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Design and analysis of an engine-start control strategy for a single-shaft parallel hybrid electric vehicle

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  • Zhao, Chen
  • Zu, Bingfeng
  • Xu, Yuliang
  • Wang, Zhen
  • Zhou, Jianwei
  • Liu, Lina

Abstract

The key point of a single-shaft parallel hybrid electric vehicle is the ability to start the engine while propelling the vehicle by utilizing a single traction motor. To pursue better driving comfort, an accurate engine-start process should be designed. For the purpose of smoothly starting the engine, this paper proposes a seven-phase engine-start control strategy with the assistance of the slipping status of the separating clutch and shifting clutch. A detailed drivetrain model of a single-shaft parallel hybrid electric vehicle is established to support the designed control strategy with consideration of the dynamic characteristics of the engine, motor and clutch. With the support of the hydraulic circuit established in this paper, feedforward-feedback controller based on proportional-integral (PI) algorithm is developed to track the demanded pressure for separating clutch. Simulation and test results validate that the proposed engine-start control strategy is feasible and effective for improving driving comfort and ensuring the drivability of the target vehicle. The key design parameters of the control strategy are optimized for the minimal equivalent fuel consumption. Test results indicate that the designed control strategy can effectively improve the fuel economy of engine-start control process compared with conventional strategy.

Suggested Citation

  • Zhao, Chen & Zu, Bingfeng & Xu, Yuliang & Wang, Zhen & Zhou, Jianwei & Liu, Lina, 2020. "Design and analysis of an engine-start control strategy for a single-shaft parallel hybrid electric vehicle," Energy, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:energy:v:202:y:2020:i:c:s0360544220307283
    DOI: 10.1016/j.energy.2020.117621
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    References listed on IDEAS

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    Cited by:

    1. Saiteja, Pemmareddy & Ashok, B., 2022. "Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    2. Dapai Shi & Junjie Guo & Kangjie Liu & Qingling Cai & Zhenghong Wang & Xudong Qu, 2023. "Research on an Improved Rule-Based Energy Management Strategy Enlightened by the DP Optimization Results," Sustainability, MDPI, vol. 15(13), pages 1-13, July.
    3. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Vehicle drivetrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 257(C).
    4. Bo, Lin & Han, Lijin & Xiang, Changle & Liu, Hui & Ma, Tian, 2022. "A Q-learning fuzzy inference system based online energy management strategy for off-road hybrid electric vehicles," Energy, Elsevier, vol. 252(C).

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