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Real-time energy-saving control for HEVs in car-following scenario with a double explicit MPC approach

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  • Ruan, Shumin
  • Ma, Yue
  • Yang, Ningkang
  • Xiang, Changle
  • Li, Xunming

Abstract

The rapid growth of electrification, automation and connectivity in the transport industries puts forward higher requirements on control strategies to improve energy efficiency, traffic safety and driving comfort. Intense efforts have developed energy management strategies (EMS) in car-following scenarios for hybrid electric vehicles (HEVs) by adopting model predictive control (MPC). However, the computational complex online optimization intrinsic to MPC hinders its real-time implementation. This paper is thus proposed to develop a framework of energy-saving controller for HEVs based on explicit MPC, taking advantage of its online computational efficiency, to enable real-time control. To achieve this, the constrained finite-time optimization control (CFTOC) problems of car-following control and energy management strategy for a hybrid electric vehicle are formulated separately. The two problems are then shifted to explicit MPC by precomputing the explicit solutions offline and the control laws are coupled together to form the control framework. Numerical simulations show that the proposed controller can improve the energy efficiency, driving safety and comfort while reduce the online computational costs. Moreover, the result of the hardware-in-the-loop experiment demonstrates the real-time performance of the proposed controller.

Suggested Citation

  • Ruan, Shumin & Ma, Yue & Yang, Ningkang & Xiang, Changle & Li, Xunming, 2022. "Real-time energy-saving control for HEVs in car-following scenario with a double explicit MPC approach," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222001682
    DOI: 10.1016/j.energy.2022.123265
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    References listed on IDEAS

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    Cited by:

    1. Jing, Hang & Li, Yan & Brandsema, Matthew J. & Chen, Yousu & Yue, Meng, 2024. "HHL algorithm with mapping function and enhanced sampling for model predictive control in microgrids," Applied Energy, Elsevier, vol. 361(C).
    2. Wang, Yong & Wu, Yuankai & Tang, Yingjuan & Li, Qin & He, Hongwen, 2023. "Cooperative energy management and eco-driving of plug-in hybrid electric vehicle via multi-agent reinforcement learning," Applied Energy, Elsevier, vol. 332(C).
    3. Hongtu Yang & Yan Sun & Changgao Xia & Hongdang Zhang, 2022. "Research on Energy Management Strategy of Fuel Cell Electric Tractor Based on Multi-Algorithm Fusion and Optimization," Energies, MDPI, vol. 15(17), pages 1-15, September.
    4. Ruan, Shumin & Ma, Yue & Yang, Ningkang & Yan, Qi & Xiang, Changle, 2023. "Multiobjective optimization of longitudinal dynamics and energy management for HEVs based on nash bargaining game," Energy, Elsevier, vol. 262(PA).
    5. Chen, Bin & Wang, Miaoben & Hu, Lin & He, Guo & Yan, Haoyang & Wen, Xinji & Du, Ronghua, 2024. "Data-driven Koopman model predictive control for hybrid energy storage system of electric vehicles under vehicle-following scenarios," Applied Energy, Elsevier, vol. 365(C).
    6. Hou, Shengyan & Yin, Hai & Xu, Fuguo & Benjamín, Pla & Gao, Jinwu & Chen, Hong, 2023. "Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles," Energy, Elsevier, vol. 266(C).

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