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A novel MPC-based adaptive energy management strategy in plug-in hybrid electric vehicles

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  • Jinquan, Guo
  • Hongwen, He
  • Jiankun, Peng
  • Nana, Zhou

Abstract

In this paper, an adaptive energy management strategy (AEMS) under model predictive control (MPC) framework is proposed. The main advantage of the AEMS is that it fully integrates the economy driving pro system (EDPS), which can provide the renewable energy consumption trajectory considering dynamic traffic information of target driving task, namely the state of charge (SOC) reference constraint for the MPC optimal calculation at each control step. Moreover, based on the dynamically updated traffic information, the SOC reference constraint will be re-planned with correction, which will further reflect the ideal energy consumption trend over the actual driving cycle. For the MPC prediction aspect, the deep neural network (DNN) is applied in this paper to predict the future short-term velocity with 5s, 10s and 15s horizon, respectively. Meanwhile, the dynamic programming (DP) is applied to calculate the optimal energy distribution at each MPC control step. Simulation results show that under the test driving cycle, the optimal MPC predictive horizon with the assistance of EDPS is 10s, and the fuel economy rate can improve up to 6.48% compared with energy management without the assistance of EDPS. Moreover, the HIL test indicates the AEMS has well real-time performance as well.

Suggested Citation

  • Jinquan, Guo & Hongwen, He & Jiankun, Peng & Nana, Zhou, 2019. "A novel MPC-based adaptive energy management strategy in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 175(C), pages 378-392.
    • Handle: RePEc:eee:energy:v:175:y:2019:i:c:p:378-392
    DOI: 10.1016/j.energy.2019.03.083
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    14. Hou, Daizheng & Sun, Qun & Bao, Chunjiang & Cheng, Xingqun & Guo, Hongqiang & Zhao, Ying, 2019. "An all-in-one design method for plug-in hybrid electric buses considering uncertain factor of driving cycles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
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    17. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
    18. Wei, Changyin & Sun, Xiuxiu & Chen, Yong & Zang, Libin & Bai, Shujie, 2021. "Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle," Energy, Elsevier, vol. 230(C).
    19. Li, Shuangqi & He, Hongwen & Zhao, Pengfei, 2021. "Energy management for hybrid energy storage system in electric vehicle: A cyber-physical system perspective," Energy, Elsevier, vol. 230(C).
    20. Wu, Chuanshen & Gao, Shan & Liu, Yu & Song, Tiancheng E. & Han, Haiteng, 2021. "A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles," Renewable Energy, Elsevier, vol. 163(C), pages 1385-1396.
    21. He, Hongwen & Meng, Xiangfei & Wang, Yong & Khajepour, Amir & An, Xiaowen & Wang, Renguang & Sun, Fengchun, 2024. "Deep reinforcement learning based energy management strategies for electrified vehicles: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    22. Guo, Xiaokai & Yan, Xianguo & Chen, Zhi & Meng, Zhiyu, 2022. "Research on energy management strategy of heavy-duty fuel cell hybrid vehicles based on dueling-double-deep Q-network," Energy, Elsevier, vol. 260(C).
    23. Wu, Chuanshen & Jiang, Sufan & Gao, Shan & Liu, Yu & Han, Haiteng, 2022. "Charging demand forecasting of electric vehicles considering uncertainties in a microgrid," Energy, Elsevier, vol. 247(C).
    24. Yuan, Jingni & Yang, Lin, 2019. "Predictive energy management strategy for connected 48V hybrid electric vehicles," Energy, Elsevier, vol. 187(C).
    25. Babar, Abdul Haseeb Khan & Ali, Yousaf, 2021. "Enhancement of electric vehicles’ market competitiveness using fuzzy quality function deployment," Technological Forecasting and Social Change, Elsevier, vol. 167(C).

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