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An Intelligent Regenerative Braking Strategy for Electric Vehicles

Author

Listed:
  • Guoqing Xu

    (Department of Electrical Engineering, Tongji University, Shanghai 200092, China
    Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China)

  • Weimin Li

    (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Kun Xu

    (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Zhibin Song

    (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

Abstract

Regenerative braking is an effective approach for electric vehicles (EVs) to extend their driving range. A fuzzy-logic-based regenerative braking strategy (RBS) integrated with series regenerative braking is developed in this paper to advance the level of energy-savings. From the viewpoint of securing car stability in braking operations, the braking force distribution between the front and rear wheels so as to accord with the ideal distribution curve are considered to prevent vehicles from experiencing wheel lock and slip phenomena during braking. Then, a fuzzy RBS using the driver’s braking force command, vehicle speed, battery SOC, battery temperature are designed to determine the distribution between friction braking force and regenerative braking force to improve the energy recuperation efficiency. The experimental results on an “LF620” prototype EV validated the feasibility and effectiveness of regenerative braking and showed that the proposed fuzzy RBS was endowed with good control performance. The maximum driving range of LF620 EV was improved by 25.7% compared with non-RBS conditions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:4:y:2011:i:9:p:1461-1477:d:14080
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    Citations

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    Cited by:

    1. 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.
    2. 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).
    3. Ilya Kulikov, 2019. "Model Analysis of Electrically Driven Vehicles by Means of Unknown Input Observers," Energies, MDPI, vol. 12(12), pages 1-17, June.
    4. 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.
    5. 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).
    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. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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).
    14. 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.
    15. 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.
    16. 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.
    17. Valery Vodovozov & Zoja Raud & Eduard Petlenkov, 2021. "Review on Braking Energy Management in Electric Vehicles," Energies, MDPI, vol. 14(15), pages 1-26, July.
    18. 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.
    19. 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.
    20. Emilia M. Szumska & Rafał S. Jurecki, 2021. "Parameters Influencing on Electric Vehicle Range," Energies, MDPI, vol. 14(16), pages 1-23, August.
    21. 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.
    22. 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.

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