Degradation Prediction of Proton Exchange Membrane Fuel Cell Under Dynamic Loading Conditions Using Algorithm‐Optimized Long Short‐Term Memory Models

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Main Authors:	Nguyen Thanh Ta, Doan Quoc Huy, Tran Ngoc Tien Phat, Nguyen Nhat Kim Ngan, Nguyen Manh Tuan, Nguyen Huynh My Tue, Vo Thi Kim Yen, Doan Thi Kim Ngan, Tran Duy Tap
Format:	Artículo Open Access
Published:	Wiley 2025
Subjects:	Fuel Cells
Online Access:	Acceder al recurso
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author	Nguyen Thanh Ta Doan Quoc Huy Tran Ngoc Tien Phat Nguyen Nhat Kim Ngan Nguyen Manh Tuan Nguyen Huynh My Tue Vo Thi Kim Yen Doan Thi Kim Ngan Tran Duy Tap
author_facet	Nguyen Thanh Ta Doan Quoc Huy Tran Ngoc Tien Phat Nguyen Nhat Kim Ngan Nguyen Manh Tuan Nguyen Huynh My Tue Vo Thi Kim Yen Doan Thi Kim Ngan Tran Duy Tap Nguyen Thanh Ta Doan Quoc Huy Tran Ngoc Tien Phat Nguyen Nhat Kim Ngan Nguyen Manh Tuan Nguyen Huynh My Tue Vo Thi Kim Yen Doan Thi Kim Ngan Tran Duy Tap
collection	Wiley Open Access
contents	Degradation Prediction of Proton Exchange Membrane Fuel Cell Under Dynamic Loading Conditions Using Algorithm‐Optimized Long Short‐Term Memory Models Nguyen Thanh Ta Doan Quoc Huy Tran Ngoc Tien Phat Nguyen Nhat Kim Ngan Nguyen Manh Tuan Nguyen Huynh My Tue Vo Thi Kim Yen Doan Thi Kim Ngan Tran Duy Tap Fuel Cells ABSTRACT Long short‐term memory (LSTM) and bidirectional long short‐term memory (Bi‐LSTM) neural networks are utilized to predict the performance degradation of proton exchange membrane fuel cells using a dataset of 550 h obtained from the Federation for Fuel Cell Research (FCLAB). The model performance is optimized using Bayesian optimization long short‐term memory (BO‐LSTM), ant colony optimization long short‐term memory (ACO‐LSTM), Bayesian optimization bidirectional long short‐term memory (BO‐Bi‐LSTM), and ant colony optimization bidirectional long short‐term memory (ACO‐Bi‐LSTM). For each above model, how different parameters such as the number of hidden nodes, dropout rate, learning rate, and number of hidden layers affect the prediction results are carefully examined. The results show that the mean absolute percent error (MAPE) of BO‐LSTM, ACO‐LSTM, BO‐Bi‐LSTM, and ACO‐Bi‐LSTM models with a training rate of 80% are approximately 0.000230, 0.000394, 0.000218, and 0.000181, respectively. Note that the obtained MAPE values are improved significantly as compared with those (MAPE = 0.0518–0.0097) of previous reports, leading to the potential application of proposed models for similar fuel cell systems. 10.1002/fuce.70030 http://onlinelibrary.wiley.com/termsAndConditions#vor
doi_str_mv	10.1002/fuce.70030
format	Artículo Open Access
id	wiley_oa_10_1002_fuce_70030
institution	Wiley Open Access
license_str_mv	http://onlinelibrary.wiley.com/termsAndConditions#vor
publishDate	2025
publisher	Wiley
record_format	wiley_oa
spellingShingle	Degradation Prediction of Proton Exchange Membrane Fuel Cell Under Dynamic Loading Conditions Using Algorithm‐Optimized Long Short‐Term Memory Models Nguyen Thanh Ta Doan Quoc Huy Tran Ngoc Tien Phat Nguyen Nhat Kim Ngan Nguyen Manh Tuan Nguyen Huynh My Tue Vo Thi Kim Yen Doan Thi Kim Ngan Tran Duy Tap Fuel Cells Degradation Prediction of Proton Exchange Membrane Fuel Cell Under Dynamic Loading Conditions Using Algorithm‐Optimized Long Short‐Term Memory Models Nguyen Thanh Ta Doan Quoc Huy Tran Ngoc Tien Phat Nguyen Nhat Kim Ngan Nguyen Manh Tuan Nguyen Huynh My Tue Vo Thi Kim Yen Doan Thi Kim Ngan Tran Duy Tap Fuel Cells ABSTRACT Long short‐term memory (LSTM) and bidirectional long short‐term memory (Bi‐LSTM) neural networks are utilized to predict the performance degradation of proton exchange membrane fuel cells using a dataset of 550 h obtained from the Federation for Fuel Cell Research (FCLAB). The model performance is optimized using Bayesian optimization long short‐term memory (BO‐LSTM), ant colony optimization long short‐term memory (ACO‐LSTM), Bayesian optimization bidirectional long short‐term memory (BO‐Bi‐LSTM), and ant colony optimization bidirectional long short‐term memory (ACO‐Bi‐LSTM). For each above model, how different parameters such as the number of hidden nodes, dropout rate, learning rate, and number of hidden layers affect the prediction results are carefully examined. The results show that the mean absolute percent error (MAPE) of BO‐LSTM, ACO‐LSTM, BO‐Bi‐LSTM, and ACO‐Bi‐LSTM models with a training rate of 80% are approximately 0.000230, 0.000394, 0.000218, and 0.000181, respectively. Note that the obtained MAPE values are improved significantly as compared with those (MAPE = 0.0518–0.0097) of previous reports, leading to the potential application of proposed models for similar fuel cell systems. 10.1002/fuce.70030 http://onlinelibrary.wiley.com/termsAndConditions#vor
title	Degradation Prediction of Proton Exchange Membrane Fuel Cell Under Dynamic Loading Conditions Using Algorithm‐Optimized Long Short‐Term Memory Models
topic	Fuel Cells
url	https://onlinelibrary.wiley.com/doi/10.1002/fuce.70030