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Towards sustainable high-speed cruising: Optimizing energy efficiency of plug-in hybrid electric vehicle via intelligent pulse-and-glide strategy

Author

Listed:
  • Tong, He
  • Chu, Liang
  • Zhang, Yuanjian
  • Zhao, Di
  • Hu, Jincheng
  • Xie, Zhihao
  • Liu, Ming

Abstract

Conventional pulse-and-glide (PnG) strategies expose issues including poor coordination with energy management systems (EMSs), discomfort and low levels of automation, this paper innovatively addresses these challenges by the comprehensive considerations of energy economy, dynamic performance, and ride comfort, proposing an intelligent pulse-and-glide (I-PnG) strategy tailored for plug-in hybrid electric vehicle (PHEV) configurations. The I-PnG strategy is aimed at optimizing economic cruising in high-speed scenarios; however, it demonstrates applicability across diverse conditions without deterioration in performance. I-PnG is a data-driven autonomous driving solution developed through a deep Q network (DQN), featuring two distinct modes: ECO and SPORT, the former prioritizes energy economy, while the latter emphasizes dynamic performance enhancement. The results indicate that our proposed strategy significantly optimizes energy management compared to the baseline methods. Specifically, during variable-speed cruising, I-PnG ECO achieves a maximum energy efficiency improvement of 16.13 % and a cost reduction of 7.74 %, while I-PnG SPORT exhibits a maximum energy efficiency improvement of 13.37 % and a cost reduction of 3.92 %. Under the Highway Fuel Economy Test (HWFET), I-PnG ECO and I-PnG SPORT further improve energy efficiency by up to 22.94 % and 19.04 %, respectively, with corresponding cost savings of 18.47 % and 13.35 %.

Suggested Citation

  • Tong, He & Chu, Liang & Zhang, Yuanjian & Zhao, Di & Hu, Jincheng & Xie, Zhihao & Liu, Ming, 2024. "Towards sustainable high-speed cruising: Optimizing energy efficiency of plug-in hybrid electric vehicle via intelligent pulse-and-glide strategy," Energy, Elsevier, vol. 311(C).
    • Handle: RePEc:eee:energy:v:311:y:2024:i:c:s0360544224031888
    DOI: 10.1016/j.energy.2024.133412
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    References listed on IDEAS

    as
    1. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
    2. Hannan, M.A. & Azidin, F.A. & Mohamed, A., 2014. "Hybrid electric vehicles and their challenges: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 135-150.
    3. Liu, Teng & Tan, Wenhao & Tang, Xiaolin & Zhang, Jinwei & Xing, Yang & Cao, Dongpu, 2021. "Driving conditions-driven energy management strategies for hybrid electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    4. Shuxian Li & Minghui Hu & Changchao Gong & Sen Zhan & Datong Qin, 2018. "Energy Management Strategy for Hybrid Electric Vehicle Based on Driving Condition Identification Using KGA-Means," Energies, MDPI, vol. 11(6), pages 1-16, June.
    5. 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.
    6. Li, Jie & Wu, Xiaodong & Xu, Min & Liu, Yonggang, 2022. "Deep reinforcement learning and reward shaping based eco-driving control for automated HEVs among signalized intersections," Energy, Elsevier, vol. 251(C).
    7. Zeyu Chen & Rui Xiong & Kunyu Wang & Bin Jiao, 2015. "Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 8(5), pages 1-18, April.
    8. Barkenbus, Jack N., 2010. "Eco-driving: An overlooked climate change initiative," Energy Policy, Elsevier, vol. 38(2), pages 762-769, February.
    9. Li, Jie & Wu, Xiaodong & Fan, Jiawei & Liu, Yonggang & Xu, Min, 2023. "Overcoming driving challenges in complex urban traffic: A multi-objective eco-driving strategy via safety model based reinforcement learning," Energy, Elsevier, vol. 284(C).
    10. Al-Alawi, Baha M. & Bradley, Thomas H., 2013. "Total cost of ownership, payback, and consumer preference modeling of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 103(C), pages 488-506.
    11. Bai, Shengxi & Liu, Chunhua, 2021. "Overview of energy harvesting and emission reduction technologies in hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    12. Sarvaiya, Shradhdha & Ganesh, Sachin & Xu, Bin, 2021. "Comparative analysis of hybrid vehicle energy management strategies with optimization of fuel economy and battery life," Energy, Elsevier, vol. 228(C).
    13. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
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