The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks
Abstract
Abstract To solve the prediction problem of proton exchange membrane fuel cell (PEMFC) remaining useful life (RUL), a novel RUL prediction approach of PEMFC based on long short-term memory (LSTM) recurrent neural networks (RNN) has been developed. The method uses regular interval sampling and locally weighted scatterplot smoothing (LOESS) to realize data reconstruction and data smoothing. Not only the primary trend of the original data can be preserved, but noise and spikes can be effectively removed. The LSTM RNN is adopted to estimate the remaining life of test data. 1154-hour experimental aging analysis of PEMFC shows that the prediction accuracy of the novel method is 99.23%, the root mean square error (RMSE) and mean absolute error (MAE) is 0.003 and 0.0026 respectively. The comparison analysis shows that the prediction accuracy of the novel method is 28.46% higher than that of back propagation neural network (BPNN). Root mean square error, relative error (RE) and mean absolute error are all much smaller than that of BPNN. Therefore, the novel method can quickly and accurately forecast the residual service life of the fuel cell.
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Cites
Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2805189 · Cited by 1,326
Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries
Chemali, E., Kollmeyer, P. J., Preindl, M., Ahmed, R., & Emadi, A. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2787586 · Cited by 906
Remaining useful life estimation of engineered systems using vanilla LSTM neural networks
Wu, Y., Yuan, M., Dong, S., Lin, L., & Liu, Y. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.05.063 · Cited by 789
Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks
Zhao, R., Yan, R., Wang, J., & Mao, K. (2017) · Sensors · doi:10.3390/s17020273 · Cited by 741
Lifetime prediction and the economic lifetime of Proton Exchange Membrane fuel cells
Chen, H., Pei, P., & Song, M. (2015) · Applied Energy · doi:10.1016/j.apenergy.2014.12.062 · Cited by 473
Robust Model-Based Fault Diagnosis for PEM Fuel Cell Air-Feed System
Liu, J., Luo, W., Yang, X., & Wu, L. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2535118 · Cited by 334
Self-citationExtended Kalman Filter for prognostic of Proton Exchange Membrane Fuel Cell
Bressel, M., Hilairet, M., Hissel, D., & Ould Bouamama, B. (2016) · Applied Energy · doi:10.1016/j.apenergy.2015.11.071 · Cited by 331
Prognostics of PEM fuel cell in a particle filtering framework
Jouin, M., Gouriveau, R., Hissel, D., Péra, M.-C., & Zerhouni, N. (2014) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2013.10.054 · Cited by 290
Xu, S., & Niu, R. (2018) · Computers & Geosciences · doi:10.1016/j.cageo.2017.10.013 · Cited by 249
Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry
Nait Aicha, A., Englebienne, G., Van Schooten, K. S., Pijnappels, M., & Kröse, B. (2018) · Sensors · doi:10.3390/s18051654 · Cited by 207
Cited by
Zhou, Y., Wang, Y., Wang, K., Kang, L., Peng, F., Wang, L., & Pang, J. (2020) · Applied Energy · doi:10.1016/j.apenergy.2019.114169 · Cited by 201
SoC estimation of lithium-ion batteries based on machine learning techniques: A filtered approach
Korkmaz, M. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108268 · Cited by 92
Ding, R., Wang, R., Ding, Y., Yin, W., Liu, Y., Li, J., & Liu, J. (2020) · Angewandte Chemie International Edition · doi:10.1002/anie.202006928 · Cited by 92
Self-citationRemaining useful life prediction of PEMFC based on matrix long short-term memory
Yi, F., Shu, X., Zhou, J., Zhang, J., Feng, C., Gong, H., Zhang, C., & Yu, W. (2025) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2025.02.302 · Cited by 73
Metadata
- Authors
- Liu, J., Li, Q., Chen, W., Yan, Y., Qiu, Y., & Cao, T.
- Venue
- International Journal of Hydrogen Energy
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 44
- Issue
- 11
- Pages
- 5470-5480
- ISSN
- 0360-3199 (ISSN-L)
- OpenAlex
- W2899336917
- Cited by
- 284
- References
- 36
- FWCI
- 7.53
- Type
- journal article
- Language
- en
- Open access
- closed
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- Liu, J., Li, Q., Chen, W., Yan, Y., Qiu, Y., & Cao, T. (2019). Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks. International Journal of Hydrogen Energy. https://doi.org/10.1016/j.ijhydene.2018.10.042
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