TwinSpace

TwinSpace enhances software for sustainable automotive technology using digital twins

Digitalisation offers enormous potential to drive sustainable transformation across all sectors of the economy. It promises innovative solutions to tackle environmental problems and shape a resource-efficient future. At the same time, however, digitalisation itself is responsible for rising resource consumption and increased carbon emissions. This is why energy-efficient software systems are indispensable. And it is why the interdisciplinary team involved in the TwinSpace project aspires to develop a new design methodology for energy-efficient software in embedded systems, particularly in the automotive industry.

The focal point of the project is the use of a digital twin – TwinSpace – which digitally maps data such as the energy consumption of software components. This makes it possible to make early and accurate predictions about the resource requirements and energy efficiency of software. These findings enable system optimisation, which leads to cost savings and improved sustainability. In addition, the use of more efficient software and the targeted selection of optimised hardware enables the use of smaller and more energy-efficient chips, which reduces material consumption.

The TwinSpace project aims to bridge the gap between software and hardware development, two areas that have often been considered separately until now. Various digital technologies are used, including a database for abstracted applications and legacy projects as well as the ‘Load Profile Description Language’ (LPDL), an interface language for analysing the software load. Tools for simulation and testing on emulated hardware platforms are also being developed.

In the automotive industry in particular, where energy-efficient software and hardware play a key role, there are many potential applications for developments arising from the TwinSpace project. In the field of electric drive systems, TwinSpace can help to optimise the energy consumption of the control software and thus improve vehicle performance. There is also potential for autonomous driving: More efficient processing of large volumes of data will not only increase energy efficiency but also improve the performance of autonomous systems.

To ensure the successful transfer of results to the market, TwinSpace is collaborating closely with industry partners and customers to ensure that the solutions developed meet the actual needs of users and offer real added value. In addition, the project follows an open and transparent research practice by publishing the results obtained in scientific publications in order to share the methodology with the wider professional community and drive progress in the industry.

Consortium:

• e:fs TechHub GmbH
• CARIAD SE
• Absint Angewandte Informatik GmbH
• BTU-Cottbus Senftenberg
• emmtrix Technologies GmbH
• Tensor Embedded GmbH
• University of Augsburg
• University of Lübeck