Application of Machine Learning in Optimizing Proton Exchange Membrane Fuel Cells: A Review

Ding, Rui; Zhang, Shiqiao; Chen, Yawen; Zhang, Rui; Ke, Hua; Wu, Yongkang; Li, Xiaoke; Duan, Xiao; Wang, Xuebing; Li, Jia; Liu, Jianguo

doi:10.1016/j.egyai.2022.100170

Cited by 80 publications

(26 citation statements)

References 160 publications

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“…Besides physics-based models, data-driven models have been developed as an efficient tool to discover the complex relationships of the CL materials, structures, and performance, which provide an alternative to address the issue of multiparameter optimization in CL development. 98,99 This type of the modeling requires a database, which plays a significant role in its prediction accuracy and can be generated by both experimental measurements and high-throughput calculations from physics-based models. The database from physics-based models plays an increasingly significant role in training a datadriven model, mainly due to their capability of obtaining more detailed information in a more efficient way than experiments.…”

Section: Accelerating Catalyst Layer Development For Ultralow Pt Load...mentioning

confidence: 99%

Towards ultralow platinum loading proton exchange membrane fuel cells

Fan

Deng²,

Zhang

et al. 2023

Energy Environ. Sci.

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Section: Accelerating Catalyst Layer Development For Ultralow Pt Load...mentioning

confidence: 99%

Towards ultralow platinum loading proton exchange membrane fuel cells

Fan

Deng²,

Zhang

et al. 2023

Energy Environ. Sci.

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“…Additionally, for a more comprehensive understanding of the underlying mathematics and physics behind ML models and their development, readers are advised to refer to previous literature reports. − These resources provide in-depth explanations and analyses of the various mathematical and statistical techniques employed in ML, making them valuable resources for anyone seeking to deepen their knowledge of this field.…”

Section: Methodsmentioning

confidence: 99%

Determining Ion Activity Coefficients in Ion-Exchange Membranes with Machine Learning and Molecular Dynamics Simulations

Dona

Olayiwola

Briceño-Mena

et al. 2023

Ind. Eng. Chem. Res.

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The activity coefficients of ions in polymeric ion-exchange membranes (IEMs) dictate the equilibrium partitioning coefficient of the ions between the membrane and the liquid. It also affects ion transport processes, such as conductivity, in ionexchange membranes. Accurately predicting the ion activity coefficient without experimental data has been elusive as most models are empirical or semi-empirical. This work employs an embedding process that maps microscopic and macroscopic properties for modeling of ion activity coefficients in IEMs with molecular dynamics (MD) and machine learning (ML). This strategy is effective for accurately predicting activity coefficients in various IEM materials, including random copolymer and block copolymer systems. ML algorithms are increasingly being used for the analysis of complex systems when limited knowledge is available. The framework uses small experimental activity coefficient datasets in conjunction with polymer structure information and molecular attributes describing the solvation of ions and polymers to predict the ion activity coefficient in IEMs. Two different ML models were developed to estimate the molecular attributes and the ion activity coefficient. The best ML model accurately predicts the solvation descriptors and ion activity coefficient with an average mean absolute error of <7 and 10%, respectively. Adopting the said approach allows for the estimation of ion activity coefficients in IEMs without the need for new time-consuming MD simulation runs and experiments.

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“…Since then, researchers have extensively applied theoretical simulation as a powerful tool for explaining the origin of the ORR activity of electrocatalysts. It should be noted that, more importantly, in recent years, researchers have applied machine learning (ML) and artificial intelligence (AI) models to boost the high throughput screening for potential ideal material components and systems [ 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ]. As surrogate models, as-trained ML models could significantly reduce the massive demand for computational resources by DFT calculations.…”

Section: Electrocatalysts For Orr In Meamentioning

confidence: 99%

Advances in Low Pt Loading Membrane Electrode Assembly for Proton Exchange Membrane Fuel Cells

et al. 2023

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Hydrogen has the potential to be one of the solutions that can address environmental pollution and greenhouse emissions from traditional fossil fuels. However, high costs hinder its large-scale commercialization, particularly for enabling devices such as proton exchange membrane fuel cells (PEMFCs). The precious metal Pt is indispensable in boosting the oxygen reduction reaction (ORR) in cathode electrocatalysts from the most crucial component, i.e., the membrane electrode assembly (MEA). MEAs account for a considerable amount of the entire cost of PEMFCs. To address these bottlenecks, researchers either increase Pt utilization efficiency or produce MEAs with enhanced performance but less Pt. Only a few reviews that explain the approaches are available. This review summarizes advances in designing nanocatalysts and optimizing the catalyst layer structure to achieve low-Pt loading MEAs. Different strategies and their corresponding effectiveness, e.g., performance in half-cells or MEA, are summarized and compared. Finally, future directions are discussed and proposed, aiming at affordable, highly active, and durable PEMFCs.

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Application of Machine Learning in Optimizing Proton Exchange Membrane Fuel Cells: A Review

Cited by 80 publications

References 160 publications

Towards ultralow platinum loading proton exchange membrane fuel cells

Towards ultralow platinum loading proton exchange membrane fuel cells

Determining Ion Activity Coefficients in Ion-Exchange Membranes with Machine Learning and Molecular Dynamics Simulations

Advances in Low Pt Loading Membrane Electrode Assembly for Proton Exchange Membrane Fuel Cells

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