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Neural Network-Based Model Reference Control of Braking Electric Vehicles

Author

Listed:
  • Valery Vodovozov

    (Department of Electrical Engineering, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Andrei Aksjonov

    (Electrical Engineering and Automation, Aalto University, FI-00076 Aalto, Finland)

  • Eduard Petlenkov

    (Department of Electrical Engineering, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Zoja Raud

    (Department of Electrical Engineering, Tallinn University of Technology, 19086 Tallinn, Estonia)

Abstract

The problem of energy recovery in braking of an electric vehicle is solved here, which ensures high quality blended deceleration using electrical and friction brakes. A model reference controller is offered, capable to meet the conflicting requirements of intensive and gradual braking scenarios at changing road surfaces. In this study, the neural network controller provides torque gradient control without a tire model, resulting in the return of maximal energy to the hybrid energy storage during braking. The torque allocation algorithm determines how to share the driver’s request between the friction and electrical brakes in such a way as to enable regeneration for all braking modes, except when the battery state of charge and voltage levels are saturated, and a solo friction brake has to be used. The simulation demonstrates the effectiveness of the proposed coupled two-layer neural network capable of capturing various dynamic behaviors that could not be included in the simplified physics-based model. A comparison of the simulation and experimental results demonstrates that the velocity, slip, and torque responses confirm the proper car performance, while the system successfully copes with the strong nonlinearity and instability of the vehicle dynamics.

Suggested Citation

  • Valery Vodovozov & Andrei Aksjonov & Eduard Petlenkov & Zoja Raud, 2021. "Neural Network-Based Model Reference Control of Braking Electric Vehicles," Energies, MDPI, vol. 14(9), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2373-:d:541338
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    References listed on IDEAS

    as
    1. Jakov Topić & Branimir Škugor & Joško Deur, 2019. "Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range," Energies, MDPI, vol. 12(7), pages 1-20, April.
    2. 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).
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    Cited by:

    1. Constantin Volosencu, 2022. "Study of the Angular Positioning of a Rotating Object Based on Some Computational Intelligence Methods," Mathematics, MDPI, vol. 10(7), pages 1-46, April.
    2. Karol Tucki & Olga Orynycz & Agnieszka Dudziak, 2022. "The Impact of the Available Infrastructure on the Electric Vehicle Market in Poland and in EU Countries," IJERPH, MDPI, vol. 19(24), pages 1-23, December.
    3. Mateusz Malarczyk & Jules-Raymond Tapamo & Marcin Kaminski, 2022. "Application of Neural Data Processing in Autonomous Model Platform—A Complex Review of Solutions, Design and Implementation," Energies, MDPI, vol. 15(13), pages 1-22, June.
    4. Marian Janusz Łopatka & Karol Cieślik & Piotr Krogul & Tomasz Muszyński & Mirosław Przybysz & Arkadiusz Rubiec & Kacper Spadło, 2023. "Research on Terrain Mobility of UGV with Hydrostatic Wheel Drive and Slip Control Systems," Energies, MDPI, vol. 16(19), pages 1-22, October.
    5. Valery Vodovozov & Zoja Raud & Eduard Petlenkov, 2021. "Review on Braking Energy Management in Electric Vehicles," Energies, MDPI, vol. 14(15), pages 1-26, July.

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