An efficient approach for mathematical modeling and parameter estimation of PEM fuel based on Young's double-slit experiment algorithm.

This paper introduces a novel optimization algorithm, Young's double-slit experiment algorithm (YSDE), for accurately estimating the unknown parameters of Proton Exchange Membrane Fuel Cell (PEMFC) models. The proposed method integrates the YDSE algorithm with five other metaheuristic techniques: the sine cosine Algorithm (SCA), moth flame optimization (MFO), Harris Hawk optimization (HHO), gray wolf optimization (GWO) and chimp optimization Algorithm (ChOA) to estimate six critical parameters of PEMFC. Comparative analysis demonstrates that the YDSE algorithm outperforms competing methods by achieving the lowest Sum of Square Error (SSE) with a minimum value of approximately 1.9454, compared to higher values in other algorithms. Statistical evaluation over 30 independent runs reveals that YDSE attains a mean SSE of 1.9454 with an exceptionally low standard deviation of 2.21 × 10[Formula: see text], indicating remarkable consistency and robustness. Furthermore, the YDSE algorithm exhibits faster convergence, reaching optimal solutions in fewer iterations than other methods, thereby enhancing computational efficiency. The proposed YSDE is validated in three different PEMFC stack configurations, using standard performance indicators such as the sum of squared errors (SSE), standard deviation (SD), and Friedman rank (FRK). Experimental results demonstrate that YSDE consistently achieves superior accuracy and robustness. It reduces average SSE values by up to 97.8% compared to GWO and 97.6% compared to SCA. The worst-case SSE is improved by up to 70.6% over IChOA, and the standard deviation is reduced by 91.3% relative to MFO. In more complex configurations, YSDE maintains a 1000-times lower SD, while enhancing average accuracy by 2.6% over IChOA and 8.5% over MFO. Overall, YSDE achieves up to 87% improvement in ranking scores based on Friedman analysis, indicating its consistent superiority across different test cases. The statistical significance of YSDE's performance is confirmed through the Wilcoxon rank-sum and multiple comparison tests. These results highlight YSDE as a highly effective and stable solution for PEMFC system identification which has significant potential to develop digital twins and control systems in automotive applications and advance renewable energy technologies.