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Global optimization energy management for multi-energy source vehicles based on “Information layer - Physical layer - Energy layer - Dynamic programming” (IPE-DP)

Author

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

Abstract

To reveal the energy-saving mechanisms of global energy management, we propose a global optimization framework of “information layer-physical layer-energy layer-dynamic programming” (IPE-DP), which can realize the unity of different information scenarios, different vehicle configurations and energy conversions. The deterministic dynamic programing (DP) and adaptive dynamic programming (ADP) are taken as the core algorithms. As a benchmark for assessing the optimality, DP strategy has four main challenges: standardization, real-time application, accuracy, and satisfactory drivability. To solve the above problems, the IPE-DP optimization framework is established, which consists of three main layers, two interface layers and an application layer. To be specific, the full-factor trip information is acquired from three scenarios in the information layer, and then the feasible work modes of the vehicle are determined in the physical layer based on the proposed conservation framework of “kinetic/potential energy & onboard energy“. The above lays a foundation for the optimal energy distribution in the energy layer. Then, a global domain-searching algorithm and action-dependent heuristic dynamic programming (ADHDP) model are developed for different information acquisition scenarios to obtain the optimal solution. To improve the computational efficiency under the deterministic information, a fast DP is developed based on the statistical rules of DP behavior, the core of which is to restrict the exploring region based on a reference SOC trajectory. Regarding the stochastic trip information, the ADHDP model is established, including determining the utility function, network design and training process. Finally, two case studies are given to compare the economic performance of the vehicle under different information acquisition scenarios, which lays a foundation for analyzing the relationship between the amount of information input and energy-saving potential of the vehicle. Simulation results demonstrate that the proposed method gains a better performance in both real-time performance and global optimality.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:312:y:2022:i:c:s0306261922001349
    DOI: 10.1016/j.apenergy.2022.118668
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    5. Li, Xue & Li, Minghai & Habibi, Mostafa & Najaafi, Neda & Safarpour, Hamed, 2023. "Optimization of hybrid energy management system based on high-energy solid-state lithium batteries and reversible fuel cells," Energy, Elsevier, vol. 283(C).
    6. 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).
    7. 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).
    8. Hou, Zhuoran & Guo, Jianhua & Chu, Liang & Hu, Jincheng & Chen, Zheng & Zhang, Yuanjian, 2023. "Exploration the route of information integration for vehicle design: A knowledge-enhanced energy management strategy," Energy, Elsevier, vol. 282(C).
    9. Kunang Li & Chunchun Jia & Xuefeng Han & Hongwen He, 2023. "A Novel Minimal-Cost Power Allocation Strategy for Fuel Cell Hybrid Buses Based on Deep Reinforcement Learning Algorithms," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
    10. Guo, Xiaokai & Yan, Xianguo & Chen, Zhi & Meng, Zhiyu, 2022. "Research on energy management strategy of heavy-duty fuel cell hybrid vehicles based on dueling-double-deep Q-network," Energy, Elsevier, vol. 260(C).
    11. Guo, Ningyuan & Zhang, Wencan & Li, Junqiu & Chen, Zheng & Li, Jianwei & Sun, Chao, 2024. "Predictive energy management of fuel cell plug-in hybrid electric vehicles: A co-state boundaries-oriented PMP optimization approach," Applied Energy, Elsevier, vol. 362(C).

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