Electric and hybrid aircraft are reshaping the future of aviation by promising lower emissions, reduced noise, and greater energy efficiency. However, developing these advanced propulsion systems introduces unprecedented complexity—managing high-voltage power, thermal loads, and integrated controls that have no direct precedent in conventional aircraft. System simulation has become the engineering backbone that makes this transformation feasible, allowing teams to design, test, and validate aircraft systems virtually long before the first prototype takes shape. By replacing costly physical experiments with digital models, simulation accelerates development cycles, improves safety margins, and enables the iterative optimization required to make electric flight commercially viable.

The Evolution of System Simulation in Aerospace

System simulation in aerospace is not new—engineers have used mathematical models for decades to predict aerodynamics, structural loads, and flight dynamics. What has changed dramatically is the scope and fidelity of those models. Early simulations focused on isolated components (a single engine, a hydraulic actuator) using linear equations and limited computing power. Modern system simulation, by contrast, treats the entire aircraft as a coupled network of electrical, thermal, mechanical, and control systems. This shift is driven partly by the rise of electric and hybrid architectures, where interactions between a battery pack, power electronics, electric motors, and thermal management are tightly entwined and highly nonlinear.

Today's tools—such as Modelica-based platforms, Simulink, and specialized aerospace simulation suites—allow engineers to run hardware-in-the-loop (HIL) and software-in-the-loop (SIL) tests that mirror real-world conditions. These approaches have been validated by agencies like the FAA and NASA for certification of critical systems, establishing simulation as a trusted complement to physical testing.

Core Benefits of Simulation for Electric and Hybrid Aircraft

While the high-level advantages of simulation are often listed as cost savings and faster iteration, the specific benefits for electric and hybrid aircraft development deserve deeper exploration.

Reducing Physical Prototyping Risk

Building a full-scale electric powertrain prototype can cost millions of dollars and take months. Simulation allows engineers to evaluate dozens of motor winding configurations, battery cell chemistries, and cooling strategies without ever ordering a single part. This virtual prototyping eliminates the time wasted on dead-end designs and ensures that the first physical build is already near-optimal.

Multi-Physics Optimization

Electric and hybrid systems exhibit strong coupling between electrical, thermal, and mechanical domains. For example, a motor’s efficiency changes with temperature, which in turn affects battery discharge profiles and thermal management loads. Simulation tools that support multi-physics co-simulation enable engineers to find trade-offs that would be impossible to calculate manually. A design that performs well electrically may overheat; simulation reveals that conflict early.

Certification by Analysis

Regulatory bodies like the FAA and EASA are increasingly open to “certification by analysis” where simulation evidence supplements or even replaces physical test data. For novel architectures such as distributed electric propulsion or hybrid series configurations, simulation can demonstrate compliance with safety standards for failure modes, load conditions, and system redundancy. This reduces the number of costly certification test flights.

Predicting Performance Across Flight Envelope

Hybrid aircraft must manage power blending between a gas turbine and electric motors across takeoff, climb, cruise, descent, and emergency scenarios. Simulation allows engineers to model energy management strategies, battery state-of-charge trajectories, and thermal responses over an entire mission profile. This level of prediction is essential for sizing batteries and defining control logic that ensures safe, efficient operation.

Key Application Areas: Where Simulation Makes the Difference

System simulation touches every subsystem of an electric or hybrid aircraft, but certain areas require especially rigorous virtual testing due to their novelty and criticality.

Electrical Power Systems

The electrical power system in a conventional aircraft is relatively simple—it generates, distributes, and stores a modest amount of DC or AC power. In an electric aircraft, the power system must handle megawatts of energy, with voltage levels reaching 800 V or higher. Simulation models here cover battery electrochemistry (including degradation and thermal runaway), power converters (inverters, DC-DC converters), protection circuits, and wiring harness impedance. Engineers use these models to design grounding schemes, fault detection algorithms, and load shedding sequences that prevent catastrophic failures.

For hybrid architectures, simulation also models the generator driven by a turbine or fuel cell, including its dynamic response to load transients. Tools like MathWorks Aircraft Electrical Power System Simulation are commonly used for this analysis.

Propulsion Systems

Electric motors and their controllers are the heart of the propulsion system. Simulation allows engineers to examine motor topologies (e.g., permanent magnet synchronous, induction, switched reluctance) under different torque and speed profiles. High-fidelity electromagnetic finite element analysis (FEA) is often combined with thermal and structural models to predict efficiency maps, cogging torque, and vibration. Virtual testing also includes inverter switching strategies and pulse-width modulation (PWM) effects, which can create harmonics that degrade motor performance or cause electromagnetic interference with avionics.

Thermal Management Systems

Heat is the enemy of electric propulsion. Batteries generate significant heat during discharge and even more during rapid charging; motors and power electronics also dissipate substantial thermal energy. Simulation models the entire thermal pathway: heat generation at the source, conduction through materials, convection in cooling loops, and rejection to ambient air via radiators or heat exchangers. Engineers can experiment with liquid cooling, phase-change materials, or ram-air cooling to maintain component temperatures within safe limits. Accurate thermal simulation is critical because exceeding temperature thresholds leads to reduced performance, accelerated aging, or catastrophic failure.

Avionics and Flight Control Systems

Electric and hybrid aircraft often integrate fly-by-wire controls and sophisticated energy management algorithms that coordinate propulsion and thermal systems. Simulation validates the software logic that governs these functions—such as limiting motor torque based on battery state-of-charge or switching between power sources during a failure. Hardware-in-the-loop (HIL) simulation with real flight controllers ensures that the control algorithms behave correctly when connected to simulated sensors and actuators.

Electromagnetic Compatibility (EMC)

High-power electrical systems generate strong electromagnetic fields that can interfere with communications, navigation, and flight control electronics. Simulation of electromagnetic interference (EMI) and compatibility (EMC) is becoming mandatory for certification. Engineers model cable routing, shielding effectiveness, and filter designs to ensure that the aircraft meets radiated and conducted emission limits. This is an area where physical testing is expensive and time-consuming, making simulation particularly valuable.

The Simulation Workflow: From Concept to Certification

Effective system simulation is not a one-time activity—it follows a structured workflow that parallels the aircraft development lifecycle.

Phase 1: Requirements and Architecture Trades

At the conceptual stage, simulation helps engineers evaluate different architectures: all-electric versus serial hybrid versus parallel hybrid versus turboelectric. Key metrics—range, payload, energy consumption, weight—are modeled using low-fidelity analytical models or surrogate models. This phase identifies the most promising configuration before detailed design begins.

Phase 2: Component and Subsystem Design

Once the architecture is chosen, each subsystem is simulated with higher fidelity. Battery packs are modeled with thermal runaway propagation, power electronics with switching losses, motors with electromagnetic FEA. This phase often uses co-simulation where different tools exchange data—for example, a motor FEA model sends loss data to a thermal model, which feeds back temperature-dependent resistance.

Phase 3: Integrated System Verification

The individual subsystem models are combined into an integrated system model that represents the complete aircraft. This model runs mission profiles—full takeoff, climb, cruise, descent, landing—including failures (e.g., one motor out, battery cell short). The behavior of control logic, protection systems, and energy management algorithms are verified against requirements. This is also where certification evidence starts to be generated.

Phase 4: Hardware-in-the-Loop and Validation

Before a prototype is built, critical components are tested in hardware-in-the-loop setups. For example, a real battery pack or a motor controller is connected to a real-time simulation that emulates the rest of the aircraft. This reveals issues like real-time communication delays, sensor noise, and control loop instabilities that pure software simulations might miss. The results are used to calibrate and refine the simulation models for subsequent phases.

Phase 5: Certification Support

Throughout the development, simulation data is documented to support certification. The simulation models themselves must be qualified (verified and validated) according to standards like DO-331 (Model-Based Development). Simulation results—such as worst-case thermal profiles, fault tolerance demonstrations, or performance margins—become part of the compliance data submitted to authorities.

Challenges and Limitations of System Simulation

Despite its power, system simulation is not a silver bullet. Engineers must be aware of its limitations to avoid overconfidence.

Model Fidelity vs. Computational Cost

High-fidelity models (e.g., 3D electromagnetic FEA, computational fluid dynamics for thermal analysis) provide accurate results but require hours or days to run on high-performance computing clusters. For design space exploration, lower-fidelity lumped-parameter models are often used, but they may miss important physical phenomena. Balancing fidelity and computational cost is an ongoing challenge, especially for optimization studies that need many simulation runs.

Model Validation Data

Simulation models are only as good as the data used to validate them. For novel technologies like solid-state batteries or high-temperature superconductors, there may be insufficient experimental data to calibrate the models. This creates uncertainty that must be bounded by sensitivity analysis and conservative design margins—which can erode the benefits of simulation.

Real-Time Simulation Constraints

Hardware-in-the-loop simulation requires real-time execution, which imposes strict timing constraints. Complex multi-physics models often cannot run fast enough, forcing engineers to simplify them. This simplification can introduce errors that affect the HIL test quality. Advances in GPU-accelerated computing and reduced-order models are mitigating this, but it remains a limitation.

Regulatory Acceptance

While certification by analysis is growing, regulators still demand physical testing for certain critical failure modes—especially those involving fire, toxic fumes, or structural collapse. Simulation can reduce the number of required tests but cannot always replace them entirely. The aviation industry is working with authorities to define acceptable methods for virtual certification, but it remains a work in progress.

The next decade will see system simulation become even more deeply embedded in aircraft development and operation.

AI-Enhanced Simulation

Machine learning is being used to create surrogate models that approximate complex physics simulations in milliseconds. These models can then be used for real-time optimization during design or even during flight. For example, a neural network trained on thousands of thermal simulation runs can predict battery temperatures in fractions of a second, enabling active thermal management that adapts to flight conditions.

Digital Twins Throughout Lifecycle

A digital twin is a continuously updated simulation model that mirrors an actual aircraft in service. It uses sensor data from the real aircraft to refine its predictions of remaining useful life, component health, and energy consumption. For electric aircraft, where battery degradation is a major operational cost, a digital twin can optimize charging schedules and power usage to extend battery life. This concept is already being explored by Lockheed Martin and others for military platforms and is expected to migrate to commercial electric aircraft.

Integrated Multi-Fidelity Frameworks

Future simulation platforms will seamlessly combine low-fidelity system-level models with high-fidelity component models, automatically swapping fidelity levels based on the analysis need. This will allow engineers to run a single simulation that starts with rough architecture trades and then zooms into detailed thermal analysis of a hot spot, all within the same environment. Such frameworks will dramatically reduce the time from concept to validated design.

Quantum Computing for Simulation

Although still in its infancy, quantum computing holds promise for simulating complex electrochemical reactions in batteries or molecular interactions in novel materials. If quantum simulators become practical, they could enable the design of battery cells with far higher energy density and safety—a game changer for electric aviation.

Conclusion

System simulation is not merely a convenience for electric and hybrid aircraft development—it is a necessity. The complexity of multi-domain interactions, the high cost of failures, and the regulatory demands for safety all push engineers to rely on virtual testing more than ever before. From initial architecture decisions to final certification data, simulation provides the insights needed to build aircraft that are not only greener but also safer and more reliable. As simulation technologies continue to evolve—incorporating AI, digital twins, and quantum computing—they will accelerate the arrival of a new era in aviation where electric flight is routine, efficient, and trusted. For the engineers and organizations that embrace these tools, the sky is no longer the limit—it is the testing ground.