Parameter identification of solid oxide fuel cell by Chaotic Binary Shark Smell Optimization method

Ya Wei and Russell J. Stanford

Energy, 2019, vol. 188, issue C

Abstract: In this work, a novel optimization method named Chaotic Binary Shark Smell Optimizer is applied to obtain the optimum parameters of the solid oxide fuel cell. In the presented literature, critical metrics of solid oxide fuel cell performance criteria are discussed in a steady-state and dynamic manners. Mean squared deviation within experimental data and modeling data of the net output voltage of the fuel cell stack is considered as the objective function. SIMULINK toolbox in the MATLAB software is used for dynamic modeling of the stack. In this study, the Chaotic Binary Shark Smell Optimizing method is illustrated on some experimental data of commercial stacks. Moreover, a comparison between the proposed optimization method results and some other well-known optimization methods results has been made to show the validation of Chaotic Binary Shark Smell Optimizer. Results of the suggested method for commercial stacks demonstrates the highly efficient performance of this scheme. Furthermore, findings of the numerical simulation in company with mandatory performance measures imply that the capability of the Chaotic Binary Shark Smell Optimizer to produce competing parameters for steady-state and dynamic models of solid oxide fuel cell that indicates the effectuality of the Chaotic Binary Shark Smell Optimizer. Keywords: Solid oxide fuel cell (SOFC), Mathematical modeling, Optimization, Chaotic Binary Shark Smell Optimizer, Parametric investigation.

Date: 2019
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:188:y:2019:i:c:s0360544219314410

DOI: 10.1016/j.energy.2019.07.100

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