An eco-drive experiment on rolling terrains for fuel consumption optimization with connected automated vehicles

Eco-drive is one of the many techniques that has been developed to increase vehicle fuel efficiency and improve the sustainability of the entire transportation system. Connected and automated vehicle (CAV) data is now being used to allow vehicles to cooperate better with current and future environments, such as traffic conditions, signal timing, and terrain information. This study proposes an eco-drive algorithm for vehicle fuel consumption optimization on rolling terrains, which frequently causes additional fuel waste due to the inefficient transformation between kinetic and potential energy. The algorithm using the Relaxed Pontryagin’s Minimum Principle (RPMP) is computationally efficient and applicable in real time. While similar algorithms have proven effective in simulations with many assumptions, it is necessary to test these algorithms in the field to better understand the algorithm performance and thus make enhancements to optimally control vehicles for eco-drive. Therefore, this study further tested and verified the newly developed algorithms on an innovative CAV platform and quantified the fuel saving benefits of eco-drive. The proposed eco-drive system is compared against conventional constant speed cruise control on a total of 7 road segments over 47 miles. Experimental data shows that more than 20 percent of fuel consumption can be saved. Detailed analysis through linear models also reveal the main geometrical contributors to the eco-drive fuel savings. This finding can provide a rough estimate of the fuel saving potential of given roadways and help state departments of transportation to identify roadways where eco-drive should be implemented. The algorithm and the experiment can also support original equipment manufacturers in developing and marketing this technology to reduce fuel consumption and emissions in the future.

• Record URL:
  https://doi.org/10.1016/j.trc.2019.01.010
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X18300561
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Ma, Jiaqi
  • Hu, Jia
  • 0000-0002-0900-7992
  • Leslie, Ed
  • Zhou, Fang
  • Huang, Peter
  • Bared, Joe
• Publication Date: 2019-3

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 125-141
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 100
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Algorithms; Autonomous vehicles; Connected vehicles; Ecodriving; Electronic controllers; Field tests; Fuel consumption; Optimization; Terrain
• Uncontrolled Terms: Rolling terrain
• Subject Areas: Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01692447
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 8 2019 1:52PM