Research International

Projekttitel: BMH - EDIH Mobility Infrastructure Decarbonisation in Mid-Germany
Projektlaufzeit: Juli 2023 - Juni 2026
Förderkennzeichen: 101120343
Projektleiter:Univ.-Prof. Dr. rer. nat. habil. Matthias Hein
Fachgebiet: Hochfrequenz- und Mikrowellentechnik

The vision of a digital and green European Union needs the common effort of businesses, public organisations, research institutions and creatives as well as a proactive citizen engagement. To foster the development and implementation of innovations, there is a high demand for digital skills, training programmes, testing and experimentation facilities, financial support, and interconnected ecosystems.

The Bauhaus.Mobility Hub incorporates this vision and these services as a European Digital Innovation Hub (EDIH) candidate with a dedicated focus on the mobility, logistics and energy sectors. It acts as a regional one-stop shop in Central Germany for the digital transformation of private companies and the public sector. With its interdisciplinary expertise on the application of artificial intelligence, high performance computing, cybersecurity and digital skill training, the Bauhaus.Mobility Hub addresses the most important challenges for a digital European Union.

By expanding the active Bauhaus.Mobility innovation ecosystem through a strong EDIH network, it offers prospective customers unique possibilities to implement forward-thinking digital solutions. The Bauhaus.Mobility Hub will foster the regional digital maturity, induce new digital products and services, stimulate significant private as well as public investments and thus be a key actor in shaping the digital future.

Projekttitel: BRAINET - Networked Distributed Neural Interfaces for Interference-Based Brain Stimulation
Projektlaufzeit: Januar 2026 - Dezember 2029
Förderkennzeichen: 101225775
Projektleiter: Prof. Dr. Jens Haueisen
Fachgebiet: Biomedizinische Technik
Fakultät: Informatik und Automatisierung

Invasive Deep Brain Stimulation (DBS) has become an important way to treat neurological diseases within the greying European society making a tether-less improvement desireable. The BRAINET team is convinced to have identified such an alternative in spatiotemporal Interference Stimulation (stIS). stIS avoids implantation into the brain and uses instead pairs of electrodes to precisely target activating beating rhythms in the depth of the brain at the therapeutic region, without requiring deep seated electrodes. In order to unleash this stimulation power in all regions of the brain, BRAINET sets out to develop skull implantable stimulation hubs which build a communication network and 3D targeting by self-organization, providing semantic information transfer and synchronization, and thus enabling virtual, self-adjusting pairs for therapeutic stIS. These stimulation hubs will be powered by novel magneto-electrical transducer stacks, driven by slowly alternating magnetic fields from the outside and will perform adjustable frequency stimulation by novel nano- modified, compliant electrodes only touching the brain. Network formation will be enabled by multiphysics approaches and ionic in- body communication instantiated in dedicated microelectronic hardware. Preclinical in-vitro and in-vivo Testung of devices and methods will allow the community to make important steps towards subsequent clinical implementation of our „Wireless Transcalvarial Brain Stimulators“. BRAINET will allow us to prepare the next generation of researchers, arming them with the knowledge, skills, and visionary insights necessary to drive neuro-technology to replace the present DBS into uncharted, but reliably safer and more flexible territories. BRAINET is an engine for excellence, interdisciplinary convergence, and innovation. Its dual mandate is to establish a training network and thus to lay the groundwork for emerging leaders.

Projekttitel: CODE4EV - Collaborative Development Framework for Electric Software-defined Vehicles
Projektlaufzeit: Januar 2025 - Dezember 2027
Förderkennzeichen: 101192739
Projektleiter: Dr. Valentin Ivanov
Fachgebiet: Regelungstechnik

The CODE4EV project aims to accelerate the development of electric software-defined vehicles (SDVs) by establishing a collaborative
development framework. This framework will support the design, production and operational phases of electric vehicles (EVs) by
demonstrating its application through selected Use Cases relevant to emerging and future SDV architectures.
The project key objectives include the elaboration of digital design tools and a trustworthy development methodology for electric
SDVs, improving the efficiency and reliability of SDV architecture component sharing, and accelerating validation processes. The
project also focuses on the implementation of a model-based design, the development of a symbolic ontology knowledge database,
and the migration from rapid prototyping environments to automotive SW environments to improve development processes and
compliance with industry standards.
In addition, CODE4EV aims to provide multi-layered benefits throughout the design, production and operational phases of EVs. This
includes methods for defining the SDV architecture, real-time runtime virtualisation approaches, and developing modular HW
architectures to optimise data usage.
The project Use Cases will demonstrate the implementation of the collaborative development framework, such as data-driven EV
optimisation, health monitoring and predictive maintenance, and smart motion control. These Use Cases aim to demonstrate
improvements in energy consumption, component life extension and overall vehicle performance.
CODE4EV plans to develop virtual, hybrid and full-scale demonstrators of electric SDVs for different vehicle categories, focusing on
efficient verification procedures and the evaluation of the scalability of the CODE4EV approach. These efforts aim to ensure
compatibility and efficiency for a range of vehicle types, including heavy-duty trucks and L-class EVs, thereby making an important
contribution to the promotion of zero-emission mobility solutions.

Projekttitel:CryoMet - Metrology for reliable liquefied energy gases measurement
Projektlaufzeit: August 2025 - Juli 2028
Förderkennzeichen:24GRD07
Projektleiter: Prof. Dr.-Ing. Thomas Fröhlich
Fachgebiet: Prozessmesstechnik
Fakultät: Maschinenbau

The fit for 55 package of legislation requires an increased usage of liquefied energy gases to reach 55 % net greenhouse gas emissions reduction by 2030. However, reliable measurement methods for (bio-)LNG and liquefied hydrogen (LH2) are not yet fully developed. Bio-LNG is defined here as (predominantly) liquefied methane originating from biomethane and/or biogas and traceable measurements for both it and LH2 are urgently needed across the supply chain. In order to address this issue, this project will develop reference data sets and technical evidence to support the acceptance of measurement methods for (bio-)LNG and LH2. This will include verification of the results and datasets under in field conditions and by SI traceable intercomparisons. The project’s outcomes should enable the European liquefied gas metrological framework to be expanded, and new SI traceable calibration procedures to be developed. The project’s liquefied gas measurement methods and good practice guidance will also be disseminated and promoted to standardisation bodies, the measurement supply chain, and end users to support their wide uptake.

Projekttitel:DENSE - Dependable Smart Energy Systems
Projektlaufzeit: Januar 2024 - Dezember 2027
Förderkennzeichen:101120278
Projektleiter: Prof. Dr.-Ing. Johann Reger
Fachgebiet:Regelungstechnik
Fakultät:Informatik und Automatisierung

DENSE is addressing individual research projects and training of early-stage researchers (ESRs) in the innovative dependable
engineering of Smart Energy Systems (SESs) with the main focus on robustness as well as preventive and corrective actions under
uncertainty.
Within this concept, the term "Smart Energy Systems (SESs)" refers to a holistic cross-sectoral approach (e.g. electricity, heating,
cooling, industry, buildings and transportation) aimed at excelling the transformation towards sustainable and achievable future energy
systems. Hence, it both covers but also extends beyond the Smart Grid approach, which is mainly focused on the electricity sector.
Consequently, SESs exhibit the following attributes:
(i) Complex interactions on sub-system and cross-sectoral systems-of-systems levels;
(ii) Cyber-physical characteristics with digital and physical network connections;
(iii) Ability to operate in non-stationary, uncertain and severe environments.
Dependability of complex networks, such as SESs, characterizes their ability to deliver service that can justifiably be trusted. Thus,
dependability comprises system attributes, such as availability, reliability, safety, integrity and maintainability. A key requirement of
dependability is the desire for providing justifiable trust in the system performance. Hence, rigorous systems engineering yielding
provable performance guarantees throughout the system's life time is already required at the design stage. This challenge is tackled
in DENSE with a focus on operational robustness as well as preventive and corrective actions in SESs. As a consequence, DENSE is
well-aligned with the EU Commission's headline ambitions on the European Green Deal as well as the strive for grasping the
opportunities from the digital age, while increasing social fairness and prosperity.

Projekttitel: EM-TECH - Innovative e-motor technologies covering e-axles and e-corners vehicle architectures for high-efficient and sustainable e-mobility
Projektlaufzeit: Januar 2023 - Dezember 2025
Förderkennzeichen: 101096083
Projektleiter: Dr. Valentin Ivanov
Fachgebiet: Regelungstechnik

EM-TECH brings together 10 participants from industry and academia to develop novel solutions to push the boundaries of electric machine technology for automotive traction, through: i) innovative direct and active cooling designs; ii) virtual sensing functionalities for the high-fidelity real-time estimation of the operating condition of the machine; iii) enhanced machine control, bringing reduced design and operating conservativeness enabled by ii); iv) electric gearing to provide enhanced operational flexibility and energy efficiency; v) digital twin based optimisation, embedding systematic consideration of Life Cycle Analysis and Life Cycle Costing aspects since the early design stages; and vi) adoption of recycled permanent magnets and circularity solutions.The proposed innovations will be implemented in new series of radial flux direct drive in-wheel motors characterised by so far unexplored levels of torque density (>150 Nm/litre, >50 Nm/kg), and on-board single stator double rotor type ironless axial flux machines providing power density and specific power levels in excess of 30 kW/litre and 10 kW/kg. The solutions will address both passenger car and van applications (continuous power levels of 50 kW - 120 kW), providing competitive costs (<6 Euro/kg for a production of 100000 units/year), and leading to significant reduction of motor energy loss during real vehicle operation (>25%), and to >60% decrease of the rare earth content, including implementation of magnet recycling solutions. EM-TECH obtained the support of several car makers (AUDI and Changan UK), which will strengthen the exploitation strategy. EM-TECH will further directly contribute to the relevant European Destination and KSO C and A, by supporting the establishment of a European leadership in the sector of key digital, enabling and emerging technologies, and the development of the respective value chains.

Projekttitel: HighScape - High efficiency, high power density, cost effective, scalable and modular power electronics and control solutions for electric vehicles
Projektlaufzeit: Januar 2023 - Dezember 2025
Förderkennzeichen: 101056824
Projektleiter: Dr.-Ing. Valentin Ivanov
Fachgebiet: Kraftfahrzeugtechnik
Bearbeiter in Ilmenau: Thüringer Innovationszentrum (ThIMo)

HighScape proposes a set of research and innovation activities to develop, test and validate innovative next-generation battery electric vehicle (BEV) solutions that can only be achieved through recent wide bandgap (WBG) technologies. Focused on BEV architectures with distributed multiple wheel drives, and, specifically, in-wheel power-trains, HighScape will explore the feasibility of a family of highly efficient, integrated, compact, cost-effective, scalable and modular power electronics components and systems, including integrated traction inverters, on-board chargers, DC/DC converters, and electric drives for auxiliaries and actuators. The proposed solutions will achieve automotive quality levels with robust and reliable functionalities and materials, which will be assessed and validated on test rigs and on two differently sized BEV prototypes carried over from previous European initiatives. The project will result in: i) component integration at a level hitherto impossible, e.g., with the incorporation of the WBG traction inverters within the in-wheel machines to achieve zero footprint of the electric power-train on the sprung mass; the functional integration of the traction inverter with the on-board charger, and the incorporation of the latter and the DC/DC converters within the battery pack; and the implementation of multi-motor and fault-tolerant inverter solutions for the auxiliaries and chassis actuators; ii) novel solutions, including the implementation of reconfigurable winding topologies of the drive, as well as integrated and predictive thermal management at the vehicle level, with the adoption of phase changing materials within the power electronics components; iii) the achievement and demonstration of significantly higher levels of power density, specific power and energy efficiency for the resulting power electronics systems and related drives; iv) major cost reductions with respect to the current state-of-the-art, thanks to the dual use of parts.

Projekttitel: HiPE - High Performance Power Electronics Integrations
Projektlaufzeit: November 2022 - Oktober 2025
Förderkennzeichen: 101056760
Projektleiter: Prof. Dr. Valentin Ivanov
Fachgebiet: Kraftfahrzeugtechnik
Fakultät: Maschinenbau

HiPE brings together 13 participants covering the whole value chain, to develop a new highly energy-efficient, cost-effective, modular, compact and integrated wide bandgap (WBG) power electronics solutions for the next generation of battery Electric vehicles (BEV), and to facilitate a significant market penetration of WBG in the automotive sector.
The project outputs will include: i) a scalable and modular family of WBG-based traction inverters and DC/DC converters with significantly improved specific cooling performance, suitable for 400V, 800V and 1200V applications, with power ratings from 50 to 250 kW, integrated into electric drives enabling drastic size and weight reductions; ii) a family of integrated WBG-based on-board chargers and DC/DC converters, with optimised innovative topologies, including use of GaN; and iii) integrated, fault-tolerant and cost-effective GaN-based power electronics for high-voltage ancillaries and chassis actuators.
The result will be an unprecedented level of functional integration, e.g., the HiPE power electronics solutions will be smart cyberphysical systems, incl. intelligent and predictive controllers to optimise performance, innovative and computationally efficient datadriven approaches to monitor the state-of-health of the relevant hardware, as well as novel digital-twin-based methodologies to tailor the component- and vehicle-level algorithms to the specific condition of the hardware installed on each individual BEV, and actively control the reliability and availability of the relevant parts. This will be achieved while preserving the expected automotive quality level without having to recur to overengineering, thanks to the innovative implementation of data-driven dependability techniques for cyber-physical systems. The extensive simulation analyses running in parallel with the design and experimental activities will
further demonstrate the scalability, modularity and wider potential impact of power electronics solutions.

Projekttitel: Lasers4NetZero - Lasers for Accelerated Net-Zero Transition
Projektlaufzeit: Januar 2024 - Dezember 2027
Förderkennzeichen: 101119711
Projektleiter: Univ.-Prof. Dr.-Ing. habil. Jean Pierre Bergmann
Fachgebiet: Fertigungstechnik

Approximate 30% of the emissions of e-vehicles comes from the manufacturing processes, due to un-optimised material utilisation, low process efficiency, product defects and waste.

Current state-of-the-art identifies laser material processing technologies the heart of e-vehicles manufacturing. Advances in laser technologies and new generation scanning optics in fuel cell and battery manufacturing have the potential to offer enhanced utilisation of materials, improved process efficiency and product quality, allowing significant reduction in C02 equivalent. Lasers4NetZero will establish an innovative training programme that aims at coaching a new generation of creative, entrepreneurial and innovative doctoral candidates (PhDs) focused on laser material processing, artificial intelligence for quality control, advanced process simulation and predictive lifecycle and sustainability analysis for e-vehicles manufacturing. This novel programme will contain both scientific and transferable training activities and will benefit from training across the network (e.g. secondments). In total, 10 PhDs will be enrolled, developing individual research projects within the project. Individual PhD projects will integrate novel methods and approaches for laser material processing (cutting and welding) aided by laser beam shaping and ultra-fast scanning technologies with the ultimate goal to enhance utilisation of materials, improve process efficiency and product quality and reduce defects and waste.

The consortium involves 6 Academic partners and 8 lndustrial partners guaranteeing that final solutions will be close to the market. The close cooperation among multidisciplinary partners will ensure knowledge transfer to cross the valley-of-death between research and implementation. To maximise impact, two demonstrators (fuel cells and battery systems) will be developed in conjunction with the lndustrial partners.

Projekttitel: MesoComp - Order at the Mesoscale: Connecting supercomputing of compressible convection to classical and quantum machine learning
Projektlaufzeit: Januar 2023 - Dezember 2027
Förderkennzeichen: 101052786
Projektleiter: Univ.-Prof. Dr. Jörg Schumacher
Fachgebiet: Theoretische Strömungsmechanik
Fakultät: Maschinenbau

Turbulent convection flows in nature display prominent patterns in the mesoscale range whose characteristic length in the horizontal directions exceeds the system scale height. Known as the turbulent superstructure of convection, they are absent on both larger and smaller scales and evolve in ways not yet understood; but they are an essential link in the heat and momentum transport to larger scales, an important driver of intermittent fluid motion at sub-mesoscales, and one major source of uncertainty in the prognosis of climate change and space weather. In MesoComp, I will investigate the formation of superstructures in massively parallel simulations of compressible turbulent convection in horizontally extended domains, aiming for a deeper understanding of their dynamical origin and role in the transport of heat and momentum. I will then use these high-fidelity simulations to build recurrent machine learning models to predict the evolution and statistics of the superstructure and thus quantify the transport fluxes beyond the mesoscale. I will also analyse the impact of the mesoscale structures on the highly intermittent statistics at the small-scale of the flow and reveal the resulting feedback in the form of improved subgrid parametrizations by means of generative machine learning. MesoComp opens additional doors to the application of quantum algorithms in machine learning which significantly improve the statistical sampling and data compression properties compared to their classical counterparts. From a longer-term perspective, my research results in a quantum advantage for the numerical analysis of classical turbulence, which accelerates the parametrizations of mesoscale convection and increases their fidelity. This work will finally lead to more precise predictions of the on-going climate change and global warming. The results will also improve solar activity models and thus solar storm prognoses with impacts on satellite communication and electrical grids.

Projekttitel: MINDnet - Neuromorphic computing and signal processing training network
Projektlaufzeit: Januar 2026 - Dezember 2029
Förderkennzeichen: 101226674
Projektleiter: Prof. Dr. Kathy Lüdge
Fachgebiet: Theoretische Physik 2

The exponential surge in artificial intelligence (AI), internet traffic, and online services demands a revolutionary leap in devices, computing architecture, and integration technologies. Current digital computing platforms, driving societal progress, are hitting fundamental limits such as quantum tunneling and facing technical challenges like energy efficiency and heat dissipation. As a result, the progress in AI is slowed down by speed bottlenecks, massive energy consumption, and limited access to AI technologies as only selected large industrial players can afford to commit the required resources. Inspired by the brain’s powerful and energy-efficient processing capabilities, neuromorphic computing presents a promising solution to these critical issues. However, to fully harness neuromorphic computing’s potential, a holistic optimization - from individual computing devices to the overall architecture,
including a focus on applications, and training methods - across multiple technological platforms - photonics, electronics, biological neurons - is critically needed. The proposal of MINDnet addresses these urgent challenges through an interdisciplinary approach, bridging traditionally separate fields. MINDnet’s mission is to train 15 Doctoral Candidates, forming the next generation of leading scientists and expert researchers in the field of neuromorphic computing. MINDnet leans on a consortium of 10 beneficiaries - 7 universities, 1 tech-transfer research center, 1 multinational, 1 SME - complemented by 6 associated partners – 1 multinational, 2 SMEs, 2 tech-transfer research centers, and 1 academic. The consortium spans over 8 European countries, combining experts in 5 research disciplines - photonics (DTU, UNITN, USTRATH), electronics (UCL, HPE, FZJ, SCS), nonlinear dynamics (TUIL) and machine learning (UNIPI, TUG), - as well as a strong focus on the 5 applications - telecom (NVIDIA, HHI), sensing (FCAP), geolocation (AT), space (NB), and biomedical (UNITN).

Projekttitel: MOCO - Motion Control Systems of Multi-Actuated Ground Vehicles
Projektlaufzeit: Januar 2025 - Dezember 2028
Förderkennzeichen: 101183051
Projektleiter: Dr. Valentin Ivanov
Fachgebiet: Regelungstechnik

MOCO is based on an intensive staff exchange to promote joint research and training between 14 universities and industrial companies from 8 European countries, Japan, the Republic of Korea, Mexico and South Africa. The proposal innovates the methods of motion control and creates a comprehensive framework for the design, identification and validation of systems for the domain of multi-actuated vehicles. With a focus on driving and functional safety, energy efficiency, driving performance and ride comfort, MOCO is dedicated to improving the robust operation of automated and electric vehicles under various driving conditions, including uncertain road surfaces, severe conditions and terrains, as well as scenarios characterised by limited information and communication infrastructures. Research and innovation activities will focus on the following:
(i) Development of sophisticated tools for estimating parameter variations and identifying system characteristics using hybrid modelbased and data-driven methods;
ii) Design of advanced vehicle motion control systems considering automated driving functions, multi- actuated configurations and different electric powertrain topologies using robust and predictive approaches;
iii) Validation and verification of real-time software and hardware-based demonstrators using state-of-the-art methods;
iv) Facilitate practical recommendations, based on the MOCO results, for researchers, OEMs and suppliers working on automotive and interdisciplinary applications;
v) Professional development of participating research and engineering staff;
vi) Knowledge transfer and promotion of the exchange of expertise between the partners.
In addition to research and training, the project will focus on relevant networking, dissemination and exploitation initiatives to maximise its impact. MOCO will cultivate the necessary competences and skills that are essential to foster successful innovation in the emerging mobility domains.

Projekttitel: A Multimodal Approach for Digitizing, Analyzing, and Simulating Traditional Musical Organs Through 3D Technologies, Acoustic Analysis and Interactive Experiences
Projektlaufzeit: Oktober 2025 - März 2029
Förderkennzeichen: 101233618
Projektleiter: Dr.-Ing. Stephan Werner
Fachgebiet: Elektronische Medientechnik

In essence, MusicSphere aims to develop tools that combine advanced technologies for preserving, studying, and providing access to the cultural heritage aspects of traditional musical organs. The project will focus on traditional wind instruments such as Pipe Organs and their ancient Greek counterpart such as ‘Hydraulis’. By employing digital technologies precise digital replicas of these instruments will be created, capturing intricate physical and mechanical details and aiding in the preservation and restoration efforts. The study will involve analysing the unique sound characteristics of the organs and utilizing acoustic simulations in order to accurately recreate their tonal qualities and the interaction of sound with surrounding architecture and environmental conditions. Virtual and interactive tools will be developed to simulate the handling of these instruments, providing platforms where users can experience them in digital or augmented reality environments. By integrating 3D visual data, acoustic properties, and interactive models into a cohesive system, the research will try to offer a holistic understanding of the instruments' artistic and functional significance. The results will contribute to the long-term preservation of these cultural artifacts by creating digital archives and enhancing public and scholarly access through interactive technologies and educational applications. Another key objective is the projection of the results to the past in an effort to approximate the acoustic characteristics of non-functional musical instruments. Surviving fragments or detailed historical records will be used to create accurate digital 3D models of the partly preserved artifacts and the missing segments will be recreated through advanced computational techniques based on similar instruments from the same period or region. Non-invasive techniques will identify the properties of the original materials, which can then be digitally and/or physically replicated.