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Data-Driven Predictive Torque Coordination Control during Mode Transition Process of Hybrid Electric Vehicles

Author

Listed:
  • Jing Sun

    (School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai 264005, China)

  • Guojing Xing

    (School of Control Science and Engineering, Shandong University, Jinan 250061, China)

  • Chenghui Zhang

    (School of Control Science and Engineering, Shandong University, Jinan 250061, China)

Abstract

Torque coordination control significantly affects the mode transition quality during the mode transition dynamic process of hybrid electric vehicles (HEV). Most of the existing torque coordination control methods are based on the mechanism model, whose control effect heavily depends on the modeling accuracy of the HEV powertrain. However, the powertrain structure is so complex, that it is difficult to establish its precise mechanism model. In this paper, a torque coordination control strategy using the data-driven predictive control (DDPC) technique is proposed to overcome the shortcomings of mechanism model-based control methods for a clutch-enabled HEV. The proposed control strategy is only based on the measured input-output data in the HEV powertrain, and no mechanism model is needed. The conflicting control requirements of comfortability and economy are included in the cost function. The actual physical constraints of actuators are also explicitly taken into account in the solving process of the data-driven predictive controller. The co-simulation results in Cruise and Simulink validate the effectiveness of the proposed control strategy and demonstrate that the DDPC method can achieve less vehicle jerk, faster mode transition and smaller clutch frictional losses compared with the traditional model predictive control (MPC) method.

Suggested Citation

  • Jing Sun & Guojing Xing & Chenghui Zhang, 2017. "Data-Driven Predictive Torque Coordination Control during Mode Transition Process of Hybrid Electric Vehicles," Energies, MDPI, vol. 10(4), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:441-:d:94700
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    References listed on IDEAS

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    1. Zeyu Chen & Rui Xiong & Kunyu Wang & Bin Jiao, 2015. "Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 8(5), pages 1-18, April.
    2. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
    3. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
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    Cited by:

    1. Huijun Yue & Jinyu Lin & Peng Dong & Zhinan Chen & Xiangyang Xu, 2023. "Configurations and Control Strategies of Hybrid Powertrain Systems," Energies, MDPI, vol. 16(2), pages 1-18, January.
    2. Kyuhyun Sim & Sang-Min Oh & Ku-Young Kang & Sung-Ho Hwang, 2017. "A Control Strategy for Mode Transition with Gear Shifting in a Plug-In Hybrid Electric Vehicle," Energies, MDPI, vol. 10(7), pages 1-15, July.
    3. Ye Yang & Youtong Zhang & Si Zhang & Jingyi Tian & Shaoyi Hu, 2019. "Control Strategy of Mode Transition with Engine Start in a Plug-in Hybrid Electric Bus," Energies, MDPI, vol. 12(15), pages 1-20, August.
    4. Rui Xiong & Hailong Li & Xuan Zhou, 2017. "Advanced Energy Storage Technologies and Their Applications (AESA2017)," Energies, MDPI, vol. 10(9), pages 1-3, September.
    5. 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.

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