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Machine Learning of PEM Fuel Cell Degradation : = Artificial Neural Network and Long Short-Term Memory Recurrent Neural Network.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Machine Learning of PEM Fuel Cell Degradation :/
Reminder of title:	Artificial Neural Network and Long Short-Term Memory Recurrent Neural Network.
Author:	Nourizadeh, Amirhossein.
Description:	1 online resource (120 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-10.
Contained By:	Masters Abstracts International84-10.
Subject:	Mechanical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798379420154

Machine Learning of PEM Fuel Cell Degradation : = Artificial Neural Network and Long Short-Term Memory Recurrent Neural Network.
Nourizadeh, Amirhossein.

Machine Learning of PEM Fuel Cell Degradation :Artificial Neural Network and Long Short-Term Memory Recurrent Neural Network. - 1 online resource (120 pages)

Source: Masters Abstracts International, Volume: 84-10.

Thesis (M.S.)--University of California, Irvine, 2023.

Includes bibliographical references

Proton exchange membrane fuel cells' (PEMFCs') degradation is a main problem that must be solved for their commercialization. In this thesis, we employ data-driven prognostic models to forecast PEMFC voltage degradation utilizing artificial neural networks (ANN), deep neural networks (DNN), and long short-term memory (LSTM) techniques. The suggested models are developed and tested using experimental information obtained through PEMFC stack testing, and their performance is assessed using a variety of metrics, such as root mean square error (RMSE). The findings show that the DNN technique performs better than the other methods, indicating that the model can reliably and precisely forecast the PEMFC stack voltage decline. In addition, we examined the models' performance in terms of the neural network designs' number of neurons and layers. The results demonstrate a trade-off between prediction accuracy and computational complexity, with an increase in neurons and layers able to enhance prediction accuracy. In conclusion, the suggested data-driven prognostic models may offer precise and trustworthy forecasts of PEMFC stack voltage degradation, which can aid in optimizing system design and operation. In order to improve the precision and effectiveness of the prediction process, future research can concentrate on investigating the applicability of these models to specific degradation modes, such as catalyst and membrane degradation, and developing more sophisticated data-driven prognostic models. Moreover, combining data-driven and physics-based models can offer a thorough strategy for PEMFC degradation prognostics.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379420154Subjects--Topical Terms:

557493
Mechanical engineering.
Subjects--Index Terms:

Artificial neural networkIndex Terms--Genre/Form:

554714
Electronic books.

Machine Learning of PEM Fuel Cell Degradation : = Artificial Neural Network and Long Short-Term Memory Recurrent Neural Network.
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520 $a Proton exchange membrane fuel cells' (PEMFCs') degradation is a main problem that must be solved for their commercialization. In this thesis, we employ data-driven prognostic models to forecast PEMFC voltage degradation utilizing artificial neural networks (ANN), deep neural networks (DNN), and long short-term memory (LSTM) techniques. The suggested models are developed and tested using experimental information obtained through PEMFC stack testing, and their performance is assessed using a variety of metrics, such as root mean square error (RMSE). The findings show that the DNN technique performs better than the other methods, indicating that the model can reliably and precisely forecast the PEMFC stack voltage decline. In addition, we examined the models' performance in terms of the neural network designs' number of neurons and layers. The results demonstrate a trade-off between prediction accuracy and computational complexity, with an increase in neurons and layers able to enhance prediction accuracy. In conclusion, the suggested data-driven prognostic models may offer precise and trustworthy forecasts of PEMFC stack voltage degradation, which can aid in optimizing system design and operation. In order to improve the precision and effectiveness of the prediction process, future research can concentrate on investigating the applicability of these models to specific degradation modes, such as catalyst and membrane degradation, and developing more sophisticated data-driven prognostic models. Moreover, combining data-driven and physics-based models can offer a thorough strategy for PEMFC degradation prognostics.
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