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Experimental Platform for Evaluation of On-Board Real-Time Motion Controllers for Electric Vehicles

Author

Listed:
  • Thanh Vo-Duy

    (CTI Laboratory for Electric Vehicles, Department of Industrial Automation, Hanoi University of Science and Technology, Hanoi 10000, Vietnam)

  • Minh C. Ta

    (CTI Laboratory for Electric Vehicles, Department of Industrial Automation, Hanoi University of Science and Technology, Hanoi 10000, Vietnam
    e-TESC Lab., University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada)

  • Bảo-Huy Nguyễn

    (CTI Laboratory for Electric Vehicles, Department of Industrial Automation, Hanoi University of Science and Technology, Hanoi 10000, Vietnam
    e-TESC Lab., University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada)

  • João Pedro F. Trovão

    (e-TESC Lab., University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
    INESC Coimbra, DEEC, University of Coimbra, Polo II, 3030-290 Coimbra, Portugal
    Polytechnic Institute of Coimbra, IPC-ISEC, DEE, 3030-199 Coimbra, Portugal)

Abstract

Electric vehicles are considered to be a greener and safer means of transport thanks to the distinguished advantages of electric motors. Studies on this object require experimental platforms for control validation purpose. Under the pressure of research, the development of these platforms must be reliable, safe, fast, and cost effective. To practically validate the control system, the controllers should be implemented in an on-board micro-controller platform; whereas, the vehicle model should be realized in a real-time emulator that behaves like the real vehicle. In this paper, we propose a signal hardware-in-the-loop simulation system for electric vehicles that are driven by four independent electric motors installed in wheels (in-wheel motor). The system is elaborately built on the basis of longitudinal, lateral, and yaw dynamics, as well as kinematic and position models, of which the characteristics are complete and comprehensive. The performance of the signal hardware-in-the-loop system is evaluated by various open-loop testing scenarios and by validation of a representative closed-loop optimal force distribution control. The proposed system can be applied for researches on active safety system of electric vehicles, including traction, braking control, force/torque distribution strategy, and electronic stability program.

Suggested Citation

  • Thanh Vo-Duy & Minh C. Ta & Bảo-Huy Nguyễn & João Pedro F. Trovão, 2020. "Experimental Platform for Evaluation of On-Board Real-Time Motion Controllers for Electric Vehicles," Energies, MDPI, vol. 13(23), pages 1-28, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6448-:d:457549
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    References listed on IDEAS

    as
    1. Yulin Yan & Chunguang Liu & Xiaojun Ma & Yunyin Zhang, 2019. "Hardware-in-loop real-time simulation of electrical vehicle using multi-simulation platform based on data fusion approach," International Journal of Distributed Sensor Networks, , vol. 15(4), pages 15501477198, April.
    2. Seung-Yun Baek & Yeon-Soo Kim & Wan-Soo Kim & Seung-Min Baek & Yong-Joo Kim, 2020. "Development and Verification of a Simulation Model for 120 kW Class Electric AWD (All-Wheel-Drive) Tractor during Driving Operation," Energies, MDPI, vol. 13(10), pages 1-15, May.
    3. Jinhyun Park & In Gyu Jang & Sung-Ho Hwang, 2018. "Torque Distribution Algorithm for an Independently Driven Electric Vehicle Using a Fuzzy Control Method: Driving Stability and Efficiency," Energies, MDPI, vol. 11(12), pages 1-22, December.
    4. Jie Tian & Jun Tong & Shi Luo, 2018. "Differential Steering Control of Four-Wheel Independent-Drive Electric Vehicles," Energies, MDPI, vol. 11(11), pages 1-18, October.
    5. Hongwen He & Jiankun Peng & Rui Xiong & Hao Fan, 2014. "An Acceleration Slip Regulation Strategy for Four-Wheel Drive Electric Vehicles Based on Sliding Mode Control," Energies, MDPI, vol. 7(6), pages 1-16, June.
    6. Cheng Lin & Zhifeng Xu, 2015. "Wheel Torque Distribution of Four-Wheel-Drive Electric Vehicles Based on Multi-Objective Optimization," Energies, MDPI, vol. 8(5), pages 1-17, April.
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    Cited by:

    1. 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.
    2. João Pedro F. Trovão & Minh Cao Ta, 2022. "Electric Vehicle Efficient Power and Propulsion Systems," Energies, MDPI, vol. 15(11), pages 1-4, May.

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