Lifetime Prediction of Direct Methanol Fuel Cells Based on Hybrid Predictive Modeling
School of Mechanical Engineering, Tianjin University of Science & Technology, Tianjin 300222, China
| Abstract: | Direct methanol fuel cells (DMFCs) face durability challenges that limit their widespread adoption. Traditional data-driven degradation models excel in short-term predictions but lack capability for long-term forecasting. To mitigate disturbances caused by the voltage rebound phenomenon in fuel cells, this paper proposes a hybrid prediction model that combines mechanistic modeling and machine learning. The core idea is to use mechanistic modeling to extract degradation-relevant internal impedance features from electrochemical impedance spectroscopy (EIS), and then leverage machine learning to predict the evolution of these features, thereby avoiding direct fitting of noisy voltage signals. Specifically, the model first fits EIS using a full-circuit equivalent circuit model (ECM) to extract key internal degradation parameters. It then combines these parameters with a convolutional neural network (CNN) to predict impedance parameter trends. Finally, by establishing a linear relationship between the predicted impedance and the output voltage, the remaining useful life (RUL) of the methanol fuel cell is reliably estimated. Comparison results between the hybrid prediction model, Gated Recurrent Unit (GRU) network, and iTransformer network show predicted failure threshold time errors of 1 hour, 11 hours, and 5 hours, respectively. These results demonstrate that the proposed hybrid model achieves the smallest time prediction error (1 hour), indicating superior performance for long-term prediction tasks. The hybrid model improves data tracking ability and prediction accuracy, and reduces dependence on large amounts of degradation data. This model provides a feasible solution for DMFC lifetime prediction. |
| Keywords: | Direct Methanol Fuel Cell; Life Prediction; Machine Learning; Voltage Rebound Phenomenon; Electrochemical Impedance Spectroscopy |
| DOI: | 10.57237/j.jest.2026.01.001 |
View:
Downloads:
收稿日期: 2026-03-19
接受日期: 2026-04-07
在线出版日期: 2026-04-08
参考文献
| [1] | Askaripour, H. (2019). "Effect of operating conditions on the performance of a PEM fuel cell." International Journal of Heat and Mass Transfer 144: 118705. https://doi.org/10.1016/j.ijheatmasstransfer.2019.118705 |
| [2] | Manupati, R., et al. (2024). "Influence of liquid electrolyte and liquid flowing electrolyte on the performance of an air breathing direct methanol fuel cell (ABDMFC)." Chemical Papers 78(16): 8647–8657. https://doi.org/10.1007/s11696-024-03691-0 |
| [3] | Alias, M. S., et al. (2020). "Active direct methanol fuel cell: An overview." International Journal of Hydrogen Energy 45(38): 19620–19641. https://doi.org/10.1016/j.ijhydene.2020.04.202 |
| [4] | Sutharssan, T., et al. (2017). "A review on prognostics and health monitoring of proton exchange membrane fuel cell." Renewable and Sustainable Energy Reviews 75: 440–450. https://doi.org/10.1016/j.rser.2016.11.009 |
| [5] | Yang, Y., et al. (2022). "One-Dimensional Modeling for Aging of Pt-Co Core-Shell Catalysts in Proton Exchange Membrane Fuel Cells." Journal of The Electrochemical Society 169(5): 054526. https://doi.org/10.1149/1945-7111/ac6e4f |
| [6] | Chen, K., et al. (2019). "Degradation prediction of proton exchange membrane fuel cell based on grey neural network model and particle swarm optimization." Energy Conversion and Management 195: 810–818. https://doi.org/10.1016/j.enconman.2019.05.045 |
| [7] | Pei, P., et al. (2019). "Nonlinear methods for evaluating and online predicting the lifetime of fuel cells." Applied Energy 254: 113730. https://doi.org/10.1016/j.apenergy.2019.113730 |
| [8] | Tang, X., et al. (2024). "Adaptive state-of-health temperature sensitivity characteristics for durability improvement of PEM fuel cells." Chemical Engineering Journal 491: 151951. https://doi.org/10.1016/j.cej.2024.151951 |
| [9] | Javed, K., et al. (2016). "Prognostics of Proton Exchange Membrane Fuel Cells stack using an ensemble of constraints based connectionist networks." Journal of Power Sources 324: 745–757. https://doi.org/10.1016/j.jpowsour.2016.05.092 |
| [10] | Ma, R., et al. (2018). "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method." Applied Energy 231: 102–115. https://doi.org/10.1016/j.apenergy.2018.09.111 |
| [11] | Vichard, L., et al. (2020). "Degradation prediction of PEM fuel cell based on artificial intelligence." International Journal of Hydrogen Energy 45(29): 14953–14963. https://doi.org/10.1016/j.ijhydene.2020.03.209 |
| [12] | Mezzi, R., et al. (2021). "An Echo State Network for fuel cell lifetime prediction under a dynamic micro-cogeneration load profile." Applied Energy 283: 116297. https://doi.org/10.1016/j.apenergy.2020.116297 |
| [13] | Dar, T. H. and S. Singh (2025). "Advanced integration of bidirectional long short-term memory neural networks and innovative extended Kalman filter for state of charge estimation of lithium-ion battery." Journal of Power Sources 628: 235893. https://doi.org/10.1016/j.jpowsour.2024.235893 |
| [14] | Ma, R., et al. (2021). "A Hybrid Prognostic Method for PEMFC With Aging Parameter Prediction." IEEE Transactions on Transportation Electrification 7(4): 2318–2331. https://doi.org/10.1109/tte.2021.3075531 |
| [15] | Wang, C., et al. (2022). "A fusion prognostics strategy for fuel cells operating under dynamic conditions." eTransportation 12: 100166. https://doi.org/10.1016/j.etran.2022.100166 |
| [16] | Zhang, D., et al. (2022). "Internal Characterization-Based Prognostics for Micro-Direct-Methanol Fuel Cells under Dynamic Operating Conditions." Sensors 22(11): 4217. https://doi.org/10.3390/s22114217 |
| [17] | Meng, X., et al. (2025). "Fuel cell life prediction considering the recovery phenomenon of reversible voltage loss." Journal of Power Sources 625: 235634. https://doi.org/10.1016/j.jpowsour.2024.235634 |
| [18] | Yuan, H., et al. (2021). "Quantitative analysis of internal polarization dynamics for polymer electrolyte membrane fuel cell by distribution of relaxation times of impedance." Applied Energy 303: 117640. https://doi.org/10.1016/j.apenergy.2021.117640 |
| [19] | Najafi, B., et al. (2020). "Rapid Fault Diagnosis of PEM Fuel Cells through Optimal Electrochemical Impedance Spectroscopy Tests." Energies 13(14): 3643. https://doi.org/10.3390/en13143643 |
| [20] | Liu, J., et al. (2022). "The life Prediction of PEMFC based on Group Method of Data handling with Savitzky–Golay Smoothing." Energy Reports 8: 565–573. https://doi.org/10.1016/j.egyr.2022.10.256 |
| [21] | Gao, W., et al. (2026). "State of health estimation for lithium-ion batteries via electro-thermal feature fusion and a GRU-Guided Attention Network." Int. J. Electr. Power Energy Syst 174: 111456. https://doi.org/10.1016/j.ijepes.2025.111456 |
| [22] | Liu, J., et al. (2024). "Household energy consumption forecasting based on adaptive signal decomposition enhanced iTransformer network." Energy and Buildings 324: 114894. https://doi.org/10.1016/j.enbuild.2024.114894 |
| [23] | Metlich, C., et al. (2025). "Damage mechanics of battery electrodes under applied stress and electrochemical cycling: Damage characterization using EIS and DMA." Electrochimica Acta 544: 147630. https://doi.org/10.1016/j.electacta.2025.147630 |
