Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic Conditions

Based on emerging sensing and communication technology, connected and automated vehicles (CAVs) receive various types of information when traveling, such as geographic data, traffic condition, signal timing, vehicle dynamics and engine status. Most types of information are temporally dynamic and spatially decentralized. For example, in a connected eco-driving system, the dynamic traffic information is a key input to designing a safe and energy-efficient trajectory of the host CAV, but the acquisition of that information is constrained by the communication and sensing range. It is a great challenge to design a robust speed profile that would adapt to the uncertain downstream traffic condition. A Markov Decision Process (MDP) based approach is therefore developed in this research. Multiple decision points are distributed within the potential queuing area, so the eco-driving process is decomposed into actions with the energy consumption as the cost function. The optimal decision at each state corresponds to an adaptive and robust eco-driving strategy that minimizes the expectation of the energy consumption of all possible following actions. Numerical experiments are also conducted to validate the proposed model under different powertrain systems, such as internal combustion engine (ICE), electric vehicle (EV) and plug-in hybrid electric vehicle (PHEV). This method provides a proactive approach rather than a passive way to adapt to the dynamic uncertainty in acquisition of the traffic information, and shows significant advantage in energy saving. A Markov Decision Process (MDP) based approach is therefore developed in this research. Multiple decision points are distributed within the potential queuing area, so the eco-driving process is decomposed into actions with the energy consumption as the cost function. The optimal decision at each state corresponds to an adaptive and robust eco-driving strategy that minimize the expectation of the energy consumption of all possible following actions. Numerical experiments are also conducted to validate the proposed model under different powertrain systems, such as ICE, EV and PHEV. This method provides a proactive approach rather than a passive way to adapt to the dynamic uncertainty in acquisition of the traffic information, and shows significant advantage in energy saving.

Language

  • English

Project

  • Status: Active
  • Funding: $79671
  • Contract Numbers:

    DOT 69A3551747114

    UCR-DOT-510

  • Sponsor Organizations:

    National Center for Sustainable Transportation

    University of California, Davis
    Davis, CA  United States 

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    National Center for Sustainable Transportation

    University of California, Davis
    Davis, CA  United States 

    University of California, Riverside

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

    Iacobucci, Lauren

  • Performing Organizations:

    National Center for Sustainable Transportation

    University of California, Davis
    Davis, CA  United States 
  • Principal Investigators:

    Hao, Peng

  • Start Date: 20180701
  • Expected Completion Date: 20190630
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01680348
  • Record Type: Research project
  • Source Agency: National Center for Sustainable Transportation
  • Contract Numbers: DOT 69A3551747114, UCR-DOT-510
  • Files: UTC, RiP
  • Created Date: Sep 14 2018 1:58PM