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Multi-objective hierarchical energy management for connected plug-in hybrid electric vehicle with cyber–physical interaction

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  • Cui, Wei
  • Cui, Naxin
  • Li, Tao
  • Du, Yi
  • Zhang, Chenghui

Abstract

Recently, the overall performances of plug-in hybrid electric vehicles (PHEVs) are expected to be further improved by integrating multiple objectives and cyber–physical interaction (CPI), which pose challenges to existing hierarchical and synchronous optimization frameworks in terms of performances improvement effect and optimization efficiency. In this paper, the CPI based hierarchical optimization framework (HOF) is proposed for the first time to explore the overall performances improvement potential of PHEVs by integrating multi-dimensional traffic information. Particularly, the decomposition based multi-objective evolutionary algorithm is employed in the cyber level to achieve motion planning by balancing various objectives including economy, comfort, safety, and traffic efficiency. Furthermore, the CPI mechanism is developed under the option-critic architecture, which achieves the bi-directional interaction between cyber and physical levels, thus contributing to obtaining superior optimization effect and computational efficiency. Benefiting from the CPI mechanism, the power distribution is finished in the physical level, in which the engine steady-transient characteristic and powertrain efficiency are highlighted comprehensively. The simulation verification with real traffic data demonstrates that the proposed strategy achieves a 4.73% and 5.91% improvement in overall performances compared to synchronous and hierarchical optimization, respectively.

Suggested Citation

  • Cui, Wei & Cui, Naxin & Li, Tao & Du, Yi & Zhang, Chenghui, 2024. "Multi-objective hierarchical energy management for connected plug-in hybrid electric vehicle with cyber–physical interaction," Applied Energy, Elsevier, vol. 360(C).
    • Handle: RePEc:eee:appene:v:360:y:2024:i:c:s0306261924001983
    DOI: 10.1016/j.apenergy.2024.122816
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    References listed on IDEAS

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    1. Saiteja, Pemmareddy & Ashok, B., 2022. "Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    2. 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).
    3. Kim, Youngki & Figueroa-Santos, Miriam & Prakash, Niket & Baek, Stanley & Siegel, Jason B. & Rizzo, Denise M., 2020. "Co-optimization of speed trajectory and power management for a fuel-cell/battery electric vehicle," Applied Energy, Elsevier, vol. 260(C).
    4. Zhou, Yang & Ravey, Alexandre & Péra, Marie-Cecile, 2020. "Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer," Applied Energy, Elsevier, vol. 258(C).
    5. 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).
    6. Ganesh, Akhil Hannegudda & Xu, Bin, 2022. "A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    7. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    8. He, Hongwen & Wang, Yunlong & Han, Ruoyan & Han, Mo & Bai, Yunfei & Liu, Qingwu, 2021. "An improved MPC-based energy management strategy for hybrid vehicles using V2V and V2I communications," Energy, Elsevier, vol. 225(C).
    9. Zhang, Shuo & Hu, Xiaosong & Xie, Shaobo & Song, Ziyou & Hu, Lin & Hou, Cong, 2019. "Adaptively coordinated optimization of battery aging and energy management in plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 256(C).
    10. Nguyễn, Bảo-Huy & Vo-Duy, Thanh & Henggeler Antunes, Carlos & Trovão, João Pedro F., 2021. "Multi-objective benchmark for energy management of dual-source electric vehicles: An optimal control approach," Energy, Elsevier, vol. 223(C).
    11. Shi, Man & He, Hongwen & Li, Jianwei & Han, Mo & Jia, Chunchun, 2021. "Multi-objective tradeoff optimization of predictive adaptive cruising control for autonomous electric buses: A cyber-physical-energy system approach," Applied Energy, Elsevier, vol. 300(C).
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