Prognostics and Health Management of Proton Exchange Membrane Fuel Cells

Prognostics and health management (PHM) of proton exchange membrane fuel cells (PEMFCs) has emerged as a vital discipline in ensuring the reliability and durability of these zero-emission energy converters. Degradation mechanisms such as catalyst ageing, membrane thinning and electrode flooding progressively impair performance, leading to loss of power output and premature system failure. By integrating on-line diagnosis, condition monitoring and remaining-useful-life prediction, PHM frameworks enable proactive maintenance scheduling, reduce downtime and lower life-cycle costs. Methods span from physics-based modelling, which captures fundamental electro-chemical and transport phenomena, to data-driven approaches employing machine learning for feature extraction and predictive analytics. Hybrid techniques combine the interpretability of mechanistic models with the adaptability of neural networks, yielding robust prognostic tools across a range of operating profiles. Globally, advances in PEMFC PHM underpin the commercial deployment of fuel cells in automotive, stationary and portable applications, supporting decarbonisation goals and enhancing energy security. Recent developments have focused on real-time implementation, uncertainty quantification and the integration of digital twins, pointing towards intelligent control systems that self-optimise under dynamic loads.

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Prognostics and Health Management of Proton Exchange Membrane Fuel Cells publication trend

The graph below shows the total number of articles in prognostics and health management of proton exchange membrane fuel cells across all publications each year (not limited to Nature Index journals).

Technical terms

Proton exchange membrane fuel cell (PEMFC): An electrochemical device that converts hydrogen and oxygen into electricity and water, using a solid polymer membrane as its electrolyte.

Prognostics and health management (PHM): An integrated methodology combining monitoring, diagnostic and predictive techniques to assess system condition and forecast degradation.

Remaining useful life (RUL): The estimated operational duration before a system or component reaches a predefined end-of-life threshold.

Polarisation curve: A plot of cell voltage against current density, reflecting electrochemical performance and losses within a fuel cell.

Gaussian process regression (GPR): A non-parametric, probabilistic machine-learning method for regression that provides uncertainty quantification.

Transformer model: A deep-learning architecture based on self-attention mechanisms, adept at capturing long-range dependencies in sequential data.

References

1. Hybrid fuel cell system degradation modeling methods: A comprehensive review. Journal of Power Sources (2021).
2. Degradation identification and prognostics of proton exchange membrane fuel cell under dynamic load. Control Engineering Practice (2022).
3. A Short-Term and Long-Term Prognostic Method for PEM Fuel Cells Based on Gaussian Process Regression. Energies (2022).
4. The Degradation Prediction of Proton Exchange Membrane Fuel Cell Performance Based on a Transformer Model. Energies (2024).