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Time Series Modelling of Temperature Distributions in Proton Exchange Membrane Fuel Cells
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DOI：10.1002/adsu.70573.png)
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Abstract
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Proton exchange membrane (PEM) fuel cells are central to net-zero transportation, with challenges in durability, thermal management, and long-term monitoring that limit their large-scale deployment. Temperature distribution within the cell serves as a key diagnostic indicator, as localized hotspots accelerate degradation of cell components. In this study, a long short-term memory (LSTM) model is developed for predicting spatial temperature distributions in PEM fuel cells using readily measurable operating parameters and current distribution data. A 500-h experimental dataset under the new European driving cycle is employed, comprising 72 input features (24 operating parameters and 48 current sensors) and 48 temperature sensor outputs across a 50 cm2 active area. Bayesian optimization is applied for hyperparameter tuning, yielding an LSTM configuration that achieves high predictive accuracy (R2 > 0.91) when trained on only 100 h (20%) of the dataset under the investigated hyperparameter settings. Sensor-wise evaluation shows minimal error for central regions with uniform heat transfer, while higher deviations occur near edges due to local non-uniformities. This suggests the need for targeted calibration and feature engineering for edge regions to further enhance predictive reliability. Overall, the framework demonstrates a computationally efficient, data-driven pathway for predictive monitoring and diagnostics in PEM fuel cells.
Keywords:
deep learning
long short-term memory (LSTM)
PEM fuel cells
temperature distribution
time series
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