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Predictive vehicle-following power management for plug-in hybrid electric vehicles

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Listed:
  • Xie, Shaobo
  • Hu, Xiaosong
  • Liu, Teng
  • Qi, Shanwei
  • Lang, Kun
  • Li, Huiling

Abstract

This paper presents a new integrated model predictive control (IMPC) method that combines power management and adaptive velocity control during vehicle-following scenarios in reality, for a plug-in hybrid electric vehicle (PHEV). Innovatively, the IMPC is able to plan the battery state of charge (SOC) and vehicular velocity trajectories, in order to improve fue economy and driving safety. To assess the performance of the IMPC, a comparison is performed with common charge-depleting and charge-sustaining (CDCS) and DP-based energy management strategies, where an improved full velocity difference model (IFVDM) is incorporated to simulate vehicle-following behavior. These solutions are examined using a real-world driving cycle. The results reveal an enormous potential of flexibly tuning the inter-vehicle distance to increase fuel economy. This is distinct from the rigid vehicle-following behavior of the IFVDM just for driving safety. Moreover, the proposed IMPC can ensure the battery charge depletion at the end of the trip. The quantitative results witness that the total cost of the IMPC with a preview horizon of 3s can be reduced by 17.9% and 36.1% for a 70 km city-bus route, compared to IFVDM-based DP and CDCS counterparts, respectively. In addition, the effect of the preview-horizon length on both fuel economy and computational time is examined.

Suggested Citation

  • Xie, Shaobo & Hu, Xiaosong & Liu, Teng & Qi, Shanwei & Lang, Kun & Li, Huiling, 2019. "Predictive vehicle-following power management for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 166(C), pages 701-714.
    • Handle: RePEc:eee:energy:v:166:y:2019:i:c:p:701-714
    DOI: 10.1016/j.energy.2018.10.129
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    References listed on IDEAS

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    1. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
    2. Tian, He & Li, Shengbo Eben & Wang, Xu & Huang, Yong & Tian, Guangyu, 2018. "Data-driven hierarchical control for online energy management of plug-in hybrid electric city bus," Energy, Elsevier, vol. 142(C), pages 55-67.
    3. Kaijiang Yu & Junqi Yang, 2014. "Performance of a Nonlinear Real-Time Optimal Control System for HEVs/PHEVs during Car Following," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-14, June.
    4. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
    5. Shaobo Xie & Huiling Li & Zongke Xin & Tong Liu & Lang Wei, 2017. "A Pontryagin Minimum Principle-Based Adaptive Equivalent Consumption Minimum Strategy for a Plug-in Hybrid Electric Bus on a Fixed Route," Energies, MDPI, vol. 10(9), pages 1-22, September.
    6. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    7. Li, Liang & You, Sixiong & Yang, Chao & Yan, Bingjie & Song, Jian & Chen, Zheng, 2016. "Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 162(C), pages 868-879.
    8. Torres, J.L. & Gonzalez, R. & Gimenez, A. & Lopez, J., 2014. "Energy management strategy for plug-in hybrid electric vehicles. A comparative study," Applied Energy, Elsevier, vol. 113(C), pages 816-824.
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