IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v7y2014i10p6459-6476d40984.html
   My bibliography  Save this article

Performance Evaluation of an Anti-Lock Braking System for Electric Vehicles with a Fuzzy Sliding Mode Controller

Author

Listed:
  • Jingang Guo

    (School of Automobile, Chang'an University, Xi'an 710061, Shaanxi, China)

  • Xiaoping Jian

    (School of Automobile, Chang'an University, Xi'an 710061, Shaanxi, China)

  • Guangyu Lin

    (School of Automobile, Chang'an University, Xi'an 710061, Shaanxi, China)

Abstract

Traditional friction braking torque and motor braking torque can be used in braking for electric vehicles (EVs). A sliding mode controller (SMC) based on the exponential reaching law for the anti-lock braking system (ABS) is developed to maintain the optimal slip value. Parameter optimizing is applied to the reaching law by fuzzy logic control (FLC). A regenerative braking algorithm, in which the motor torque is taken full advantage of, is adopted to distribute the braking force between the motor braking and the hydraulic braking. Simulations were carried out with Matlab/Simulink. By comparing with a conventional Bang-bang ABS controller, braking stability and passenger comfort is improved with the proposed SMC controller, and the chatting phenomenon is reduced effectively with the parameter optimizing by FLC. With the increasing proportion of the motor braking torque, the tracking of the slip ratio is more rapid and accurate. Furthermore, the braking distance is shortened and the conversion energy is enhanced.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:10:p:6459-6476:d:40984
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/7/10/6459/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/7/10/6459/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guoqing Xu & Weimin Li & Kun Xu & Zhibin Song, 2011. "An Intelligent Regenerative Braking Strategy for Electric Vehicles," Energies, MDPI, vol. 4(9), pages 1-17, September.
    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Valery Vodovozov & Zoja Raud & Eduard Petlenkov, 2021. "Review on Braking Energy Management in Electric Vehicles," Energies, MDPI, vol. 14(15), pages 1-26, July.
    8. 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.

    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. López, I. & Ibarra, E. & Matallana, A. & Andreu, J. & Kortabarria, I., 2019. "Next generation electric drives for HEV/EV propulsion systems: Technology, trends and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    2. Wasbari, F. & Bakar, R.A. & Gan, L.M. & Tahir, M.M. & Yusof, A.A., 2017. "A review of compressed-air hybrid technology in vehicle system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 935-953.
    3. Emilia M. Szumska & Rafał S. Jurecki, 2021. "Parameters Influencing on Electric Vehicle Range," Energies, MDPI, vol. 14(16), pages 1-23, August.
    4. Duo Zhang & Guohai Liu & Wenxiang Zhao & Penghu Miao & Yan Jiang & Huawei Zhou, 2014. "A Neural Network Combined Inverse Controller for a Two-Rear-Wheel Independently Driven Electric Vehicle," Energies, MDPI, vol. 7(7), pages 1-15, July.
    5. Jiangbo Wang & Kai Liu & Toshiyuki Yamamoto, 2017. "Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations," Energies, MDPI, vol. 10(1), pages 1-12, January.
    6. 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.
    7. Petronilla Fragiacomo & Francesco Piraino & Matteo Genovese & Lorenzo Flaccomio Nardi Dei & Daria Donati & Michele Vincenzo Migliarese Caputi & Domenico Borello, 2022. "Sizing and Performance Analysis of Hydrogen- and Battery-Based Powertrains, Integrated into a Passenger Train for a Regional Track, Located in Calabria (Italy)," Energies, MDPI, vol. 15(16), pages 1-20, August.
    8. Yang, Chao & Sun, Tonglin & Wang, Weida & Li, Ying & Zhang, Yuhang & Zha, Mingjun, 2024. "Regenerative braking system development and perspectives for electric vehicles: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 198(C).
    9. Jinhyun Park & Houn Jeong & In Gyu Jang & Sung-Ho Hwang, 2015. "Torque Distribution Algorithm for an Independently Driven Electric Vehicle Using a Fuzzy Control Method," Energies, MDPI, vol. 8(8), pages 1-25, August.
    10. 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.
    11. Xiang Liu & Min Xu & Mian Li, 2015. "New TA Index-Based Rollover Prevention System for Electric Vehicles," Energies, MDPI, vol. 8(3), pages 1-24, March.
    12. Yang Yang & Xiaolong He & Yi Zhang & Datong Qin, 2018. "Regenerative Braking Compensatory Control Strategy Considering CVT Power Loss for Hybrid Electric Vehicles," Energies, MDPI, vol. 11(3), pages 1-15, February.
    13. Branimir Škugor & Joško Petrić, 2018. "Optimization of Control Variables and Design of Management Strategy for Hybrid Hydraulic Vehicle," Energies, MDPI, vol. 11(10), pages 1-24, October.
    14. Maksymilian Mądziel & Tiziana Campisi & Artur Jaworski & Giovanni Tesoriere, 2021. "The Development of Strategies to Reduce Exhaust Emissions from Passenger Cars in Rzeszow City—Poland. A Preliminary Assessment of the Results Produced by the Increase of E-Fleet," Energies, MDPI, vol. 14(4), pages 1-21, February.
    15. Wu, Fuliang & Bektaş, Tolga & Dong, Ming & Ye, Hongbo & Zhang, Dali, 2021. "Optimal driving for vehicle fuel economy under traffic speed uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 175-206.
    16. Serrano, José Ramón & García, Antonio & Monsalve-Serrano, Javier & Martínez-Boggio, Santiago, 2021. "High efficiency two stroke opposed piston engine for plug-in hybrid electric vehicle applications: Evaluation under homologation and real driving conditions," Applied Energy, Elsevier, vol. 282(PA).
    17. He, Hongwen & Wang, Chen & Jia, Hui & Cui, Xing, 2020. "An intelligent braking system composed single-pedal and multi-objective optimization neural network braking control strategies for electric vehicle," Applied Energy, Elsevier, vol. 259(C).
    18. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2018. "Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption," Applied Energy, Elsevier, vol. 227(C), pages 324-331.
    19. Ilya Kulikov, 2019. "Model Analysis of Electrically Driven Vehicles by Means of Unknown Input Observers," Energies, MDPI, vol. 12(12), pages 1-17, June.
    20. Emilia M. Szumska & Rafał Jurecki, 2022. "The Analysis of Energy Recovered during the Braking of an Electric Vehicle in Different Driving Conditions," Energies, MDPI, vol. 15(24), pages 1-16, December.

    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:7:y:2014:i:10:p:6459-6476:d:40984. 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.