IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i17p6294-d900507.html
   My bibliography  Save this article

Data-Driven Voltage Prognostic for Solid Oxide Fuel Cell System Based on Deep Learning

Author

Listed:
  • Mingfei Li

    (Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China
    These authors contributed equally to this work.)

  • Jiajian Wu

    (Key Laboratory of Imaging Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
    These authors contributed equally to this work.)

  • Zhengpeng Chen

    (Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China)

  • Jiangbo Dong

    (Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China)

  • Zhiping Peng

    (Guangdong Huizhou Lng Power Co., Ltd., Huizhou 516081, China)

  • Kai Xiong

    (Guangdong Energy Group Co., Ltd., Guangzhou 510630, China)

  • Mumin Rao

    (Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China)

  • Chuangting Chen

    (Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 511466, China)

  • Xi Li

    (Key Laboratory of Imaging Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
    Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518055, China)

Abstract

A solid oxide fuel cell (SOFC) is an innovative power generation system that is green, efficient, and promising for a wide range of applications. The prediction and evaluation of the operation state of a solid oxide fuel cell system is of great significance for the stable and long-term operation of the power generation system. Prognostics and Health Management (PHM) technology is widely used to perform preventive and predictive maintenance on equipment. Unlike prediction based on the SOFC mechanistic model, the combination of PHM and deep learning has shown wide application prospects. Therefore, this study first obtains an experimental dataset through short-term degradation experiments of a 1 kW SOFC system, and then proposes an encoder-decoder RNN-based SOFC state prediction model. Based on the experimental dataset, the model can accurately predict the voltage variation of the SOFC system. The prediction results of the four different prediction models developed are compared and analyzed, namely, long short-term memory (LSTM), gated recurrent unit (GRU), encoder–decoder LSTM, and encoder–decoder GRU. The results show that for the SOFC test set, the mean square error of encoder–decoder LSTM and encoder–decoder GRU are 0.015121 and 0.014966, respectively, whereas the corresponding error results of LSTM and GRU are 0.017050 and 0.017456, respectively. The encoder–decoder RNN model displays high prediction precision, which proves that it can improve the accuracy of prediction, which is expected to be combined with control strategies and further help the implementation of PHM in fuel cells.

Suggested Citation

  • Mingfei Li & Jiajian Wu & Zhengpeng Chen & Jiangbo Dong & Zhiping Peng & Kai Xiong & Mumin Rao & Chuangting Chen & Xi Li, 2022. "Data-Driven Voltage Prognostic for Solid Oxide Fuel Cell System Based on Deep Learning," Energies, MDPI, vol. 15(17), pages 1-20, August.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6294-:d:900507
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/17/6294/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/17/6294/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hou, Qinlong & Zhao, Hongbin & Yang, Xiaoyu, 2019. "Economic performance study of the integrated MR-SOFC-CCHP system," Energy, Elsevier, vol. 166(C), pages 236-245.
    2. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    3. Damo, U.M. & Ferrari, M.L. & Turan, A. & Massardo, A.F., 2019. "Solid oxide fuel cell hybrid system: A detailed review of an environmentally clean and efficient source of energy," Energy, Elsevier, vol. 168(C), pages 235-246.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jingxuan Peng & Dongqi Zhao & Yuanwu Xu & Xiaolong Wu & Xi Li, 2023. "Comprehensive Analysis of Solid Oxide Fuel Cell Performance Degradation Mechanism, Prediction, and Optimization Studies," Energies, MDPI, vol. 16(2), pages 1-23, January.

    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. Gabriele Loreti & Andrea Luigi Facci & Stefano Ubertini, 2021. "High-Efficiency Combined Heat and Power through a High-Temperature Polymer Electrolyte Membrane Fuel Cell and Gas Turbine Hybrid System," Sustainability, MDPI, vol. 13(22), pages 1-24, November.
    2. Yue Teng & Ho Yeon Lee & Haesu Lee & Yoon Ho Lee, 2022. "Effect of Sputtering Pressure on the Nanostructure and Residual Stress of Thin-Film YSZ Electrolyte," Sustainability, MDPI, vol. 14(15), pages 1-9, August.
    3. Kamei, Sayaka & Taghipour, Sharareh, 2023. "A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    4. Zuo, Jian & Cadet, Catherine & Li, Zhongliang & Bérenguer, Christophe & Outbib, Rachid, 2024. "A deterioration-aware energy management strategy for the lifetime improvement of a multi-stack fuel cell system subject to a random dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    5. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Lewis, Austin D. & Groth, Katrina M., 2022. "Metrics for evaluating the performance of complex engineering system health monitoring models," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    7. Tanveer, Waqas Hassan & Rezk, Hegazy & Nassef, Ahmed & Abdelkareem, Mohammad Ali & Kolosz, Ben & Karuppasamy, K. & Aslam, Jawad & Gilani, Syed Omer, 2020. "Improving fuel cell performance via optimal parameters identification through fuzzy logic based-modeling and optimization," Energy, Elsevier, vol. 204(C).
    8. Michail Cheliotis & Evangelos Boulougouris & Nikoletta L Trivyza & Gerasimos Theotokatos & George Livanos & George Mantalos & Athanasios Stubos & Emmanuel Stamatakis & Alexandros Venetsanos, 2021. "Review on the Safe Use of Ammonia Fuel Cells in the Maritime Industry," Energies, MDPI, vol. 14(11), pages 1-20, May.
    9. Roberta De Robbio, 2023. "Micro Gas Turbine Role in Distributed Generation with Renewable Energy Sources," Energies, MDPI, vol. 16(2), pages 1-37, January.
    10. Fiammetta Rita Bianchi & Arianna Baldinelli & Linda Barelli & Giovanni Cinti & Emilio Audasso & Barbara Bosio, 2020. "Multiscale Modeling for Reversible Solid Oxide Cell Operation," Energies, MDPI, vol. 13(19), pages 1-16, September.
    11. Park, Heejin & Jung, Yoonju & Park, Chungi & Lee, Jaeseung & Ghasemi, Masoomeh & Alam, Afroz & Kim, Hyeonjin & Kim, Jinwook & Park, Sojin & Choi, Kyungshik & You, Hyunseok & Ju, Hyunchul, 2023. "Performance evaluation and economic feasibility of a PAFC-based multi-energy hub system in South Korea," Energy, Elsevier, vol. 278(PB).
    12. Zaitseva, Elena & Levashenko, Vitaly & Rabcan, Jan, 2023. "A new method for analysis of Multi-State systems based on Multi-valued decision diagram under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    13. Ghotkar, Rhushikesh & Milcarek, Ryan J., 2020. "Investigation of flame-assisted fuel cells integrated with an auxiliary power unit gas turbine," Energy, Elsevier, vol. 204(C).
    14. Iliya Krastev Iliev & Antonina Andreevna Filimonova & Andrey Alexandrovich Chichirov & Natalia Dmitrievna Chichirova & Alexander Vadimovich Pechenkin & Artem Sergeevich Vinogradov, 2023. "Theoretical and Experimental Studies of Combined Heat and Power Systems with SOFCs," Energies, MDPI, vol. 16(4), pages 1-17, February.
    15. Lyu, Dongzhen & Niu, Guangxing & Liu, Enhui & Zhang, Bin & Chen, Gang & Yang, Tao & Zio, Enrico, 2022. "Time space modelling for fault diagnosis and prognosis with uncertainty management: A general theoretical formulation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    16. 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).
    17. Chmielniak, Tadeusz & Remiorz, Leszek, 2020. "Entropy analysis of hydrogen production in electrolytic processes," Energy, Elsevier, vol. 211(C).
    18. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    19. Han, Te & Li, Yan-Fu, 2022. "Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    20. Chen, Rentong & Zhang, Chao & Wang, Shaoping & Zio, Enrico & Dui, Hongyan & Zhang, Yadong, 2023. "Importance measures for critical components in complex system based on Copula Hierarchical Bayesian Network," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

    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:15:y:2022:i:17:p:6294-:d:900507. 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.