A study into Proton Exchange Membrane Fuel Cell power and voltage prediction using Artificial Neural Network

Wilberforce, Tabbi; Biswas, M. A. Rafe

doi:10.1016/j.egyr.2022.09.104

Cited by 13 publications

(3 citation statements)

References 26 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]. Seyhan et al [40] implement an ANN to optimize a wavy serpentine flow channel.…”

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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“…Long et al developed an ANN model to predict the remaining useful life of the PEMFC in comparison with other data-driven models [37]. Wilberforce et al proposed an ANN model to predict the dynamic electrical and thermal performance of the PEMFC stack under various operating conditions [38,39]. Musharavati et al investigated a bio-inspired ANN model to find the optimal design and control variables for fuel cell dynamic operations [40].…”

Section: Introductionmentioning

confidence: 99%

A Dimension-Reduced Artificial Neural Network Model for the Cell Voltage Consistency Prediction of a Proton Exchange Membrane Fuel Cell Stack

et al. 2022

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The voltage consistency of hundreds of cells in a proton exchange membrane fuel cell stack significantly influences the stack’s performance and lifetime. Using the physics-based model to estimate the cell voltage consistency is highly challenging due to the massive calculation efforts and the complicated fuel cell designs. In this research, an artificial neural network (ANN) model is developed to efficiently predict the cell voltage distribution and the consistency of a commercial-size fuel cell stack. To balance the computation efficiency and accuracy, a dimension-reduced method is proposed with different output-grouping strategies to optimize the ANN structure based on the experiment test of a 100-cell stack. The model’s training time falls nonlinearly from 16 min to 6 s with the output neuron number decreasing from 100 to 5, while the model can still predict the cell voltage distribution trends. With the proposed model, the stack’s cell voltage distributions could be reproduced with significantly lowered computation time, which is beneficial to evaluate the fuel cell status and optimize the control strategies.

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“…Then the dimensions of the PEMFC stack must be less than these dimensions; the following is an explanation of the PEMFC dimensions.a. Bipolar PlateThe bipolar plate is an important component of PEMFC, which is used to direct hydrogen gas flow to the anode and oxygen gas flow to the cathode.The fluid flow path has a width and height of 1 mmx 1 mm, respectively(Wilberforce, 2022). In this study, 6 serpentines type flows were made which were used for homogeneous gas distribution on the cathode and anode sides, shown in the following figure.…”

mentioning

confidence: 99%

Designing Size and Stack Number of Fuel Cell Urban Vehicle

Nazwa,

Satria,

Jannah

et al. 2024

fwl untirta

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A study into Proton Exchange Membrane Fuel Cell power and voltage prediction using Artificial Neural Network

Cited by 13 publications

References 26 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

A Dimension-Reduced Artificial Neural Network Model for the Cell Voltage Consistency Prediction of a Proton Exchange Membrane Fuel Cell Stack

Designing Size and Stack Number of Fuel Cell Urban Vehicle

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