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Real-Time Energy Management of Parallel Hybrid Electric Vehicles Using Linear Quadratic Regulation

Author

Listed:
  • Bảo-Huy Nguyễn

    (e-TESC Lab., University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
    Centrale Lille, Arts et Métiers Institute of Technology, Univ. Lille, Yncrea Hauts-de-France, ULR 2697-L2EP, F-59000 Lille, France
    MEGEVH, French Scientific Network on Hybrid and Electric Vehicles, F-59000 Lille, France)

  • João Pedro F. Trovão

    (e-TESC Lab., University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
    Canada Research Chair in Efficient Electric Vehicles with Hybridized Energy Storage Systems, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada)

  • Ronan German

    (Centrale Lille, Arts et Métiers Institute of Technology, Univ. Lille, Yncrea Hauts-de-France, ULR 2697-L2EP, F-59000 Lille, France
    MEGEVH, French Scientific Network on Hybrid and Electric Vehicles, F-59000 Lille, France)

  • Alain Bouscayrol

    (Centrale Lille, Arts et Métiers Institute of Technology, Univ. Lille, Yncrea Hauts-de-France, ULR 2697-L2EP, F-59000 Lille, France
    MEGEVH, French Scientific Network on Hybrid and Electric Vehicles, F-59000 Lille, France)

Abstract

Optimization-based methods are of interest for developing energy management strategies due to their high performance for hybrid electric vehicles. However, these methods are often complicated and may require strong computational efforts, which can prevent them from real-world applications. This paper proposes a novel real-time optimization-based torque distribution strategy for a parallel hybrid truck. The strategy aims to minimize the engine fuel consumption while ensuring battery charge-sustaining by using linear quadratic regulation in a closed-loop control scheme. Furthermore, by reformulating the problem, the obtained strategy does not require the information of the engine efficiency map like the previous works in literature. The obtained strategy is simple, straightforward, and therefore easy to be implemented in real-time platforms. The proposed method is evaluated via simulation by comparison to dynamic programming as a benchmark. Furthermore, the real-time ability of the proposed strategy is experimentally validated by using power hardware-in-the-loop simulation.

Suggested Citation

  • Bảo-Huy Nguyễn & João Pedro F. Trovão & Ronan German & Alain Bouscayrol, 2020. "Real-Time Energy Management of Parallel Hybrid Electric Vehicles Using Linear Quadratic Regulation," Energies, MDPI, vol. 13(21), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5538-:d:433073
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    References listed on IDEAS

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    Cited by:

    1. Naoui Mohamed & Flah Aymen & Abdullah Altamimi & Zafar A. Khan & Sbita Lassaad, 2022. "Power Management and Control of a Hybrid Electric Vehicle Based on Photovoltaic, Fuel Cells, and Battery Energy Sources," Sustainability, MDPI, vol. 14(5), pages 1-20, February.
    2. Hoai-Linh T. Nguyen & Bảo-Huy Nguyễn & Thanh Vo-Duy & João Pedro F. Trovão, 2021. "A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles," Energies, MDPI, vol. 14(12), pages 1-23, June.
    3. Leone Martellucci & Roberto Capata, 2022. "High Performance Hybrid Vehicle Concept—Preliminary Study and Vehicle Packaging," Energies, MDPI, vol. 15(11), pages 1-20, May.
    4. Waruna Maddumage & Malika Perera & Rahula Attalage & Patrick Kelly, 2021. "Power Management Strategy of a Parallel Hybrid Three-Wheeler for Fuel and Emission Reduction," Energies, MDPI, vol. 14(7), pages 1-30, March.

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