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A length ratio based neural network energy management strategy for online control of plug-in hybrid electric city bus

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  • Tian, He
  • Lu, Ziwang
  • Wang, Xu
  • Zhang, Xinlong
  • Huang, Yong
  • Tian, Guangyu

Abstract

Because of the limited resources of micro-controller, rule-based energy management strategies are still very popular for online control of plug-in hybrid electric vehicles, however, the control results may deviate from the optimal control results. Since the city bus routes are predetermined, the speed profiles of the certain bus route do not make much difference, this indeed creates an opportunity to design a novel energy management strategy that can reduce the micro-controller resources usage and achieve close to optimal control performance. To accomplish these goals, the single parameter of length ratio was introduced to represent trip information, and a novel efficient neural network module structure was designed to reduce the calculation time and memory usage of micro-controller. Finally, the length ratio based neural network energy management strategy was proposed for online control of plug-in hybrid electric city bus. Simulation results show that the proposed strategy can greatly decrease the total cost compared with the charge-depleting and charge-sustaining control strategy and can be regarded as an approximated global optimal energy management strategy.

Suggested Citation

  • Tian, He & Lu, Ziwang & Wang, Xu & Zhang, Xinlong & Huang, Yong & Tian, Guangyu, 2016. "A length ratio based neural network energy management strategy for online control of plug-in hybrid electric city bus," Applied Energy, Elsevier, vol. 177(C), pages 71-80.
    • Handle: RePEc:eee:appene:v:177:y:2016:i:c:p:71-80
    DOI: 10.1016/j.apenergy.2016.05.086
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    Cited by:

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    3. Wang, Hao & He, Hongwen & Bai, Yunfei & Yue, Hongwei, 2022. "Parameterized deep Q-network based energy management with balanced energy economy and battery life for hybrid electric vehicles," Applied Energy, Elsevier, vol. 320(C).
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    5. Min, Qingyun & Li, Junqiu & Liu, Bo & Li, Jianwei & Sun, Fengchun & Sun, Chao, 2021. "Guided model predictive control for connected vehicles with hybrid energy systems," Energy, Elsevier, vol. 230(C).
    6. Wu, Yitao & Zhang, Yuanjian & Li, Guang & Shen, Jiangwei & Chen, Zheng & Liu, Yonggang, 2020. "A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks," Energy, Elsevier, vol. 208(C).
    7. Chaoying Xia & Zhiming DU & Cong Zhang, 2017. "A Single-Degree-of-Freedom Energy Optimization Strategy for Power-Split Hybrid Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-23, July.
    8. López-Ibarra, Jon Ander & Gaztañaga, Haizea & Saez-de-Ibarra, Andoni & Camblong, Haritza, 2020. "Plug-in hybrid electric buses total cost of ownership optimization at fleet level based on battery aging," Applied Energy, Elsevier, vol. 280(C).
    9. Hongwei Liu & Chantong Wang & Xin Zhao & Chong Guo, 2018. "An Adaptive-Equivalent Consumption Minimum Strategy for an Extended-Range Electric Bus Based on Target Driving Cycle Generation," Energies, MDPI, vol. 11(7), pages 1-26, July.
    10. 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.
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