Key Takeaways
- Switching detail sets the ceiling on what a converter model can tell you about loss, stress and stability.
- Solver type and time step decide both switching fidelity and how long a study takes on a desktop machine.
- A model fitted against one hardware build and documented properly keeps predicting later design revisions.
Power electronics simulation earns its keep when it predicts how a converter behaves through every switching event, long before anyone commits to a board layout.
Converters now sit between almost every generator and almost every load on the grid. Wind and utility-scale solar reached 17% of US electricity generation in 2025, up from under 1% in 2005, and each of those plants pushes its output through inverters that switch thousands of times a second. Accuracy at those instants separates a study you can act on from a waveform that looks plausible.
Good simulation work is a series of honest choices about which physics to keep, which numerical method to solve it with, and how small a time step you can afford. Keep the right detail and a desktop run tells you where the losses sit and which component runs hottest. Skip it and you’ll get clean pictures that quietly disagree with the bench.
What power electronics simulation covers and what it produces
Power electronics simulation reproduces the electrical behaviour of a switching converter from component equations, so you can watch currents, voltages, losses and control response before any hardware exists. A full study covers the power stage, the passive components, the gate signals and the feedback loop as one connected system.
Picture a 3 kW boost stage for a solar string. You model the inductor with its winding resistance, the switch with its on-state drop and turn-off time, the diode with its reverse recovery, and the capacitor with its series resistance. One run then shows the current ripple, the ringing on the switch node and the output voltage settling.
What you do with those results is the point. Ripple current sizes the capacitor, the ringing tells you what stray inductance is doing to your switch, and the settling shape tells you the compensator has enough phase margin. That’s three decisions you would otherwise make with a rule of thumb and correct on the bench.
Why accurate switching behaviour decides what a model is worth
Switching events set the losses, the voltage stress and the stability limits of a converter, so a model that smooths over them will mispredict all three. Averaged models give you the operating point and the low-frequency loop response. A switched model tells you what the silicon and the magnetics actually experience.
The stakes are easy to quantify. Around 25% of electric motor system electricity use could be saved cost-effectively, cutting total global electricity use by about 10%, and most of that saving arrives through the converters feeding those motors. A single point of efficiency found in modelling rather than in a late redesign multiplies across every unit shipped.
Switching accuracy also changes what you can claim about thermal behaviour. Conduction loss follows the current waveform you compute, and switching loss follows the overlap of voltage and current during each transition. Get the transition wrong and the junction temperature estimate drifts far enough to send you to the wrong heatsink entirely.
“Averaged models give you the operating point and the low-frequency loop response.”
How solver choice and time step govern switching accuracy
Solver type and time step set the ceiling on how faithfully a run resolves each switching edge and how long that run takes on a desktop machine. A fixed-step discrete solver holds the sample grid constant and keeps timing repeatable. A variable-step continuous solver chases fast edges and slows down.
Arithmetic settles most of these arguments. Running a 100 kHz converter with a 1 microsecond step puts 10 samples across each switching period, far too coarse for a 200 nanosecond turn-off. Drop to 20 nanoseconds and the edge resolves, though the run needs 50 times as many steps.
| Modelling choice | What you gain | What it costs |
| Fixed-step discrete solver | Results repeat exactly, so runs stay comparable. | Switching edges snap to step boundaries. |
| Variable-step continuous solver | Fast transitions resolve sharply because the solver shortens its step. | Run time gets hard to predict, and stiff circuits stall the solver. |
| Time step far below the switching period | Loss and ripple figures converge, so quoted numbers hold up. | An overnight thermal study becomes a multi-day one. |
| Averaged converter model | Loop design and system studies finish in seconds. | Ripple, switching loss and device stress vanish from the results. |
| Device model with parasitics | Ringing and reverse recovery appear where the bench shows them. | Extra states stiffen the circuit and force a smaller step. |
The pairing you choose should follow the question you’re asking. Sizing a heatsink justifies the small step and the long wait. Tuning a voltage loop across a hundred operating points does not, and an averaged model gets you there in an afternoon. Most projects need both, with the switched model confirming what the averaged one assumed.
Which converter topologies you can model and study
Almost any topology built from switches, diodes, inductors, capacitors and a control loop can be modelled, from a two-switch buck stage up to a multi-level grid-tied inverter. The limit is rarely the topology itself. It’s the level of device detail you keep and the length of run you’re prepared to wait for.
Most production work falls into a handful of families.
- Non-isolated DC/DC stages such as buck, boost and buck-boost converters for point-of-load supplies.
- Isolated DC/DC stages such as flyback, forward and LLC resonant converters, where the transformer model carries the accuracy.
- Single-phase and three-phase inverters for motor drives, where the machine model matters as much as the bridge.
- Grid-tied converters with phase-locked loops, current control and fault ride-through logic.
- Active front ends and power factor correction stages that shape input current.
Component libraries decide how much of that ground you cover without writing equations yourself. SPS ships blocks for these families with the underlying equations left open, so you can inspect what a diode or a machine model is solving and adjust it when your part behaves differently.
How close simulation results come to measured hardware behaviour
A carefully parameterized switched model will track bench measurements closely for efficiency, ripple and loop response. Agreement breaks down where the datasheet stops, which usually means layout parasitics, thermal drift of on-state resistance and the reverse recovery behaviour of the specific diode you bought.
Anyone who has compared a simulated switch node against a measured one has seen the gap. The model shows a clean 400 V step and the oscilloscope shows 120 V of overshoot with a 30 MHz ring, because the loop inductance of the commutation path never made it into the schematic. Add 15 nanohenries and a package capacitance and the traces converge.
Calibration like that turns a model into a measuring instrument. Once a handful of parasitics are fitted against one hardware build, the same model predicts the next revision without a fresh prototype. You don’t need a perfect model. You need one whose disagreements you understand.
Common modelling mistakes that quietly distort your results
Most bad results come from a small set of repeated errors. A time step too close to the switching period, ideal switches used for a loss study, a control loop discretized at the wrong rate, and passives entered without series resistance each produce output that looks reasonable and reads wrong.
Ideal switches cause the most trouble. Set on-state resistance to zero and turn-off time to nothing, and a converter that really runs at 95% efficiency simulates at 99.8%, with a heatsink sized three sizes too small. The waveforms look textbook-clean, which is exactly why nobody questions them.
Sampling errors are subtler and just as damaging. A digital current loop sampled at the switching frequency sees the average inductor current only if the sample lands at the midpoint of the on-time. Move that instant and your reading picks up a ripple-dependent offset that no controller tuning will remove. It’s worth checking sample placement against the gate signals early.
“You don’t need a perfect model. You need one whose disagreements you understand.”
What careful modelling habits give you across a project
Simulation pays off when you treat it as an engineering record rather than a one-off check. Models carrying documented parameters, a stated time step and a note on what was verified against hardware stay useful for years, while undocumented ones get rebuilt from scratch by whoever needs them next.
The habit that matters most is writing down what you assumed. A model annotated with the diode part number, the fitted loop inductance and the temperature it was fitted at can be handed to a colleague and trusted. One built quickly on default parameters cannot, and a single wrong value in a mask dialog will cost you the whole set of results.
That is where transparent models earn their reputation. Because SPS keeps component equations open and editable inside MATLAB and Simulink, the assumptions behind a model stay visible to whoever opens it next, including a student meeting a converter for the first time. Accuracy in power electronics simulation comes from that ordinary discipline, applied across many small choices, more than from any feature of a tool.
