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Data-driven hierarchical control for online energy management of plug-in hybrid electric city bus

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  • Tian, He
  • Li, Shengbo Eben
  • Wang, Xu
  • Huang, Yong
  • Tian, Guangyu

Abstract

The pre-determined city bus routes and the availability of partial-trip information obtained through vehicular connectivity provides new opportunities for plug-in vehicles to plan electric energy reasonably. This paper presents a data-driven hierarchical control method for online energy management of plug-in hybrid electric city buses, which can learn from globally optimal solutions based on historical accumulated cycles while taking advantage of connectivity-enabled partial-trip information. The devised scheme comprises two levels of control modules. The upper battery state-of-charge planner trained using historical optimal data is employed for deriving a reference state-of-charge based on the current battery state, remaining trip length, and low/high speed ratios. The lower powertrain controller is then applied to regulate the engine operation according to the reference state-of-charge and powertrain states. This article presents two contributions: (1) both accumulated historical optimal data and partial-trip information are assimilated to augment the applicability of the control hierarchy, thus achieving better resilience to “unseen” driving patterns; (2) given limited resources of micro-controllers, the control strategy is proven to be a real-time implementable, close-to-optimal solution. A variety of results show that the proposed approach can achieve significant fuel savings (4.99%–14.80%) as compared to the charge depleting and charge sustaining strategy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:142:y:2018:i:c:p:55-67
    DOI: 10.1016/j.energy.2017.09.061
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    11. Xiaobin Ning & Jiazheng Wang & Yuming Yin & Jiarong Shangguan & Nanxin Bao & Ning Li, 2023. "Regenerative Braking Algorithm for Parallel Hydraulic Hybrid Vehicles Based on Fuzzy Q-Learning," Energies, MDPI, vol. 16(4), pages 1-18, February.
    12. Guo, Hongqiang & Lu, Silong & Hui, Hongzhong & Bao, Chunjiang & Shangguan, Jinyong, 2019. "Receding horizon control-based energy management for plug-in hybrid electric buses using a predictive model of terminal SOC constraint in consideration of stochastic vehicle mass," Energy, Elsevier, vol. 176(C), pages 292-308.
    13. Maroto Estrada, Pedro & de Lima, Daniela & Bauer, Peter H. & Mammetti, Marco & Bruno, Joan Carles, 2023. "Deep learning in the development of energy Management strategies of hybrid electric Vehicles: A hybrid modeling approach," Applied Energy, Elsevier, vol. 329(C).
    14. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Lang, Kun, 2018. "An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 163(C), pages 837-848.
    15. Guo, Lingxiong & Liu, Hui & Han, Lijin & Yang, Ningkang & Liu, Rui & Xiang, Changle, 2023. "Predictive energy management strategy of dual-mode hybrid electric vehicles combining dynamic coordination control and simultaneous power distribution," Energy, Elsevier, vol. 263(PA).
    16. 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).
    17. Lei, Zhenzhen & Qin, Datong & Hou, Liliang & Peng, Jingyu & Liu, Yonggang & Chen, Zheng, 2020. "An adaptive equivalent consumption minimization strategy for plug-in hybrid electric vehicles based on traffic information," Energy, Elsevier, vol. 190(C).
    18. 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.
    19. Li, Xunming & Han, Lijin & Liu, Hui & Wang, Weida & Xiang, Changle, 2019. "Real-time optimal energy management strategy for a dual-mode power-split hybrid electric vehicle based on an explicit model predictive control algorithm," Energy, Elsevier, vol. 172(C), pages 1161-1178.
    20. Zhuang, Weichao & Li (Eben), Shengbo & Zhang, Xiaowu & Kum, Dongsuk & Song, Ziyou & Yin, Guodong & Ju, Fei, 2020. "A survey of powertrain configuration studies on hybrid electric vehicles," Applied Energy, Elsevier, vol. 262(C).
    21. Chen, Z. & Liu, Y. & Ye, M. & Zhang, Y. & Chen, Z. & Li, G., 2021. "A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    22. Hou, Shengyan & Yin, Hai & Xu, Fuguo & Benjamín, Pla & Gao, Jinwu & Chen, Hong, 2023. "Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles," Energy, Elsevier, vol. 266(C).
    23. Zhang, Yuanjian & Liu, Yonggang & Huang, Yanjun & Chen, Zheng & Li, Guang & Hao, Wanming & Cunningham, Geoff & Early, Juliana, 2021. "An optimal control strategy design for plug-in hybrid electric vehicles based on internet of vehicles," Energy, Elsevier, vol. 228(C).

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