Optimization of Gradient Catalyst Layers in PEMFCs Based on Neural Network Models
The high cost of platinum (Pt) catalysts impedes the widespread commercialization of proton exchange membrane fuel cells (PEMFCs). Reducing Pt loading will increase local oxygen transport resistance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="in...
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| author | Guo-Rui Zhao Wen-Zhen Fang Zi-Hao Xuan Wen-Quan Tao |
| author_facet | Guo-Rui Zhao Wen-Zhen Fang Zi-Hao Xuan Wen-Quan Tao |
| author_sort | Guo-Rui Zhao |
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| description | The high cost of platinum (Pt) catalysts impedes the widespread commercialization of proton exchange membrane fuel cells (PEMFCs). Reducing Pt loading will increase local oxygen transport resistance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mrow><mi>Pt</mi></mrow><mrow><msub><mi mathvariant="normal">O</mi><mn>2</mn></msub></mrow></msubsup></mrow></semantics></math></inline-formula>) and decrease performance. Due to the oxygen transport resistance, the reactants in the cathode catalyst layer (CCL) are not evenly distributed. The gradient structure can cooperate with the unevenly distributed reactants in CL to enhance the Pt utilization. In this work, a one-dimensional gradient CCL model considering <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mrow><mi>Pt</mi></mrow><mrow><msub><mi mathvariant="normal">O</mi><mn>2</mn></msub></mrow></msubsup></mrow></semantics></math></inline-formula> is established, and the optimal gradient structure is optimized by combining the artificial neural network (ANN) model and the genetic algorithm (GA). The optimal structure parameters of non-gradient CCL are <i>l</i><sub>CL</sub> equal to 8.86 μm, <i>r</i><sub>C</sub> equal to 36.82 nm, and I/C equal to 0.48, with the objective of maximum current density (<i>I</i><sub>max</sub>); <i>l</i><sub>CL</sub> equal to 4.24 μm, <i>r</i><sub>C</sub> equal to 36.60 nm, and I/C equal to 0.76, with the objective of maximum power density (<i>P</i><sub>max</sub>). For the gradient CCL, the best gradient distribution enables Pt loading to increase from the membrane (MEM) side to the gas diffusion layer (GDL) side and the ionomer volume fraction to decrease from the MEM side to the GDL side. |
| format | Article |
| id | doaj-art-0a6dd73ec8c740639adf4012fc6d7ccd |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-05-01 |
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| series | Energies |
| spelling | doaj-art-0a6dd73ec8c740639adf4012fc6d7ccd2025-08-20T03:14:39ZengMDPI AGEnergies1996-10732025-05-011810257010.3390/en18102570Optimization of Gradient Catalyst Layers in PEMFCs Based on Neural Network ModelsGuo-Rui Zhao0Wen-Zhen Fang1Zi-Hao Xuan2Wen-Quan Tao3Key Laboratory of Thermo-Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaKey Laboratory of Thermo-Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaKey Laboratory of Thermo-Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaKey Laboratory of Thermo-Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaThe high cost of platinum (Pt) catalysts impedes the widespread commercialization of proton exchange membrane fuel cells (PEMFCs). Reducing Pt loading will increase local oxygen transport resistance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mrow><mi>Pt</mi></mrow><mrow><msub><mi mathvariant="normal">O</mi><mn>2</mn></msub></mrow></msubsup></mrow></semantics></math></inline-formula>) and decrease performance. Due to the oxygen transport resistance, the reactants in the cathode catalyst layer (CCL) are not evenly distributed. The gradient structure can cooperate with the unevenly distributed reactants in CL to enhance the Pt utilization. In this work, a one-dimensional gradient CCL model considering <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mrow><mi>Pt</mi></mrow><mrow><msub><mi mathvariant="normal">O</mi><mn>2</mn></msub></mrow></msubsup></mrow></semantics></math></inline-formula> is established, and the optimal gradient structure is optimized by combining the artificial neural network (ANN) model and the genetic algorithm (GA). The optimal structure parameters of non-gradient CCL are <i>l</i><sub>CL</sub> equal to 8.86 μm, <i>r</i><sub>C</sub> equal to 36.82 nm, and I/C equal to 0.48, with the objective of maximum current density (<i>I</i><sub>max</sub>); <i>l</i><sub>CL</sub> equal to 4.24 μm, <i>r</i><sub>C</sub> equal to 36.60 nm, and I/C equal to 0.76, with the objective of maximum power density (<i>P</i><sub>max</sub>). For the gradient CCL, the best gradient distribution enables Pt loading to increase from the membrane (MEM) side to the gas diffusion layer (GDL) side and the ionomer volume fraction to decrease from the MEM side to the GDL side.https://www.mdpi.com/1996-1073/18/10/2570proton exchange membrane fuel cellcathode catalyst layersgradient catalyst layerdata-driven optimization |
| spellingShingle | Guo-Rui Zhao Wen-Zhen Fang Zi-Hao Xuan Wen-Quan Tao Optimization of Gradient Catalyst Layers in PEMFCs Based on Neural Network Models Energies proton exchange membrane fuel cell cathode catalyst layers gradient catalyst layer data-driven optimization |
| title | Optimization of Gradient Catalyst Layers in PEMFCs Based on Neural Network Models |
| title_full | Optimization of Gradient Catalyst Layers in PEMFCs Based on Neural Network Models |
| title_fullStr | Optimization of Gradient Catalyst Layers in PEMFCs Based on Neural Network Models |
| title_full_unstemmed | Optimization of Gradient Catalyst Layers in PEMFCs Based on Neural Network Models |
| title_short | Optimization of Gradient Catalyst Layers in PEMFCs Based on Neural Network Models |
| title_sort | optimization of gradient catalyst layers in pemfcs based on neural network models |
| topic | proton exchange membrane fuel cell cathode catalyst layers gradient catalyst layer data-driven optimization |
| url | https://www.mdpi.com/1996-1073/18/10/2570 |
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