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An energy management strategy for plug-in hybrid electric vehicles based on deep learning and improved model predictive control

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  • Sun, Xilei
  • Fu, Jianqin
  • Yang, Huiyong
  • Xie, Mingke
  • Liu, Jingping

Abstract

In order to reasonably allocate the power needs and manage the energy of the plug-in hybrid electric vehicle (PHEV) more efficiently, an energy management strategy (EMS) based on deep learning and improved model predictive control was proposed. Firstly, the vehicle energy flow test was carried out for the PHEV, and the multi-physics (mechanical-electrical-thermal-hydraulic) model was constructed and validated. Secondly, six prediction models were built based on different algorithms and the effects were compared and analyzed in detail. Finally, a long short-term memory based improved model predictive control algorithm (LSTM-IMPC) was developed, and the effects of three EMSs based on the charge-depleting charge-sustaining rule (CD-CS), dynamic programming (DP) and LSTM-IMPC were investigated under Worldwide Light-duty Test Cycle (WLTC), New European Driving Cycle (NEDC) and real driving cycle (RDC). The results show that the fuel-saving rates of the LSTM-IMPC-based EMS under these three cycles are respectively 3.81%, 5.6% and 18.71% compared with the CD-CS-based EMS, which prove the good fuel-saving performance and strong robustness of the proposed EMS. The fuel-saving rates of the LSTM-IMPC-based EMS are close to the DP-based EMS, which are the global optimal under these three cycles.

Suggested Citation

  • Sun, Xilei & Fu, Jianqin & Yang, Huiyong & Xie, Mingke & Liu, Jingping, 2023. "An energy management strategy for plug-in hybrid electric vehicles based on deep learning and improved model predictive control," Energy, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:energy:v:269:y:2023:i:c:s0360544223001664
    DOI: 10.1016/j.energy.2023.126772
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    References listed on IDEAS

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    1. Guo, Lingxiong & Zhang, Xudong & Zou, Yuan & Guo, Ningyuan & Li, Jianwei & Du, Guodong, 2021. "Cost-optimal energy management strategy for plug-in hybrid electric vehicles with variable horizon speed prediction and adaptive state-of-charge reference," Energy, Elsevier, vol. 232(C).
    2. 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.
    3. 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.
    4. 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.
    5. Yang, Chao & Wang, Muyao & Wang, Weida & Pu, Zesong & Ma, Mingyue, 2021. "An efficient vehicle-following predictive energy management strategy for PHEV based on improved sequential quadratic programming algorithm," Energy, Elsevier, vol. 219(C).
    6. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Real-time energy management for fuel cell electric vehicle using speed prediction-based model predictive control considering performance degradation," Applied Energy, Elsevier, vol. 304(C).
    7. Wen, Jianping & Zhao, Dan & Zhang, Chuanwei, 2020. "An overview of electricity powered vehicles: Lithium-ion battery energy storage density and energy conversion efficiency," Renewable Energy, Elsevier, vol. 162(C), pages 1629-1648.
    8. Chen, Zheng & Hu, Hengjie & Wu, Yitao & Zhang, Yuanjian & Li, Guang & Liu, Yonggang, 2020. "Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning," Energy, Elsevier, vol. 211(C).
    9. Feng, Renhua & Li, Guanghua & Sun, Zhengwei & Hu, Xiulin & Deng, Banglin & Fu, Jianqin, 2023. "Potential of emission reduction of a turbo-charged non-road diesel engine without aftertreatment under multiple operating scenarios," Energy, Elsevier, vol. 263(PB).
    10. Chen, Zheng & Gu, Hongji & Shen, Shiquan & Shen, Jiangwei, 2022. "Energy management strategy for power-split plug-in hybrid electric vehicle based on MPC and double Q-learning," Energy, Elsevier, vol. 245(C).
    11. Hassanzadeh, Mojtaba & Rahmani, Zahra, 2022. "A predictive controller for real-time energy management of plug-in hybrid electric vehicles," Energy, Elsevier, vol. 249(C).
    12. Zhang, Shuo & Hu, Xiaosong & Xie, Shaobo & Song, Ziyou & Hu, Lin & Hou, Cong, 2019. "Adaptively coordinated optimization of battery aging and energy management in plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 256(C).
    13. Biswas, Dhrupad & Ghosh, Susenjit & Sengupta, Somnath & Mukhopadhyay, Siddhartha, 2022. "Energy Management of a Parallel Hybrid Electric Vehicle using Model Predictive Static Programming," Energy, Elsevier, vol. 250(C).
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    4. Rofhiwa Lutendo Edward Takalani & Lesedi Masisi, 2023. "Development of an Optimal Port Crane Trajectory for Reduced Energy Consumption," Energies, MDPI, vol. 16(20), pages 1-20, October.

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