Modelling DCM Flyback Converter with MATLAB/Simulink

In summary, the person is planning to use a simulation platform on MATLAB to model their flyback converter circuit and modify the inductor with a permanent magnet for smaller dimensions. They have come across a DCM buck-boost converter that can be modified for their purposes and intend to compare simulation graphs with actual readings. They have two questions: how to incorporate the transformer turns ratio and the physics of the biased inductor into the simulation. They also forgot to attach the Simulink models.
  • #1
bentayyy
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I have an actual flyback converter on a chip and I'm thinking of having a simulation platform to model the circuit on MATLAB. This is because somewhere down the road I intend to modify the inductor by biasing it with a permanent magnet in order to prevent premature saturation. It is hoped that this allows for the use of a smaller core in the transformer which will reduce the overall physical dimensions of my converter. My flyback converter operates in DCM.

In any case, I have trawled the web and came across the following DCM buck-boost converter which can be modified slightly for my purposes. It basically compiles the state-space equations in a u-->y block and plots the graphs out. I have an oscilloscope so I was thinking of comparing my readings to the simulation graphs as a first pass. Barring things like losses I hope that my simulation graphs will closely approach my actual graphs.

My questions are:
-I know for a fact that my transformer turns ratio is 64:5, how do I incomporate this info in my buck-boost simulation to accurately simulate my turns ratio?
-A slightly more challenging part is incorporating the physics of my biased inductor in the simulation: I intend to use FEMM to get flux-current relationships for my biased inductor -- any idea how to incorporate this in the model?

Any suggestions/responses would be great, thanks.
 
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  • #2
Sorry, forgot to attach the Simulink models
 

Attachments

  • Buck-Boost.zip
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1. What is a DCM Flyback Converter?

A DCM (Discontinuous Conduction Mode) Flyback Converter is a type of DC-DC converter that uses a transformer to convert a DC input voltage to a different DC output voltage. It is called a "flyback" converter because energy is stored in the transformer during the "ON" time of the switching transistor and released during the "OFF" time.

2. Why is MATLAB/Simulink used for modelling DCM Flyback Converters?

MATLAB/Simulink is a powerful software tool that allows for the simulation and analysis of complex systems. It offers a user-friendly interface and a wide range of tools and functions for modelling and simulating various types of systems, including DCM Flyback Converters. It also allows for easy visualization and analysis of the simulation results.

3. What are the advantages of modelling DCM Flyback Converters with MATLAB/Simulink?

One of the main advantages of using MATLAB/Simulink for modelling DCM Flyback Converters is the ability to quickly and easily simulate different scenarios and parameters. This allows for faster design iterations and optimization. Additionally, MATLAB/Simulink has a vast library of pre-built components and models that can be used to build the converter, making the modelling process more efficient.

4. How accurate are the results obtained from modelling DCM Flyback Converters with MATLAB/Simulink?

The accuracy of the results obtained from modelling DCM Flyback Converters with MATLAB/Simulink depends on the accuracy of the model used and the accuracy of the input parameters. The more accurate the model and input parameters, the more accurate the results will be. Additionally, the accuracy of the results can be improved by performing sensitivity analyses and adjusting the model accordingly.

5. Can the model developed in MATLAB/Simulink be used for practical applications?

Yes, the model developed in MATLAB/Simulink can be used for practical applications. Once the model is validated and verified, it can be used to design and optimize the converter for a specific application. However, it is important to note that the model may need to be adjusted or modified to account for real-world factors and limitations, such as component tolerances and parasitic effects.

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