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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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    Cited by:

    1. Sun, Bin & Zhang, Qijun & Wu, Zhong & Mao, Hongjun, 2024. "Impact of early-stage cooperative vehicle infrastructure systems on traffic and energy consumption under various traffic conditions: From application to policy," Energy, Elsevier, vol. 309(C).
    2. Wang, Zhuoer & Zhang, Hongjuan & Li, Bijun & Fan, Xiaoyi & Ma, Zhenqiang & Zhou, Jian, 2024. "An IMFO-LSTM_BIGRU combined network for long-term multiple battery states prediction for electric vehicles," Energy, Elsevier, vol. 309(C).

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