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A comparative study of energy-oriented driving strategy for connected electric vehicles on freeways with varying slopes

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Listed:
  • Li, Bingbing
  • Zhuang, Weichao
  • Zhang, Hao
  • Zhao, Ruixuan
  • Liu, Haoji
  • Qu, Linghu
  • Zhang, Jianrun
  • Chen, Boli

Abstract

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies.

Suggested Citation

  • Li, Bingbing & Zhuang, Weichao & Zhang, Hao & Zhao, Ruixuan & Liu, Haoji & Qu, Linghu & Zhang, Jianrun & Chen, Boli, 2024. "A comparative study of energy-oriented driving strategy for connected electric vehicles on freeways with varying slopes," Energy, Elsevier, vol. 289(C).
    • Handle: RePEc:eee:energy:v:289:y:2024:i:c:s0360544223033108
    DOI: 10.1016/j.energy.2023.129916
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    References listed on IDEAS

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    1. Hua, Min & Zhang, Cetengfei & Zhang, Fanggang & Li, Zhi & Yu, Xiaoli & Xu, Hongming & Zhou, Quan, 2023. "Energy management of multi-mode plug-in hybrid electric vehicle using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 348(C).
    2. Barkenbus, Jack N., 2010. "Eco-driving: An overlooked climate change initiative," Energy Policy, Elsevier, vol. 38(2), pages 762-769, February.
    3. Hu, Xiaosong & Zou, Yuan & Yang, Yalian, 2016. "Greener plug-in hybrid electric vehicles incorporating renewable energy and rapid system optimization," Energy, Elsevier, vol. 111(C), pages 971-980.
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    Cited by:

    1. Zhang, Hao & Chen, Boli & Lei, Nuo & Li, Bingbing & Chen, Chaoyi & Wang, Zhi, 2024. "Coupled velocity and energy management optimization of connected hybrid electric vehicles for maximum collective efficiency," Applied Energy, Elsevier, vol. 360(C).
    2. Zhang, Hao & Lei, Nuo & Wang, Zhi, 2024. "Ammonia-hydrogen propulsion system for carbon-free heavy-duty vehicles," Applied Energy, Elsevier, vol. 369(C).

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