And they allow it to happen continuously throughout the development cycle. <\/p>\n\n\n\n
This makes it possible to frontload key design questions: <\/p>\n\n\n\n
- \n
- Will a new powertrain architecture compromise cooling performance during peak load?\u00a0<\/li>\n<\/ul>\n\n\n\n
- \n
- Can we trust a new software control loop to handle nonlinear terrain transitions safely?\u00a0<\/li>\n<\/ul>\n\n\n\n
- \n
- How will reconfiguring the hydraulic system affect machine efficiency or operator comfort?\u00a0<\/li>\n<\/ul>\n\n\n\n
By surfacing these interactions early, virtual prototypes reduce costly late-stage changes, de-risk innovation and align performance expectations across teams. <\/p>\n\n\n\n
Supporting cross-domain engineering without slowing down <\/h2>\n\n\n\n
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\u2019t just expensive, it\u2019s impossible. <\/p>\n\n\n\n
With a virtual prototype that enables high-fidelity engineering simulation, teams can: <\/p>\n\n\n\n
- \n
- Evaluate control strategies<\/strong> against virtual physics in edge-case scenarios\u00a0<\/li>\n<\/ul>\n\n\n\n
- \n
- Analyze subsystem interactions<\/strong> during full-duty cycles, including terrain variation, tool changes or thermal buildup\u00a0<\/li>\n<\/ul>\n\n\n\n
- \n
- Dramatically reduce iterations<\/strong> by identifying failure modes or inefficiencies before hardware exists\u00a0<\/li>\n<\/ul>\n\n\n\n
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. <\/p>\n\n\n\n
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. <\/p>\n\n\n\n
Making simulation practical at scale <\/h2>\n\n\n\n
A virtual prototype doesn\u2019t succeed in isolation. To drive real business value, it must be part of a repeatable, scalable process \u2014 one that connects to design tools, test data and analytics environments. Siemens\u2019 Predictive Performance Engineering digital thread is built for this level of integration. <\/p>\n\n\n\n
It connects virtual prototypes to: <\/p>\n\n\n\n
- \n
- Requirements management and system architecture definitions\u00a0<\/li>\n<\/ul>\n\n\n\n
- \n
- CAE and test systems\u00a0<\/li>\n<\/ul>\n\n\n\n
- \n
- PLM systems that manage model configurations, reuse and traceability\u00a0<\/li>\n<\/ul>\n\n\n\n
- \n
- Post-processing and analytics environments for deeper insight and optimization\u00a0<\/li>\n<\/ul>\n\n\n\n
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. <\/p>\n\n\n\n
The result: fewer surprises late in development, and more time spent on engineering better solutions. <\/p>\n\n\n\n
Not a replacement \u2014 an evolution of the process<\/strong> <\/h2>\n\n\n\n
Virtual prototypes won\u2019t 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. <\/p>\n\n\n\n
Virtual prototypes also enable new possibilities, like: <\/p>\n\n\n\n
- \n
- Testing in silico<\/strong> for safety-critical scenarios that are too dangerous or impractical to reproduce physically\u00a0<\/li>\n<\/ul>\n\n\n\n
- \n
- Rapid exploration<\/strong> of design variants to discover radically new designs that deliver unmatched performance\u00a0<\/li>\n<\/ul>\n\n\n\n
- \n
- Continuous validation<\/strong>, where the same models that support early design are extended into V&V and even into in-field monitoring\u00a0<\/li>\n<\/ul>\n\n\n\n
- \n
- Training and certification <\/strong>of algorithms for autonomous capabilities\u00a0<\/li>\n<\/ul>\n\n\n\n
In short, they bridge the gap between the conceptual and the physical, giving engineers a way to manage uncertainty with data instead of guesswork. <\/p>\n\n\n\n
Engineering the next generation of heavy equipment <\/h2>\n\n\n\n
As the heavy equipment<\/a> 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. <\/p>\n\n\n\n
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. <\/p>\n\n\n\n
They aren\u2019t a replacement for physical testing. But in today\u2019s environment, they\u2019re becoming essential for managing complexity, accelerating innovation and effectively delivering equipment that performs reliably in the real world. <\/p>\n\n\n\n
![\"\"](\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/42\/2025\/09\/PPE-ABM-Evaluate-Copy-1-970x250-1.jpg\")
<\/a><\/figure>\n\n\n\nAdditional heavy equipment simulation and testing resources can be found here:<\/strong> <\/p>\n\n\n\n
Ebook: Create next generation heavy equipment designs with physics simulation<\/a> <\/p>\n\n\n\n
- Training and certification <\/strong>of algorithms for autonomous capabilities\u00a0<\/li>\n<\/ul>\n\n\n\n
- Continuous validation<\/strong>, where the same models that support early design are extended into V&V and even into in-field monitoring\u00a0<\/li>\n<\/ul>\n\n\n\n
- Rapid exploration<\/strong> of design variants to discover radically new designs that deliver unmatched performance\u00a0<\/li>\n<\/ul>\n\n\n\n
- Testing in silico<\/strong> for safety-critical scenarios that are too dangerous or impractical to reproduce physically\u00a0<\/li>\n<\/ul>\n\n\n\n
- Post-processing and analytics environments for deeper insight and optimization\u00a0<\/li>\n<\/ul>\n\n\n\n
- PLM systems that manage model configurations, reuse and traceability\u00a0<\/li>\n<\/ul>\n\n\n\n
- CAE and test systems\u00a0<\/li>\n<\/ul>\n\n\n\n
- Requirements management and system architecture definitions\u00a0<\/li>\n<\/ul>\n\n\n\n
- Dramatically reduce iterations<\/strong> by identifying failure modes or inefficiencies before hardware exists\u00a0<\/li>\n<\/ul>\n\n\n\n
- Analyze subsystem interactions<\/strong> during full-duty cycles, including terrain variation, tool changes or thermal buildup\u00a0<\/li>\n<\/ul>\n\n\n\n
- Evaluate control strategies<\/strong> against virtual physics in edge-case scenarios\u00a0<\/li>\n<\/ul>\n\n\n\n
- How will reconfiguring the hydraulic system affect machine efficiency or operator comfort?\u00a0<\/li>\n<\/ul>\n\n\n\n
- Can we trust a new software control loop to handle nonlinear terrain transitions safely?\u00a0<\/li>\n<\/ul>\n\n\n\n