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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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    7. 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).
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    10. Maciej Dzikuć & Rafał Miśko & Szymon Szufa, 2021. "Modernization of the Public Transport Bus Fleet in the Context of Low-Carbon Development in Poland," Energies, MDPI, vol. 14(11), pages 1-12, June.
    11. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    12. Jinquan, Guo & Hongwen, He & Jianwei, Li & Qingwu, Liu, 2022. "Driving information process system-based real-time energy management for the fuel cell bus to minimize fuel cell engine aging and energy consumption," Energy, Elsevier, vol. 248(C).
    13. Lin, Xinyou & Wu, Jiayun & Wei, Yimin, 2021. "An ensemble learning velocity prediction-based energy management strategy for a plug-in hybrid electric vehicle considering driving pattern adaptive reference SOC," Energy, Elsevier, vol. 234(C).
    14. Sanchari Deb & Xiao-Zhi Gao, 2022. "Prediction of Charging Demand of Electric City Buses of Helsinki, Finland by Random Forest," Energies, MDPI, vol. 15(10), pages 1-18, May.
    15. 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).
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    17. 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).

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