Hello, I am Nicolas Kieny with Siemens PLM Software.
Today, I'm going to present you how to use magnetic Finite Elements results, to set reduced order
models of Permanent Magnet Synchronous Machine drives, in LMS Imagine.lab Amesim.
Permanent Magnet Synchronous Machines, or PMSM, are very used in
automotive applications because of their high power density, and excellent efficiency.
The design of such device requires Finite Elements for a good
magnetic sizing, with a good geometry definition and material definition,
and also system validation.
For example, using simulation and LMS Amesim.
Both are important, and therefore the process of reducing Finite Elements model
into system simulation model, is a key step.
To do that, the methodology is to use Finite Elements results, to generate the right
characterization data, that we can use as input parameters into LMS Amesim.
The characterization data consists in three sets of maps.
First, flux linkages, that will represent the machine magnetic saturation
and the related non-linearities.
Then, the iron losses of the machine.
Finally, control related data, such as torque limits
and optimal current setpoint, for the various operating conditions.
How to obtain this data is explained in the demonstration examples
of the electric motors and drives library, that we can access through this path.
This example explains the model reduction process, and in this first step,
the right simulation to perform with the Finite Elements tool,
to obtain the data in the right format for LMS Amesim is explained,
and also, scripting templates to reuse and adapt are proposed.
In the second step, this data that we obtained can be post-processed,
to get the right control related data, and here again, templates are proposed.
Now we can use this data into a vehicle model in LMS Amesim.
That features an electric powertrain, with a battery, a torque control,
a voltage source inverter, and a PMSM.
The flux linkage and iron losses can be input as text parameters here,
in the parameters list of this component.
It is a quasi-static component, using a space phasor representation.
The current dynamics are neglected for the fastest stimulations.
This approach is very efficient for a vehicle simulation, and I can launch a simulation here.
It is very fast, and we can get the results with this saved plots here.
We can observe the various losses in the system,
battery, inverter, machine electrical and mechanical losses, as well as the energy
conversion from the battery, to the wheel.
But also machine temperature assessment, or the battery current load.
So, with this analysis, we are able to evaluate the performance of a design,
but also to identify the mechanical, electrical or thermal constraint of each
component of the system, and we can refine the individual requirements of those components.
On the second step, we can perform design of exploration
to narrow down the design space of the PMSM drive.
Now, we can also use the data we discussed, in a PMSM drive model,
reduced order model, that features the dynamic of the currents.
So here they are controlled, in closed-loop, with a field oriented vector control,
and we can observe their time variation, and their regulation.
We can also look at the electromagnetic torque, and observe how
it evolves with time and how it is regulated.
So this model enables to validate the performance of a control,
and eventually ensures a better system design.
So, to conclude, we showed how to use magnetic Finite Elements results to get
the right characterization data that we can use in reduced order model of
Permanent Magnet Synchronous Machines drives in LMS Amesim.
This approach enables to front-load the development effort and ensures
a better system design.
Thank you for watching, for more information please visit our website.