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Health-aware energy management strategy for fuel cell hybrid bus considering air-conditioning control based on TD3 algorithm

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  • Jia, Chunchun
  • Li, Kunang
  • He, Hongwen
  • Zhou, Jiaming
  • Li, Jianwei
  • Wei, Zhongbao

Abstract

The air-conditioning system (ACS), as a high-power component on the fuel cell hybrid electric bus (FCHEB), has a significant impact on the whole vehicle economy while maintaining comfortable temperature. Achieving cabin comfort in a way that reduces the whole vehicle operating cost is a great challenge. This task requires excellent coordination between the ACS and the on-board energy sources. Given that, this paper proposes an energy management strategy (EMS) with on-board energy sources health awareness considering air-conditioning control. Specifically, firstly, cabin comfort is combined with fuel cell/battery durability control to minimize total vehicle operating cost while satisfying cabin comfort. Secondly, the state-of-the-art twin delayed deep deterministic policy gradient algorithm is adopted to improve the training efficiency and optimization capability of the EMS to achieve the best power allocation. Finally, comparative analysis is performed to verify the effectiveness of the proposed EMS, and the results show that the proposed strategy can enhance training efficiency by 56.7% and decrease total operating cost by 8.58% compared to the benchmark strategy based on deep deterministic policy gradient.

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  • Jia, Chunchun & Li, Kunang & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao, 2023. "Health-aware energy management strategy for fuel cell hybrid bus considering air-conditioning control based on TD3 algorithm," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s036054422301856x
    DOI: 10.1016/j.energy.2023.128462
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    References listed on IDEAS

    as
    1. Xie, Shaobo & Hu, Xiaosong & Xin, Zongke & Brighton, James, 2019. "Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 236(C), pages 893-905.
    2. Hussain, Syed Asad & Razi, Faran & Hewage, Kasun & Sadiq, Rehan, 2023. "The perspective of energy poverty and 1st energy crisis of green transition," Energy, Elsevier, vol. 275(C).
    3. Wang, Weida & Guo, Xinghua & Yang, Chao & Zhang, Yuanbo & Zhao, Yulong & Huang, Denggao & Xiang, Changle, 2022. "A multi-objective optimization energy management strategy for power split HEV based on velocity prediction," Energy, Elsevier, vol. 238(PA).
    4. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    5. Chen, Dongfang & Pei, Pucheng & Meng, Yining & Ren, Peng & Li, Yuehua & Wang, Mingkai & Wang, Xizhong, 2022. "Novel extraction method of working condition spectrum for the lifetime prediction and energy management strategy evaluation of automotive fuel cells," Energy, Elsevier, vol. 255(C).
    6. Shi, Wenzhuo & Huangfu, Yigeng & Xu, Liangcai & Pang, Shengzhao, 2022. "Online energy management strategy considering fuel cell fault for multi-stack fuel cell hybrid vehicle based on multi-agent reinforcement learning," Applied Energy, Elsevier, vol. 328(C).
    7. Huang, Yanjun & Khajepour, Amir & Bagheri, Farshid & Bahrami, Majid, 2016. "Optimal energy-efficient predictive controllers in automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 184(C), pages 605-618.
    8. Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang & Shi, Man, 2023. "A novel energy management strategy for hybrid electric bus with fuel cell health and battery thermal- and health-constrained awareness," Energy, Elsevier, vol. 271(C).
    9. 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).
    10. Tang, Xiaolin & Zhou, Haitao & Wang, Feng & Wang, Weida & Lin, Xianke, 2022. "Longevity-conscious energy management strategy of fuel cell hybrid electric Vehicle Based on deep reinforcement learning," Energy, Elsevier, vol. 238(PA).
    11. Tanzim Meraj, Sheikh & Zaihar Yahaya, Nor & Hasan, Kamrul & Hossain Lipu, M.S. & Madurai Elavarasan, Rajvikram & Hussain, Aini & Hannan, M.A. & Muttaqi, Kashem M., 2022. "A filter less improved control scheme for active/reactive energy management in fuel cell integrated grid system with harmonic reduction ability," Applied Energy, Elsevier, vol. 312(C).
    12. Hou, Shengyan & Yin, Hai & Xu, Fuguo & Benjamín, Pla & Gao, Jinwu & Chen, Hong, 2023. "Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles," Energy, Elsevier, vol. 266(C).
    13. Nie, Zhigen & Jia, Yuan & Wang, Wanqiong & Chen, Zheng & Outbib, Rachid, 2022. "Co-optimization of speed planning and energy management for intelligent fuel cell hybrid vehicle considering complex traffic conditions," Energy, Elsevier, vol. 247(C).
    14. Zeng, Tao & Zhang, Caizhi & Zhang, Yanyi & Deng, Chenghao & Hao, Dong & Zhu, Zhongwen & Ran, Hongxu & Cao, Dongpu, 2021. "Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle," Energy, Elsevier, vol. 227(C).
    15. Chen, Zhihang & Liu, Yonggang & Zhang, Yuanjian & Lei, Zhenzhen & Chen, Zheng & Li, Guang, 2022. "A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles," Energy, Elsevier, vol. 243(C).
    16. Huang, Ruchen & He, Hongwen & Zhao, Xuyang & Wang, Yunlong & Li, Menglin, 2022. "Battery health-aware and naturalistic data-driven energy management for hybrid electric bus based on TD3 deep reinforcement learning algorithm," Applied Energy, Elsevier, vol. 321(C).
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    Cited by:

    1. Cui, Can & Xue, Jing, 2024. "Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning," Energy, Elsevier, vol. 292(C).
    2. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2023. "A novel health-aware deep reinforcement learning energy management for fuel cell bus incorporating offline high-quality experience," Energy, Elsevier, vol. 282(C).

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