Super capacitor non-linear three-branch equivalent circuit model and parameter identification method

Abstract

The invention discloses a super capacitor non-linear three-branch equivalent circuit model and a parameter identification method. The super capacitor non-linear three-branch equivalent circuit model is established, charging and discharging tests on a super capacitor are carried out, model parameters of the equivalent circuit model are acquired according to the test data acquired through the tests, and the test data includes an end voltage, a charging current and the charging time of the super capacitor during each charging and discharging test. Through the model, an actual work state and performance of the super capacitor can be reliably reflected, external electrical characteristics of the super capacitor can be excellently represented in a longer time scope, moreover, parameter selection is more simplified, parameter identification can be realized, high accuracy is realized, practicality is strong, and the model is practically convenient for engineering application.

Description

Technical field [0001] The invention belongs to the field of super capacitor modeling and parameter identification, and specifically relates to a super capacitor nonlinear three-branch equivalent circuit model and parameter identification method. Background technique [0002] Supercapacitors have shown a rising industry trend in the field of new energy. Many developed countries have made supercapacitor projects as a national key research and development project. The domestic and foreign markets of supercapacitors are showing unprecedented prosperity. At present, the potential of supercapacitors has received more and more attention. In the design of energy equalization devices with good performance for supercapacitors and other important applications of supercapacitors, it is necessary to obtain the best design scheme through repeated circuit simulation and parameter optimization. Among them, the most basic but also the most critical link Just need an accurate super capacitor mod...

AI Technical Summary

Problems solved by technology

The second type of method is called the maximum likelihood method. The biggest disadvantage of the maximum likelihood method is that the distribution form must be known first, and the maximum likelihood function is usually difficult to save. The maximum likelihood method is too complicated to calculate.

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