IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v253y2019ic70.html
   My bibliography  Save this article

Pore-scale and multiscale study of effects of Pt degradation on reactive transport processes in proton exchange membrane fuel cells

Author

Listed:
  • Zhang, Ruiyuan
  • Min, Ting
  • Chen, Li
  • Kang, Qinjun
  • He, Ya-Ling
  • Tao, Wen-Quan

Abstract

Understanding catalyst degradation mechanisms and their effects on reactive transport in proton exchange membrane fuel cell (PEMFC) is critical for prolonging cell lifetime. In this study, for the first time pore-scale numerical studies are conducted to explore effects of catalyst degradation on transport and electrochemical reactions in catalyst layers (CLs) of PEMFCs. High-resolution nanoscale structures of pristine and degraded CLs are reconstructed, in which detailed distributions of carbon, Pt, electrolyte and pores are resolved. Different particle size distributions of Pt agglomerates are also considered during the reconstruction. Based on the lattice Boltzmann method, pore-scale models for oxygen diffusion, interfacial dissolution, and electrochemical reaction are developed. Pore-scale modeling is then conducted to evaluate effects of Pt degradation on Pt utilization, active surface area, limiting current density and local transport resistance. It is found that total reaction rate is reduced by approximately 10–30% due to Pt degradation. Such negative effects are more prominent when Pt loading is low or more Pt is distributed in the CL interior, causing 25% and 45% increased transport resistance, respectively. Further, a multi-scale simulation strategy is proposed, and upscaling schemes for integrating pore-scale results into cell-scale models are proposed. The present study demonstrates that pore-scale simulation is a useful tool for understanding coupled mechanisms between Pt degradation and reactive transport phenomena within CLs, and is helpful for providing practical guidance for CL fabrication.

Suggested Citation

  • Zhang, Ruiyuan & Min, Ting & Chen, Li & Kang, Qinjun & He, Ya-Ling & Tao, Wen-Quan, 2019. "Pore-scale and multiscale study of effects of Pt degradation on reactive transport processes in proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  • Handle: RePEc:eee:appene:v:253:y:2019:i:c:70
    DOI: 10.1016/j.apenergy.2019.113590
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919312644
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2019.113590?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wu, Horng-Wen, 2016. "A review of recent development: Transport and performance modeling of PEM fuel cells," Applied Energy, Elsevier, vol. 165(C), pages 81-106.
    2. Xing, Lei & Liu, Xiaoteng & Alaje, Taiwo & Kumar, Ravi & Mamlouk, Mohamed & Scott, Keith, 2014. "A two-phase flow and non-isothermal agglomerate model for a proton exchange membrane (PEM) fuel cell," Energy, Elsevier, vol. 73(C), pages 618-634.
    3. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
    4. Zhang, Tong & Wang, Peiqi & Chen, Huicui & Pei, Pucheng, 2018. "A review of automotive proton exchange membrane fuel cell degradation under start-stop operating condition," Applied Energy, Elsevier, vol. 223(C), pages 249-262.
    5. Jouin, Marine & Bressel, Mathieu & Morando, Simon & Gouriveau, Rafael & Hissel, Daniel & Péra, Marie-Cécile & Zerhouni, Noureddine & Jemei, Samir & Hilairet, Mickael & Ould Bouamama, Belkacem, 2016. "Estimating the end-of-life of PEM fuel cells: Guidelines and metrics," Applied Energy, Elsevier, vol. 177(C), pages 87-97.
    6. Chen, Huicui & Pei, Pucheng & Song, Mancun, 2015. "Lifetime prediction and the economic lifetime of Proton Exchange Membrane fuel cells," Applied Energy, Elsevier, vol. 142(C), pages 154-163.
    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. Dou, Shaojun & Hao, Liang & Liu, Hong, 2023. "Effects of carbon aggregates and ionomer distribution on the performance of PEM fuel cell catalyst layer: A pore-scale study," Renewable Energy, Elsevier, vol. 217(C).
    2. Li, Xiao-Yang & Chen, Da-Yu & Wu, Ji-Peng & Kang, Rui, 2022. "3-Dimensional general ADT modeling and analysis: Considering epistemic uncertainties in unit, time and stress dimension," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    3. Li, Bing & Wan, Kechuang & Xie, Meng & Chu, Tiankuo & Wang, Xiaolei & Li, Xiang & Yang, Daijun & Ming, Pingwen & Zhang, Cunman, 2022. "Durability degradation mechanism and consistency analysis for proton exchange membrane fuel cell stack," Applied Energy, Elsevier, vol. 314(C).
    4. Yu, Rui Jiao & Guo, Hang & Ye, Fang & Chen, Hao, 2022. "Multi-parameter optimization of stepwise distribution of parameters of gas diffusion layer and catalyst layer for PEMFC peak power density," Applied Energy, Elsevier, vol. 324(C).
    5. Dou, Shaojun & Hao, Liang & Wang, Qianqian & Liu, Hong, 2024. "Effects of agglomerate structure and operating humidity on the catalyst layer performance of PEM fuel cells," Applied Energy, Elsevier, vol. 355(C).
    6. Zhang, Ruiyuan & Min, Ting & Chen, Li & Li, Hailong & Yan, Jinyue & Tao, Wen-Quan, 2022. "Pore-scale study of effects of relative humidity on reactive transport processes in catalyst layers in PEMFC," Applied Energy, Elsevier, vol. 323(C).
    7. Wan, Yue & Qiu, Diankai & Yi, Peiyun & Peng, Linfa & Lai, Xinmin, 2022. "Design and optimization of gradient wettability pore structure of adaptive PEM fuel cell cathode catalyst layer," Applied Energy, Elsevier, vol. 312(C).
    8. Namazi, Mohammadmehdi & Nayebi, Mohammadreza & Isazadeh, Amin & Modarresi, Ali & Marzbali, Iman Ghasemi & Hosseinalipour, Seyed Mostafa, 2022. "Experimental and numerical study of catalytic combustion and pore-scale numerical study of mass diffusion in high porosity fibrous porous media," Energy, Elsevier, vol. 238(PB).
    9. Fu, Ya-Lu & Zhang, Biao & Zhu, Xun & Ye, Ding-Ding & Sui, Pang-Chieh & Djilali, Ned, 2020. "Pore-scale modeling of oxygen transport in the catalyst layer of air-breathing cathode in membraneless microfluidic fuel cells," Applied Energy, Elsevier, vol. 277(C).
    10. Guo, Lingyi & Chen, Li & Zhang, Ruiyuan & Peng, Ming & Tao, Wen-Quan, 2022. "Pore-scale simulation of two-phase flow and oxygen reactive transport in gas diffusion layer of proton exchange membrane fuel cells: Effects of nonuniform wettability and porosity," Energy, Elsevier, vol. 253(C).

    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. Huu-Linh Nguyen & Sang-Min Lee & Sangseok Yu, 2023. "A Comprehensive Review of Degradation Prediction Methods for an Automotive Proton Exchange Membrane Fuel Cell," Energies, MDPI, vol. 16(12), pages 1-32, June.
    2. Mumin Rao & Li Wang & Chuangting Chen & Kai Xiong & Mingfei Li & Zhengpeng Chen & Jiangbo Dong & Junli Xu & Xi Li, 2022. "Data-Driven State Prediction and Analysis of SOFC System Based on Deep Learning Method," Energies, MDPI, vol. 15(9), pages 1-15, April.
    3. He, Wenbin & Liu, Ting & Ming, Wuyi & Li, Zongze & Du, Jinguang & Li, Xiaoke & Guo, Xudong & Sun, Peiyan, 2024. "Progress in prediction of remaining useful life of hydrogen fuel cells based on deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    4. Ke Song & Yimin Wang & Xiao Hu & Jing Cao, 2020. "Online Prediction of Vehicular Fuel Cell Residual Lifetime Based on Adaptive Extended Kalman Filter," Energies, MDPI, vol. 13(23), pages 1-21, November.
    5. Zuo, Jian & Lv, Hong & Zhou, Daming & Xue, Qiong & Jin, Liming & Zhou, Wei & Yang, Daijun & Zhang, Cunman, 2021. "Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application," Applied Energy, Elsevier, vol. 281(C).
    6. Chen, Hong & Zhan, Zhigang & Jiang, Panxing & Sun, Yahao & Liao, Liwen & Wan, Xiongbiao & Du, Qing & Chen, Xiaosong & Song, Hao & Zhu, Ruijie & Shu, Zhanhong & Li, Shang & Pan, Mu, 2022. "Whole life cycle performance degradation test and RUL prediction research of fuel cell MEA," Applied Energy, Elsevier, vol. 310(C).
    7. Liu, Hao & Chen, Jian & Hissel, Daniel & Lu, Jianguo & Hou, Ming & Shao, Zhigang, 2020. "Prognostics methods and degradation indexes of proton exchange membrane fuel cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    8. Lorenzo, Charles & Bouquain, David & Hibon, Samuel & Hissel, Daniel, 2021. "Synthesis of degradation mechanisms and of their impacts on degradation rates on proton-exchange membrane fuel cells and lithium-ion nickel–manganese–cobalt batteries in hybrid transport applicati," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    9. Pei, Pucheng & Chen, Dongfang & Wu, Ziyao & Ren, Peng, 2019. "Nonlinear methods for evaluating and online predicting the lifetime of fuel cells," Applied Energy, Elsevier, vol. 254(C).
    10. Chen, Kui & Laghrouche, Salah & Djerdir, Abdesslem, 2019. "Degradation model of proton exchange membrane fuel cell based on a novel hybrid method," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    11. Zhang, Xiaojie & Zhang, Tong & Chen, Huicui & Cao, Yinliang, 2021. "A review of online electrochemical diagnostic methods of on-board proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 286(C).
    12. Ahmed Mohmed Dafalla & Lin Wei & Bereket Tsegai Habte & Jian Guo & Fangming Jiang, 2022. "Membrane Electrode Assembly Degradation Modeling of Proton Exchange Membrane Fuel Cells: A Review," Energies, MDPI, vol. 15(23), pages 1-26, December.
    13. Xu, Liangfei & Fang, Chuan & Li, Jianqiu & Ouyang, Minggao & Lehnert, Werner, 2018. "Nonlinear dynamic mechanism modeling of a polymer electrolyte membrane fuel cell with dead-ended anode considering mass transport and actuator properties," Applied Energy, Elsevier, vol. 230(C), pages 106-121.
    14. Hou, Junbo & Yang, Min & Ke, Changchun & Zhang, Junliang, 2020. "Control logics and strategies for air supply in PEM fuel cell engines," Applied Energy, Elsevier, vol. 269(C).
    15. Wang, Chu & Li, Zhongliang & Outbib, Rachid & Dou, Manfeng & Zhao, Dongdong, 2022. "Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 305(C).
    16. Petrone, Giovanni & Zamboni, Walter & Spagnuolo, Giovanni, 2019. "An interval arithmetic-based method for parametric identification of a fuel cell equivalent circuit model," Applied Energy, Elsevier, vol. 242(C), pages 1226-1236.
    17. Tzelepis, Stefanos & Kavadias, Kosmas A. & Marnellos, George E. & Xydis, George, 2021. "A review study on proton exchange membrane fuel cell electrochemical performance focusing on anode and cathode catalyst layer modelling at macroscopic level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    18. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
    19. Chen, Kui & Badji, Abderrezak & Laghrouche, Salah & Djerdir, Abdesslem, 2022. "Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm," Applied Energy, Elsevier, vol. 318(C).
    20. Xu, Liangfei & Fang, Chuan & Hu, Junming & Cheng, Siliang & Li, Jianqiu & Ouyang, Minggao & Lehnert, Werner, 2017. "Parameter extraction of polymer electrolyte membrane fuel cell based on quasi-dynamic model and periphery signals," Energy, Elsevier, vol. 122(C), pages 675-690.

    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:eee:appene:v:253:y:2019:i:c:70. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.