article · Scientific Reports
Accurate parameter estimation for proton exchange membrane fuel cells is essential for producing reliable current-voltage curves. An improved gorilla troops technique addresses this by integrating a dual migration approach to enhance exploitation and prevent convergence on local minima, alongside a tangent flight strategy for efficient search space exploration. The method was evaluated using two commercial fuel cell stacks, the BCS 500W and Modular SR-12, under varied temperatures and pressures, as well as ten standard benchmark functions. When benchmarked against alternative optimisers, including the standard gorilla troops technique, grey wolf algorithm, and particle swarm optimisation, the improved approach outperformed them across the majority of statistical indices. It recorded minimal sum of squared errors of 0.0117 for the BCS 500W stack and 0.000142 for the Modular SR-12 stack, demonstrating competitive precision in model parameter identification.
Proton exchange membrane fuel cells are important clean energy devices, but designing and operating them effectively requires accurate computational models of their electrical behaviour. By refining algorithmic parameter extraction, engineers can predict fuel cell voltage and current responses under diverse operating temperatures and pressures with greater mathematical certainty, reducing errors in simulation and operational performance forecasting.
This algorithmic tool is aimed at engineers and researchers modelling proton exchange membrane fuel cells for clean energy systems. By improving the accuracy of fuel cell simulation models using real stack data, it could assist in system design and monitoring. As the research focuses on mathematical validation against benchmark data and existing commercial cell specifications, it represents early-stage computational development requiring integration into engineering workflows before commercial use.
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The parameter extraction of the proton exchange membrane fuel cells (PEMFCs) is an active study area over the past few years to achieve accurate current/voltage (I/V) curves. This work proposes an advanced version of an improved gorilla troops technique (IGTT) to precisely estimate the PEMFC's model parameters. The GTT's dual implementation of the migration approach enables boosting the exploitation phase and preventing becoming trapped in the local minima. Besides, a Tangent Flight Strategy (TFS) is incorporated with the exploitation stage for efficiently searching the search space. Using two common PEMFCs stacks of BCS 500W, and Modular SR-12, the developed IGTT is effectively applied. Furthermore, the two models are evaluated under varied partial temperature and pressure. In addition to this, different new recently inspired optimizers are employed for comparative validations namely supply demand optimization (SDO), flying foxes optimizer (FFO) and red fox optimizer (RFO). Also, a comparative assessment of the developed IGTT and the original GTT are tested to ten unconstrained benchmark functions following to the Congress on Evolutionary Computation (CEC) 2017. The proposed IGTT outperforms the standard GTT, grey wolf algorithm (GWA) and Particle swarm optimizer (PSO) in 92.5%, 87.5% and 92.5% of the statistical indices. Moreover, the viability of the IGTT is proved in comparison to various previously published frameworks-based parameter's identification of PEMFCs stacks. The obtained sum of squared errors (SSE) and the standard deviations (STD) are among the difficult approaches in this context and are quite competitive. For the PEMFCs stacks being studied, the developed IGTT achieves exceedingly small SSE values of 0.0117 and 0.000142 for BCS 500 and SR-12, respectively. Added to that, the IGTT gives superior performance compared to GTT, SDO, FFO and RFO obtaining the smallest SSE objective with the least STD ever.
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DOI: 10.1038/s41598-023-35581-y
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