IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i15p2989-d254397.html
   My bibliography  Save this article

Control Strategy of Mode Transition with Engine Start in a Plug-in Hybrid Electric Bus

Author

Listed:
  • Ye Yang

    (Laboratory of Low Emission Vehicle, Beijing Institute of Technology, Beijing 100081, China
    Qing Gong College, North China University of Science and Technology, University Road No. 11, Tangshan 063000, China)

  • Youtong Zhang

    (Laboratory of Low Emission Vehicle, Beijing Institute of Technology, Beijing 100081, China)

  • Si Zhang

    (Qing Gong College, North China University of Science and Technology, University Road No. 11, Tangshan 063000, China)

  • Jingyi Tian

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Shaoyi Hu

    (Qing Gong College, North China University of Science and Technology, University Road No. 11, Tangshan 063000, China)

Abstract

Torque coordinated control of the relevant power sources has an important impact on the vehicle dynamics and driving performance during the mode transition of the hybrid electric vehicles(HEVs). Considering the dynamic impact problem caused by mode transition, this paper, based upon the structural features of axially paralleled hybrid power system, introduces the bumpless mode switching control theory to analyze multi-mode transition. Firstly, the state transition process is abstracted as the state space transition problem of hybrid system. Secondly, the mode transition is divided into four sub-states, and the state model of each sub-state is established. Thirdly, taking the cost functions as the optimization objective, the state switching process is solved, and the control vectors of each switching process are obtained. Simulation and experimental results show that the proposed control strategy can effectively suppress torque fluctuation, avoid longitudinal acceleration impact, and improve driving performance.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2989-:d:254397
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/15/2989/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/15/2989/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ye Yang & Youtong Zhang & Jingyi Tian & Si Zhang, 2018. "Research on a Plug-In Hybrid Electric Bus Energy Management Strategy Considering Drivability," Energies, MDPI, vol. 11(8), pages 1-22, August.
    2. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yongjian Zhou & Rong Yang & Song Zhang & Kejun Lan & Wei Huang, 2023. "Optimization of Power-System Parameters and Energy-Management Strategy Research on Hybrid Heavy-Duty Trucks," Energies, MDPI, vol. 16(17), pages 1-21, August.
    2. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    3. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Li, Shuangqi & Zhao, Pengfei & Gu, Chenghong & Huo, Da & Zeng, Xianwu & Pei, Xiaoze & Cheng, Shuang & Li, Jianwei, 2022. "Online battery-protective vehicle to grid behavior management," Energy, Elsevier, vol. 243(C).
    5. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    6. Vitale, F. & Rispoli, N. & Sorrentino, M. & Rosen, M.A. & Pianese, C., 2021. "On the use of dynamic programming for optimal energy management of grid-connected reversible solid oxide cell-based renewable microgrids," Energy, Elsevier, vol. 225(C).
    7. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    8. Guo, Hongqiang & Sun, Qun & Wang, Chong & Wang, Qinpu & Lu, Silong, 2018. "A systematic design and optimization method of transmission system and power management for a plug-in hybrid electric vehicle," Energy, Elsevier, vol. 148(C), pages 1006-1017.
    9. Du, Guodong & Zou, Yuan & Zhang, Xudong & Liu, Teng & Wu, Jinlong & He, Dingbo, 2020. "Deep reinforcement learning based energy management for a hybrid electric vehicle," Energy, Elsevier, vol. 201(C).
    10. Anselma, Pier Giuseppe, 2022. "Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints," Applied Energy, Elsevier, vol. 307(C).
    11. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    12. Chen, Z. & Liu, Y. & Ye, M. & Zhang, Y. & Chen, Z. & Li, G., 2021. "A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    13. Chen, Jiaxin & Shu, Hong & Tang, Xiaolin & Liu, Teng & Wang, Weida, 2022. "Deep reinforcement learning-based multi-objective control of hybrid power system combined with road recognition under time-varying environment," Energy, Elsevier, vol. 239(PC).
    14. Ke Song & Yimin Wang & Cancan An & Hongjie Xu & Yuhang Ding, 2021. "Design and Validation of Energy Management Strategy for Extended-Range Fuel Cell Electric Vehicle Using Bond Graph Method," Energies, MDPI, vol. 14(2), pages 1-31, January.
    15. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    16. Qi, Chunyang & Zhu, Yiwen & Song, Chuanxue & Yan, Guangfu & Xiao, Feng & Da wang, & Zhang, Xu & Cao, Jingwei & Song, Shixin, 2022. "Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle," Energy, Elsevier, vol. 238(PA).
    17. Cheng, Shen & Zhao, Gaiju & Gao, Ming & Shi, Yuetao & Huang, Mingming & Yousefi, Nasser, 2021. "Optimal hybrid energy system for locomotive utilizing improved Locust Swarm optimizer," Energy, Elsevier, vol. 218(C).
    18. López-Ibarra, Jon Ander & Gaztañaga, Haizea & Saez-de-Ibarra, Andoni & Camblong, Haritza, 2020. "Plug-in hybrid electric buses total cost of ownership optimization at fleet level based on battery aging," Applied Energy, Elsevier, vol. 280(C).
    19. Shi, Wenzhuo & Huangfu, Yigeng & Xu, Liangcai & Pang, Shengzhao, 2022. "Online energy management strategy considering fuel cell fault for multi-stack fuel cell hybrid vehicle based on multi-agent reinforcement learning," Applied Energy, Elsevier, vol. 328(C).
    20. Huang, Ruchen & He, Hongwen & Zhao, Xuyang & Wang, Yunlong & Li, Menglin, 2022. "Battery health-aware and naturalistic data-driven energy management for hybrid electric bus based on TD3 deep reinforcement learning algorithm," Applied Energy, Elsevier, vol. 321(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2989-:d:254397. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.