FPGA simulation for power electronics and motor drives

Key Takeaways

• Solver architecture follows the fastest phenomenon under test, so a converter switching above 100 kHz rules out a CPU timestep before any other requirement is weighed.
• CPU solvers hold between 10 and 50 microseconds while FPGA solvers reach 200 to 500 nanoseconds, and that gap decides which switching effects appear at all.
• FPGA resolution costs flexibility through fixed-point range, a capped model size and long bitstream builds, which makes early partitioning between CPU and FPGA the call that matters most.

FPGA simulation exists to close a control loop faster than any CPU can finish a single timestep.

Power converters keep pushing switching frequencies upward while the models used to validate them sit at microsecond resolution. Electric motor systems already account for 53% of global electricity consumption, and nearly all of them now sit behind a switching converter that a test bench has to reproduce accurately. The gap between what a CPU solver samples and what the hardware actually does is where validation quietly fails.

The architecture question isn’t about raw speed. It’s about resolving switching events at the timescale your controller sees them. A CPU solver at 25 microseconds and an FPGA solver at 250 nanoseconds don’t produce slightly different results on a 100 kHz converter. One returns a usable waveform and the other returns an average that hides the behaviour you’re testing for.

What FPGA simulation means for a real-time model

FPGA simulation moves the solver out of sequential software and into logic gates. Circuit equations become a fixed arithmetic pipeline laid out in the fabric, and that pipeline produces a new solution every clock cycle. Timesteps land between 200 and 500 nanoseconds for converter models, with input and output paths measured in tens of nanoseconds.

A three-phase two-level inverter shows the difference plainly. Six switches, their antiparallel diodes and the associated node equations compile into fixed-point arithmetic blocks that all evaluate in parallel. The solver never fetches an instruction, never waits on a cache line and never yields to an operating system scheduler. What comes back is the same latency on every step, which is the property that matters when a physical controller closes a current loop against the model.

That determinism carries a design consequence. Model size is fixed when the bitstream is built, so device resources cap how many switches, nodes and machine models you carry at once. Engineers who treat the FPGA as an accelerator they can extend mid-project discover this late.

Why CPU-based timesteps stop near 10 microseconds

A CPU core evaluates a model one operation after another, so the timestep has to cover the full matrix solve plus interrupt handling and I/O transfer. Real-time power system models settle between 10 and 50 microseconds on a dedicated core. Pushing below that leaves no margin for overruns.

The limit only becomes visible when switching speed rises. A 60 Hz distribution feeder solved at 50 microseconds carries more than 300 samples per fundamental cycle, which is plenty. Point the same solver at a 20 kHz inverter and each switching period gets one sample, so the duty cycle the model applies quantizes to the timestep and the ripple current it reports is fiction. Time-stamped gate capture and interpolation recover some of that accuracy, and for a 5 kHz drive that’s often enough, but the technique can’t recover a 500 nanosecond dead time or a desaturation transient that occurs entirely between samples.

“Point the same solver at a 20 kHz inverter and each switching period gets one sample, so the duty cycle the model applies quantizes to the timestep and the ripple current it reports is fiction.”

CPU and FPGA solvers compared for switching accuracy

The main difference between CPU and FPGA solvers is where the arithmetic happens. A CPU runs the model as instructions in sequence, while an FPGA lays the same equations out spatially and solves them in parallel on every clock tick. That structural difference is what separates a 25 microsecond timestep from a 250 nanosecond one.

Neither architecture wins outright. A 400 node distribution network with protection logic runs comfortably on CPU cores and would consume an entire FPGA before you modelled half of it. A 150 kHz silicon carbide converter with device level switching losses cannot run on a CPU at any useful fidelity.

Compile turnaround is the item teams underestimate most. A CPU model edit is ready in seconds, while an FPGA bitstream build runs for tens of minutes, which reshapes how you plan a session rather than how fast the model runs.

What timestep your switching frequency actually requires

Pick the timestep from the switching period rather than from the hardware already on the bench. Accurate ripple and loss figures need roughly 50 to 100 samples inside each switching period, so the converter’s switching frequency sets the floor before anything else enters the calculation.

Working through the arithmetic makes the boundaries concrete.

• A 10 kHz IGBT drive has a 100 microsecond switching period, so a 1 microsecond timestep holds duty cycle error near 1%.
• A 100 kHz silicon carbide or gallium nitride stage has a 10 microsecond period, which pushes the timestep to about 100 nanoseconds.
• A 500 nanosecond dead time vanishes completely at any timestep coarser than half a microsecond.
• Ripple current amplitude reads low once fewer than 20 samples land inside each switching period.
• Gate capture resolution sets the practical floor, since a duty command the simulator cannot time stamp cannot be reproduced.

These numbers explain why so many benches end up mixed. Thermal networks and mechanical loads move over milliseconds and waste FPGA resources, while converter switching moves over nanoseconds and returns meaningless numbers on a CPU.

Motor drive testing that only FPGA resolution supports

Motor drive models push FPGA solvers harder than converters alone, because the machine and the inverter have to solve at the same rate. Saturation, spatial harmonics and rotor position all move within a single switching period on a high-speed permanent magnet machine, and averaging them removes the effects you’re testing.

A traction motor spinning at 18,000 rpm with 4 pole pairs produces a 1,200 Hz electrical fundamental beneath a 20 kHz inverter carrier. Torque ripple at that combination comes from flux linkage that varies with both current and rotor angle, which is why FPGA machine models carry finite element derived lookup tables instead of constant inductance values. Electric car sales exceeded 20 million in 2025, and the validation workload behind those powertrains is what pushed machine modelling into the FPGA fabric.

Fault testing raises the stakes again. An inter-turn short in a stator winding develops across a few electrical degrees, and a desaturation event in the inverter resolves in under a microsecond. Running the machine library and the switching model on the same device, as OPAL-RT does on its OP5707XG class simulators, keeps both inside one deterministic solve so the controller under test sees the fault at hardware timing.

Where FPGA models cost you flexibility and compile time

FPGA solvers trade convenience for resolution. Models run in fixed-point arithmetic with a numerical range set at build time, the switch and node budget is capped by device resources, and every structural edit requires a full bitstream rebuild that takes tens of minutes instead of seconds.

Numerical range causes the most surprises. A fixed-point solver configured for an 800 V DC bus and 400 A phase currents will clip or lose precision if the same model is reused for a 1,500 V string inverter without rescaling. Debugging visibility costs you as well, since probing a signal inside the fabric means routing it to a monitoring output and rebuilding. Neither constraint argues against FPGA solvers. Both argue for deciding early which parts of the model genuinely need nanosecond resolution.

Matching solver architecture to the tests that matter

The right architecture follows the fastest phenomenon you intend to observe. If your controller responds to events shorter than a microsecond, an FPGA solver is the only option that will reproduce them faithfully. If the quickest thing in the model is a millisecond mechanical transient, FPGA resources buy you nothing.

Most benches that hold up over several years were partitioned deliberately. The grid, the mechanical load and the supervisory logic sit on CPU cores at 25 to 50 microseconds, the converter and the machine sit on the FPGA at a few hundred nanoseconds, and the interface between them is documented well enough to extend later.

Discipline in that first partitioning call compounds over time. Choosing solver architecture against the physics rather than against a hardware catalogue is what keeps a test bench useful as the converters under test get faster, and it’s the reasoning OPAL-RT applies when engineers ask which parts of a drive model belong in the fabric. Get the boundary right early and you’ll spend your time testing the controller instead of arguing with the simulator.

“The right architecture follows the fastest phenomenon you intend to observe.”