Improved Leader Slime Mould Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cells

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Chinese explanation / 中文解读

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Original abstract

Abstract Accurate parameter identification is essential for reliable modeling, performance prediction, and control of proton exchange membrane fuel cells (PEMFCs). This paper proposes an Improved Leader Slime Mould Algorithm (ILSMA) for estimating the parameters of a semi-empirical PEMFC model by minimizing the sum of squared errors between experimental and simulated stack voltages. Compared with the original Leader Slime Mould Algorithm, the proposed approach incorporates adaptive weighting, annealed Gaussian perturbation, and an enhanced leader-guided search strategy to improve convergence speed and reduce the risk of premature convergence. The effectiveness of ILSMA is evaluated on three commercial PEMFC stacks, namely Horizon 500~W, BCS 500~W, and NedStack PS6, and is further examined using standard benchmark optimization functions. The results demonstrate that ILSMA yields accurate parameter estimates, stable convergence behavior, and competitive performance compared with several representative metaheuristic optimization methods. These findings indicate that ILSMA is a promising and efficient method for PEMFC parameter estimation and for improving the predictive accuracy of fuel cell models used in monitoring, control, and energy management applications.

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