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A Control Strategy for Driving Mode Switches of Plug-in Hybrid Electric Vehicles

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  • Yuping Zeng

    (Jiangxi Province Key Laboratory of Precision Drive & Control, Nanchang Institute of Technology, Nanchang 330099, China
    State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China)

  • Zhikai Huang

    (Jiangxi Province Key Laboratory of Precision Drive & Control, Nanchang Institute of Technology, Nanchang 330099, China)

  • Yang Cai

    (Jiangxi Province Key Laboratory of Precision Drive & Control, Nanchang Institute of Technology, Nanchang 330099, China)

  • Yonggang Liu

    (State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China)

  • Yue Xiao

    (Jiangxi Province Key Laboratory of Precision Drive & Control, Nanchang Institute of Technology, Nanchang 330099, China)

  • Yang Shang

    (State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China)

Abstract

Driving mode switches of hybrid vehicles are significant events. Due to the different dynamic characteristics of the engine, motor, and wet clutch, it is difficult to coordinate torque fluctuations caused by mode switches. This paper focused on a control strategy for driving mode switches of plug-in hybrid electric vehicles (PHEVs) with a multi-disk wet clutch. First, the dynamic model of the PHEV was established, and a rule-based control strategy was proposed to divide the working mode regions and distribute the torque between engine and motor. Second, the dual fuzzy control strategy for a wet clutch and the coordinated torque control strategy for driving mode switches were proposed. The dual fuzzy logic control system consisted of the initial pulse-width modulation (PWM)’s duty cycle control and the changing rate of the PWM’s duty cycle control. Considering the difference in the dynamic characteristics between engine, motor, and wet clutch, a coordinated control strategy for the driving mode switches of PHEVs was put forward. Third, simulations of driving mode switches between pure electric driving mode and only engine driving mode were conducted. The results showed that the proposed control strategy could reduce the torque ripple and the jerk of the vehicle, completely satisfying the requirements of China. Finally, the control strategy for the motor-assisted engine starting process was tested on the bench. The experiment results indicated that the proposed control strategy was effective.

Suggested Citation

  • Yuping Zeng & Zhikai Huang & Yang Cai & Yonggang Liu & Yue Xiao & Yang Shang, 2018. "A Control Strategy for Driving Mode Switches of Plug-in Hybrid Electric Vehicles," Sustainability, MDPI, vol. 10(11), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:4237-:d:183401
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    References listed on IDEAS

    as
    1. Yang Yang & Chao Wang & Quanrang Zhang & Xiaolong He, 2017. "Torque Coordination Control during Braking Mode Switch for a Plug-in Hybrid Electric Vehicle," Energies, MDPI, vol. 10(11), pages 1-16, October.
    2. Yuping Zeng & Yang Cai & Changbao Chu & Guiyue Kou & Wei Gao, 2018. "Integrated Energy and Catalyst Thermal Management for Plug-In Hybrid Electric Vehicles," Energies, MDPI, vol. 11(7), pages 1-29, July.
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    Citations

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    Cited by:

    1. Julian M. Müller, 2019. "Comparing Technology Acceptance for Autonomous Vehicles, Battery Electric Vehicles, and Car Sharing—A Study across Europe, China, and North America," Sustainability, MDPI, vol. 11(16), pages 1-17, August.
    2. Hong, Xianqian & Wu, Jinglai & Zhang, Nong & Wang, Bing, 2022. "Energy efficiency optimization of Simpson planetary gearset based dual-motor powertrains for electric vehicles," Energy, Elsevier, vol. 259(C).
    3. 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).
    4. Julian Marius Müller & Raphael Kunderer, 2019. "Ex-Ante Prediction of Disruptive Innovation: The Case of Battery Technologies," Sustainability, MDPI, vol. 11(19), pages 1-19, September.

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