https://doi.org/10.1140/epjs/s11734-024-01408-8
Regular Article
Prediction of PEM fuel cell performance degradation using bidirectional long short-term memory with chimp optimization algorithm
1
Electrical and Electronics Engineering Department, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, Turkey
2
Computer Engineering Department, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, Turkey
Received:
1
October
2024
Accepted:
14
November
2024
Published online:
2
December
2024
The proton exchange membrane fuel cells (PEMFC) are among the most promising technologies for efficiently converting hydrogen into electricity with minimal emissions. Significant advancements have been made in enhancing the performance, durability, and cost-effectiveness of PEMFC. However, these cells still face challenges related to performance degradation over time. Therefore, this study focuses on voltage prediction, which is one of the most important key factors for assessing fuel cell performance and extending its lifetime. This study combines the chimpanzee optimization algorithm (ChOA) with long short-term memory (LSTM), stacked LSTM, and bidirectional LSTM (BiLSTM) networks to predict performance degradation in PEM fuel cells. Initially, features from the PEMFC time-series data are reduced using the ChOA to select the most informative ones. These selected features are subsequently input into the corresponding LSTM networks to enhance the accuracy of PEMFC performance degradation predictions. The experimental results in terms of root mean squared error (RMSE) indicate that the ChOA variants—specifically, ChOALSTM, ChOAStackedLSTM, and ChOABiLSTM—achieved prediction accuracies of 0.012, 0.014, and 0.007 on the IEEE PHM 2014 DATA Challenge dataset, respectively. The comparative and statistical results obtained from the proposed ChOABiLSTM model demonstrate its superior accuracy and robustness compared to its variants and other state-of-the-art algorithms.
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjs/s11734-024-01408-8.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
