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System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information

Author

Listed:
  • Felipe Jiménez

    (University Institute for Automobile Research (INSIA), Technical University of Madrid (UPM), Madrid 28031, Spain)

  • Wilmar Cabrera-Montiel

    (University Institute for Automobile Research (INSIA), Technical University of Madrid (UPM), Madrid 28031, Spain)

Abstract

Nowadays, reducing the energy and fuel consumption of road vehicles is a key issue. Different strategies have been proposed. One of them is to promote Eco-driving behaviour among drivers. Most Eco-driving tips take into account only the road stretch where the vehicle is located. However, larger improvements could be achieved if information from subsequent stretches is used. The main objective of this work is to develop a system to warn the driver in real time of the optimal speed that should be maintained on every road segment in order to optimize the energy used and the fuel consumed while observing a time schedule. The system takes into account the road vertical profile, the fixed and variable speed limits and the traffic information retrieved using V2V and V2I communications. The system has been tested on real road sections with satisfactory results in fuel savings.

Suggested Citation

  • Felipe Jiménez & Wilmar Cabrera-Montiel, 2014. "System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information," Energies, MDPI, vol. 7(6), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:6:p:3576-3598:d:36703
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    References listed on IDEAS

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    Cited by:

    1. Patel Nilanchal & Mukherjee Alok Bhushan, 2015. "Assessment of network traffic congestion through Traffic Congestability Value (TCV): a new index," Bulletin of Geography. Socio-economic Series, Sciendo, vol. 30(30), pages 123-134, December.
    2. Fiamma Perez-Prada & Andres Monzon & Cristina Valdes, 2017. "Managing Traffic Flows for Cleaner Cities: The Role of Green Navigation Systems," Energies, MDPI, vol. 10(6), pages 1-18, June.
    3. Thomas Levermore & M. Necip Sahinkaya & Yahya Zweiri & Ben Neaves, 2016. "Real-Time Velocity Optimization to Minimize Energy Use in Passenger Vehicles," Energies, MDPI, vol. 10(1), pages 1-18, December.
    4. Teng Liu & Yuan Zou & Dexing Liu & Fengchun Sun, 2015. "Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle," Energies, MDPI, vol. 8(7), pages 1-18, July.

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