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Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption

Author

Listed:
  • Yesid Bello

    (Energy and Embedded Systems for Transportation Research Department, ESTACA-LAB, 78066 Montigny-Le-Bretonneux, France
    Javeriana Electronics Department, Pontificia Universidad, Bogotá 110231, Colombia)

  • Juan Sebastian Roncancio

    (Energy and Embedded Systems for Transportation Research Department, ESTACA-LAB, 78066 Montigny-Le-Bretonneux, France
    Javeriana Electronics Department, Pontificia Universidad, Bogotá 110231, Colombia)

  • Toufik Azib

    (Energy and Embedded Systems for Transportation Research Department, ESTACA-LAB, 78066 Montigny-Le-Bretonneux, France)

  • Diego Patino

    (Javeriana Electronics Department, Pontificia Universidad, Bogotá 110231, Colombia)

  • Cherif Larouci

    (Energy and Embedded Systems for Transportation Research Department, ESTACA-LAB, 78066 Montigny-Le-Bretonneux, France)

  • Moussa Boukhnifer

    (Université de Lorraine, LCOMS, F-57000 Metz, France)

  • Nassim Rizoug

    (Energy and Embedded Systems for Transportation Research Department, ESTACA-LAB, 78066 Montigny-Le-Bretonneux, France)

  • Fredy Ruiz

    (Systems and Control Department Italy, Politecnico de Milano, 20158 Milan, Italy)

Abstract

Increasing the range of electric vehicles (EVs) is possible with the help of eco-driving techniques, which are algorithms that consider internal and external factors, like performance limits and environmental conditions, such as weather. However, these constraints must include critical variables in energy consumption, such as driver preferences and external vehicle conditions. In this article, a reasonable energy-efficient non-linear model predictive control (NMPC) is built for an electric two-wheeler vehicle, considering the Paris-Brussels route with different driving profiles and driver preferences. Here, NMPC is successfully implemented in a test bed, showing how to obtain the different parameters of the optimization problem and the estimation of the energy for the closed-loop system from a practical point of view. The efficiency of the brushless DC motor (BLCD) is also included for this test bed. In addition, this document shows that the proposal increases the chance of traveling the given route with a distance accuracy of approximately 1.5% while simultaneously boosting the vehicle autonomy by almost 20%. The practical result indicates that the strategy based on an NMPC algorithm can significantly boost the driver’s chance of completing the journey. If the vehicle energy is insufficient to succeed in the trip, the algorithm can guide the minimal State of Charge (SOC) required to complete the journey to reduce the driver energy-related uncertainty to a minimum.

Suggested Citation

  • Yesid Bello & Juan Sebastian Roncancio & Toufik Azib & Diego Patino & Cherif Larouci & Moussa Boukhnifer & Nassim Rizoug & Fredy Ruiz, 2023. "Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption," Energies, MDPI, vol. 16(4), pages 1-26, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1950-:d:1070007
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    References listed on IDEAS

    as
    1. Aminu Bugaje & Mathias Ehrenwirth & Christoph Trinkl & Wilfried Zörner, 2021. "Electric Two-Wheeler Vehicle Integration into Rural Off-Grid Photovoltaic System in Kenya," Energies, MDPI, vol. 14(23), pages 1-27, November.
    2. Alessia Musa & Michele Pipicelli & Matteo Spano & Francesco Tufano & Francesco De Nola & Gabriele Di Blasio & Alfredo Gimelli & Daniela Anna Misul & Gianluca Toscano, 2021. "A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems," Energies, MDPI, vol. 14(23), pages 1-24, November.
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