Machine Learning Applications in Proton Exchange Membrane Fuel Cells

Proton exchange membrane fuel cells (PEMFCs) occupy a central role in the transition towards zero-emission energy systems, yet their commercial uptake is constrained by cost, durability and operational complexity. Machine learning (ML) has emerged as a transformative tool to address these challenges by extracting patterns from experimental and operational data, and by augmenting or replacing conventional physics-based models. Techniques such as artificial neural networks, support vector machines and random forests enable high-fidelity prediction of voltage–current (polarisation) behaviour across multiple operating conditions, accounting for nonlinear dependencies on temperature, humidity and reactant flow. Adaptive-neuro fuzzy inference systems further enhance interpretability by linking fuzzy logic rules with neural architectures, supporting fault diagnosis and real-time control. In parallel, data-driven surrogate models and digital twins integrate ML with multi-physics simulations to accelerate design optimisation, reduce computational expense and guide accelerated ageing studies. Reinforcement learning and Bayesian optimisation are now being explored for dynamic stack-level control, maximising efficiency under variable load and extending system lifetime. Collectively, these advances demonstrate the global significance of ML for enhancing PEMFC performance, lowering barriers to scale-up and informing the next generation of hydrogen-based energy infrastructures.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Machine Learning Applications in Proton Exchange Membrane Fuel Cells publication trend

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

Technical terms

Proton Exchange Membrane Fuel Cell (PEMFC): A device that converts hydrogen and oxygen into electricity, heat and water via an ion‐conducting polymer membrane.

Artificial Neural Network (ANN): A data-driven computational framework of interconnected nodes that learns complex, nonlinear mappings between inputs and outputs.

Digital Twin: A real-time virtual replica of a physical system, built from sensor data and models, to simulate performance and predict future states.

Surrogate Model: A simplified data-driven approximation of a detailed physics-based model, offering rapid prediction with quantified accuracy.

Polarisation Curve: The characteristic plot of cell voltage versus current density, used to assess fuel cell performance and efficiency under different loads.

References

1. Multi-physics-resolved digital twin of proton exchange membrane fuel cells with a data-driven surrogate model. Energy and AI (2020).
2. On neural network modeling to maximize the power output of PEMFCs. Electrochimica Acta (2020).
3. Performance Prediction of Proton Exchange Membrane Fuel Cells (PEMFC) Using Adaptive Neuro Inference System (ANFIS). Sustainability (2020).
4. Application of Machine Learning in Fuel Cell Research. Energies (2023).