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Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses

Author

Listed:
  • Li, Liang
  • You, Sixiong
  • Yang, Chao
  • Yan, Bingjie
  • Song, Jian
  • Chen, Zheng

Abstract

Driving cycles of a city bus is statistically characterized by some repetitive features, which makes the predictive energy management strategy very desirable to obtain approximate optimal fuel economy of a plug-in hybrid electric bus. But dealing with the complicated traffic conditions and finding an approximated global optimal strategy which is applicable to the plug-in hybrid electric bus still remains a challenging technique. To solve this problem, a novel driving-behavior-aware modified stochastic model predictive control method is proposed for the plug-in hybrid electric bus. Firstly, the K-means is employed to classify driving behaviors, and the driver models based on Markov chains is obtained under different kinds of driving behaviors. While the obtained driver behaviors are regarded as stochastic disturbance inputs, the local minimum fuel consumption might be obtained with a traditional stochastic model predictive control at each step, taking tracking the reference battery state of charge trajectory into consideration in the finite predictive horizons. However, this technique is still accompanied by some working points with reduced/worsened fuel economy. Thus, the stochastic model predictive control is modified with the equivalent consumption minimization strategy to eliminate these undesirable working points. The results in real-world city bus routines show that the proposed energy management strategy could greatly improve the fuel economy of a plug-in hybrid electric bus in whole driving cycles, compared with the popular charge depleting–charge sustaining strategy and it may offer some useful insights for realizing the approximate global optimal energy management for the plug-in hybrid electric vehicles.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:162:y:2016:i:c:p:868-879
    DOI: 10.1016/j.apenergy.2015.10.152
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    References listed on IDEAS

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    1. Hu, Xiaosong & Murgovski, Nikolce & Johannesson, Lars & Egardt, Bo, 2013. "Energy efficiency analysis of a series plug-in hybrid electric bus with different energy management strategies and battery sizes," Applied Energy, Elsevier, vol. 111(C), pages 1001-1009.
    2. Zhang, Shuo & Xiong, Rui & Zhang, Chengning, 2015. "Pontryagin’s Minimum Principle-based power management of a dual-motor-driven electric bus," Applied Energy, Elsevier, vol. 159(C), pages 370-380.
    3. He, Hongwen & Xiong, Rui & Zhao, Kai & Liu, Zhentong, 2013. "Energy management strategy research on a hybrid power system by hardware-in-loop experiments," Applied Energy, Elsevier, vol. 112(C), pages 1311-1317.
    4. Hou, Cong & Ouyang, Minggao & Xu, Liangfei & Wang, Hewu, 2014. "Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 115(C), pages 174-189.
    5. Chen, Bo-Chiuan & Wu, Yuh-Yih & Tsai, Hsien-Chi, 2014. "Design and analysis of power management strategy for range extended electric vehicle using dynamic programming," Applied Energy, Elsevier, vol. 113(C), pages 1764-1774.
    6. 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.
    7. Hu, Xiaosong & Johannesson, Lars & Murgovski, Nikolce & Egardt, Bo, 2015. "Longevity-conscious dimensioning and power management of the hybrid energy storage system in a fuel cell hybrid electric bus," Applied Energy, Elsevier, vol. 137(C), pages 913-924.
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