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Stochastic Optimal Control of Parallel Hybrid Electric Vehicles

Author

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  • Feiyan Qin

    (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China)

  • Guoqing Xu

    (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China)

  • Yue Hu

    (Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China)

  • Kun Xu

    (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Weimin Li

    (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    Jining Institutes of Advanced Technology, Chinese Academy of Sciences, Jining 272000, China)

Abstract

Energy management strategies (EMSs) in hybrid electric vehicles (HEVs) are highly related to the fuel economy and emission performances. However, EMS constitutes a challenging problem due to the complex structure of a HEV and the unknown or partially known driving cycles. To meet this problem, this paper adopts a stochastic dynamic programming (SDP) method for the EMS of a specially designed vehicle, a pre-transmission single-shaft torque-coupling parallel HEV. In this parallel HEV, the auto clutch output is connected to the transmission input through an electric motor, which benefits an efficient motor assist operation. In this EMS, demanded torque of driver is modeled as a one-state Markov process to represent the uncertainty of future driving situations. The obtained EMS has been evaluated with ADVISOR2002 over two standard government drive cycles and a self-defined one, and compared with a dynamic programming (DP) one and a rule-based one. Simulation results have shown the real-time performance of the proposed approach, and potential vehicle performance improvement relative to the rule-based one.

Suggested Citation

  • Feiyan Qin & Guoqing Xu & Yue Hu & Kun Xu & Weimin Li, 2017. "Stochastic Optimal Control of Parallel Hybrid Electric Vehicles," Energies, MDPI, vol. 10(2), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:214-:d:90185
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    References listed on IDEAS

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    Cited by:

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    2. Wiesław Grzesikiewicz & Lech Knap & Michał Makowski & Janusz Pokorski, 2018. "Study of the Energy Conversion Process in the Electro-Hydrostatic Drive of a Vehicle," Energies, MDPI, vol. 11(2), pages 1-22, February.
    3. Wang, Chun & Yang, Ruixin & Yu, Quanqing, 2019. "Wavelet transform based energy management strategies for plug-in hybrid electric vehicles considering temperature uncertainty," Applied Energy, Elsevier, vol. 256(C).
    4. Branimir Škugor & Joško Petrić, 2018. "Optimization of Control Variables and Design of Management Strategy for Hybrid Hydraulic Vehicle," Energies, MDPI, vol. 11(10), pages 1-24, October.
    5. Chien-Hsun Wu & Yong-Xiang Xu, 2019. "The Optimal Control of Fuel Consumption for a Heavy-Duty Motorcycle with Three Power Sources Using Hardware-in-the-Loop Simulation," Energies, MDPI, vol. 13(1), pages 1-16, December.

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