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Real-Time Velocity Optimization to Minimize Energy Use in Passenger Vehicles

Author

Listed:
  • Thomas Levermore

    (Faculty of Science, Engineering and Computing, Kingston University London, London SW15 3DW, UK)

  • M. Necip Sahinkaya

    (Faculty of Science, Engineering and Computing, Kingston University London, London SW15 3DW, UK)

  • Yahya Zweiri

    (Faculty of Science, Engineering and Computing, Kingston University London, London SW15 3DW, UK)

  • Ben Neaves

    (Jaguar Land Rover Limited, Gaydon CV35 0RR, UK)

Abstract

Energy use in internal combustion engine passenger vehicles contributes directly to CO 2 emissions and fuel consumption, as well as producing a number of air pollutants. Optimizing the vehicle velocity by utilising upcoming road information is an opportunity to minimize vehicle energy use without requiring mechanical design changes. Dynamic programming is capable of such an optimization task and is shown in simulation to produce fuel savings, on average 12%, compared to real driving data; however, in this paper it is also applied in real time on a Raspberry Pi, a low cost miniature computer, in situ in a vehicle. A test drive was undertaken with driver feedback being provided by a dynamic programming algorithm, and the results are compared to a simulated intelligent cruise control system that can follow the algorithm results precisely. An 8% reduction in fuel with no loss in time is reported compared to the test driver.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:10:y:2016:i:1:p:30-:d:86264
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    References listed on IDEAS

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    1. 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.
    2. Wenchen Shen & Huilong Yu & Yuhui Hu & Junqiang Xi, 2016. "Optimization of Shift Schedule for Hybrid Electric Vehicle with Automated Manual Transmission," Energies, MDPI, vol. 9(3), pages 1-11, March.
    3. Monastyrsky, V. V. & Golownykh, I. M., 1993. "Rapid computation of optimal control for vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 27(3), pages 219-227, June.
    4. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
    5. Paul Minett & John Pearce, 2011. "Estimating the Energy Consumption Impact of Casual Carpooling," Energies, MDPI, vol. 4(1), pages 1-14, January.
    6. Barkenbus, Jack N., 2010. "Eco-driving: An overlooked climate change initiative," Energy Policy, Elsevier, vol. 38(2), pages 762-769, February.
    7. Ximing Wang & Hongwen He & Fengchun Sun & Jieli Zhang, 2015. "Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles," Energies, MDPI, vol. 8(4), pages 1-20, April.
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    Cited by:

    1. Abdullah Mohiuddin & Tarek Taha & Yahya Zweiri & Dongming Gan, 2019. "UAV Payload Transportation via RTDP Based Optimized Velocity Profiles," Energies, MDPI, vol. 12(16), pages 1-25, August.
    2. Subramaniam Saravana Sankar & Yiqun Xia & Julaluk Carmai & Saiprasit Koetniyom, 2020. "Optimal Eco-Driving Cycles for Conventional Vehicles Using a Genetic Algorithm," Energies, MDPI, vol. 13(17), pages 1-15, August.

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