IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i7p1728-d1369935.html
   My bibliography  Save this article

Temperature Control of Fuel Cell Based on PEI-DDPG

Author

Listed:
  • Zichen Lu

    (School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Ying Yan

    (School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China)

Abstract

Proton exchange membrane fuel cells (PEMFCs) constitute nonlinear systems that are challenging to model accurately. Therefore, a controller with robustness and adaptability is imperative for temperature control within the PEMFC stack. This paper introduces a data-driven controller utilizing deep reinforcement learning for stack temperature control. Given the PEMFC system’s characteristics, such as nonlinearity, uncertainty, and environmental conditions, we propose a novel deep reinforcement learning algorithm—the deep deterministic policy gradient with priority experience playback and importance sampling method (PEI-DDPG). Algorithm design incorporates technologies such as priority experience playback, importance sampling, and optimized sample data storage structure, enhancing the controller’s performance. Simulation results demonstrate the proposed algorithm’s superior effectiveness in temperature control for PEMFC, leveraging the PEI-DDPG algorithm’s high adaptability and robustness. The proposed algorithm’s effectiveness is additionally validated on the RT-LAB experimental platform. The proposed PEI-DDPG algorithm reduces the average adjustment time by 8.3%, 17.13%, and 24.56% and overshoots by 2.12 times, 4.16 times, and 4.32 times compared to the TD3, GA-PID, and PID algorithms, respectively.

Suggested Citation

  • Zichen Lu & Ying Yan, 2024. "Temperature Control of Fuel Cell Based on PEI-DDPG," Energies, MDPI, vol. 17(7), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1728-:d:1369935
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/7/1728/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/7/1728/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sun, Li & Jin, Yuhui & You, Fengqi, 2020. "Active disturbance rejection temperature control of open-cathode proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 261(C).
    2. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Feedback linearization-based MIMO model predictive control with defined pseudo-reference for hydrogen regulation of automotive fuel cells," Applied Energy, Elsevier, vol. 293(C).
    3. Chen, Fengxiang & Pei, Yaowang & Jiao, Jieran & Chi, Xuncheng & Hou, Zhongjun, 2023. "Energy flow and thermal voltage analysis of water-cooled PEMFC stack under normal operating conditions," Energy, Elsevier, vol. 275(C).
    4. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    5. Siwen Gu & Jiaan Wang & Xinmin You & Yu Zhuang, 2023. "Investigating the Parameter-Driven Cathode Gas Diffusion of PEMFCs with a Piecewise Linearization Model," Energies, MDPI, vol. 16(9), pages 1-12, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Teresa Donateo, 2023. "Semi-Empirical Models for Stack and Balance of Plant in Closed-Cathode Fuel Cell Systems for Aviation," Energies, MDPI, vol. 16(22), pages 1-40, November.
    2. Ma, Zhenxi & Cai, Liang & Sun, Li & Zhang, Xiao & Zhang, Xiaosong, 2024. "Thermodynamics and flexibility assessment on integrated high-temperature PEMFC and double-effect absorption heating/cooling cogeneration cycle," Energy, Elsevier, vol. 290(C).
    3. Tulika Saha & Sriparna Saha & Pushpak Bhattacharyya, 2020. "Towards sentiment aided dialogue policy learning for multi-intent conversations using hierarchical reinforcement learning," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-28, July.
    4. Mahmoud Mahfouz & Angelos Filos & Cyrine Chtourou & Joshua Lockhart & Samuel Assefa & Manuela Veloso & Danilo Mandic & Tucker Balch, 2019. "On the Importance of Opponent Modeling in Auction Markets," Papers 1911.12816, arXiv.org.
    5. Imen Azzouz & Wiem Fekih Hassen, 2023. "Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach," Energies, MDPI, vol. 16(24), pages 1-18, December.
    6. Sun, Li & Sun, Wen & You, Fengqi, 2020. "Core temperature modelling and monitoring of lithium-ion battery in the presence of sensor bias," Applied Energy, Elsevier, vol. 271(C).
    7. Jacob W. Crandall & Mayada Oudah & Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael A. Goodrich & Iyad Rahwan, 2018. "Cooperating with machines," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
      • Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," TSE Working Papers 17-806, Toulouse School of Economics (TSE).
      • Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," IAST Working Papers 17-68, Institute for Advanced Study in Toulouse (IAST).
      • Jacob Crandall & Mayada Oudah & Fatimah Ishowo-Oloko Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael Goodrich & Iyad Rahwan, 2018. "Cooperating with machines," Post-Print hal-01897802, HAL.
    8. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    9. Yassine Chemingui & Adel Gastli & Omar Ellabban, 2020. "Reinforcement Learning-Based School Energy Management System," Energies, MDPI, vol. 13(23), pages 1-21, December.
    10. Woo Jae Byun & Bumkyu Choi & Seongmin Kim & Joohyun Jo, 2023. "Practical Application of Deep Reinforcement Learning to Optimal Trade Execution," FinTech, MDPI, vol. 2(3), pages 1-16, June.
    11. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    12. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    13. Huang, Ruchen & He, Hongwen & Gao, Miaojue, 2023. "Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework," Applied Energy, Elsevier, vol. 346(C).
    14. Michelle M. LaMar, 2018. "Markov Decision Process Measurement Model," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 67-88, March.
    15. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    16. Wang, Xuan & Shu, Gequn & Tian, Hua & Wang, Rui & Cai, Jinwen, 2020. "Operation performance comparison of CCHP systems with cascade waste heat recovery systems by simulation and operation optimisation," Energy, Elsevier, vol. 206(C).
    17. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    18. Parvez Farazi, Nahid & Zou, Bo & Tulabandhula, Theja, 2022. "Dynamic On-Demand Crowdshipping Using Constrained and Heuristics-Embedded Double Dueling Deep Q-Network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    19. Louback, Eduardo & Biswas, Atriya & Machado, Fabricio & Emadi, Ali, 2024. "A review of the design process of energy management systems for dual-motor battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    20. Brammer, Janis & Lutz, Bernhard & Neumann, Dirk, 2022. "Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 299(1), pages 75-86.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1728-:d:1369935. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.