Evaluating Fuel Economy of Connected and Autonomous Vehicles under Different Traffic Factors

Connected and autonomous vehicle (CAV) technology has become one of the focused research topics to reduce automobile energy consumption and emissions. Many CAV applications have been developed that use a computer rather than a human to determine the vehicle’s trajectory. This brings new challenges to evaluate their fuel economy as different CAV controllers will drive the vehicle differently and many traffic factors might affect the fuel economy such as penetration rates, congestion levels, etc. The current fuel economy testing procedure cannot be directly applied since it evaluates all vehicles with the same standard driving cycles and different traffic factors are not considered. This work studies the effects of different traffic factors on the fuel economy of a CAV and uses eco-corporative adaptive cruise control (Eco-CACC) as a representative CAV application. Two scenarios are investigated: a driving cycle based scenario with no lane changes and a complex traffic network scenario with lane changes. The results provide a systematic analysis of variations of CAV fuel benefits due to different penetration rates of communication, penetration rates of automation, and locations of the CVs and CAVs. It shows that in a platoon of mixed human-driven vehicles and Eco-CACC controlled CAVs, the target CAV can gain significant benefits as long as there is another preceding CAV in front. It also demonstrates that the human-driven vehicles can gain about 10% fuel benefit when following an Eco-CACC controlled CAV. In addition, the traffic flow of the platoon is smoothed and shockwaves are mitigated.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ADC70 Standing Committee on Transportation Energy.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Oo, Ye Lwin
  • Shao, Yunli
  • Sun, Zongxuan
• Conference:
  • Transportation Research Board 98th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2019-1-13 to 2019-1-17
• Date: 2019

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: 16p

Subject/Index Terms

• TRT Terms: Autonomous intelligent cruise control; Autonomous vehicles; Connected vehicles; Fuel consumption; Lane changing; Traffic flow; Traffic platooning; Vehicle mix
• Uncontrolled Terms: Driving cycles
• Subject Areas: Data and Information Technology; Energy; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01698022
• Record Type: Publication
• Report/Paper Numbers: 19-05828
• Files: TRIS, TRB, ATRI
• Created Date: Mar 1 2019 3:51PM