Block structure optimization in PEMFC flow channels using a data-driven surrogate model based on random forest

Zheng, Jiayang; Qin, Yanzhou; Guo, Qiaoyu; Dong, Zizhe; Wang, Yulin

doi:10.1080/15435075.2022.2118540

Cited by 9 publications

(2 citation statements)

References 32 publications

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“…In order to optimize the design parameters of the bi-polar plate [40,322,323], ML has proven to be an efficient tool. In addition to that, data-driven ML also utilizes PEMFC optimization of operating conditions and performance predictions [324][325][326][327][328].…”

Section: In the Field Of Bpmentioning

confidence: 99%

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Kaiser,

Ahn,

Kim

et al. 2024

Processes

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For mitigating global warming, polymer electrolyte membrane fuel cells have become promising, clean, and sustainable alternatives to existing energy sources. To increase the energy density and efficiency of polymer electrolyte membrane fuel cells (PEMFC), a comprehensive numerical modeling approach that can adequately predict the multiphysics and performance relative to the actual test such as an acceptable depiction of the electrochemistry, mass/species transfer, thermal management, and water generation/transportation is required. However, existing models suffer from reliability issues due to their dependency on several assumptions made for the sake of modeling simplification, as well as poor choices and approximations in material characterization and electrochemical parameters. In this regard, data-driven machine learning models could provide the missing and more appropriate parameters in conventional computational fluid dynamics models. The purpose of the present overview is to explore the state of the art in computational fluid dynamics of individual components of the modeling of PEMFC, their issues and limitations, and how they can be significantly improved by hybrid modeling techniques integrating with machine learning approaches. Furthermore, a detailed future direction of the proposed solution related to PEMFC and its impact on the transportation sector is discussed.

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Section: In the Field Of Bpmentioning

confidence: 99%

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Kaiser,

Ahn,

Kim

et al. 2024

Processes

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“…The model took approximately 0.52 s to predict the cell performance (output voltage), with an average absolute percentage error of 1.97% for RF, as shown in Figure 27. Guo et al [89] combined a random forest trained alternative model with a GA to achieve block structure optimization applied to a novel two-block channel. The genetic algorithm was used to optimize the block geometry (length, width, and height) in the flow channel.…”

Section: Random Forestmentioning

confidence: 99%

Application of Machine Learning in Fuel Cell Research

Zheng

et al. 2023

Energies

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A fuel cell is an energy conversion device that utilizes hydrogen energy through an electrochemical reaction. Despite their many advantages, such as high efficiency, zero emissions, and fast startup, fuel cells have not yet been fully commercialized due to deficiencies in service life, cost, and performance. Efficient evaluation methods for performance and service life are critical for the design and optimization of fuel cells. The purpose of this paper was to review the application of common machine learning algorithms in fuel cells. The significance and status of machine learning applications in fuel cells are briefly described. Common machine learning algorithms, such as artificial neural networks, support vector machines, and random forests are introduced, and their applications in fuel cell performance prediction and optimization are comprehensively elaborated. The review revealed that machine learning algorithms can be successfully used for performance prediction, service life prediction, and fault diagnosis in fuel cells, with good accuracy in solving nonlinear problems. Combined with optimization algorithms, machine learning models can further carry out the optimization of design and operating parameters to achieve multiple optimization goals with good accuracy and efficiency. It is expected that this review paper could help the reader comprehend the state of the art of machine learning applications in fuel fuels and shed light on further development directions in fuel cell research.

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Behavior analysis of PEMFC with geometric configuration variation during multiple-step loading reduction process

et al. 2023

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Block structure optimization in PEMFC flow channels using a data-driven surrogate model based on random forest

Cited by 9 publications

References 32 publications

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Application of Machine Learning in Fuel Cell Research

Behavior analysis of PEMFC with geometric configuration variation during multiple-step loading reduction process

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