IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i7p6820-6840d52203.html
   My bibliography  Save this article

Wheel Slip Control for Improving Traction-Ability and Energy Efficiency of a Personal Electric Vehicle

Author

Listed:
  • Kanghyun Nam

    (School of Mechanical Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan 712-749, Korea)

  • Yoichi Hori

    (Department of Advanced Energy, Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Chiba 277-8561, Japan)

  • Choonyoung Lee

    (School of Mechanical Engineering, Kyungpook National University, 80 Daehak-ro, Bukgu, Daegu 702-701, Korea)

Abstract

In this paper, a robust wheel slip control system based on a sliding mode controller is proposed for improving traction-ability and reducing energy consumption during sudden acceleration for a personal electric vehicle. Sliding mode control techniques have been employed widely in the development of a robust wheel slip controller of conventional internal combustion engine vehicles due to their application effectiveness in nonlinear systems and robustness against model uncertainties and disturbances. A practical slip control system which takes advantage of the features of electric motors is proposed and an algorithm for vehicle velocity estimation is also introduced. The vehicle velocity estimator was designed based on rotational wheel dynamics, measurable motor torque, and wheel velocity as well as rule-based logic. The simulations and experiments were carried out using both CarSim software and an experimental electric vehicle equipped with in-wheel-motors. Through field tests, traction performance and effectiveness in terms of energy saving were all verified. Comparative experiments with variations of control variables proved the effectiveness and practicality of the proposed control design.

Suggested Citation

  • Kanghyun Nam & Yoichi Hori & Choonyoung Lee, 2015. "Wheel Slip Control for Improving Traction-Ability and Energy Efficiency of a Personal Electric Vehicle," Energies, MDPI, vol. 8(7), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:7:p:6820-6840:d:52203
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/7/6820/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/8/7/6820/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anith Khairunnisa Ghazali & Mohd Khair Hassan & Mohd Amran Mohd Radzi & Azizan As’arry, 2023. "Optimizing Energy Harvesting: A Gain-Scheduled Braking System for Electric Vehicles with Enhanced State of Charge and Efficiency," Energies, MDPI, vol. 16(12), pages 1-19, June.
    2. Xudong Zhang & Dietmar Göhlich, 2017. "Integrated Traction Control Strategy for Distributed Drive Electric Vehicles with Improvement of Economy and Longitudinal Driving Stability," Energies, MDPI, vol. 10(1), pages 1-18, January.
    3. Zhenpo Wang & Changhui Qu & Lei Zhang & Jin Zhang & Wen Yu, 2018. "Integrated Sizing and Energy Management for Four-Wheel-Independently-Actuated Electric Vehicles Considering Realistic Constructed Driving Cycles," Energies, MDPI, vol. 11(7), pages 1-22, July.
    4. Binh-Minh Nguyen & Hung Van Nguyen & Minh Ta-Cao & Michihiro Kawanishi, 2020. "Longitudinal Modelling and Control of In-Wheel-Motor Electric Vehicles as Multi-Agent Systems," Energies, MDPI, vol. 13(20), pages 1-28, October.
    5. Idris Idris Sunusi & Jun Zhou & Chenyang Sun & Zhenzhen Wang & Jianlei Zhao & Yongshuan Wu, 2021. "Development of Online Adaptive Traction Control for Electric Robotic Tractors," Energies, MDPI, vol. 14(12), pages 1-24, June.
    6. Valery Vodovozov & Zoja Raud & Eduard Petlenkov, 2021. "Review on Braking Energy Management in Electric Vehicles," Energies, MDPI, vol. 14(15), pages 1-26, July.

    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. Jiangyi Lv & Hongwen He & Wei Liu & Yong Chen & Fengchun Sun, 2019. "Vehicle Velocity Estimation Fusion with Kinematic Integral and Empirical Correction on Multi-Timescales," Energies, MDPI, vol. 12(7), pages 1-24, April.
    2. Ran Chen & Zongxia Jiao & Liang Yan & Yaoxing Shang & Shuai Wu, 2019. "Nonlinear Synchronous Control for H-Type Gantry Stage Used in Electric Vehicles Manufacturing," Energies, MDPI, vol. 12(12), pages 1-16, June.
    3. Lingfei Wu & Jinfang Gou & Lifang Wang & Junzhi Zhang, 2015. "Acceleration Slip Regulation Strategy for Distributed Drive Electric Vehicles with Independent Front Axle Drive Motors," Energies, MDPI, vol. 8(5), pages 1-30, May.
    4. 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.
    5. Xudong Zhang & Dietmar Göhlich, 2017. "A hierarchical estimator development for estimation of tire-road friction coefficient," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-21, February.
    6. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Electric vehicle powertrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 238(PC).
    7. Binh-Minh Nguyen & Hung Van Nguyen & Minh Ta-Cao & Michihiro Kawanishi, 2020. "Longitudinal Modelling and Control of In-Wheel-Motor Electric Vehicles as Multi-Agent Systems," Energies, MDPI, vol. 13(20), pages 1-28, October.
    8. 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.
    9. Xudong Zhang & Dietmar Göhlich, 2017. "Integrated Traction Control Strategy for Distributed Drive Electric Vehicles with Improvement of Economy and Longitudinal Driving Stability," Energies, MDPI, vol. 10(1), pages 1-18, January.
    10. Wanke Cao & Helin Liu & Cheng Lin & Yuhua Chang & Zhiyin Liu & Antoni Szumanowski, 2017. "Co-Design Based Lateral Motion Control of All-Wheel-Independent-Drive Electric Vehicles with Network Congestion," Energies, MDPI, vol. 10(10), pages 1-16, October.

    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:8:y:2015:i:7:p:6820-6840:d:52203. 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.