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A driving pattern recognition-based energy management for plug-in hybrid electric bus to counter the noise of stochastic vehicle mass

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  • Guo, Hongqiang
  • Hou, Daizheng
  • Du, Shangye
  • Zhao, Ling
  • Wu, Jian
  • Yan, Ning

Abstract

Because the strong coupling relationship between energy management and required power, the Pontryagin’s Minimum Principle (PMP)-based energy management should consider the noise of stochastic vehicle mass for plug-in hybrid electric bus (PHEB). However, if the vehicle mass is evaluated on-line, the control complexity will be greatly increased. This paper proposes a driving pattern recognition method to address the problem. The method is constituted by a look-up table and the K-nearest neighbor algorithm (KNN). The look-up table is used to recognize the robust design value (the inverse value of the robust co-state), where the average velocity at every bus station is taken as input, and the robust design value is taken as output. More importantly, the robust design value is found off-line by Design For Six Sigma (DFSS) method, and can counter the noise of stochastic vehicle mass. Because of this, the noise of the stochastic vehicle mass can be neglected in adaptive energy management control. The Monte Carlo Simulation (MCS) and simulation test results show that the proposed method is reasonable, robust and applicable; the fuel economy can be averagely improved by 34.36%, compared to a rule-based energy management.

Suggested Citation

  • Guo, Hongqiang & Hou, Daizheng & Du, Shangye & Zhao, Ling & Wu, Jian & Yan, Ning, 2020. "A driving pattern recognition-based energy management for plug-in hybrid electric bus to counter the noise of stochastic vehicle mass," Energy, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:energy:v:198:y:2020:i:c:s0360544220303960
    DOI: 10.1016/j.energy.2020.117289
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    References listed on IDEAS

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    1. van Velzen, Arjan & Annema, Jan Anne & van de Kaa, Geerten & van Wee, Bert, 2019. "Proposing a more comprehensive future total cost of ownership estimation framework for electric vehicles," Energy Policy, Elsevier, vol. 129(C), pages 1034-1046.
    2. Taner, Tolga & Sivrioglu, Mecit, 2015. "Energy–exergy analysis and optimisation of a model sugar factory in Turkey," Energy, Elsevier, vol. 93(P1), pages 641-654.
    3. Xie, Shaobo & Hu, Xiaosong & Xin, Zongke & Brighton, James, 2019. "Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 236(C), pages 893-905.
    4. Huang, Yanjun & Wang, Hong & Khajepour, Amir & Li, Bin & Ji, Jie & Zhao, Kegang & Hu, Chuan, 2018. "A review of power management strategies and component sizing methods for hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 132-144.
    5. Weichao Zhuang & Xiaowu Zhang & Huei Peng & Liangmo Wang, 2016. "Simultaneous Optimization of Topology and Component Sizes for Double Planetary Gear Hybrid Powertrains," Energies, MDPI, vol. 9(6), pages 1-17, May.
    6. M. Sabri, M.F. & Danapalasingam, K.A. & Rahmat, M.F., 2016. "A review on hybrid electric vehicles architecture and energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1433-1442.
    7. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    8. Yang, Chao & Li, Liang & You, Sixiong & Yan, Bingjie & Du, Xian, 2017. "Cloud computing-based energy optimization control framework for plug-in hybrid electric bus," Energy, Elsevier, vol. 125(C), pages 11-26.
    9. Li, Gaopeng & Zhang, Jieli & He, Hongwen, 2017. "Battery SOC constraint comparison for predictive energy management of plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 194(C), pages 578-587.
    10. 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.
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    3. Chen, Xu & Li, Mince & Chen, Zonghai, 2023. "Meta rule-based energy management strategy for battery/supercapacitor hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
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    5. Wang, Qiaochu & Ding, Yan & Kong, Xiangfei & Tian, Zhe & Xu, Linrui & He, Qing, 2022. "Load pattern recognition based optimization method for energy flexibility in office buildings," Energy, Elsevier, vol. 254(PC).
    6. Shi, Junzhe & Xu, Bin & Shen, Yimin & Wu, Jingbo, 2022. "Energy management strategy for battery/supercapacitor hybrid electric city bus based on driving pattern recognition," Energy, Elsevier, vol. 243(C).
    7. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2023. "Investigation of novel intelligent energy management strategies for connected HEB considering global planning of fixed-route information," Energy, Elsevier, vol. 263(PB).
    8. Makeen, Peter & Ghali, Hani A. & Memon, Saim & Duan, Fang, 2022. "Impacts of electric vehicle fast charging under dynamic temperature and humidity: Experimental and theoretically validated model analyses," Energy, Elsevier, vol. 261(PB).

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