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Interval Type-2 Fuzzy Logic Anti-Lock Braking Control for Electric Vehicles under Complex Road Conditions

Author

Listed:
  • Linfeng Lv

    (Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Juncheng Wang

    (Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Jiangqi Long

    (School of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China)

Abstract

To simultaneously track the ideal slip rate and realize ideal energy recovery efficiency under different complex road conditions, an electro-hydraulic compound anti-lock braking system based on interval type-2 fuzzy logic control strategy and its corresponding braking torque allocation strategy have been developed for electric vehicles. The proposed interval type-2 fuzzy logic controller aims to calculate the ideal total braking torque by four steps, namely, fuzzification, fuzzy inference, type reduction, and defuzzification. The slip rate error and the change rate of slip rate error are utilized as inputs in the fuzzification, and then, the membership degree interval of fuzzy variables determined by the upper and lower membership functions is used to calculate the activation degree interval of different fuzzy rules in the fuzzy inference process, which enhances the anti-interference ability to external uncertainties and internal uncertainties. The braking torque allocation strategy is proposed to maintain the maximum energy recovery efficiency on the premise of safe braking. The software of MATLAB/Simulink is applied to simulate the process of anti-lock braking control under two complex road conditions. Simulation results corroborate the proposed interval type-2 fuzzy logic anti-lock braking control system can not only obtain better slip rate control effect and outstanding robustness but also achieve ideal regenerative braking energy recovery efficiency under both joint- μ and split- μ road surfaces.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:20:p:11531-:d:659534
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Man-Wen Tian & Khalid Alattas & Fayez El-Sousy & Abdullah Alanazi & Ardashir Mohammadzadeh & Jafar Tavoosi & Saleh Mobayen & Paweł Skruch, 2022. "A New Short Term Electrical Load Forecasting by Type-2 Fuzzy Neural Networks," Energies, MDPI, vol. 15(9), pages 1-14, April.

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