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Dynamic Coordinated Shifting Control of Automated Mechanical Transmissions without a Clutch in a Plug-In Hybrid Electric Vehicle

Author

Listed:
  • Hongwen He

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Zhentong Liu

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Liming Zhu

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Xinlei Liu

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

Abstract

On the basis of the shifting process of automated mechanical transmissions (AMTs) for traditional hybrid electric vehicles (HEVs), and by combining the features of electric machines with fast response speed, the dynamic model of the hybrid electric AMT vehicle powertrain is built up, the dynamic characteristics of each phase of shifting process are analyzed, and a control strategy in which torque and speed of the engine and electric machine are coordinatively controlled to achieve AMT shifting control for a plug-in hybrid electric vehicle (PHEV) without clutch is proposed. In the shifting process, the engine and electric machine are well controlled, and the shift jerk and power interruption and restoration time are reduced. Simulation and real car test results show that the proposed control strategy can more efficiently improve the shift quality for PHEVs equipped with AMTs.

Suggested Citation

  • Hongwen He & Zhentong Liu & Liming Zhu & Xinlei Liu, 2012. "Dynamic Coordinated Shifting Control of Automated Mechanical Transmissions without a Clutch in a Plug-In Hybrid Electric Vehicle," Energies, MDPI, vol. 5(8), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:8:p:3094-3109:d:19512
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    References listed on IDEAS

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    1. Hong-Wen He & Rui Xiong & Yu-Hua Chang, 2010. "Dynamic Modeling and Simulation on a Hybrid Power System for Electric Vehicle Applications," Energies, MDPI, vol. 3(11), pages 1-10, November.
    2. Rui Xiong & Hongwen He & Fengchun Sun & Kai Zhao, 2012. "Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach," Energies, MDPI, vol. 5(5), pages 1-15, May.
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    Cited by:

    1. Keun-Young Yoon & Soo-Whang Baek, 2019. "Robust Design Optimization with Penalty Function for Electric Oil Pumps with BLDC Motors," Energies, MDPI, vol. 12(1), pages 1-14, January.
    2. Ming Ye & Yitao Long & Yi Sui & Yonggang Liu & Qiao Li, 2019. "Active Control and Validation of the Electric Vehicle Powertrain System Using the Vehicle Cluster Environment," Energies, MDPI, vol. 12(19), pages 1-21, September.
    3. Wang, Zhenzhen & Zhou, Jun & Rizzoni, Giorgio, 2022. "A review of architectures and control strategies of dual-motor coupling powertrain systems for battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    4. Jianjun Hu & Lingling Zheng & Meixia Jia & Yi Zhang & Tao Pang, 2018. "Optimization and Model Validation of Operation Control Strategies for a Novel Dual-Motor Coupling-Propulsion Pure Electric Vehicle," Energies, MDPI, vol. 11(4), pages 1-14, March.
    5. Hongwen He & Chao Sun & Xiaowei Zhang, 2012. "A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network," Energies, MDPI, vol. 5(9), pages 1-18, September.

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