 Nonlinear simulating of the Proton Exchange Membrane Fuel Cells utilizing Ridgelet Neural Network optimized using a hybrid form of Northern Goshawk OptimizerRuiheng Li, Hao Tian, Yi Di and Sarmistha Hossain Applied Energy, 2024, vol. 360, issue C, No S0306261924001508 Abstract: The utilization of modeling techniques for Proton Exchange Membrane Fuel Cells (PEMFC) presents a viable and proficient approach for comprehending, enhancing, and increasing the efficiency of these sustainable energy sources. This is of paramount importance in tackling worldwide anxieties regarding ecological contamination and the lessening of non-renewable resources of energy. The authors of this research paper introduce a novel approach for the efficient detection of output voltage in PEMFC. This approach involves the utilization of an optimized Ridgelet Neural Network (RNN) in conjunction with a Hybrid Northern Goshawk Optimization (HNGO) algorithm. The primary aim of the proposed approach is to reduce the discrepancy among the determined and anticipated resulted voltages of the PEMFC. This is a vital aspect in increasing the fuel cells' efficacy. The authors conducted simulations to assess the efficacy of the proposed approach. The outcomes designated that the RNN/HNGO method outperforms existing methods when it comes to accuracy in tracking voltage signal predictions. Moreover, the proposed approach yields a comparatively lower level of forecast error compared to previously published studies. The aforementioned results underscored the possibility of utilizing the suggested approach in enhancing the performance of PEMFC through practical means. Additionally, the authors propose that the methodology they have presented has the potential to be expanded to encompass other performance metrics of PEMFCs, including power and efficiency. The authors propose the integration of the RNN/HNGO method with additional optimization techniques or its utilization in hybrid systems as a means of augmenting its performance. The authors concluded by acknowledging the need for additional research to experimentally validate the proposed methodology and optimizing its parameters in order to ensure its reliability and applicability in real-world scenarios. Keywords: Ridgelet Neural Network (RNN); PEMFC (Proton Exchange Membrane Fuel Cell); Hybrid Northern Goshawk Optimization (HNGO); Output voltage prediction; Nonlinear modeling; Optimization algorithm (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:360:y:2024:i:c:s0306261924001508 Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2024.122767 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
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