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Model predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of discharge

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  • Xie, Shaobo
  • Hu, Xiaosong
  • Qi, Shanwei
  • Tang, Xiaolin
  • Lang, Kun
  • Xin, Zongke
  • Brighton, James

Abstract

When developing an energy management strategy (EMS) including a battery aging model for plug-in hybrid electric vehicles, the trade-off between the energy consumption cost (ECC) and the equivalent battery life loss cost (EBLLC) should be considered to minimize the total cost of both and improve the life cycle value. Unlike EMSs with a lower State of Charge (SOC) boundary value given in advance, this paper proposes a model predictive control of EMS based on an optimal battery depth of discharge (DOD) for a minimum sum of ECC and EBLLC. First, the optimal DOD is identified using Pontryagin's Minimum Principle and shooting method. Then a reference SOC is constructed with the optimal DOD, and a model predictive controller (MPC) in which the conflict between the ECC and EBLC is optimized in a moving horizon is implemented. The proposed EMS is examined by real-world driving cycles under different preview horizons, and the results indicate that MPCs with a battery aging model lower the total cost by 1.65%, 1.29% and 1.38%, respectively, for three preview horizons (5, 10 and 15 s) under a city bus route of about 70 km, compared to those unaware of battery aging. Meanwhile, global optimization algorithms like the dynamic programming and Pontryagin's Minimum Principle, as well as a rule-based method, are compared with the predictive controller, in terms of computational expense and accuracy.

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  • Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Tang, Xiaolin & Lang, Kun & Xin, Zongke & Brighton, James, 2019. "Model predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of discharge," Energy, Elsevier, vol. 173(C), pages 667-678.
    • Handle: RePEc:eee:energy:v:173:y:2019:i:c:p:667-678
    DOI: 10.1016/j.energy.2019.02.074
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    15. Zhang, Yuanjian & Gao, Bingzhao & Jiang, Jingjing & Liu, Chengyuan & Zhao, Dezong & Zhou, Quan & Chen, Zheng & Lei, Zhenzhen, 2023. "Cooperative power management for range extended electric vehicle based on internet of vehicles," Energy, Elsevier, vol. 273(C).
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    19. Lin, Tianliang & Lin, Yuanzheng & Ren, Haoling & Chen, Haibin & Chen, Qihuai & Li, Zhongshen, 2020. "Development and key technologies of pure electric construction machinery," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    20. Xiaobo Sun & Weirong Liu & Mengfei Wen & Yue Wu & Heng Li & Jiahao Huang & Chao Hu & Zhiwu Huang, 2021. "A Real-Time Optimal Car-Following Power Management Strategy for Hybrid Electric Vehicles with ACC Systems," Energies, MDPI, vol. 14(12), pages 1-17, June.
    21. Zhang, Mingze & Li, Weidong & Yu, Samson Shenglong & Wen, Kerui & Zhou, Chen & Shi, Peng, 2021. "A unified configurational optimization framework for battery swapping and charging stations considering electric vehicle uncertainty," Energy, Elsevier, vol. 218(C).
    22. Qin, Yechen & Tang, Xiaolin & Jia, Tong & Duan, Ziwen & Zhang, Jieming & Li, Yinong & Zheng, Ling, 2020. "Noise and vibration suppression in hybrid electric vehicles: State of the art and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(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. Tejas-Dilipsing Patil & Emmanuel Vinot & Simone Ehrenberger & Rochdi Trigui & Eduardo Redondo-Iglesias, 2023. "Sensitivity Analysis of Battery Aging for Model-Based PHEV Use Scenarios," Energies, MDPI, vol. 16(4), pages 1-17, February.
    25. Chen, Jiaxin & Shu, Hong & Tang, Xiaolin & Liu, Teng & Wang, Weida, 2022. "Deep reinforcement learning-based multi-objective control of hybrid power system combined with road recognition under time-varying environment," Energy, Elsevier, vol. 239(PC).

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