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Robust parameter estimation in solid oxide fuel cells using a multi strategy improved crayfish optimization algorithm
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DOI：10.1016/j.rineng.2025.107823.png)
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Abstract
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• Novel Algorithm: Introduces the Multi-strategy Improved Crayfish Optimization Algorithm (MICOA), integrating cave selection, food attraction, and Cauchy mutation for robust SOFC parameter estimation. • Superior Accuracy: Achieves the lowest MSE (2.23 × 10⁻⁵) and minimal variance across ten SOFC operating conditions, outperforming nine state-of-the-art metaheuristics. • Comprehensive Validation: Rigorously tested under varying temperatures (1073–1273 K) and pressures (1–9 atm), confirming alignment with electrochemical model trends. • Statistical Dominance: Secures top Friedman rank (∼1.1), demonstrating faster convergence and enhanced stability compared to peer algorithms. • Practical Impact: Enables precise control-oriented modeling, supporting digital twins, fault diagnosis, and real-world SOFC optimization.
Keywords:
Solid oxide fuel cell (SOFC)
Parameter estimation
Metaheuristic optimization
Multi-strategy improved crayfish optimization algorithm (MICOA)
Electrochemical modeling
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