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

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
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    Cited by:

    1. 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).
    2. Ouyang, Tiancheng & Jin, Song & Lu, Yucai & Peng, Weijie & Li, Yanzhou, 2024. "Improved energy utilization in heavy-duty automatic transmission: Advanced modeling and multi-objective optimization," Energy, Elsevier, vol. 303(C).
    3. 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).

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