Multiphysics simulation helps electric-racing engineers see how a change in one subsystem can affect the whole car before they commit to hardware. A battery that supplies more power may run hotter; a cooling solution can add mass or aerodynamic drag; and added downforce can improve grip while consuming more energy. Modeling those interactions helps teams compare designs and strategies against specific tracks, temperatures, loads, and energy limits. It can inform faster, more reliable designs, but it cannot guarantee either outcome or replace physical testing.
Why electric race-car design is a coupled problem
An electric race car is not a collection of independent components. Its battery, inverter, motor, cooling circuits, structure, tires, and aerodynamic surfaces exchange energy, heat, forces, and constraints. The car’s performance depends on how those systems behave together over a lap or a race stint—not just on the peak output of any one part.
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For example, increasing motor power may improve acceleration, but it can also increase electrical demand and heat. More cooling could help control temperatures, yet the cooling hardware and airflow it requires may affect mass, packaging, or drag. More aerodynamic downforce may help the tires generate grip, but the accompanying drag can raise energy consumption. The relevant design question is therefore not simply whether a component is better in isolation; it is whether the full vehicle can use that improvement within its operating limits.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsFormula E’s October 2026 announcement of its collaboration with COMSOL identifies electrical, thermal, structural, electromagnetic, fluid, and battery systems among the interacting challenges. COMSOL describes its software as an integrated environment for physics-based modeling and coupled-physics analysis. Those are examples of the domains engineers may need to consider; a useful model includes only the couplings material to the decision being made.
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What the different simulation tools do
“Simulation” can mean several different kinds of analysis. They can feed into one another, but they answer different questions and operate at different levels of detail.
| Approach | What it represents | Typical design question |
|---|---|---|
| Component or multiphysics model | Interactions such as battery electrical behavior and heat, motor electromagnetic behavior and temperature, or structural response to loads. | Will a component meet its electrical, thermal, or load limits under a defined operating profile? |
| Computational fluid dynamics (CFD) | Airflow around the car or flow through cooling passages, including forces such as drag and downforce. | How might a bodywork or cooling-flow change affect aerodynamic forces or heat removal? |
| Vehicle and lap simulation | Vehicle dynamics, component behavior, energy constraints, and track conditions combined to estimate vehicle or lap behavior. | How does a setup or operating strategy perform over a specific track scenario? |
| Optimization | A search across candidate designs or control strategies against specified objectives and constraints, using underlying models. | Which candidate best meets the defined objective without violating modeled limits? |
A high-detail component model and a reduced-order system model may be used for different purposes. Detailed analyses can represent local behavior, while reduced-order models can make repeated or system-level evaluations more practical. Neither is automatically the right choice: fidelity, runtime, available data, and the decision at hand all matter.
How engineers use simulation during design
A typical workflow begins with a concrete decision—such as comparing cooling layouts or a torque-distribution strategy—not with the assumption that one model should reproduce every detail of the car. Engineers set the operating scenario, select the physics that affect the decision, compare candidates, and check the predictions against measurements.
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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →- Define the scenario. Specify the track or duty cycle, speed or power profile, relevant ambient and component temperatures, loads, and regulatory or energy constraints. A race-strategy comparison should reflect the event or scenario being modeled rather than treating every circuit and condition as interchangeable.
- Select the coupled physics. Include the interactions that could change the result. A battery study might couple electrical performance with heat; an aero study could examine airflow and aerodynamic forces; a structural question may require assessing loads and deformation.
- Build a model at a useful level of detail. Use detailed component or CFD analyses where local behavior matters, and reduced-order or system-level representations where many repeated runs are needed. The model must be suitable for the question, not merely complex.
- Compare candidate designs or controls. Evaluate alternatives such as cooling layouts, motor sizing, powertrain choices, torque distribution, aerodynamic packages, or energy-management strategies under the same stated scenario and constraints.
- Check predictions against evidence. Compare model outputs with relevant component tests, rig or wind-tunnel data, track measurements, or telemetry. Agreement in tested conditions builds confidence within that range; it does not establish accuracy for every track, setup, or operating state.
- Refine and decide. Use discrepancies or new measurements to revisit assumptions, then weigh predicted performance against energy use, temperature, loads, mass, durability, and uncertainty before choosing what to build or test next.
Virtual comparisons can help teams avoid spending physical-prototype effort on candidates that look unsuitable under the modeled conditions. In its Porsche Formula E case study, Ansys (now part of Synopsys) describes virtual evaluation as helping decrease the need for physical prototypes. That is a vendor’s description of a particular use case, not a measured universal reduction in prototype count or development time.
Battery temperature and energy management
Battery state and temperature are linked. Electrical behavior determines how the battery responds to power demand, while temperature can affect its electrical performance. That makes a battery model more useful for race planning when it represents both behavior and heat rather than treating the available energy as a static number.
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The Ansys case study describes coupling an equivalent-circuit electrical model with a reduced-order thermal model for a Porsche Formula E use case. Against a track-specific power profile, the model estimates state of charge, voltage, and temperature. In practical terms, that kind of analysis can help engineers examine whether a power demand or energy strategy remains compatible with modeled battery behavior and thermal limits over the chosen profile.
Energy and thermal management also matter at the vehicle level. A driver or control strategy that uses power aggressively may change not only lap pace but also remaining energy and heat. The best strategy depends on the event scenario, the model’s constraints, and the objective being optimized—for instance, a qualifying lap versus an energy-limited race pace. A predicted strategy is not a guarantee that the same outcome will occur in competition, where actual conditions and measurements matter.
Downforce, drag, and the energy tradeoff
Aerodynamic choices illustrate why faster does not always mean more power or more downforce. Downforce can help tires generate grip, which can support cornering performance. But aerodynamic load comes with drag, and overcoming drag costs energy. The balance depends on the track, speed profile, grip needs, energy budget, and the rest of the car’s setup.
Olivier Champenois, Technical Project Leader Formula E at Porsche Motorsport, described the tradeoff in Porsche’s April 20, 2026 announcement of its GEN4 Formula E car, the 975 RSE: “Within roughly a decade, Formula E has become so fast that aerodynamic downforce is now a necessity. However, downforce always comes with drag and increases energy consumption.” That statement describes Porsche’s view of Formula E’s development and the design tradeoff; it is not a numerical rule for every circuit or electric race car.
CFD can help teams compare airflow and aerodynamic forces across candidate shapes or conditions. Those results can then inform vehicle or lap analyses that account for the car’s energy and performance constraints. Simulation is useful here because a change that appears favorable in an aerodynamic analysis may have a different value once its effects on energy use and lap behavior are considered.
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What published examples and figures do—and do not—show
Several recent examples illustrate the range of questions simulation can address. Their numbers should not be compared as if they measured the same car or outcome: they come from different vehicle generations, models, objectives, and methods.
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| Reported result | Scope and qualification |
|---|---|
| 600 kW (816 PS) | Porsche’s April 2026 figure for its announced GEN4 Formula E car, the 975 RSE. Porsche also described permanent all-wheel drive and new aerodynamic packages. |
| More than 97% drivetrain efficiency; less than 3% of energy lost from battery to wheels | Porsche’s 2026 claim for its current GEN3 Evo-generation 99X Electric. It is not a general efficiency figure for all electric race cars. |
| More than 4% improvement in achievable lap time | Broere, van Kampen, and Salazar’s 2021 Formula Student Netherlands simulation case: torque vectoring compared with a fixed power split for an all-wheel-drive electric race car. This is a modeled case result, not a measured Formula E gain. |
| 0.9 seconds per lap at the same energy target at three car lengths; around 1.4 seconds per lap at 0.5 car lengths | Results described by Canopy Simulation Services in July 2026 for a following car in Alexandra Gray’s MSc research, using an energy-limited Formula E Gen 3 simulation case. They are specific to that modeled scenario, not a universal benefit of following another car. |
| A 47-kilometer horizon computed in 2.5 seconds | Geisberger et al.’s 2024 preprint abstract describes this computation for its proposed optimization-based energy and thermal management approach. It is a research result, not a production-car benchmark. |
The figures answer different questions. Porsche’s power and efficiency figures describe particular cars and generations; the optimization studies report outcomes under stated simulation methods and comparisons. A modeled lap-time improvement is not equivalent to a track-measured improvement, and a computation time for an optimization horizon is not itself a measure of car performance.
How optimization helps—and where its answer stops
Optimization searches the modeled design or control space for candidates that satisfy constraints while pursuing a selected objective. That may involve seeking a lap-time outcome, managing energy, or balancing another performance measure against limits. It is a way to use a model systematically, not a substitute for engineering judgment.
The result depends on what the model allows the optimizer to vary, what it holds fixed, how the objective is defined, and which limits are imposed. If a model omits an important effect or uses inputs that do not match the real operating scenario, the mathematical optimum may not be the best real-world choice. Engineers therefore need to review candidate solutions for feasibility and validate relevant predictions against evidence.
The 2021 Formula Student study by Broere, van Kampen, and Salazar reports more than 4% better achievable lap time for torque vectoring than for a fixed power split in its simulation case. It demonstrates what optimization can reveal for that case and comparator; it does not establish the same gain for Formula E, another vehicle, or a different race scenario.
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Using simulation to support reliability
Reliability is supported by finding and addressing risks, not by running a simulation alone. Thermal analysis can help identify conditions in which a component may overheat; structural analysis can examine how a modeled component responds to loads; and coupled analyses can expose when a performance change creates a new temperature or stress concern elsewhere in the system.
For a model to inform a design decision, its inputs and assumptions must be appropriate to that decision, and its predictions should be checked against relevant tests or measurements. Validation only supports confidence over the conditions and outputs that were actually checked. An agreement in one tested configuration does not prove that a model covers every race, track surface, ambient temperature, or future design change.
The published examples described here establish engineering methods and selected case results, but they do not quantify a whole-car reliability improvement or show that simulation guarantees a dependable race car. Nor do they support a general percentage reduction in development time. Those outcomes depend on the car, process, evidence, and conditions involved.
Formula E’s current simulation context
On October 7, 2026, Formula E named COMSOL its Official Multiphysics Partner. The announcement says the collaboration will use COMSOL Multiphysics and explore simulation apps and digital twins. COMSOL’s announcement also says that version 2027 is scheduled for release later in autumn 2026, with electric-motor modeling and system-level simulation among the announced capabilities. That is a forward-looking company statement as of the announcement date, not confirmation that the version has shipped.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11“Formula E is a great example of why multiphysics simulation matters, as multiple phenomena are at play and constantly interact with one another. We are excited to work directly with Formula E’s technology team and put COMSOL Multiphysics to work on these kinds of real-world challenges.”
— Bjorn Sjodin, SVP of Product Management at COMSOL
The partnership is a current example of the sport’s interest in coupled modeling. It does not mean every team uses the same tool, model, or workflow, and a partnership announcement alone is not evidence of a quantified performance or reliability gain.
A practical way to judge a simulation claim
When evaluating a prediction or a reported result, ask what decision the analysis was meant to support and how closely its scenario resembles the question you care about. A lap-time result from one simulated student race car should not be read as a Formula E forecast; a battery prediction based on a particular power profile should not be generalized to a different circuit or ambient condition.
- Physics included: Which domains are modeled, and which interactions are coupled?
- Model fidelity and runtime: Is it a detailed component or CFD model, a reduced-order model, or a system-level analysis?
- Operating scenario: Does it represent a qualifying lap, an energy-limited race pace, an endurance stint, a particular track, or stated ambient conditions?
- Validation evidence: Was the prediction compared with component tests, rig or wind-tunnel measurements, or telemetry—and under what conditions?
- Decision and tradeoff: Is the analysis intended to guide component sizing, cooling, setup, strategy, or optimization? What does the chosen objective trade against, such as lap time versus energy use or grip versus drag?
Those details determine how far a result can be carried beyond the modeled case. The most useful interpretation is usually the narrowest one supported by the model and its validation.
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