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Energy optimization of multi-mode coupling drive plug-in hybrid electric vehicles based on speed prediction

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  • Zhang, LiPeng
  • Liu, Wei
  • Qi, BingNan

Abstract

By integrating the functions of centralized drive and distributed drive into a series-parallel hybrid system, the multi-mode coupling drive system can greatly improve the fuel economy of a plug-in hybrid electric vehicle (PHEV). However, some simple energy management strategies do not give full play to the advantages of the drive system. In order to get better fuel economy, after the system working principle analysis and modeling, a vehicle speed prediction model combining Markov and BP neural network algorithm was developed to predict the speed of the next 5s, and an adaptive equivalent consumption minimum strategy (AECMS) based on the combined vehicle speed prediction is proposed to optimize the drive modes selection and power distribution. The vehicle speed prediction accuracy was verified by the actual vehicle road test and the energy management effect was verified by the simulation. The research results show that, the prediction accuracy of the combined vehicle speed prediction can be improved by 27.9% compared with the ordinary single speed prediction, and the proposed control strategy improves the energy consumption of 3.7% for the PHEV under the same driving cycle condition when compared with the rule-based optimization strategy.

Suggested Citation

  • Zhang, LiPeng & Liu, Wei & Qi, BingNan, 2020. "Energy optimization of multi-mode coupling drive plug-in hybrid electric vehicles based on speed prediction," Energy, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:energy:v:206:y:2020:i:c:s0360544220312330
    DOI: 10.1016/j.energy.2020.118126
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    References listed on IDEAS

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    Cited by:

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    5. Jin, Yue & Yang, Lin & Du, Mao & Qiang, Jiaxi & Li, Jingzhong & Chen, Yuxuan & Tu, Jiayu, 2023. "Two-scale based energy management for connected plug-in hybrid electric vehicles with global optimal energy consumption and state-of-charge trajectory prediction," Energy, Elsevier, vol. 267(C).
    6. Yang, Chao & Wang, Muyao & Wang, Weida & Pu, Zesong & Ma, Mingyue, 2021. "An efficient vehicle-following predictive energy management strategy for PHEV based on improved sequential quadratic programming algorithm," Energy, Elsevier, vol. 219(C).
    7. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Real-time energy management for fuel cell electric vehicle using speed prediction-based model predictive control considering performance degradation," Applied Energy, Elsevier, vol. 304(C).
    8. Dapai Shi & Junjie Guo & Kangjie Liu & Qingling Cai & Zhenghong Wang & Xudong Qu, 2023. "Research on an Improved Rule-Based Energy Management Strategy Enlightened by the DP Optimization Results," Sustainability, MDPI, vol. 15(13), pages 1-13, July.
    9. Lin, Xinyou & Wu, Jiayun & Wei, Yimin, 2021. "An ensemble learning velocity prediction-based energy management strategy for a plug-in hybrid electric vehicle considering driving pattern adaptive reference SOC," Energy, Elsevier, vol. 234(C).
    10. Wei, Hongqian & Zhang, Nan & Liang, Jun & Ai, Qiang & Zhao, Wenqiang & Huang, Tianyi & Zhang, Youtong, 2022. "Deep reinforcement learning based direct torque control strategy for distributed drive electric vehicles considering active safety and energy saving performance," Energy, Elsevier, vol. 238(PB).
    11. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Vehicle drivetrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 257(C).
    12. Tobias Frambach & Ralf Kleisch & Ralf Liedtke & Jochen Schwarzer & Egbert Figgemeier, 2022. "Environmental Impact Assessment and Classification of 48 V Plug-in Hybrids with Real-Driving Use Case Simulations," Energies, MDPI, vol. 15(7), pages 1-21, March.
    13. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
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