The electrical modelling of magnetic components was intended for use during topology pre-selection performed by the knowledge-base (KB) and at simulation during the pre-design of the finally selected and parameterised topology. It should yield accurate power losses, size and set-up predictions including electrical circuit properties. Due to the fact, that the demands for accuracy and execution time are different at pre-selection, and pre-design stage, two model types of low (level 0) and high accuracy (level 1) were developed.
The level 0 model is dedicated to be applied for a coarse prediction of the relevant properties in order to derive a single step, non-iterative optimisation of the component.
The level 1 model is intended to achieve accuracy as high as possible considering all major parasitic effects by utilisation of analytical methods aided by Finite-Element parameter studies. This model is dedicated for the application in an iterative parameter optimisation (CAO).
It also contains coarse predictions of magnetic components parasitics like short circuit impedances and main inductances of transformers. The turn ratio n of the transformers and the desired inductance L of inductors is also given. But there is no detailed design of all magnetic components available containing all the necessary information to build a device.
The design and optimisation tool CAEOMAG is integrated into the circuit simulator SIMPLORER, via a graphical user interface. All stress quantities are determined and collected in a file and a magnetic component can be designed and optimised with CAEOMAG.
This design and optimisation tool allows a pre-optimisation of the device with respect to simulated stress quantities yielding a core choice and starting values for the parameter model used by a succeeding parameter optimisation utilising state-of-the-art genetic algorithms. The parameter model allows the automated optimisation of e.g. the layer thickness or core dimensions with respect to an objective function, which can be size, costs, etc. and constraints like temperature rise, maximum saturation induction etc. Several hundred core-winding set-ups are evaluated automatically with varying parameters during such an optimisation, so that a component model is used, which is efficient to compute. The model developed uses analytical methods as shown in modelling chapter .Two thermal models are supported, one with 2 temperature nodes and one with 9, predict the temperature rise with a given ambient temperature and air velocity.