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A Model Predictive Control Approach for Fuel Economy Improvement of a Series Hydraulic Hybrid Vehicle

Author

Listed:
  • Tri-Vien Vu

    (Department of Mechanical and Automation Engineering, Da-yeh University, 168 University Road, Dacun, Changhua 51591, Taiwan
    These authors contributed equally to this work.)

  • Chih-Keng Chen

    (Department of Mechanical and Automation Engineering, Da-yeh University, 168 University Road, Dacun, Changhua 51591, Taiwan)

  • Chih-Wei Hung

    (Department of Mechanical and Automation Engineering, Da-yeh University, 168 University Road, Dacun, Changhua 51591, Taiwan
    These authors contributed equally to this work.)

Abstract

This study applied a model predictive control (MPC) framework to solve the cruising control problem of a series hydraulic hybrid vehicle (SHHV). The controller not only regulates vehicle velocity, but also engine torque, engine speed, and accumulator pressure to their corresponding reference values. At each time step, a quadratic programming problem is solved within a predictive horizon to obtain the optimal control inputs. The objective is to minimize the output error. This approach ensures that the components operate at high efficiency thereby improving the total efficiency of the system. The proposed SHHV control system was evaluated under urban and highway driving conditions. By handling constraints and input-output interactions, the MPC-based control system ensures that the system operates safely and efficiently. The fuel economy of the proposed control scheme shows a noticeable improvement in comparison with the PID-based system, in which three Proportional-Integral-Derivative (PID) controllers are used for cruising control.

Suggested Citation

  • Tri-Vien Vu & Chih-Keng Chen & Chih-Wei Hung, 2014. "A Model Predictive Control Approach for Fuel Economy Improvement of a Series Hydraulic Hybrid Vehicle," Energies, MDPI, vol. 7(11), pages 1-24, October.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:11:p:7017-7040:d:41875
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    Citations

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    Cited by:

    1. Izaskun Garrido & Aitor J. Garrido & Stefano Coda & Hoang B. Le & Jean Marc Moret, 2016. "Real Time Hybrid Model Predictive Control for the Current Profile of the Tokamak à Configuration Variable (TCV)," Energies, MDPI, vol. 9(8), pages 1-14, August.
    2. Armin Norouzi & Hamed Heidarifar & Mahdi Shahbakhti & Charles Robert Koch & Hoseinali Borhan, 2021. "Model Predictive Control of Internal Combustion Engines: A Review and Future Directions," Energies, MDPI, vol. 14(19), pages 1-40, October.
    3. 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.
    4. Feras Alasali & Stephen Haben & Victor Becerra & William Holderbaum, 2017. "Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems," Energies, MDPI, vol. 10(10), pages 1-18, October.

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