HIL-Tests für Elektroflugzeuge

Impedyme HIL/RCP-Box: The Hardware and Communication Backbone

Processing and Heterogeneous Compute Architecture

To handle the demanding computational tasks required for modern aerospace simulation, the Impedyme HIL/RCP-Box employs a heterogeneous computing architecture. It integrates a high-performance dual-core ARM processor alongside a multi-core, user-programmable Xilinx Ultrascale+ FPGA.

The ARM processor is optimized for running high-level supervisory controls, communication bus protocols (such as CAN, CAN-FD, and Ethernet), and soft real-time mathematical operations. The Ultrascale+ FPGA handles the execution of high-frequency physical systems, including high-order, non-linear motor models and switching power converter dynamics. This co-processor configuration enables the HIL/RCP-Box to achieve execution frequencies up to 250 kHz for closed-loop control algorithms, while executing hardware-synthesized models at sub-microsecond time steps.

Co-Location of I/O and Processing for Zero-Latency Operation

In conventional real-time simulators, communication between the central processing unit and the input/output (I/O) cards occurs over a shared backplane bus, such as PCIe or PXI. This architecture introduces bus-transfer latency, which can degrade stability and accuracy when simulating fast-switching power electronics.

The Impedyme HIL/RCP-Box bypasses this bottleneck by co-locating the processing cores and the high-speed I/O interfaces directly on the same silicon chip. By routing the analog-to-digital converters (ADCs), digital inputs, and communication interfaces straight to the Ultrascale+ FPGA fabric, the system eliminates bus-transfer latency. Signals are captured, processed within the physical model, and outputted as simulated feedback within a single clock cycle, enabling deterministic, sample-by-sample execution.

RealSync Technology and Multi-Unit Scalability

Aerospace systems verification often requires thousands of I/O channels to fully simulate an aircraft’s complete “Iron Bird” or power distribution grid. To scale the simulation infrastructure, Impedyme utilizes a modular stacking design.

Up to 64 HIL/RCP-Box units can be stacked and interconnected to form a highly networked, multi-node simulator. To ensure that these distributed units execute in unison, Impedyme uses its proprietary RealSync Technology. Operating over high-speed Small Form-factor Pluggable (SFP+) optical ports, RealSync delivers sub-microsecond data transfer latency and nanosecond-level clock synchronization across all stacked units. This allows the entire networked cluster to operate as a single, unified simulation engine with perfectly synchronized execution clocks.

Demonstration 1: eVTOL Electric Powertrain HIL

Consider validating an eVTOL propulsion system with two real controllers on the bench: a motor controller unit governing 200 kW electric drives, and a battery management system consisting of a BMS controller plus cell monitoring units, all communicating over a CAN network.

Motor drive simulation. The FPGA-based motor drive model emulates the PMSM motors and their switching inverters. Because the controller drives real PWM, the bench captures PWM pulses at 5 ns resolution while the FPGA machine model runs at 200 ns — fast enough to reproduce high switching dynamics, inject faults, and emulate encoder and phase-current feedback. MotorSim Studio parameterizes the machines (PMSM, BLDC, induction, IPM) with angle-dependent flux, torque, and saliency maps, and offers quadrature encoder and resolver options so the controller sees exactly the position feedback it expects. Field-oriented control loops close against this emulated machine as if a real motor were spinning.

Battery simulation and cell emulation. On the DC side, BatterySim Studio emulates a 400 V battery. At the cell level, the bench emulates individual cells — in this demonstration, 24 of 100 cells are emulated at cell level, with the Real-Time Battery Emulator’s daisy-chaining capability scaling up to 252 cells. Fault insertion happens at the cell level: short-circuit faults, broken-wire faults, voltage override, and temperature override can all be scripted against the BMS. The setup validates passive cell balancing by observing balancing currents in the tens of milliamps (around 78 mA in this case) — proving the BMS balancing algorithm actually works. 

Testing outcome. Both unit-level and system-level automated, requirements-based tests run with generated test reports. The team validates the motor controller and the BMS together, over CAN, across normal operation and a battery of fault scenarios — none of which risk a real 200 kW drive or a real high-voltage pack.

Demonstration 2: Power HIL for a 300 kW Inverter

Signal-level HIL validates control logic, but it does not exercise the power stage. That is where Power Hardware-in-the-Loop (PHIL) comes in.

In a PHIL setup, the inverter control unit is tested along with a real 300 kW motor inverter at actual power levels. Instead of connecting a real motor and battery, the bench emulates the electric motor and the batteries at rated power using regenerative power amplifiers. The CHP Series’ power stage sources and sinks real current and voltage, so the inverter experiences genuine electrical loading. Low-latency fiber-optic communication links the power amplifier to the FPGA real-time core, keeping the closed loop stable at full power. 

The payoff: engineers verify the inverter design at full power without building a dynamometer, procuring a real traction pack, or risking hardware and personnel. PHIL delivers the realism of a full-power test with the safety, repeatability, and cost profile of simulation.

Demonstration 3: More Electric Aircraft Emergency Power

Electric aircraft HIL testing is not limited to eVTOL propulsion. Consider a more electric aircraft emergency power system that combines a fuel cell, a lithium-ion battery, and an ultracapacitor — a hybrid architecture designed to keep essential systems alive when primary generation fails. 

A HIL bench can emulate this entire energy system and stage an emergency landing with a generator-failure scenario: the primary generator drops offline, and the power management controller must orchestrate the fuel cell, battery, and ultracapacitor to sustain flight-critical loads through the landing. The bench communicates with the real controller over an ARINC 429 avionics interface, exercising the same messaging the aircraft uses in flight. 

Staging a generator failure on a real aircraft is out of the question. On an FPGA-based HIL bench, it is a repeatable test case — one that can be run across dozens of energy-state and fault combinations, each producing traceable test evidence for the safety case.

Unit-Level vs System-Level Testing

Certification-grade programs test at two levels, and HIL supports both.

Impedyme’s platform scales from a focused unit-level bench to a full system-level e-bird because every emulator — MotorSim Studio, BatterySim Studio, GridSim Studio, DroneSim Studio: — runs on the same CHP hardware and is orchestrated from one PowerHIL Studio environment.

eVTOL Powertrain Validation: High-Fidelity Twin Simulation

Unit-Level vs. System-Level Testing Workflows

Validating a complex eVTOL propulsion system requires a progressive testing strategy, starting at the individual component level (unit-level) and moving up to full-scale multi-system integration (system-level) :

• Unit-Level Validation: Focuses on isolating a single controller (such as a Battery Management System or a Motor Control Unit) . This step isolates the controller from external noise and verifies that its specific firmware, logic boundaries, and sensor front-ends perform as expected.
• System-Level Validation: Integrates all individual controllers (e.g., the primary Flight Control Computer, multiple Motor Control Units, and the central Batteriemanagementsystem) into a unified real-time test environment . System-level testing validates cross-communication protocols (such as CAN-FD messaging rates), latency propagation, power distribution transients, and multi-controller fault handling .

The eVTOL Digital Twin Architecture

To validate flight operations in real time, the HIL simulator must run a highly complex, multi-domain digital twin of the eVTOL aircraft . This digital twin consists of several interconnected mathematical submodels :

• Flight Controller Submodel: Runs the virtual autopilot or flight computer, converting operator inputs and path navigation coordinates into motor speed commands.
• Propulsion Submodel: Simulates the four (or more) rotary propulsors, including permanent magnet synchronous motors (PMSM) and three-phase inverter drives .
• Energy Storage Submodel: Models the high-voltage battery pack (typically simulated around 400V or 800V depending on the aircraft class) and cell-balancing circuitry .
• Flight Dynamics Submodel: Incorporates gravitational vectors, air density, wind shear, gyroscopic forces, and lifting aerodynamics to compute the vehicle’s actual movement in three-dimensional space.

By linking these models together in a closed-loop real-time environment, engineers can observe how a momentary drop in battery voltage affects motor thrust, and how that thrust reduction impacts the aircraft’s altitude and flight path .

Unreal Engine Photorealistic Visualization and Guidance Testing

To validate mission-level guidance, navigation, and control (GNC) algorithms, modern HIL setups integrate the real-time simulation engine with photorealistic 3D visualization environments, such as Epic Games’ Unreal Engine .

Using the custom workflow of specialized UAV simulation toolboxes, the aircraft’s 3D spatial coordinates are streamed from the real-time dynamics model directly to the visualization engine . This displays the aircraft flying within highly detailed, photorealistic urban environments.

During a guidance test, the operator can monitor the vehicle’s automated takeoff, transition to forward flight, hover adjustments, and vertical landing . The real-time interface displays motor speeds, terminal battery voltage, state of charge, and total current draw, enabling engineers to assess how environmental factors like crosswinds and turbulence impact battery life and propulsion efficiency.