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Acquisition of full-factor trip information for global optimization energy management in multi-energy source vehicles and the measure of the amount of information to be transmitted

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  • Kong, Yan
  • Xu, Nan
  • Zhang, Yuanjian
  • Sui, Yan
  • Ju, Hao
  • Liu, Heng
  • Xu, Zhe

Abstract

Dynamic programming (DP), as a typical global optimization method, requires the prior knowledge of the future driving conditions. To standardize the DP optimizing process, a hierarchical optimization framework of “information layer - physical layer - energy layer - dynamic programming” (IPE-DP) is proposed. The trip information, as the prerequisite for implementing global optimization energy management, is acquired in the information layer. Firstly, full-factor trip information, including the vehicle speed, slope and slip rate, is acquired from three scenarios: deterministic information, information with constraints and information supported by historical data. If only the relevant constraints are available, a “drivers-vehicles-roads” full-factor constraint model is proposed to limit the trip information. Then, information entropy is introduced to measure the uncertainty of the trip information. Particularly, for information with constraints, the independence of various constraints ensures the additivity of the entropy as quantified by the drivers, vehicles and roads. Based on the above, the amount of information to be transmitted is analyzed at the end. To a certain extent, the proposed constraint model can lower the limit on data transfer rate. Furthermore, information entropy provides a theoretical basis for determining the amount of information required to optimize vehicle fuel economy and regional energy consumption.

Suggested Citation

  • Kong, Yan & Xu, Nan & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2021. "Acquisition of full-factor trip information for global optimization energy management in multi-energy source vehicles and the measure of the amount of information to be transmitted," Energy, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:energy:v:236:y:2021:i:c:s0360544221016716
    DOI: 10.1016/j.energy.2021.121423
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    References listed on IDEAS

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    Cited by:

    1. Xu, Nan & Kong, Yan & Zhang, Yuanjian & Yue, Fenglai & Sui, Yan & Li, Xiaohan & Liu, Heng & Xu, Zhe, 2022. "Determination of vehicle working modes for global optimization energy management and evaluation of the economic performance for a certain control strategy," Energy, Elsevier, vol. 251(C).
    2. Yu, Xiao & Lin, Cheng & Xie, Peng & Liang, Sheng, 2022. "A novel real-time energy management strategy based on Monte Carlo Tree Search for coupled powertrain platform via vehicle-to-cloud connectivity," Energy, Elsevier, vol. 256(C).
    3. 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).
    4. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    5. Yu, Xiao & Lin, Cheng & Tian, Yu & Zhao, Mingjie & Liu, Huimin & Xie, Peng & Zhang, JunZhi, 2023. "Real-time and hierarchical energy management-control framework for electric vehicles with dual-motor powertrain system," Energy, Elsevier, vol. 272(C).
    6. Kong, Yan & Xu, Nan & Liu, Qiao & Sui, Yan & Yue, Fenglai, 2023. "A data-driven energy management method for parallel PHEVs based on action dependent heuristic dynamic programming (ADHDP) model," Energy, Elsevier, vol. 265(C).

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