IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v180y2019icp714-727.html
   My bibliography  Save this article

Discretely variable speed ratio control strategy for continuously variable transmission system considering hydraulic energy loss

Author

Listed:
  • Hu, Jianjun
  • Mei, Bo
  • Peng, Hang
  • Guo, Zihan

Abstract

By using the continuously variable transmission (CVT) to change the speed ratio continuously, the power resource components can operate in the high efficiency region for plug-in hybrid electric vehicle (PHEV) equipped with CVT, which improves the driving efficiency of powertrain. However, the frequent change of CVT speed ratio causes large energy loss of CVT hydraulic system and reduces the energy economy of PHEV. In view of this issue, the characteristic of energy loss for CVT hydraulic system is studied and the influence of speed ratio rate of change on energy loss of hydraulic system is obtained. Then, the simulation based on the continuously variable speed ratio control strategy (CVSRCS) is carried out and the results indicate that there is large energy loss of the CVT hydraulic system due to the frequent change of CVT speed ratio, which influences energy economy of the vehicle. Moreover, excessive driving jerk is generated, which significantly affects ride comfort of the vehicle. In order to reduce the adverse impact of frequent changes of CVT speed ratio on energy economy and ride comfort, a discretely variable speed ratio control strategy (DVSRCS) is proposed and discrete speed ratio is optimized by genetic algorithm. A comparative simulation for PHEV’s performance by adopting the CVSRCS and the proposed discretely variable speed ratio control strategy is carried out under a comprehensive driving cycle. The results of this study demonstrate that the proposed control strategy can not only significantly reduce the energy loss of CVT hydraulic system and enhance the energy economy, but also improve ride comfort.

Suggested Citation

  • Hu, Jianjun & Mei, Bo & Peng, Hang & Guo, Zihan, 2019. "Discretely variable speed ratio control strategy for continuously variable transmission system considering hydraulic energy loss," Energy, Elsevier, vol. 180(C), pages 714-727.
    • Handle: RePEc:eee:energy:v:180:y:2019:i:c:p:714-727
    DOI: 10.1016/j.energy.2019.05.086
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219309636
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.05.086?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yang Yang & Xiaolong He & Yi Zhang & Datong Qin, 2018. "Regenerative Braking Compensatory Control Strategy Considering CVT Power Loss for Hybrid Electric Vehicles," Energies, MDPI, vol. 11(3), pages 1-15, February.
    2. Cai, Y. & Ouyang, M.G. & Yang, F., 2017. "Impact of power split configurations on fuel consumption and battery degradation in plug-in hybrid electric city buses," Applied Energy, Elsevier, vol. 188(C), pages 257-269.
    3. 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.
    4. Hongwen, He & Jinquan, Guo & Jiankun, Peng & Huachun, Tan & Chao, Sun, 2018. "Real-time global driving cycle construction and the application to economy driving pro system in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 152(C), pages 95-107.
    5. Xiang, Changle & Ding, Feng & Wang, Weida & He, Wei, 2017. "Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control," Applied Energy, Elsevier, vol. 189(C), pages 640-653.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liao, Peng & Tang, Tie-Qiao & Liu, Ronghui & Huang, Hai-Jun, 2021. "An eco-driving strategy for electric vehicle based on the powertrain," Applied Energy, Elsevier, vol. 302(C).
    2. Yang, Jian & Liu, Bo & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin, 2023. "Multi-parameter controlled mechatronics-electro-hydraulic power coupling electric vehicle based on active energy regulation," Energy, Elsevier, vol. 263(PC).

    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. Liu, Hanwu & Lei, Yulong & Fu, Yao & Li, Xingzhong, 2022. "A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle," Energy, Elsevier, vol. 247(C).
    2. Zhuang, Weichao & Zhang, Xiaowu & Li, Daofei & Wang, Liangmo & Yin, Guodong, 2017. "Mode shift map design and integrated energy management control of a multi-mode hybrid electric vehicle," Applied Energy, Elsevier, vol. 204(C), pages 476-488.
    3. 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.
    4. 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.
    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. 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).
    7. Do, Tri Cuong & Dinh, Truong Quang & Yu, Yingxiao & Ahn, Kyoung Kwan, 2023. "Innovative powertrain and advanced energy management strategy for hybrid hydraulic excavators," Energy, Elsevier, vol. 282(C).
    8. Pei Zhang & Wangda Lu & Changqing Du & Jie Hu & Fuwu Yan, 2024. "A Comparative Study of Vehicle Velocity Prediction for Hybrid Electric Vehicles Based on a Neural Network," Mathematics, MDPI, vol. 12(4), pages 1-27, February.
    9. He, Hongwen & Wang, Yunlong & Han, Ruoyan & Han, Mo & Bai, Yunfei & Liu, Qingwu, 2021. "An improved MPC-based energy management strategy for hybrid vehicles using V2V and V2I communications," Energy, Elsevier, vol. 225(C).
    10. 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).
    11. Xixue Liu & Datong Qin & Shaoqian Wang, 2019. "Minimum Energy Management Strategy of Equivalent Fuel Consumption of Hybrid Electric Vehicle Based on Improved Global Optimization Equivalent Factor," Energies, MDPI, vol. 12(11), pages 1-17, May.
    12. 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.
    13. Massimiliano Passalacqua & Mauro Carpita & Serge Gavin & Mario Marchesoni & Matteo Repetto & Luis Vaccaro & Sébastien Wasterlain, 2019. "Supercapacitor Storage Sizing Analysis for a Series Hybrid Vehicle," Energies, MDPI, vol. 12(9), pages 1-15, May.
    14. Wang, Yue & Zeng, Xiaohua & Song, Dafeng, 2020. "Hierarchical optimal intelligent energy management strategy for a power-split hybrid electric bus based on driving information," Energy, Elsevier, vol. 199(C).
    15. Planakis, Nikolaos & Papalambrou, George & Kyrtatos, Nikolaos, 2022. "Ship energy management system development and experimental evaluation utilizing marine loading cycles based on machine learning techniques," Applied Energy, Elsevier, vol. 307(C).
    16. Yunfeng Jiang & Louis J. Shrinkle & Raymond A. de Callafon, 2019. "Autonomous Demand-Side Current Scheduling of Parallel Buck Regulated Battery Modules," Energies, MDPI, vol. 12(11), pages 1-20, May.
    17. Babar, Abdul Haseeb Khan & Ali, Yousaf, 2021. "Enhancement of electric vehicles’ market competitiveness using fuzzy quality function deployment," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    18. Li, Guozhen & Zhang, Zhenyu & Shi, Wankai & Li, Wenyong, 2023. "Energy management strategy and simulation analysis of a hybrid train based on a comprehensive efficiency optimization," Applied Energy, Elsevier, vol. 349(C).
    19. Guanghai Zhu & Jianbin Lin & Qingwu Liu & Hongwen He, 2019. "Research on the Energy-Saving Strategy of Path Planning for Electric Vehicles Considering Traffic Information," Energies, MDPI, vol. 12(19), pages 1-14, September.
    20. Shen, Peihong & Zhao, Zhiguo & Zhan, Xiaowen & Li, Jingwei & Guo, Qiuyi, 2018. "Optimal energy management strategy for a plug-in hybrid electric commercial vehicle based on velocity prediction," Energy, Elsevier, vol. 155(C), pages 838-852.

    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:eee:energy:v:180:y:2019:i:c:p:714-727. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.