article · Energies
Mathematical modelling of polymer electrolyte membrane fuel cells is vital for optimal control and performance analysis, relying heavily on precise parameter estimation. A multi-strategy tuna swarm optimisation algorithm has been developed to estimate the unknown parameters of fuel cell voltage models. In this approach, unknown fuel cell factors serve as decision variables that are tuned to minimise the sum of square errors between estimated and measured operating data. The method was evaluated using three benchmark datasets, specifically BCS500W, NedStackPS6, and harizon500W, alongside laboratory data gathered from a 25 square centimetre single cell on a Greenlight G20 test platform operated at 353 Kelvin. Comparative evaluations against alternative optimisers, including differential evolution and particle swarm optimisation, show that the multi-strategy tuna swarm optimisation approach achieves superior accuracy and faster convergence speed.
Proton exchange membrane fuel cells are important clean energy conversion devices, but designing effective control systems requires highly accurate mathematical representations. Improving the precision and speed of parameter estimation helps researchers and engineers better predict fuel cell voltage behaviours under diverse conditions, supporting more reliable system design and operation.
This research provides a computational tool that could assist fuel cell developers, control systems engineers, and manufacturers in characterising cell performance and designing model-based controllers. Because the technique has been tested on standard commercial datasets and laboratory cell test benches, it represents applied research that could be integrated into fuel cell simulation and design software environments.
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Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated.
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DOI: 10.3390/en17122917
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