IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i5p1294-d506679.html
   My bibliography  Save this article

Design, Analysis and Application of Single-Wheel Test Bench for All-Electric Antilock Braking System in Electric Vehicles

Author

Listed:
  • Xiangdang XUE

    (Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, China)

  • Ka Wai Eric CHENG

    (Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, China)

  • Wing Wa CHAN

    (Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, China)

  • Yat Chi FONG

    (Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, China)

  • Kin Lung Jerry KAN

    (Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, China)

  • Yulong FAN

    (Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, China)

Abstract

An antilock braking system (ABS) is one of the most important components in a road vehicle, which provides active protection during braking, to prevent the wheels from locking-up and achieve handling stability and steerability. The all-electric ABS without any hydraulic components is a potential candidate for electric vehicles. To demonstrate and examine the all-electric ABS algorithms, this article proposes a single-wheel all-electric ABS test bench, which mainly includes the vehicle wheel, the roller, the flywheels, and the electromechanical brake. To simulate dynamic operation of a real vehicle’s wheel, the kinetic energy of the total rotary components in the bench is designed to match the quarter of the one of a commercial car. The vertical force to the wheel is adjustable. The tire-roller contact simulates the real tire-road contact. The roller’s circumferential velocity represents the longitudinal vehicle velocity. The design and analysis of the proposed bench are described in detail. For the developed prototype, the rated clamping force of the electromechanical brake is 11 kN, the maximum vertical force to the wheel reaches 300 kg, and the maximum roller (vehicle) velocity reaches 100 km/h. The measurable bandwidth of the wheel speed is 4 Hz–2 kHz and the motor speed is 2.5 Hz–50 kHz. The measured results including the roller (vehicle) velocity, the wheel velocity, and the wheel slip are satisfactory. This article offers the effective tools to verify all-electric ABS algorithms in a laboratory, hence saving time and cost for the subsequent test on a real road.

Suggested Citation

  • Xiangdang XUE & Ka Wai Eric CHENG & Wing Wa CHAN & Yat Chi FONG & Kin Lung Jerry KAN & Yulong FAN, 2021. "Design, Analysis and Application of Single-Wheel Test Bench for All-Electric Antilock Braking System in Electric Vehicles," Energies, MDPI, vol. 14(5), pages 1-12, February.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1294-:d:506679
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/5/1294/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/5/1294/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jinhong Sun & Xiangdang Xue & Ka Wai Eric Cheng, 2019. "Fuzzy Sliding Mode Wheel Slip Ratio Control for Smart Vehicle Anti-Lock Braking System," Energies, MDPI, vol. 12(13), pages 1-22, June.
    2. Jingang Guo & Xiaoping Jian & Guangyu Lin, 2014. "Performance Evaluation of an Anti-Lock Braking System for Electric Vehicles with a Fuzzy Sliding Mode Controller," Energies, MDPI, vol. 7(10), pages 1-18, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peter Girovský & Jaroslava Žilková & Ján Kaňuch, 2020. "Optimization of Vehicle Braking Distance Using a Fuzzy Controller," Energies, MDPI, vol. 13(11), pages 1-15, June.
    2. Jose A. Ruz-Hernandez & Larbi Djilali & Mario Antonio Ruz Canul & Moussa Boukhnifer & Edgar N. Sanchez, 2022. "Neural Inverse Optimal Control of a Regenerative Braking System for Electric Vehicles," Energies, MDPI, vol. 15(23), pages 1-19, November.
    3. Linfeng Lv & Juncheng Wang & Jiangqi Long, 2021. "Interval Type-2 Fuzzy Logic Anti-Lock Braking Control for Electric Vehicles under Complex Road Conditions," Sustainability, MDPI, vol. 13(20), pages 1-23, October.
    4. Mojtaba Ahmadieh Khanesar & David Branson, 2022. "Robust Sliding Mode Fuzzy Control of Industrial Robots Using an Extended Kalman Filter Inverse Kinematic Solver," Energies, MDPI, vol. 15(5), pages 1-17, March.
    5. Zebin Yang & Ling Wan & Xiaodong Sun & Fangli Li & Lin Chen, 2016. "Sliding Mode Variable Structure Control of a Bearingless Induction Motor Based on a Novel Reaching Law," Energies, MDPI, vol. 9(6), pages 1-14, June.
    6. Hanwu Liu & Yulong Lei & Yao Fu & Xingzhong Li, 2020. "An Optimal Slip Ratio-Based Revised Regenerative Braking Control Strategy of Range-Extended Electric Vehicle," Energies, MDPI, vol. 13(6), pages 1-21, March.
    7. Raja Mazuir Raja Ahsan Shah & Richard Peter Jones & Caizhen Cheng & Alessandro Picarelli & Abd Rashid Abd Aziz & Mansour Al Qubeissi, 2021. "Model-Based Energy Path Analysis of Tip-In Event in a 2WD Vehicle with Range-Extender Electric Powertrain Architecture," Energies, MDPI, vol. 14(18), pages 1-18, September.
    8. Changran He & Guoye Wang & Zhangpeng Gong & Zhichao Xing & Dongxin Xu, 2018. "A Control Algorithm for the Novel Regenerative–Mechanical Coupled Brake System with by-Wire Based on Multidisciplinary Design Optimization for an Electric Vehicle," Energies, MDPI, vol. 11(9), pages 1-18, September.
    9. 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.
    10. Valery Vodovozov & Zoja Raud & Eduard Petlenkov, 2021. "Review on Braking Energy Management in Electric Vehicles," Energies, MDPI, vol. 14(15), pages 1-26, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1294-:d:506679. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.