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Hierarchical speed planning and energy management for autonomous plug-in hybrid electric vehicle in vehicle-following environment

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  • Liu, Yonggang
  • Huang, Bin
  • Yang, Yang
  • Lei, Zhenzhen
  • Zhang, Yuanjian
  • Chen, Zheng

Abstract

In this paper, a hierarchical energy management control strategy is investigated for autonomous plug-in hybrid electric vehicle in vehicle-following environment. With the target of safety and comfort, the designed algorithm is divided into two layers. The grey neural network is leveraged in the upper layer controller to predict the future speed trend of preceding vehicle, and the target speed of ego vehicle is planned by fuzzy adaptive control algorithm. By combining with the planned state of charge reference trajectory, genetic algorithm is exploited in the adaptive equivalent consumption minimization strategy-based lower layer controller to determine the initial equivalent factor map by offline iterative calculation, and the fuzzy logic algorithm is employed to update the equivalent factor in real time according to the state of charge difference. Finally, the simulation and hardware-in-the-loop experiment are conducted to validate the performance of the proposed strategy. The simulation results highlight the capability of the proposed strategy in solving multi-objective optimization for autonomous plug-in hybrid electric vehicle in vehicle-following environment, and the experiment results validate that the energy consumption economy of the proposed strategy reaches 95.43% optimality of the results derived by dynamic programming while ensuring the satisfied driving comfort and safety.

Suggested Citation

  • Liu, Yonggang & Huang, Bin & Yang, Yang & Lei, Zhenzhen & Zhang, Yuanjian & Chen, Zheng, 2022. "Hierarchical speed planning and energy management for autonomous plug-in hybrid electric vehicle in vehicle-following environment," Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:energy:v:260:y:2022:i:c:s0360544222021028
    DOI: 10.1016/j.energy.2022.125212
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Hao & Lei, Nuo & Liu, Shang & Fan, Qinhao & Wang, Zhi, 2023. "Data-driven predictive energy consumption minimization strategy for connected plug-in hybrid electric vehicles," Energy, Elsevier, vol. 283(C).
    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. Gao, Kai & Luo, Pan & Xie, Jin & Chen, Bin & Wu, Yue & Du, Ronghua, 2023. "Energy management of plug-in hybrid electric vehicles based on speed prediction fused driving intention and LIDAR," Energy, Elsevier, vol. 284(C).
    4. Chen, Shuiwang & Hu, Lu & Yao, Zhihong & Zhu, Juanxiu & Zhao, Bin & Jiang, Yangsheng, 2022. "Efficient and environmentally friendly operation of intermittent dedicated lanes for connected autonomous vehicles in mixed traffic environments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P2).
    5. Chen, Zheng & Wu, Simin & Shen, Shiquan & Liu, Yonggang & Guo, Fengxiang & Zhang, Yuanjian, 2023. "Co-optimization of velocity planning and energy management for autonomous plug-in hybrid electric vehicles in urban driving scenarios," Energy, Elsevier, vol. 263(PF).

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