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A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles

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  • Chen, Zhihang
  • Liu, Yonggang
  • Zhang, Yuanjian
  • Lei, Zhenzhen
  • Chen, Zheng
  • Li, Guang

Abstract

For plug-in hybrid electric vehicles, the equivalent consumption minimum strategy is typically regarded as a battery state of charge reference tracking method. Thus, the corresponding control performance is strongly dependent on the quality of state of charge reference generation. This paper proposes an intelligent equivalent consumption minimum strategy based on dual neural networks and a novel equivalent factor correction, which can adaptively regulate the equivalent factor to achieve the near-optimal fuel economy without the support of the state of charge reference. The Bayesian regularization neural network is constructed to predict the near-optimal equivalent factor online, while the backpropagation neural network is designed to forecast the engine on/off with the aim of improving the quality of equivalent factor prediction. The corresponding neural network training takes advantage of the global optimality of dynamic programming. Besides, the novel equivalent factor correction can guarantee that the electrical energy is gradually consumed along the trip and the terminal battery state of charge satisfies the preset constraints. A series of virtual simulations under a total of nine driving cycles demonstrates that the proposed method can deliver a competitive fuel economy comparing to the optimal solution derived from the dynamic programming, as well as regulating the battery state of charge to reach the desired terminal value at the end of the trip.

Suggested Citation

  • Chen, Zhihang & Liu, Yonggang & Zhang, Yuanjian & Lei, Zhenzhen & Chen, Zheng & Li, Guang, 2022. "A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles," Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544221029765
    DOI: 10.1016/j.energy.2021.122727
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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. Hou, Zhuoran & Guo, Jianhua & Chu, Liang & Hu, Jincheng & Chen, Zheng & Zhang, Yuanjian, 2023. "Exploration the route of information integration for vehicle design: A knowledge-enhanced energy management strategy," Energy, Elsevier, vol. 282(C).
    3. Jaikumar Shanmuganathan & Aruldoss Albert Victoire & Gobu Balraj & Amalraj Victoire, 2022. "Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    4. Guo, Xiaokai & Yan, Xianguo & Chen, Zhi & Meng, Zhiyu, 2022. "Research on energy management strategy of heavy-duty fuel cell hybrid vehicles based on dueling-double-deep Q-network," Energy, Elsevier, vol. 260(C).
    5. Wilberforce, Tabbi & Anser, Afaaq & Swamy, Jangam Aishwarya & Opoku, Richard, 2023. "An investigation into hybrid energy storage system control and power distribution for hybrid electric vehicles," Energy, Elsevier, vol. 279(C).
    6. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    7. Wei, Xiaodong & Wang, Jiaqi & Sun, Chao & Liu, Bo & Huo, Weiwei & Sun, Fengchun, 2023. "Guided control for plug-in fuel cell hybrid electric vehicles via vehicle to traffic communication," Energy, Elsevier, vol. 267(C).
    8. Abd-Elhaleem, Sameh & Shoeib, Walaa & Sobaih, Abdel Azim, 2023. "A new power management strategy for plug-in hybrid electric vehicles based on an intelligent controller integrated with CIGPSO algorithm," Energy, Elsevier, vol. 265(C).

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