Circulatory system-based optimization: A biologically inspired metaheuristic approach for accurately identifying a PEMFC's parameters.

Hydrogen's increasing prominence as a sustainable energy carrier point out its transformative potential in the global energy transition. Among the technologies enabling this shift, Proton Exchange Membrane Fuel Cells (PEMFCs) stand out as a vital solution, offering a clean and efficient alternative to fossil fuels. Accurate parameter identification is critical for optimizing PEMFC performance and advancing their practical applications. This paper introduces a novel biologically inspired optimization algorithm, Circulatory System-Based Optimization (CSBO), designed specifically to identify the unknown parameters of PEMFC models with high precision and efficiency. Mimicking the functionality of the body's circulatory system, comprising pulmonary and systemic circuits, CSBO addresses challenges in convergence speed and solution accuracy inherent in traditional methods. The algorithm's effectiveness was rigorously validated against experimental voltage-current data from four commercial PEMFC stacks (250 W, BCS 500 W, SR-12, H-12), demonstrating superior performance over state-of-the-art optimization approaches. Key performance metrics, including the sum of squared errors (SSE), standard deviation (STD), computational efficiency, and statistical robustness, confirmed the algorithm's capability to enhance stability, accelerate convergence, and improve accuracy.