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An intelligent braking system composed single-pedal and multi-objective optimization neural network braking control strategies for electric vehicle

Author

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  • He, Hongwen
  • Wang, Chen
  • Jia, Hui
  • Cui, Xing

Abstract

The braking system is significant to improve the total energy efficiency and ensure driving security of electric vehicles. An intelligent braking system (IBS) composed single-pedal and multi-objective optimization neural network braking control strategies is proposed in this paper to improve the energy economy, braking stability and driving intelligence for electric vehicles. The braking operations of drivers are divided into two parts: (1) releasing the accelerator pedal and (2) stepping on brake pedal. In the first braking operation, a single-pedal regenerative braking control strategy (RBCS) of accelerator pedal based on adaptive fuzzy control algorithm is proposed to improve energy recovery and driving intelligence. Simulation results illustrate that the simulation velocities under the control of adaptive single-pedal RBCS can follow several standard test cycles (including US06, UDDS, LA92 and ECE) very well. The braking energy can be recovered effectively with a less usage of brake pedal. In the second braking operation, a neural network (NN) controller for the composite braking system (CBS) is proposed to optimize the energy economy and braking stability at the same time. The control effect is verified by simulation results under NEDC cycles. The hardware-in-loop (HIL) experiments of the IBS are also conducted in this paper. Compared with a parallel braking strategy used in EU260 electric vehicles, the energy economy of IBS is improved by 3.67% than EU260 in 3 NEDC cycles. IBS performs more closely to the I curve in a specific braking condition with a decreasing braking severity. The time ratio of hydraulic braking in IBS is 2.27% less than EU260 with an increasing driving intelligence.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:259:y:2020:i:c:s0306261919318598
    DOI: 10.1016/j.apenergy.2019.114172
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    References listed on IDEAS

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    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.
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    Full references (including those not matched with items on IDEAS)

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    1. He, Qiang & Yang, Yang & Luo, Chang & Zhai, Jun & Luo, Ronghua & Fu, Chunyun, 2022. "Energy recovery strategy optimization of dual-motor drive electric vehicle based on braking safety and efficient recovery," Energy, Elsevier, vol. 248(C).
    2. 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.
    3. Tong Wu & Jing Li & Xuan Qin, 2021. "Braking performance oriented multi–objective optimal design of electro–mechanical brake parameters," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-31, May.
    4. Yang, Jian & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Zhao, Qinghai & Meng, Zewen, 2021. "Research on driving control strategy and Fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle," Energy, Elsevier, vol. 233(C).
    5. Xinyu Zhao & Lu Xiong & Guirong Zhuo & Wei Tian & Jing Li & Qiang Shu & Xuanbai Zhao & Guodong Xu, 2024. "A Review of One-Box Electro-Hydraulic Braking System: Architecture, Control, and Application," Sustainability, MDPI, vol. 16(3), pages 1-31, January.
    6. Dong, Haoxuan & Zhuang, Weichao & Chen, Boli & Wang, Yan & Lu, Yanbo & Liu, Ying & Xu, Liwei & Yin, Guodong, 2022. "A comparative study of energy-efficient driving strategy for connected internal combustion engine and electric vehicles at signalized intersections," Applied Energy, Elsevier, vol. 310(C).
    7. Zhang, Junjiang & Yang, Yang & Hu, Minghui & Yang, Zhong & Fu, Chunyun, 2021. "Longitudinal–vertical comprehensive control for four-wheel drive pure electric vehicle considering energy recovery and ride comfort," Energy, Elsevier, vol. 236(C).
    8. Zhang, Yuanjian & Huang, Yanjun & Chen, Haibo & Na, Xiaoxiang & Chen, Zheng & Liu, Yonggang, 2021. "Driving behavior oriented torque demand regulation for electric vehicles with single pedal driving," Energy, Elsevier, vol. 228(C).
    9. Li, Shicheng & Xu, Lin & Du, Xiaofang & Wang, Nian & Lin, Feng & Abdelkareem, Mohamed A.A., 2023. "Combined single-pedal and low adhesion control systems for enhanced energy regeneration in electric vehicles: Modeling, simulation, and on-field test," Energy, Elsevier, vol. 269(C).
    10. Yang, Jian & Liu, Bo & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin, 2023. "Multi-parameter controlled mechatronics-electro-hydraulic power coupling electric vehicle based on active energy regulation," Energy, Elsevier, vol. 263(PC).
    11. Fengrui Xu & Xuelin Liang & Mengqiao Chen & Wensheng Liu, 2022. "Robust Self-Learning PID Control of an Aircraft Anti-Skid Braking System," Mathematics, MDPI, vol. 10(8), pages 1-18, April.
    12. Wei, Hongqian & Ai, Qiang & Zhao, Wenqiang & Zhang, Youtong, 2022. "Modelling and experimental validation of an EV torque distribution strategy towards active safety and energy efficiency," Energy, Elsevier, vol. 239(PA).
    13. 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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