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Decoding the optimal charge depletion behavior in energy domain for predictive energy management of series plug-in hybrid electric vehicle

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  • Zhou, Wei
  • Cai, Xuan
  • Chen, Yaoqi
  • Li, Junqiu
  • Peng, Xiaoyan

Abstract

A critical issue for designing predictive energy management (PEM) strategy of Plug-in Hybrid Electric Vehicles is the planning of optimal global charge trajectory. Existing planning methods have flaws in terms of optimality or computational efficiency due to their lack of in-depth consideration about optimal charge depletion behaviors. To address this issue, rigorous theoretical analysis on the aggregated local and global optimal charge depletion behaviors in energy domain is conducted by combining Pontryagin’s Minimum Principle-based analytical derivations and some qualitative reasoning. Fundamental understanding on how the optimal charge depletion rates behave in different driving conditions and why they exhibit such behaviors is provided. The theoretical analysis is further validated through model-in-the-loop tests using an experimentally validated high-fidelity vehicle simulator. The insights gained from the analysis of this paper establish a fundamental knowledge foundation and may pave a new path for more scientific PEM design in the future.

Suggested Citation

  • Zhou, Wei & Cai, Xuan & Chen, Yaoqi & Li, Junqiu & Peng, Xiaoyan, 2022. "Decoding the optimal charge depletion behavior in energy domain for predictive energy management of series plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 316(C).
  • Handle: RePEc:eee:appene:v:316:y:2022:i:c:s0306261922004858
    DOI: 10.1016/j.apenergy.2022.119098
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    1. Min, Qingyun & Li, Junqiu & Liu, Bo & Li, Jianwei & Sun, Fengchun & Sun, Chao, 2021. "Guided model predictive control for connected vehicles with hybrid energy systems," Energy, Elsevier, vol. 230(C).
    2. Guo, Ningyuan & Zhang, Xudong & Zou, Yuan & Guo, Lingxiong & Du, Guodong, 2021. "Real-time predictive energy management of plug-in hybrid electric vehicles for coordination of fuel economy and battery degradation," Energy, Elsevier, vol. 214(C).
    3. Yang, Chao & Du, Siyu & Li, Liang & You, Sixong & Yang, Yiyong & Zhao, Yue, 2017. "Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 203(C), pages 883-896.
    4. Cordiner, Stefano & Galeotti, Matteo & Mulone, Vincenzo & Nobile, Matteo & Rocco, Vittorio, 2016. "Trip-based SOC management for a plugin hybrid electric vehicle," Applied Energy, Elsevier, vol. 164(C), pages 891-905.
    5. Mahmoodi-k, Mehdi & Montazeri, Morteza & Madanipour, Vahid, 2021. "Simultaneous multi-objective optimization of a PHEV power management system and component sizing in real world traffic condition," Energy, Elsevier, vol. 233(C).
    6. Xu, Nan & Kong, Yan & Yan, Jinyue & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2022. "Global optimization energy management for multi-energy source vehicles based on “Information layer - Physical layer - Energy layer - Dynamic programming” (IPE-DP)," Applied Energy, Elsevier, vol. 312(C).
    7. Onori, Simona & Tribioli, Laura, 2015. "Adaptive Pontryagin’s Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt," Applied Energy, Elsevier, vol. 147(C), pages 224-234.
    8. Zhou, Wei & Chen, Yaoqi & Zhai, Haoran & Zhang, Weigang, 2021. "Predictive energy management for a plug-in hybrid electric vehicle using driving profile segmentation and energy-based analytical SoC planning," Energy, Elsevier, vol. 220(C).
    9. Hongwen, He & Jinquan, Guo & Jiankun, Peng & Huachun, Tan & Chao, Sun, 2018. "Real-time global driving cycle construction and the application to economy driving pro system in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 152(C), pages 95-107.
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    2. Yao, Yongming & Wang, Jie & Zhou, Zhicong & Li, Hang & Liu, Huiying & Li, Tianyu, 2023. "Grey Markov prediction-based hierarchical model predictive control energy management for fuel cell/battery hybrid unmanned aerial vehicles," Energy, Elsevier, vol. 262(PA).
    3. Zhang, Hao & Liu, Shang & Lei, Nuo & Fan, Qinhao & Wang, Zhi, 2022. "Leveraging the benefits of ethanol-fueled advanced combustion and supervisory control optimization in hybrid biofuel-electric vehicles," Applied Energy, Elsevier, vol. 326(C).
    4. Fan, Likang & Wang, Jun & Peng, Yiqiang & Sun, Hongwei & Bao, Xiuchao & Zeng, Baoquan & Wei, Hongqian, 2024. "Real-time energy management strategy with dynamically updating equivalence factor for through-the-road (TTR) hybrid vehicles," Energy, Elsevier, vol. 298(C).

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