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Jerk Analysis of a Power-Split Hybrid Electric Vehicle Based on a Data-Driven Vehicle Dynamics Model

Author

Listed:
  • Xiaohua Zeng

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

  • Haoyong Cui

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

  • Dafeng Song

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

  • Nannan Yang

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

  • Tong Liu

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

  • Huiyong Chen

    (Zhengzhou Yutong Bus Co., Ltd., Zhengzhou 450016, China)

  • Yinshu Wang

    (Zhengzhou Yutong Bus Co., Ltd., Zhengzhou 450016, China)

  • Yulong Lei

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China)

Abstract

Given its highly coupled multi-power sources with diverse dynamic response characteristics, the mode transition process of a power-split Hybrid Electric Vehicle (HEV) can easily lead to unanticipated passenger-felt jerks. Moreover, difficulties in parameter estimation, especially power-source dynamic torque estimation, result in new challenges for jerk reduction. These two aspects entangle with each other and constitute a complicated coupling problem which obstructs the realization of a valid anti-jerk method. In this study, a vehicle dynamics model with reference to a data-driven modeling method is first established, integrating a full-time artificial neural network engine dynamic model that can accurately predict engine dynamic torque. Then the essential reason for the occurrence of vehicle jerks in real driving conditions is analyzed. Finally, to smooth the mode transition process, a more practical anti-jerk strategy based on power-source torque changing rate limitation (TCRL) is proposed. Verification studies indicate that the data-driven vehicle dynamics model has enough accuracy to reflect the vehicle dynamic characteristics, and the proposed TCRL strategy could reduce the vehicle jerk by up to 85.8%, without any sacrifice of vehicle performance. This research provides a feasible method for precise modeling of vehicle dynamics and a reference for improving the riding comfort of hybrid electric vehicles.

Suggested Citation

  • Xiaohua Zeng & Haoyong Cui & Dafeng Song & Nannan Yang & Tong Liu & Huiyong Chen & Yinshu Wang & Yulong Lei, 2018. "Jerk Analysis of a Power-Split Hybrid Electric Vehicle Based on a Data-Driven Vehicle Dynamics Model," Energies, MDPI, vol. 11(6), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1537-:d:152244
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    References listed on IDEAS

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    3. Rezaei, Javad & Shahbakhti, Mahdi & Bahri, Bahram & Aziz, Azhar Abdul, 2015. "Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks," Applied Energy, Elsevier, vol. 138(C), pages 460-473.
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    Cited by:

    1. Andrzej Łebkowski, 2018. "Steam and Oxyhydrogen Addition Influence on Energy Usage by Range Extender—Battery Electric Vehicles," Energies, MDPI, vol. 11(9), pages 1-20, September.
    2. Xu, Yueru & Zheng, Yuan & Yang, Ying, 2021. "On the movement simulations of electric vehicles: A behavioral model-based approach," Applied Energy, Elsevier, vol. 283(C).

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