Optimization Techniques for Fuel Cell Parameter Estimation

Accurate parameter estimation is essential for reliable modelling of fuel cells, enabling design optimisation, performance prediction and control in applications ranging from stationary power generation to automotive propulsion. The complex electrochemical and thermal behaviour of both proton exchange membrane fuel cells (PEMFCs) and solid oxide fuel cells (SOFCs) gives rise to numerous uncertain parameters that cannot be measured directly. To address this, a range of metaheuristic and artificial intelligence–driven optimisation techniques has been developed. These methods harness stochastic search strategies and heuristic rules inspired by natural processes—such as evolutionary adaptation, swarm intelligence and physics-based interactions—to navigate high-dimensional, non-linear parameter spaces. Key advances include hybridising search paradigms, embedding adaptive mechanisms for exploration–exploitation balance, and integrating statistical metrics to avoid premature convergence. Collectively, these optimisation frameworks deliver enhanced convergence rates, reduced estimation error and robust performance across varying operating conditions, thereby supporting more precise simulation, fault diagnosis and adaptive control in fuel cell systems worldwide.

Research from Nature Portfolio

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Research from all publishers

Recent reviews have systematically categorised AI-based optimisers applied to SOFC parameter estimation, illustrating thirty state-of-the-art algorithms classified into evolutionary, physics-inspired, swarm-based and nature-inspired families. This work provides comparative performance benchmarks and highlights best practices in algorithm selection for different stack architectures and operating regimes. In parallel, a novel enhanced Bald Eagle Search algorithm has been tailored to the design-variable estimation of PEMFCs. By introducing adaptive search operators and pressure-temperature perturbation modelling, the method achieves lower error metrics than conventional alternatives and maintains stability under transient conditions. More recently, an optimisation strategy inspired by rabbit foraging behaviour has been demonstrated for PEMFC stacks, delivering rapid convergence to optimal parameter sets across multiple commercial cell types. Statistical analyses confirm its robustness and minimal deviation between simulated and empirical polarization curves, underscoring the practical value of bio-inspired heuristics in real-world fuel cell modelling.

Optimization Techniques for Fuel Cell Parameter Estimation publication trend

The graph below shows the total number of articles in optimization techniques for fuel cell parameter estimation across all publications each year (not limited to Nature Index journals).

Technical terms

Metaheuristic optimiser: A high-level algorithmic framework that guides subordinate heuristic searches to find near-optimal solutions in complex, non-linear spaces.

Proton exchange membrane fuel cell (PEMFC): A low-temperature electrochemical device converting hydrogen and oxygen into electricity, heat and water through a polymer electrolyte membrane.

Solid oxide fuel cell (SOFC): A high-temperature fuel cell employing a ceramic electrolyte to facilitate ion conduction and enable direct fuel utilisation.

Sum of squared errors (SSE): An objective function quantifying the deviation between experimental data and model predictions, commonly minimised during parameter fitting.

Convergence rate: The speed at which an optimisation algorithm approaches its objective minimum, reflecting computational efficiency and reliability.

Lévy flight: A probabilistic search mechanism characterised by occasional long jumps, enhancing global exploration and reducing entrapment in local optima.

References

1. A comprehensive survey of artificial intelligence-based techniques for performance enhancement of solid oxide fuel cells: Test cases with debates. Artificial Intelligence Review (2024).
2. A PEMFC model optimization using the enhanced bald eagle algorithm. Ain Shams Engineering Journal (2022).
3. Identifying the PEM Fuel Cell Parameters Using Artificial Rabbits Optimization Algorithm. Sustainability (2023).