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Real-time energy management strategy with dynamically updating equivalence factor for through-the-road (TTR) hybrid vehicles

Author

Listed:
  • Fan, Likang
  • Wang, Jun
  • Peng, Yiqiang
  • Sun, Hongwei
  • Bao, Xiuchao
  • Zeng, Baoquan
  • Wei, Hongqian

Abstract

Through-the-road–coupled hybrid electric vehicles, comprising a forward-driving internal combustion engine (ICE) and two rear-mounted hub motors, have been attracting increasing attention for their energy efficiency and excellent road passability. However, real-time energy allocation in the ICE and motors is challenging because of complex road scenarios, nonlinear characteristics of components, and the requirement of real-time executability of energy management strategies (EMSs). This study developed an online EMS with an equivalent factor (EF) which is timely updated through the offline rule extraction and online parameter feedback based on real-time driving scenarios. Initially, the utilization of a global dynamic programming approach aims at delineating the potential switching boundary, thus substantially mitigating the computational burden on the controller. Subsequently, an innovative Model Predictive Control-based Equivalent Fuel Consumption Strategy (MPC-ECMS) is introduced. This method integrates real-time velocity prediction with State of Charge (SOC) feedback to enhance fuel efficiency. Notably, the efficiency factor (EF) within ECMS undergoes optimization through a genetic algorithm, with adaptive corrections based on driving condition recognition outcomes. Consequently, the EF is continuously adjusted to ensure convergence of actual SOC towards the reference SOC within the MPC time frame. Through rigorous Hardware in Loop experimentation, the efficacy of the proposed EMS is validated. The outcomes demonstrate its superiority over traditional charge-depleting mode–charge sustaining mode strategies, yielding a notable 13.3 % reduction in total fuel consumption during high power demand scenarios. Furthermore, the method's feasibility within embedded systems is convincingly affirmed.

Suggested Citation

  • Fan, Likang & Wang, Jun & Peng, Yiqiang & Sun, Hongwei & Bao, Xiuchao & Zeng, Baoquan & Wei, Hongqian, 2024. "Real-time energy management strategy with dynamically updating equivalence factor for through-the-road (TTR) hybrid vehicles," Energy, Elsevier, vol. 298(C).
    • Handle: RePEc:eee:energy:v:298:y:2024:i:c:s0360544224011162
    DOI: 10.1016/j.energy.2024.131343
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    References listed on IDEAS

    as
    1. Wei, Zhen & Xu, John & Halim, Dunant, 2017. "HEV power management control strategy for urban driving," Applied Energy, Elsevier, vol. 194(C), pages 705-714.
    2. 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).
    3. Yang, Ye & Zhang, Youtong & Tian, Jingyi & Li, Tao, 2020. "Adaptive real-time optimal energy management strategy for extender range electric vehicle," Energy, Elsevier, vol. 197(C).
    4. Khayyam, Hamid & Bab-Hadiashar, Alireza, 2014. "Adaptive intelligent energy management system of plug-in hybrid electric vehicle," Energy, Elsevier, vol. 69(C), pages 319-335.
    5. Min, Dehao & Song, Zhen & Chen, Huicui & Wang, Tianxiang & Zhang, Tong, 2022. "Genetic algorithm optimized neural network based fuel cell hybrid electric vehicle energy management strategy under start-stop condition," Applied Energy, Elsevier, vol. 306(PB).
    6. Xie, Shaobo & Qi, Shanwei & Lang, Kun & Tang, Xiaolin & Lin, Xianke, 2020. "Coordinated management of connected plug-in hybrid electric buses for energy saving, inter-vehicle safety, and battery health," Applied Energy, Elsevier, vol. 268(C).
    7. Chen, Zheng & Xia, Bing & You, Chenwen & Mi, Chunting Chris, 2015. "A novel energy management method for series plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 145(C), pages 172-179.
    8. Fan, Likang & Wang, Yufei & Wei, Hongqian & Zhang, Youtong & Zheng, Pengyu & Huang, Tianyi & Li, Wei, 2022. "A GA-based online real-time optimized energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 241(C).
    9. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    10. Maino, Claudio & Misul, Daniela & Musa, Alessia & Spessa, Ezio, 2021. "Optimal mesh discretization of the dynamic programming for hybrid electric vehicles," Applied Energy, Elsevier, vol. 292(C).
    11. Xie, Shanshan & He, Hongwen & Peng, Jiankun, 2017. "An energy management strategy based on stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 196(C), pages 279-288.
    12. Chen, Zeyu & Xiong, Rui & Cao, Jiayi, 2016. "Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions," Energy, Elsevier, vol. 96(C), pages 197-208.
    13. Guo, Chenyu & Wang, Xin & Zheng, Yihui & Zhang, Feng, 2022. "Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    14. 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.
    15. Zhou, Wei & Cai, Xuan & Chen, Yaoqi & Li, Junqiu & Peng, Xiaoyan, 2022. "Decoding the optimal charge depletion behavior in energy domain for predictive energy management of series plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 316(C).
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