Abstract
The proton exchange membrane fuel cell (PEMFC) is regarded as a promising option for a sustainable and eco-friendly energy source. Accurate modeling of PEMFCs to identify their polarization curves and thoroughly understand their operational characteristics has captivated numerous researchers. This paper explores the application of innovative meta-heuristic optimization methods to determine the unknown parameters of PEMFC models, particularly focusing on variants of Differential Evolution such as the dynamic Historical Population-based mutation strategy in Differential Evolution (HiP-DE) augmented with a novel diversity metric. The efficacy of these optimization algorithms was evaluated across six different commercial PEMFC stacks: BCS 500-W PEM, Nedstack PS6 PEM, BCS 250-W PEM, HORIZON 500W PEM, H12 12W PEM, and 500W SR-12P, tested under a variety of operating conditions, resulting in analyses of twelve distinct PEMFCs. The objective function for the optimization problem was the sum of squared errors (SSE) between the parameter-derived results and the experimentally measured outcomes from the fuel cell stacks. To confirm the effectiveness of the proposed methods, comparative analyses were conducted with results from previous studies. Additionally, the I/V and P/V curves derived from the HiP-DE application closely matched the datasheet curves for all cases examined. Ultimately, the PEMFC model utilizing the HiP-DE technique outperformed all compared JADE, SaDE, LSHADE, iLSHADE, PalmDE, PSO-DE, jSO, LPalmDE, and HARD-DE algorithms in terms of solution accuracy and convergence speed.