Research creates knowledge for the benefit of our customers

The European Climate Law sets the target of reducing net greenhouse gas emissions by at least 55% by 2030 and for Europe to become climate-neutral by 2050. To meet these ambitious goals open standards and established modelling and simulation tools need be enhanced to better support large-scale systems and distributed controllers optimized to minimize energy consumption and greenhouse gas emissions.

Several industrial demonstrators will showcase how the OpenSCALING innovations are applied in the domains of

• energy,
• buildings,
• aviation,
• and automotive

through green hydrogen production, more efficient heat pumps, fuel cell propulsion and electrified vehicles.

Significant improvements are expected in terms of managing magnitudes larger system models in the virtual engineering process using the open standards Modelica, FMI, eFMI and SSP. This includes:

• reduction of compilation and simulation times,
• speed-up of simulations by means of machine learning surrogate models,
• and the tool support of traceable and credible modeling and simulation processes.

PARTNER

The ITEA4 project is conducted in close cooperation of european partners from six countries.

Research Funding

The research project OpenSCALING is supported by the Federal Ministry of Education and Research (number 01IS23062K) and XRG participates from April 2025 to December 2026.

The vehicle of the future is “smart”. It is expected that a vehicle can react flexible on changes of its environment and make decisions independently in order to optimally adapt to changing boundary conditions. This requires a high degree of "self-awareness", i.e. the ability to predict the effects of its own behavior in interaction with the environment. Thus, creating such models of vehicle and environment quickly, cost-effectively and in consideration of fidelity and performance is a key competence. Classic model-based approaches are often associated with high development costs. Advances in artificial intelligence open up new options, but are data-intensive and involve other risks. In this project, hybrid (data and physics-based) approaches are to be evaluated in concrete applications in order to be able to generate scalable “proper models” in a data-efficient manner using existing physical knowledge. In future, this will make it possible to develop innovative product properties in a significantly shorter time and to implement them in vehicles.

In this project, XRG provides a physical vehicle cabin model from which a real-time capable model is to be derived. The accelerated model offers a wide range of possible uses in HiL applications and enables the relevant model information to be transferred to control hardware. At the same time, it opens up the possibility of significantly accelerating conceptual optimization. XRG will develop methods with its project partners and then implement them as a prototype extension for the XRG software SCORE. The developed routines form the basis for the final demonstration of the creation of a proper model taking into account several different tool chains.

PARTNER

The following german institutions are partnering in this joined project:

• Robert Bosch GmbH, Stuttgart,
• Universität Augsburg, Augsburg,
• Technische Universität Braunschweig, Braunschweig,
• Fachhochschule Bielefeld, Bielefeld,
• TLK-Thermo GmbH, Braunschweig,
• ESI ITI GmbH, Dresden,
• LTX Simulation GmbH, München,
• Modelon Deutschland GmbH, Hamburg.

Research Funding

The research project PHyMoS is supported by the Federal Ministry of Economic Affairs and Energy (number 19I20022F) and runs from March 2021 to February 2024.