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A hierarchical eco-driving strategy for hybrid electric vehicles via vehicle-to-cloud connectivity

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  • Liu, Rui
  • Liu, Hui
  • Nie, Shida
  • Han, Lijin
  • Yang, Ningkang

Abstract

The emergence of the intelligent transportation system and cloud computing technology has brought the available traffic information and increasing computing power, which lead to a significant improvement in driving performance. In order to enhance energy economy and mobility simultaneously, a hierarchical eco-driving strategy is proposed in this paper, which is comprised of the cloud-level controller and the vehicle-level controller. The dynamic programming-based cloud-level controller optimizes the velocity and battery state-of-charge utilizing the global traffic information obtained from the intelligent transportation system. However, the global traffic information suffers from uncertainties, which deteriorates the effectiveness of the cloud-level controller. The vehicle-level controller is constructed on the model predictive control framework, aiming to cope with the uncertainties, improve fuel economy and reduce travel time. Besides, a transfer learning-based particle swarm optimization algorithm is presented for solving the optimization problem in model predictive control, which can achieve great control performance utilizing the knowledge from the cloud-level controller. To validate the effectiveness of the proposed strategy, simulation tests are conducted. The results demonstrate that the proposed strategy can achieve near-global-optimal performance in fuel economy and mobility. Moreover, the real-time performance of the proposed strategy is validated through the hardware-in-loop test.

Suggested Citation

  • Liu, Rui & Liu, Hui & Nie, Shida & Han, Lijin & Yang, Ningkang, 2023. "A hierarchical eco-driving strategy for hybrid electric vehicles via vehicle-to-cloud connectivity," Energy, Elsevier, vol. 281(C).
    • Handle: RePEc:eee:energy:v:281:y:2023:i:c:s0360544223016250
    DOI: 10.1016/j.energy.2023.128231
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    References listed on IDEAS

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    1. Hu, Xiaosong & Zhang, Xiaoqian & Tang, Xiaolin & Lin, Xianke, 2020. "Model predictive control of hybrid electric vehicles for fuel economy, emission reductions, and inter-vehicle safety in car-following scenarios," Energy, Elsevier, vol. 196(C).
    2. Shangguan, Jinyong & Guo, Hongqiang & Yue, Ming, 2020. "Robust energy management of plug-in hybrid electric bus considering the uncertainties of driving cycles and vehicle mass," Energy, Elsevier, vol. 203(C).
    3. Yang, Chao & Du, Siyu & Li, Liang & You, Sixong & Yang, Yiyong & Zhao, Yue, 2017. "Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 203(C), pages 883-896.
    4. Guo, Lingxiong & Zhang, Xudong & Zou, Yuan & Han, Lijin & Du, Guodong & Guo, Ningyuan & Xiang, Changle, 2022. "Co-optimization strategy of unmanned hybrid electric tracked vehicle combining eco-driving and simultaneous energy management," Energy, Elsevier, vol. 246(C).
    5. Wang, Siyang & Lin, Xianke, 2020. "Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios," Applied Energy, Elsevier, vol. 271(C).
    6. 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).
    7. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    8. Hou, Jun & Song, Ziyou, 2020. "A hierarchical energy management strategy for hybrid energy storage via vehicle-to-cloud connectivity," Applied Energy, Elsevier, vol. 257(C).
    Full references (including those not matched with items on IDEAS)

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