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Research on Starting Control Method of New-Energy Vehicle Based on State Machine

Author

Listed:
  • Yezhen Wu

    (Internal Combustion Engine Research Institute, Tianjin University, Tianjin 300072, China
    School of Mechanical Engineering, Tianjin University, Tianjin 300072, China)

  • Yuliang Xu

    (Internal Combustion Engine Research Institute, Tianjin University, Tianjin 300072, China
    School of Mechanical Engineering, Tianjin University, Tianjin 300072, China)

  • Jianwei Zhou

    (Tianjin Trumpjet Power Technology Co. Ltd., Tianjin 300072, China)

  • Zhen Wang

    (Internal Combustion Engine Research Institute, Tianjin University, Tianjin 300072, China)

  • Haopeng Wang

    (Internal Combustion Engine Research Institute, Tianjin University, Tianjin 300072, China)

Abstract

In order to improve the starting smoothness of new-energy vehicles under multiple working conditions and meet the driving intention better, and to make the control strategy have high portability and integration, a starting control method for vehicle based on state machine is designed. Based on inclination, starting of vehicle is divided into three working conditions: flat road, slight slope and steep slope. The method of vehicle starting control is designed, which includes five control states: default state control, torque pre-loading control, anti-rollback control, pedal control and PI (Proportion-Intergral) creep control. The simulation is carried out under the conditions of flat road, slight slope and steep slope. In terms of flat road and light slope, the vehicle travels below 3 km/h according to the driver’s intention, the speed is stable at 8 km/h during the creeping control phase and the jerk is lower than 5 m/s 3 . In terms of steep slope, the speed is controlled at 0 km/h basically and the 10 s-rollback distance is less than 0.04 m. The results show that the strategy can fully meet the driver’s intention with lower jerk, better dynamic and stability, and the method can achieve the demand of new-energy vehicle starting control.

Suggested Citation

  • Yezhen Wu & Yuliang Xu & Jianwei Zhou & Zhen Wang & Haopeng Wang, 2020. "Research on Starting Control Method of New-Energy Vehicle Based on State Machine," Energies, MDPI, vol. 13(23), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6249-:d:452160
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    References listed on IDEAS

    as
    1. Wang, Jing & Zhao, Changhong & Pratt, Annabelle & Baggu, Murali, 2018. "Design of an advanced energy management system for microgrid control using a state machine," Applied Energy, Elsevier, vol. 228(C), pages 2407-2421.
    2. Jianhao Zhou & Jing Sun & Longqiang He & Yi Ding & Hanzhang Cao & Wanzhong Zhao, 2019. "Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach," Energies, MDPI, vol. 12(13), pages 1-20, June.
    3. Yang Yang & Yundong He & Zhong Yang & Chunyun Fu & Zhipeng Cong, 2020. "Torque Coordination Control of an Electro-Hydraulic Composite Brake System During Mode Switching Based on Braking Intention," Energies, MDPI, vol. 13(8), pages 1-19, April.
    4. Shen, Peihong & Zhao, Zhiguo & Zhan, Xiaowen & Li, Jingwei, 2017. "Particle swarm optimization of driving torque demand decision based on fuel economy for plug-in hybrid electric vehicle," Energy, Elsevier, vol. 123(C), pages 89-107.
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