Natural Flood Algorithm for Efficient Parameter Identification in a Proton Exchange Membrane Fuel Cell Models

Attaining sustainability in energy systems is a critical task in confronting global environmental issues. Hydrogen fuel cells, especially proton exchange membrane fuel cells (PEMFCs), present a viable avenue for clean and efficient energy solutions. Precise identification of the characteristics influencing PEMFC models is crucial for improving their effectiveness, reliability, and flexibility in real-world applications. This paper presents a unique optimization method, the Flood Algorithm (FLA), for effective and accurate parameter determination in PEMFC models. The FLA, influenced by natural flood dynamics, incorporates mathematical models of essential physical phenomena, including water flow on inclines, temporal flow rate variations, soil permeability, and water level changes induced by precipitation and evaporation. These concepts direct the algorithm toward global optimization by methodically balancing exploration and exploitation. The FLA functions through two principal phases: a regular movement phase that guarantees convergence and a flooding phase that promotes diversification to circumvent local optima. The proposed methodology is confirmed by experimental data from four commercial PEMFC stacks: 250 W, H-12, BCS 500 W, Temasek, and SR-12 by minimizing the sum of squared errors (SSE). The optimal SSE values of 0.624709, 0.096533, 0.0115561, 0.117086, and 1.056369779 were attained, indicating enhanced accuracy relative to contemporary metaheuristic algorithms and extensively cited methodologies in the literature. The findings highlight the resilience and effectiveness of the FLA in achieving accurate PEMFC parameter estimation, supported by comparisons of SSE and statistical indicators.