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Two-scale based energy management for connected plug-in hybrid electric vehicles with global optimal energy consumption and state-of-charge trajectory prediction

Author

Listed:
  • Jin, Yue
  • Yang, Lin
  • Du, Mao
  • Qiang, Jiaxi
  • Li, Jingzhong
  • Chen, Yuxuan
  • Tu, Jiayu

Abstract

Traffic conditions of the road network significantly affect the energy consumption (EC) of plug-in hybrid electric vehicles (PHEVs). However, they are not effectively used in existing energy management strategies (EMSs). In this paper, a two-scale based global optimal EMS is proposed based on macro traffic parameters (MTPs) for connected PHEVs (cPHEVs). First, MTPs are used to describe the whole trip-oriented optimal EC of the cPHEV over each path, and a hierarchical internal-feedback-driven general regression neural network is proposed to predict it. In this way, a path with the maximum energy saving potential can be searched for the cPHEV, and the global optimization of the EMS can be achieved at the whole network scale. Further, at the whole trip scale, a bidirectional long-short-term-memory model is developed based on MTPs for the first time to predict the whole trip-oriented optimal state-of-charge reference trajectory (SOC-RT) over the whole searched path. It provides the optimal reference for achieving the lowest EC during the whole trip. Finally, the adaptive equivalent consumption minimization strategy is employed for tracking the predicted SOC-RT to realize the EMS optimization at two scales. Compared to the state-of-art EMS, the proposed EMS can improve the fuel economy by 27.82% on average.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:267:y:2023:i:c:s0360544222033849
    DOI: 10.1016/j.energy.2022.126498
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    References listed on IDEAS

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    1. Zafar, Muhammad Hamza & Mansoor, Majad & Abou Houran, Mohamad & Khan, Noman Mujeeb & Khan, Kamran & Raza Moosavi, Syed Kumayl & Sanfilippo, Filippo, 2023. "Hybrid deep learning model for efficient state of charge estimation of Li-ion batteries in electric vehicles," Energy, Elsevier, vol. 282(C).
    2. 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).
    3. 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).

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