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Performance Comparison of Pure Electric Vehicles with Two-Speed Transmission and Adaptive Gear Shifting Strategy Design

Author

Listed:
  • Bolin He

    (Tianjin Key Laboratory of New Energy Automobile Power Transmission and Safety Technology, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China)

  • Yong Chen

    (Tianjin Key Laboratory of New Energy Automobile Power Transmission and Safety Technology, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China)

  • Qiang Wei

    (Key Laboratory of Hebei Province on Scale-Span Intelligent Equipment Technology, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Cong Wang

    (School of Vehicle and Mobility, Tsinghua University, Beijing 100190, China)

  • Changyin Wei

    (Tianjin Key Laboratory of New Energy Automobile Power Transmission and Safety Technology, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China)

  • Xiaoyu Li

    (Tianjin Key Laboratory of New Energy Automobile Power Transmission and Safety Technology, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China)

Abstract

The two-speed automatic transmission can adjust the drive motor speed of electric vehicles and expand their output torque range. This study proposes a rule-based partitioned gear-shifting strategy for pure electric vehicles equipped with a two-speed dual-clutch transmission, combining economic and dynamic shifting strategies to ensure low energy consumption and strong power. Specifically, fuzzy logic is applied to adaptively modify the partition shifting strategy online, to reduce invalid gearshifts, increase the service life of the transmission, and improve driving comfort. Finally, we compare the economic performance and dynamic performance of pure electric vehicles equipped with a two-speed dual-clutch transmission and a single-speed final drive. The results show that the vehicle equipped with the two-speed dual-clutch transmission has better economic and dynamic performance. In addition, its maximum climbing ability was verified by rig testing. These results prove that the two-speed dual-clutch automatic transmission and the gear-shifting strategy proposed in this study can comprehensively improve the performance of pure electric vehicles.

Suggested Citation

  • Bolin He & Yong Chen & Qiang Wei & Cong Wang & Changyin Wei & Xiaoyu Li, 2023. "Performance Comparison of Pure Electric Vehicles with Two-Speed Transmission and Adaptive Gear Shifting Strategy Design," Energies, MDPI, vol. 16(7), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:3007-:d:1107184
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    References listed on IDEAS

    as
    1. Wenchen Shen & Huilong Yu & Yuhui Hu & Junqiang Xi, 2016. "Optimization of Shift Schedule for Hybrid Electric Vehicle with Automated Manual Transmission," Energies, MDPI, vol. 9(3), pages 1-11, March.
    2. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo & Xie, Jiale, 2022. "A data-fusion framework for lithium battery health condition Estimation Based on differential thermal voltammetry," Energy, Elsevier, vol. 239(PC).
    3. Lin, Cheng & Zhao, Mingjie & Pan, Hong & Yi, Jiang, 2019. "Blending gear shift strategy design and comparison study for a battery electric city bus with AMT," Energy, Elsevier, vol. 185(C), pages 1-14.
    4. Polychronis Spanoudakis & Nikolaos C. Tsourveloudis & Lefteris Doitsidis & Emmanuel S. Karapidakis, 2019. "Experimental Research of Transmissions on Electric Vehicles’ Energy Consumption," Energies, MDPI, vol. 12(3), pages 1-15, January.
    5. Wei, Changyin & Sun, Xiuxiu & Chen, Yong & Zang, Libin & Bai, Shujie, 2021. "Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle," Energy, Elsevier, vol. 230(C).
    6. 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).
    7. Li, Liang & Li, Xujian & Wang, Xiangyu & Song, Jian & He, Kai & Li, Chenfeng, 2016. "Analysis of downshift’s improvement to energy efficiency of an electric vehicle during regenerative braking," Applied Energy, Elsevier, vol. 176(C), pages 125-137.
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