Virtual prototypes in heavy equipment: A practical tool for managing complexity

By Veronica Drake

Designing next-generation heavy equipment is fundamentally different. Electric drivetrains, embedded control systems and autonomous functionality are changing how machines are engineered and validated. But even as technical complexity rises, time-to-market expectations haven’t moved.

You can’t solve that gap with iterative methods and recurrent prototyping.

That’s why OEMs are redefining the role of virtual prototypes: they are no longer merely a tool for validation purposes and to gain specific insights, but also to drive decisions for optimizing performance and improving reliability before anything physical is built.

From disconnected models to predictive engineering

Engineering teams often have many engineering simulation tools that focus on individual domains. But solving today’s most critical design problems, like software-controlled drivetrains or thermally sensitive battery systems, requires coordination across domains. And it requires answers earlier in the development cycle, not at the prototype or test bench stage.

That’s where virtual prototypes come into play.

Unlike domain-specific engineering simulation models, virtual prototypes are built to represent the full system, integrating mechanical, electrical, thermal, hydraulic and control models into a shared digital environment. They allow engineers to virtually test realistic, real-time interactions across subsystems under a wide range of operating conditions, even before detailed CAD geometry is complete.

And they allow it to happen continuously throughout the development cycle.

This makes it possible to frontload key design questions:

Will a new powertrain architecture compromise cooling performance during peak load?

Can we trust a new software control loop to handle nonlinear terrain transitions safely?

How will reconfiguring the hydraulic system affect machine efficiency or operator comfort?

By surfacing these interactions early, virtual prototypes reduce costly late-stage changes, de-risk innovation and align performance expectations across teams.

Supporting cross-domain engineering without slowing down

Electrification and autonomy fundamentally shift how machines behave and lead to systems where mechanical, software and thermal domains are tightly coupled. That makes integration more complex than ever. Relying on physical prototypes to debug these interactions isn’t just expensive, it’s impossible.

With a virtual prototype that enables high-fidelity engineering simulation, teams can:

Evaluate control strategies against virtual physics in edge-case scenarios

Analyze subsystem interactions during full-duty cycles, including terrain variation, tool changes or thermal buildup

Dramatically reduce iterations by identifying failure modes or inefficiencies before hardware exists

For example, in the context of electric excavators, engineers can simulate thermal propagation from battery cells under varying ambient and load conditions, then test how control logic impacts cooling system demand. Instead of relying on best guesses or waiting on field tests, they can make informed decisions in days, not months.

This approach is particularly valuable for companies who want to differentiate themselves from the competition by bringing innovation faster to market and with greater confidence.

Making simulation practical at scale

A virtual prototype doesn’t succeed in isolation. To drive real business value, it must be part of a repeatable, scalable process — one that connects to design tools, test data and analytics environments. Siemens’ Predictive Performance Engineering digital thread is built for this level of integration.

It connects virtual prototypes to:

Requirements management and system architecture definitions

CAE and test systems

PLM systems that manage model configurations, reuse and traceability

Post-processing and analytics environments for deeper insight and optimization

That connection ensures virtual prototypes reflect real design intent and operational needs. It also helps teams avoid the trap of starting from scratch each time, by enabling libraries of validated sub-models and architectures that can be reused and adapted across programs.

The result: fewer surprises late in development, and more time spent on engineering better solutions.

Not a replacement — an evolution of the process

Virtual prototypes won’t eliminate the need for hardware. Physical testing will continue to play a key role in final validation and certification and will also be necessary to support better modeling assumptions. But when and how issues are found is now different, and how confidently systems can be designed in the first place.

Virtual prototypes also enable new possibilities, like:

Testing in silico for safety-critical scenarios that are too dangerous or impractical to reproduce physically

Rapid exploration of design variants to discover radically new designs that deliver unmatched performance

Continuous validation, where the same models that support early design are extended into V&V and even into in-field monitoring

Training and certification of algorithms for autonomous capabilities

In short, they bridge the gap between the conceptual and the physical, giving engineers a way to manage uncertainty with data instead of guesswork.

Engineering the next generation of heavy equipment

As the heavy equipment industry pushes toward electrification, autonomy and smarter software, the stakes for getting integration right have never been higher. OEMs need tools that help them move fast without sacrificing reliability. They need confidence in performance before the first bolt is tightened.

Virtual prototypes deliver that confidence, not just by simulating components, but by using engineering simulation to reveal how complex systems behave in the real world.

They aren’t a replacement for physical testing. But in today’s environment, they’re becoming essential for managing complexity, accelerating innovation and effectively delivering equipment that performs reliably in the real world.