Determining the parameters of supercapacitors used in the spacecraft electrical power generation system using stochastic optimization algorithms

Researcher:
Researcher/ Wael Ahmed Mortada Atwa

Summary of project:

In certain cases, applying conventional measurement techniques and related procedures to extract supercapacitor coefficients in the time domain may be an unnecessarily time-consuming process, which also requires different sets of measurement equipment, and is generally expensive. Therefore, the strong motivation to develop parameter extraction procedures for supercapacitors is to be compatible with the above.

Obtaining accurate values of supercapacitor parameters is one of the most vital stages. To provide an accurate simulation of the behavior of a supercapacitor, these parameters must be specified correctly. Therefore, a selection method based on the algorithms based on stochastic methods proposed in this research is proposed to extract the optimal parameters of the supercapacitor model. A stochastic method-based algorithm optimizer that provides exceptional performance is implemented in this application. In addition, these algorithms provide an accurate solution due to their convergence mechanism. Double layer electrolytic capacitors were considered in the simulation with values of 470 farad and 1500 farad. However, the values of eight parameters that affect the performance of the supercapacitor must be well determined. It is suggested that the desired function is the sum of the squared error between the actual and estimated voltage difference. To evaluate the qualification of the proposed methodology, the results of algorithms based on stochastic methods are compared with some recent and well-known optimization approaches. The resulting values are expected to show that the proposed methods are not only promising in building a reliable equivalent circuit for supercapacitors, but they also estimate the values of the supercapacitor coefficients very close to the real values. Moreover, several comprehensive statistical tests will confirm the robustness of the proposed algorithms compared to other algorithmic techniques based on random methods.

The objective of the project:

Implementing an efficient and accurate mathematical model for super capacitors.

Extracting ultracapacitor parameters using the latest biological mental optimization algorithms.

A comparative study of the latest optimization algorithms in terms of accuracy, performance and execution time.

An intensive study of the coupling, exploration and exploitation of state-of-the-art biological mental optimization algorithms. This is to choose the best algorithms used in the problem of extracting super capacitor coefficients.

The most important outputs:

Accurate and reliable models of supercapacitors in MATLAB/SIMULINK.

Implementation of BES, AEO, and state-of-the-art optimization algorithms.

Estimating supercapacitor parameters using the latest optimization algorithms.

Published research papers.

Status reports.

Phase reports.

the final report.

A collection of images that express the outputs