Solid oxide fuel cell parameter estimation using enhanced LSHADE algorithm and Newton Raphson method

doi:10.1002/er.8633

2022-09-02

PRE

Manish Kumar Singla *

Jyoti Gupta

Parag Nijhawan

DOI：10.1002/er.8633

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摘要

En 中文

Enormous research has been undertaken on an academic and industrial scale in recent decades to establish new sources of energy that are completely created to fulfill the demands of today's human beings, using science and technology. A fuel cell is also one of those alternative sources that utilizes own particular energy. A fuel cell is a non-polluting energy source that turns the chemical energy in a fuel directly into electrical energy through two distinct electrochemical processes. It is vital to provide a good model for the Solid Oxide Fuel Cell (SOFC) as one of the usable fuel cells in order to reduce design costs. Due to the problem's intricacy and extremely nonlinear nature, it was a challenging task to solve. Specifically, the model for SOFCs should be tuned under pressure and different operating temperatures. For optimal identification of the SOFC parameters, the current works present a newly developed LSHADE (ELSHADE) approach for determining SOFC model parameters by including immense contribution in the algorithm itself and the objective function. The population of ELSHADE is split into two stages: the first is handled by a robust mutation system, and the second is handled by a chaotic-guided approach. Furthermore, an enhanced Newton Raphson (INR) approach is proposed to successfully handle the chaotic behavior of the I-V curve equation. By comparing it to state-of-the-art algorithms, the findings show that the proposed ELSHADE-INR can exactly discover global solutions, and its supremacy is proved in numerous statistical criteria using real experimental data.

Keyword:

LSHADE-INR

mathematical modeling

optimization

parameter estimation

solid oxide fuel cell