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Velocity prediction and profile optimization based real-time energy management strategy for Plug-in hybrid electric buses

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  • Zhang, Zhendong
  • He, Hongwen
  • Guo, Jinquan
  • Han, Ruoyan

Abstract

The Plug-in hybrid vehicle (PHEV) has been progressively penetrated in the urban public transport system and seen a foreseeable fast growth in the future. Within this horizon, energy management is an enabling technique for the cost-efficient operation of the PHEV. In this paper, a model predictive control (MPC)-based real-time energy management strategy (EMS) combining a cloud-enabled velocity profile optimizer (VPO) and vehicle-side velocity predictor is proposed for the Plug-in hybrid bus (PHEB) under the intelligent transportation systems (ITS). Particularly, the velocity profile and the state of charge (SOC) sequences are optimized by incorporating the genetic algorithm (GA) with the dynamic programming (DP), giving rise to a novel GA-DP-based VPO. In the case that the vehicle can be hardly decoupled from the traffic flow, a multi-feature predictor based on Long Short Term Memory (LSTM) Network is triggered to replace the cloud-enabled VPO to predict the short-term velocity. Results show that the prediction accuracy can be improved by 5.4% by employing the multi-feature training. The equivalent fuel consumption with the mode-switching EMS in the optimized UDDS cycle can be reduced by 14.9% compared with the state of the art. The proposed strategy is validated with a real-time performance by performing the hardware in the loop (HIL) experiment.

Suggested Citation

  • Zhang, Zhendong & He, Hongwen & Guo, Jinquan & Han, Ruoyan, 2020. "Velocity prediction and profile optimization based real-time energy management strategy for Plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:appene:v:280:y:2020:i:c:s030626192031446x
    DOI: 10.1016/j.apenergy.2020.116001
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    References listed on IDEAS

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    Cited by:

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    14. Cui, Wei & Cui, Naxin & Li, Tao & Cui, Zhongrui & Du, Yi & Zhang, Chenghui, 2022. "An efficient multi-objective hierarchical energy management strategy for plug-in hybrid electric vehicle in connected scenario," Energy, Elsevier, vol. 257(C).
    15. Piras, M. & De Bellis, V. & Malfi, E. & Novella, R. & Lopez-Juarez, M., 2024. "Hydrogen consumption and durability assessment of fuel cell vehicles in realistic driving," Applied Energy, Elsevier, vol. 358(C).
    16. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    17. Yang, Chao & Liu, Kaijia & Jiao, Xiaohong & Wang, Weida & Chen, Ruihu & You, Sixiong, 2022. "An adaptive firework algorithm optimization-based intelligent energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 239(PB).
    18. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2023. "Investigation of novel intelligent energy management strategies for connected HEB considering global planning of fixed-route information," Energy, Elsevier, vol. 263(PB).
    19. Firouzi, Mehdi & Setayesh Nazar, Mehrdad & Shafie-khah, Miadreza & Catalão, João P.S., 2023. "Integrated framework for modeling the interactions of plug-in hybrid electric vehicles aggregators, parking lots and distributed generation facilities in electricity markets," Applied Energy, Elsevier, vol. 334(C).
    20. Li, Shuangqi & He, Hongwen & Zhao, Pengfei, 2021. "Energy management for hybrid energy storage system in electric vehicle: A cyber-physical system perspective," Energy, Elsevier, vol. 230(C).
    21. Luis B. Elvas & Joao C Ferreira, 2021. "Intelligent Transportation Systems for Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-9, September.
    22. Zhang, Yahui & Wei, Zeyi & Wang, Zhong & Tian, Yang & Wang, Jizhe & Tian, Zhikun & Xu, Fuguo & Jiao, Xiaohong & Li, Liang & Wen, Guilin, 2024. "Hierarchical eco-driving control strategy for connected automated fuel cell hybrid vehicles and scenario-/hardware-in-the loop validation," Energy, Elsevier, vol. 292(C).

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