IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0179526.html
   My bibliography  Save this article

Tire-road friction estimation and traction control strategy for motorized electric vehicle

Author

Listed:
  • Li-Qiang Jin
  • Mingze Ling
  • Weiqiang Yue

Abstract

In this paper, an optimal longitudinal slip ratio system for real-time identification of electric vehicle (EV) with motored wheels is proposed based on the adhesion between tire and road surface. First and foremost, the optimal longitudinal slip rate torque control can be identified in real time by calculating the derivative and slip rate of the adhesion coefficient. Secondly, the vehicle speed estimation method is also brought. Thirdly, an ideal vehicle simulation model is proposed to verify the algorithm with simulation, and we find that the slip ratio corresponds to the detection of the adhesion limit in real time. Finally, the proposed strategy is applied to traction control system (TCS). The results showed that the method can effectively identify the state of wheel and calculate the optimal slip ratio without wheel speed sensor; in the meantime, it can improve the accelerated stability of electric vehicle with traction control system (TCS).

Suggested Citation

  • Li-Qiang Jin & Mingze Ling & Weiqiang Yue, 2017. "Tire-road friction estimation and traction control strategy for motorized electric vehicle," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-18, June.
    • Handle: RePEc:plo:pone00:0179526
    DOI: 10.1371/journal.pone.0179526
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0179526
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0179526&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0179526?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hu, Xiaosong & Zou, Yuan & Yang, Yalian, 2016. "Greener plug-in hybrid electric vehicles incorporating renewable energy and rapid system optimization," Energy, Elsevier, vol. 111(C), pages 971-980.
    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. Xiang Fu & Jiaqi Wan & Daoyuan Liu & Song Huang & Sen Wu & Zexuan Liu & Jijie Wang & Qianfeng Ruan & Tianqi Yang, 2024. "Synthetic Optimization of Trafficability and Roll Stability for Off-Road Vehicles Based on Wheel-Hub Drive Motors and Semi-Active Suspension," Mathematics, MDPI, vol. 12(12), pages 1-29, June.

    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. Ming Foong Soong & Rahizar Ramli & Ahmad Saifizul, 2017. "Between simplicity and accuracy: Effect of adding modeling details on quarter vehicle model accuracy," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-23, June.
    2. Jing Li & Tong Wu & Tianxin Fan & Yan He & Lingshuai Meng & Zuoyue Han, 2020. "Clamping force control of electro–mechanical brakes based on driver intentions," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-30, September.
    3. Shaobo Xie & Xiaosong Hu & Kun Lang & Shanwei Qi & Tong Liu, 2018. "Powering Mode-Integrated Energy Management Strategy for a Plug-In Hybrid Electric Truck with an Automatic Mechanical Transmission Based on Pontryagin’s Minimum Principle," Sustainability, MDPI, vol. 10(10), pages 1-23, October.
    4. Liu, Hui & Duan, Zhu & Chen, Chao, 2020. "Wind speed big data forecasting using time-variant multi-resolution ensemble model with clustering auto-encoder," Applied Energy, Elsevier, vol. 280(C).
    5. García-Vázquez, Carlos A. & Llorens-Iborra, Francisco & Fernández-Ramírez, Luis M. & Sánchez-Sainz, Higinio & Jurado, Francisco, 2017. "Comparative study of dynamic wireless charging of electric vehicles in motorway, highway and urban stretches," Energy, Elsevier, vol. 137(C), pages 42-57.
    6. Wu, Xiaohua & Hu, Xiaosong & Yin, Xiaofeng & Zhang, Caiping & Qian, Shide, 2017. "Optimal battery sizing of smart home via convex programming," Energy, Elsevier, vol. 140(P1), pages 444-453.
    7. Colmenar-Santos, A. & de Palacio-Rodriguez, Carlos & Rosales-Asensio, Enrique & Borge-Diez, David, 2017. "Estimating the benefits of vehicle-to-home in islands: The case of the Canary Islands," Energy, Elsevier, vol. 134(C), pages 311-322.
    8. Fei Liu & Zhiyuan Zeng & Rong Jiang, 2017. "A video-based real-time adaptive vehicle-counting system for urban roads," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-16, November.
    9. Chattopadhyay, Kabitri & Kies, Alexander & Lorenz, Elke & von Bremen, Lüder & Heinemann, Detlev, 2017. "The impact of different PV module configurations on storage and additional balancing needs for a fully renewable European power system," Renewable Energy, Elsevier, vol. 113(C), pages 176-189.
    10. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Lang, Kun, 2018. "An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 163(C), pages 837-848.
    11. Peng, Fei & Zhao, Yuanzhe & Chen, Ting & Zhang, Xuexia & Chen, Weirong & Zhou, Donghua & Li, Qi, 2018. "Development of robust suboptimal real-time power sharing strategy for modern fuel cell based hybrid tramways considering operational uncertainties and performance degradation," Applied Energy, Elsevier, vol. 226(C), pages 503-521.
    12. Yang, Chao & Li, Liang & You, Sixiong & Yan, Bingjie & Du, Xian, 2017. "Cloud computing-based energy optimization control framework for plug-in hybrid electric bus," Energy, Elsevier, vol. 125(C), pages 11-26.
    13. Noman Khan & Fath U Min Ullah & Ijaz Ul Haq & Samee Ullah Khan & Mi Young Lee & Sung Wook Baik, 2021. "AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting," Mathematics, MDPI, vol. 9(19), pages 1-18, October.
    14. Pirouzi, Sasan & Aghaei, Jamshid & Niknam, Taher & Shafie-khah, Miadreza & Vahidinasab, Vahid & Catalão, João P.S., 2017. "Two alternative robust optimization models for flexible power management of electric vehicles in distribution networks," Energy, Elsevier, vol. 141(C), pages 635-651.
    15. Zheng, Fengying & Chen, Yuang & Zhang, Jingyang & Cheng, Fengna & Zhang, Jingzhou, 2023. "A two-stage energy management for integrated thermal/energy optimization of aircraft airborne system based on Stackelberg game," Energy, Elsevier, vol. 269(C).
    16. Li, Yapeng & Tang, Xiaolin & Lin, Xianke & Grzesiak, Lech & Hu, Xiaosong, 2022. "The role and application of convex modeling and optimization in electrified vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    17. Zengliang Han & Dongqing Wang & Feng Liu & Zhiyong Zhao, 2017. "Multi-AGV path planning with double-path constraints by using an improved genetic algorithm," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-16, July.
    18. Lyu, Lin & Yang, Xinran & Xiang, Yue & Liu, Junyong & Jawad, Shafqat & Deng, Runqi, 2020. "Exploring high-penetration electric vehicles impact on urban power grid based on voltage stability analysis," Energy, Elsevier, vol. 198(C).
    19. Gaizka Saldaña & Jose Ignacio San Martin & Inmaculada Zamora & Francisco Javier Asensio & Oier Oñederra, 2019. "Electric Vehicle into the Grid: Charging Methodologies Aimed at Providing Ancillary Services Considering Battery Degradation," Energies, MDPI, vol. 12(12), pages 1-37, June.
    20. WenBo Xiao & Gina Nazario & HuaMing Wu & HuaMing Zhang & Feng Cheng, 2017. "A neural network based computational model to predict the output power of different types of photovoltaic cells," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-8, September.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0179526. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.