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A systematic design and optimization method of transmission system and power management for a plug-in hybrid electric vehicle

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  • Guo, Hongqiang
  • Sun, Qun
  • Wang, Chong
  • Wang, Qinpu
  • Lu, Silong

Abstract

The transmission system together with power management will simultaneously affect the fuel economy of the plug-in hybrid electric vehicle (PHEV) with automated mechanical transmission (AMT). This paper strives to make three contributions to realize systematic design and optimization of the transmission and power management. Firstly, a design of experiment method is proposed to redesign the current transmission system of the PHEV with Optimal Latin Hypercube Design algorithm. Then, a co-optimization method is proposed to insight into the preferable speed ratios of the redesigned transmission system with multi-island genetic and dynamic programming algorithms. Finally, a Pontryagin’s Minimum Principle-based self-identification controller is proposed to realize adaptive power management control, based on a significant finding that the constant solution of the co-state from off-line iteration optimization can be approximately identified by the mean value of the co-state with time-moving in the self-identification controller. Results demonstrate that the current 6-speed ratio AMT can be reduced to 4, the fuel economy of the redesigned transmission system can be improved by 2.9% compared to the current transmission system and the self-identification controller can further improve the fuel economy of the PHEV compared to the conventional PI controller.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:148:y:2018:i:c:p:1006-1017
    DOI: 10.1016/j.energy.2018.01.152
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    References listed on IDEAS

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    Cited by:

    1. Xiaodong Liu & Hongqiang Guo & Xingqun Cheng & Juan Du & Jian Ma, 2022. "A Robust Design of the Model-Free-Adaptive-Control-Based Energy Management for Plug-In Hybrid Electric Vehicle," Energies, MDPI, vol. 15(20), pages 1-24, October.
    2. 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).
    3. Jinquan, Guo & Hongwen, He & Jiankun, Peng & Nana, Zhou, 2019. "A novel MPC-based adaptive energy management strategy in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 175(C), pages 378-392.
    4. Wu, Wei & Luo, Junlin & Zou, Tiangang & Liu, Yin & Yuan, Shihua & Xiao, Bingqing, 2022. "Systematic design and power management of a novel parallel hybrid electric powertrain for heavy-duty vehicles," Energy, Elsevier, vol. 253(C).
    5. Chung, Cheng-Ta & Wu, Chien-Hsun & Hung, Yi-Hsuan, 2021. "A design methodology for selecting energy-efficient compound split e-CVT hybrid systems with planetary gearsets based on electric circulation," Energy, Elsevier, vol. 230(C).
    6. 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.
    7. Bhattacharjee, Debraj & Ghosh, Tamal & Bhola, Prabha & Martinsen, Kristian & Dan, Pranab K., 2019. "Data-driven surrogate assisted evolutionary optimization of hybrid powertrain for improved fuel economy and performance," Energy, Elsevier, vol. 183(C), pages 235-248.
    8. Roberto Finesso & Daniela Misul & Ezio Spessa & Mattia Venditti, 2018. "Optimal Design of Power-Split HEVs Based on Total Cost of Ownership and CO 2 Emission Minimization," Energies, MDPI, vol. 11(7), pages 1-28, July.
    9. Guo, Hongqiang & Lu, Silong & Hui, Hongzhong & Bao, Chunjiang & Shangguan, Jinyong, 2019. "Receding horizon control-based energy management for plug-in hybrid electric buses using a predictive model of terminal SOC constraint in consideration of stochastic vehicle mass," Energy, Elsevier, vol. 176(C), pages 292-308.
    10. Chung, Cheng-Ta & Wu, Chien-Hsun & Hung, Yi-Hsuan, 2020. "Evaluation of driving performance and energy efficiency for a novel full hybrid system with dual-motor electric drive and integrated input- and output-split e-CVT," Energy, Elsevier, vol. 191(C).
    11. Jichao Liu & Yanyan Liang & Zheng Chen & Hai Yang, 2023. "An ECMS Based on Model Prediction Control for Series Hybrid Electric Mine Trucks," Energies, MDPI, vol. 16(9), pages 1-26, May.
    12. Sellali, M. & Betka, A. & Drid, S. & Djerdir, A. & Allaoui, L. & Tiar, M., 2019. "Novel control implementation for electric vehicles based on fuzzy -back stepping approach," Energy, Elsevier, vol. 178(C), pages 644-655.
    13. Hou, Daizheng & Sun, Qun & Bao, Chunjiang & Cheng, Xingqun & Guo, Hongqiang & Zhao, Ying, 2019. "An all-in-one design method for plug-in hybrid electric buses considering uncertain factor of driving cycles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    14. Shangguan, Jinyong & Guo, Hongqiang & Yue, Ming, 2020. "Robust energy management of plug-in hybrid electric bus considering the uncertainties of driving cycles and vehicle mass," Energy, Elsevier, vol. 203(C).
    15. 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.

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