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.

• Record URL:
  https://doi.org/10.7922/G2NP22N6
• Dataset URL:
  https://doi.org/10.6086/D1FW9G
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/42373
• Dataset URL:
  https://rosap.ntl.bts.gov/view/dot/42505
• Supplemental Notes:
  • Supporting datasets available at: https://doi.org/10.6086/D1FW9G; https://rosap.ntl.bts.gov/view/dot/42505 This document was sponsored by the U.S Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Office of the Assistant Secretary for Research and Technology

  Department of Transportation
  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590

  Office of the Assistant Secretary for Research and Technology

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

  University of California, Riverside

  College of Engineering, Center for Environmental Research and Technology
  1084 Columbia Avenue
  Riverside, CA  United States  92507

  National Center for Sustainable Transportation

  One Shields Avenue
  Davis, CA  United States  95616
• Authors:
  • Wu, Guoyuan
  • 0000-0001-6707-6366
  • Ye, Fei
  • Hao, Peng
  • 0000-0001-5864-7358
  • Esaid, Danial
  • Boriboonsomsin, Kanok
  • 0000-0003-2558-5343
  • Barth, Matthew J
  • 0000-0002-4735-5859
• Publication Date: 2019-5

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; References; Tables;
• Pagination: 37p

Subject/Index Terms

• TRT Terms: Algorithms; Automatic speed control; Ecodriving; Electric vehicles; Energy conservation; Energy consumption; Machine learning; Simulation; Traffic speed
• Identifier Terms: VISSIM (Computer model)
• Uncontrolled Terms: Deep learning
• Subject Areas: Data and Information Technology; Energy; Environment; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01707915
• Record Type: Publication
• Report/Paper Numbers: NCST-UCR-RR-19-03
• Contract Numbers: USDOT Grant 69A3551747114
• Files: BTRIS, UTC, NTL, TRIS, ATRI, USDOT
• Created Date: Jun 14 2019 9:49AM