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A Rule-Based Energy Management Strategy Based on Dynamic Programming for Hydraulic Hybrid Vehicles

Author

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  • Haicheng Zhou
  • Zhaoping Xu
  • Liang Liu
  • Dong Liu
  • Lingling Zhang

Abstract

Energy management strategy is very important for hydraulic hybrid vehicles to improve fuel economy. The rule-based energy management strategies are widely used in engineering practice due to their simplicity and practicality. However, their performances differ a lot from different parameters and control actions. A rule-based energy management strategy is designed in this paper to realize real-time control of a novel hydraulic hybrid vehicle, and a control parameter selection method based on dynamic programming is proposed to optimize its performance. Firstly, the simulation model of the hydraulic hybrid vehicle is built and validated by the data tested from prototype experimental platform. Based on the simulation model, the optimization method of dynamic programming is used to find the global optimal solution of the engine control for the UDDS drive cycle. Then, the engine control parameters of the rule-based energy management strategy are selected according to the engine control trajectory of the global optimal solution. The simulation results show that the 100 km fuel consumption of the proposed rule-based energy management strategy is 12.7L, which is very close to the global optimal value of 12.4L and is suboptimal.

Suggested Citation

  • Haicheng Zhou & Zhaoping Xu & Liang Liu & Dong Liu & Lingling Zhang, 2018. "A Rule-Based Energy Management Strategy Based on Dynamic Programming for Hydraulic Hybrid Vehicles," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, October.
    • Handle: RePEc:hin:jnlmpe:9492026
    DOI: 10.1155/2018/9492026
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    Cited by:

    1. Jin, Yue & Yang, Lin & Du, Mao & Qiang, Jiaxi & Li, Jingzhong & Chen, Yuxuan & Tu, Jiayu, 2023. "Two-scale based energy management for connected plug-in hybrid electric vehicles with global optimal energy consumption and state-of-charge trajectory prediction," Energy, Elsevier, vol. 267(C).
    2. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(23), pages 1-22, November.
    3. Shilei Zhou & Paul Walker & Yang Tian & Cong Thanh Nguyen & Nong Zhang, 2021. "Comparison on Energy Economy and Vibration Characteristics of Electric and Hydraulic in-Wheel Drive Vehicles," Energies, MDPI, vol. 14(8), pages 1-15, April.
    4. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    5. Umberto Previti & Sebastian Brusca & Antonio Galvagno & Fabio Famoso, 2022. "Influence of Energy Management System Control Strategies on the Battery State of Health in Hybrid Electric Vehicles," Sustainability, MDPI, vol. 14(19), pages 1-20, September.

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