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Reinforcement learning based power management integrating economic rotational speed of turboshaft engine and safety constraints of battery for hybrid electric power system

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  • Wei, Zhengchao
  • Ma, Yue
  • Yang, Ningkang
  • Ruan, Shumin
  • Xiang, Changle

Abstract

Hybrid electric power system (HEPS) with turboshaft engine is a promising solution for the land and air vehicle, and the power management strategy (PMS) is the key to obtaining better performance of HEPS. In this paper, a reinforcement learning (RL)-based PMS integrating economic rotational speed (ERS) of turboshaft engine and safety constraints-based variable action space (SC-VAS) approach is proposed. First, an efficient algorithm based on the turbine performance map for calculating ERS is proposed, with low complexity which is 5.5% of the conventional algorithm. Second, based on the ERS feature, the SC-VAS approach is presented to further optimize the action space to prevent the discharging/charging power and state of charge of the battery from violating the safety constraints. Comparison results show that with no violation of constraints of battery, the convergence speed of RL agent incorporating the SC-VAS approach increases by 5 times, and the size of the optimized Q table decreases to 21.9% of that of the basic Q table. The proposed PMS with the ERS feature and SC-VAS approach can bring a 4.29% reduction in the fuel consumption under an air-land driving condition. Moreover, the results of the hardware-in-the-loop experiment demonstrate the real-time performance of the proposed strategy.

Suggested Citation

  • Wei, Zhengchao & Ma, Yue & Yang, Ningkang & Ruan, Shumin & Xiang, Changle, 2023. "Reinforcement learning based power management integrating economic rotational speed of turboshaft engine and safety constraints of battery for hybrid electric power system," Energy, Elsevier, vol. 263(PB).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pb:s036054422202638x
    DOI: 10.1016/j.energy.2022.125752
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    1. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    2. Li, Xunming & Han, Lijin & Liu, Hui & Wang, Weida & Xiang, Changle, 2019. "Real-time optimal energy management strategy for a dual-mode power-split hybrid electric vehicle based on an explicit model predictive control algorithm," Energy, Elsevier, vol. 172(C), pages 1161-1178.
    3. Omar Mohamed & Ashraf Khalil, 2020. "Progress in Modeling and Control of Gas Turbine Power Generation Systems: A Survey," Energies, MDPI, vol. 13(9), pages 1-26, May.
    4. Song, Ke & Wang, Xiaodi & Li, Feiqiang & Sorrentino, Marco & Zheng, Bailin, 2020. "Pontryagin’s minimum principle-based real-time energy management strategy for fuel cell hybrid electric vehicle considering both fuel economy and power source durability," Energy, Elsevier, vol. 205(C).
    5. Xiang, Changle & Ding, Feng & Wang, Weida & He, Wei, 2017. "Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control," Applied Energy, Elsevier, vol. 189(C), pages 640-653.
    6. Ceusters, Glenn & Rodríguez, Román Cantú & García, Alberte Bouso & Franke, Rüdiger & Deconinck, Geert & Helsen, Lieve & Nowé, Ann & Messagie, Maarten & Camargo, Luis Ramirez, 2021. "Model-predictive control and reinforcement learning in multi-energy system case studies," Applied Energy, Elsevier, vol. 303(C).
    7. Bao, Shuyue & Sun, Ping & Zhu, Jianxin & Ji, Qian & Liu, Junheng, 2022. "Improved multi-dimensional dynamic programming energy management strategy for a vehicle power-split hybrid powertrain," Energy, Elsevier, vol. 256(C).
    8. Yang, Ningkang & Han, Lijin & Xiang, Changle & Liu, Hui & Li, Xunmin, 2021. "An indirect reinforcement learning based real-time energy management strategy via high-order Markov Chain model for a hybrid electric vehicle," Energy, Elsevier, vol. 236(C).
    9. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    10. Maino, Claudio & Misul, Daniela & Musa, Alessia & Spessa, Ezio, 2021. "Optimal mesh discretization of the dynamic programming for hybrid electric vehicles," Applied Energy, Elsevier, vol. 292(C).
    11. Qi, Chunyang & Zhu, Yiwen & Song, Chuanxue & Yan, Guangfu & Xiao, Feng & Da wang, & Zhang, Xu & Cao, Jingwei & Song, Shixin, 2022. "Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle," Energy, Elsevier, vol. 238(PA).
    12. Collins, Jeffrey M. & McLarty, Dustin, 2020. "All-electric commercial aviation with solid oxide fuel cell-gas turbine-battery hybrids," Applied Energy, Elsevier, vol. 265(C).
    13. Tian, Xiang & Cai, Yingfeng & Sun, Xiaodong & Zhu, Zhen & Xu, Yiqiang, 2019. "An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses," Energy, Elsevier, vol. 189(C).
    14. Zhengchao Wei & Yue Ma & Changle Xiang & Dabo Liu & Weixiang Zhou, 2021. "Power Prediction-Based Model Predictive Control for Energy Management in Land and Air Vehicle with Turboshaft Engine," Complexity, Hindawi, vol. 2021, pages 1-24, August.
    15. Ezzat, M.F. & Dincer, I., 2019. "Development and exergetic assessment of a new hybrid vehicle incorporating gas turbine as powering option," Energy, Elsevier, vol. 170(C), pages 112-119.
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