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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

    as
    1. Guo, Lingxiong & Zhang, Xudong & Zou, Yuan & Guo, Ningyuan & Li, Jianwei & Du, Guodong, 2021. "Cost-optimal energy management strategy for plug-in hybrid electric vehicles with variable horizon speed prediction and adaptive state-of-charge reference," Energy, Elsevier, vol. 232(C).
    2. Guang, Hao & Jin, Hui, 2019. "Fuel consumption model optimization based on transient correction," Energy, Elsevier, vol. 169(C), pages 508-514.
    3. Li, Pengshun & Zhang, Yi & Zhang, Yi & Zhang, Kai & Jiang, Mengyan, 2021. "The effects of dynamic traffic conditions, route characteristics and environmental conditions on trip-based electricity consumption prediction of electric bus," Energy, Elsevier, vol. 218(C).
    4. Waag, Wladislaw & Käbitz, Stefan & Sauer, Dirk Uwe, 2013. "Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application," Applied Energy, Elsevier, vol. 102(C), pages 885-897.
    5. 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).
    6. 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).
    7. Hegde, Bharatkumar & Ahmed, Qadeer & Rizzoni, Giorgio, 2020. "Velocity and energy trajectory prediction of electrified powertrain for look ahead control," Applied Energy, Elsevier, vol. 279(C).
    8. Hu, Jiayi & Li, Jianqiu & Hu, Zunyan & Xu, Liangfei & Ouyang, Minggao, 2021. "Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming," Energy, Elsevier, vol. 215(PA).
    9. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.
    10. Li, Pengshun & Zhang, Yuhang & Zhang, Yi & Zhang, Yi & Zhang, Kai, 2021. "Prediction of electric bus energy consumption with stochastic speed profile generation modelling and data driven method based on real-world big data," Applied Energy, Elsevier, vol. 298(C).
    11. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng, 2020. "Energy consumption analysis and prediction of electric vehicles based on real-world driving data," Applied Energy, Elsevier, vol. 275(C).
    12. Zhang, Yuanjian & Chu, Liang & Fu, Zicheng & Xu, Nan & Guo, Chong & Zhao, Di & Ou, Yang & Xu, Lei, 2020. "Energy management strategy for plug-in hybrid electric vehicle integrated with vehicle-environment cooperation control," Energy, Elsevier, vol. 197(C).
    13. Haicheng Zhou & Zhaoping Xu & Liang Liu & Dong Liu & Lingling Zhang, 2018. "A Rule-Based Energy Management Strategy Based on Dynamic Programming for Hydraulic Hybrid Vehicles," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, October.
    Full references (including those not matched with items on IDEAS)

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    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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