings vs. computational efforts when compared to the baseline scenarios where no EAD is implemented and the optimal solution (in terms of energy savings) is provided by a graph-based trajectory planning algorithm.

Language

  • English

Project

  • Status: Completed
  • Funding: $70000
  • Contract Numbers:

    69A3551747114

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590

    National Center for Sustainable Transportation

    University of California, Davis
    Davis, CA  United States 
  • Managing Organizations:

    National Center for Sustainable Transportation

    University of California, Davis
    Davis, CA  United States 
  • Project Managers:

    Iacobucci, Lauren

  • Performing Organizations:

    University of California, Riverside

    Center for Environmental Research and Technology
    900 University Avenue
    Riverside, CA  United States  92521-0425
  • Principal Investigators:

    Wu, Guoyuan

  • Start Date: 20170701
  • Expected Completion Date: 20180630
  • Actual Completion Date: 20190506
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01645972
  • Record Type: Research project
  • Source Agency: National Center for Sustainable Transportation
  • Contract Numbers: 69A3551747114
  • Files: UTC, RiP
  • Created Date: Sep 12 2017 2:27PM