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High accuracy parameter estimation of solid oxide fuel cells using a surrogate assisted differential evolution with multi sampling mechanism
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DOI：10.1016/j.elecom.2025.108060.png)
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Abstract
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• Innovative Hybrid Optimization: Introduces SADE-MSM, a novel surrogate-assisted evolutionary algorithm combining Radial Basis Function (RBF) surrogates, adaptive multi-sampling, and Differential Evolution to efficiently optimize SOFC parameters with minimal expensive function evaluations. • Unprecedented Accuracy: Achieves superior parameter estimation precision (lowest MSE) across 10 diverse SOFC operational cases, outperforming nine state-of-the-art metaheuristics by balancing global exploration and local exploitation. • Computational Efficiency: Reduces computational cost by 80–90 % via surrogate modeling, enabling faster convergence while maintaining robustness in high-dimensional, nonlinear SOFC landscapes. • Dynamic Adaptability: Features a Multi-Sampling Mechanism (MSM) that dynamically shifts from broad exploration to focused refinement, avoiding premature convergence and enhancing solution quality. • Practical Impact: Validated on real-world SOFC models, enabling high-fidelity simulations for control, diagnostics, and digital twins—critical for applications in microgrids and hybrid energy systems.
Keywords:
Solid oxide fuel cells (SOFCs)
Parameter estimation
Surrogate-assisted optimization
Differential evolution
Multi-sampling mechanism
Metaheuristic algorithms
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