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Convex optimization-based predictive and bi-level energy management for plug-in hybrid electric vehicles

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  • Li, Yapeng
  • Wang, Feng
  • Tang, Xiaolin
  • Hu, Xiaosong
  • Lin, Xianke

Abstract

Energy management is one of the key technologies to improve the energy efficiency of electrified vehicles. In the existing real-time powertrain control strategies, most studies focus on improving fuel economy based on high-fidelity powertrain models without adequately exploring the impact of model accuracy on computational efficiency and energy saving. To address this research gap, this paper proposes a hierarchical control framework to minimize the fuel consumption of a plug-in hybrid electric vehicle. Specifically, three main contributions are presented to distinguish our efforts from existing research. First, two types of powertrain models are used in the optimization framework. In the upper control layer, an approximated model is employed to generate the optimal reference state of charge trajectory using convex optimization. Then in the lower control layer, by integrating the equivalent consumption minimize strategy into the model predictive control framework, the fuel consumption is minimized in real-time by using a high-fidelity powertrain model. Second, optimization results from the other three real-time control strategies and two predictive energy management strategies are presented and analyzed to verify the effectiveness of the proposed method. Finally, the robustness with respect to prediction horizon length, initial co-state value, and gain coefficient value are discussed.

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  • Li, Yapeng & Wang, Feng & Tang, Xiaolin & Hu, Xiaosong & Lin, Xianke, 2022. "Convex optimization-based predictive and bi-level energy management for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 257(C).
  • Handle: RePEc:eee:energy:v:257:y:2022:i:c:s0360544222015754
    DOI: 10.1016/j.energy.2022.124672
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    References listed on IDEAS

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    Cited by:

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    2. Lyu, Chenghao & Zhang, Yuchen & Bai, Yilin & Yang, Kun & Song, Zhengxiang & Ma, Yuhang & Meng, Jinhao, 2024. "Inner-outer layer co-optimization of sizing and energy management for renewable energy microgrid with storage," Applied Energy, Elsevier, vol. 363(C).
    3. Pampa Sinha & Kaushik Paul & Sanchari Deb & Sulabh Sachan, 2023. "Comprehensive Review Based on the Impact of Integrating Electric Vehicle and Renewable Energy Sources to the Grid," Energies, MDPI, vol. 16(6), pages 1-39, March.
    4. Zhang, Hao & Lei, Nuo & Liu, Shang & Fan, Qinhao & Wang, Zhi, 2023. "Data-driven predictive energy consumption minimization strategy for connected plug-in hybrid electric vehicles," Energy, Elsevier, vol. 283(C).
    5. Wilberforce, Tabbi & Anser, Afaaq & Swamy, Jangam Aishwarya & Opoku, Richard, 2023. "An investigation into hybrid energy storage system control and power distribution for hybrid electric vehicles," Energy, Elsevier, vol. 279(C).
    6. Yang, Chao & Du, Xuelong & Wang, Weida & Yuan, Lijuan & Yang, Liuquan, 2024. "Variable optimization domain-based cooperative energy management strategy for connected plug-in hybrid electric vehicles," Energy, Elsevier, vol. 290(C).

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