Funding projects

3D-i-GeneralTol (FKZ 03TN0057A) - 3D Modeling with Implicit General Tolerances based on DIN ISO 2768-1/2, or DIN 2769 for the Further Development of ISO 22081 - 3D-i-GeneralTol

Duration: October 1, 2023 - September 30, 2025

Siemens AG (SAG), Siemens Energy Global GmbH & Co. KG (SE), and Technische Universität Dresden (TUD) have joined forces to facilitate the continuous introduction of a forward-looking, 3D-based, digital working method in German industry. For this purpose, a standardized convention is to be developed that allows tolerated surfaces to be determined directly from 3D CAD geometry models, without the need to explicitly specify dimensions and form. This convention complements and simplifies the application of the existing GPS standard system (ISO 16792, ISO 22081), especially for functionally/technologically subordinate geometric elements. By embedding it into the existing and industry-wide accepted GPS standard system, the use of the new standard will be ensured. The proven idea of general tolerances/free dimensions is extended such that these tolerance specifications can also be applied directly in the 3D CAD model for undimensioned and untoleranced geometric elements, thereby enabling significant time savings.

Consortium Partners:

Siemens Energy Global GmbH & Co. KG

Technische Universität Dresden

Contacts:

Project Manager: christian.lipp@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMWE

AdaPEdge (FKZ 16ME0547K) - Edge Computing Modules for Resilient Electronics Manufacturing with Adaptive Process Optimization

Duration: July 1, 2022 - June 30, 2025

This project aims to optimize the manufacturing of products in an Industry 4.0 environment. The overarching goal of the project is adaptive process optimization in complex, even spatially distributed, manufacturing environments through the use and evaluation of freely available information/data. For various scenarios, this data can be used to optimize manufacturing productivity and quality to a target value. This applies, on the one hand, to highly efficient manufacturing where productivity gains can still be achieved, for example, by adjusting the chosen test strategy. However, it also applies to manufacturing that is thrown out of balance by inferior or counterfeit raw materials or even by a targeted attack on individual machines, and which can be quickly brought back into a reliable operating mode through early detection of unstable operating modes. To this end, the project will combine data evaluation methods using AI/federated learning with the use of edge computing modules featuring processors specialized in data fusion and an associated universal sensor network. This network will be used for local collection of freely available data – for example, environmental data such as temperature, humidity, acceleration, and operating noise are measured, but alternative values like UV radiation, air quality, E- and B-fields, etc., are also possible. The data will be processed, condensed, and evaluated by AI; after comparison with the manufacturing's quality and productivity parameters, a condition level for the ongoing production will be determined. This condition level is designed to be independent of the existing digital manufacturing infrastructure and thus serves as an independent data source for manufacturing optimization decisions. Through a feedback loop, the AI is intended to continuously learn and improve its decisions.

Consortium Partners:

• Universität Bielefeld (University of Bielefeld)

• Sensorik-Bayern GmbH

• Creonic GmbH

• Technische Universität Berlin (Technical University of Berlin)

• Micro Systems Engineering GmbH

• Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung eingetragener Verein (Fraunhofer Society for the Advancement

Contacts:

Project Manager: matthias.heimann@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMFTR

AdHyBau2 (20M2202A) - Overall Project: Development of a Certifiable Hydrogen-Electric Propulsion System for Aircraft and Testing of the Cryogenically Cooled Electric Motor - AdHyBau2; - Sub-project: Digital Development Methods and Innovative Manufacturing Concepts for Highly Efficient Electric Drives with Cryogenic Cooling.

Duration: August 1, 2024 - July 31, 2028

The project focuses on the further development of digitalization methods, innovative approaches to additive manufacturing, and the investigation of disruptive technologies for electrical machines. The project builds on the results of the predecessor project and expands them with important aspects for future aviation certification. Specifically, the mapping of a hydrogen-cooled electric drive in a Digital Twin is aimed for. With so-called model order reduction, computing time and resources are to be saved, so that the Digital Twin can be used in the industrial metaverse and digitalization in research and development can be accelerated. Furthermore, within the scope of the project, advanced manufacturing methods will be developed, and materials and components will be investigated regarding their properties and quality. In particular, supplementary material parameters for the complete description of the elasticity tensor play a significant role in the design. With a view to aviation certification, it is essential to thoroughly research the new manufacturing processes and to produce the generated materials reproducibly with consistent quality. The safe mastery of the processes forms the basis. In detail, the cold gas spraying of copper is to be further developed, with the focus of research being on the creation of copper hollow structures through the first-time combination of two additive processes. In the electric drive, studies on the potential of disruptive technologies are carried out. The focus here is on investigating the potential of superconductivity in the drivetrain for the favored application. The feasibility of a cryogenically cooled electric drive is demonstrated on a laboratory demonstrator. The construction, commissioning, and experimental testing of the drive under laboratory conditions are an essential part of the project.

Consortium Partners:

Karlsruher Institut für Technologie (Karlsruhe Institute of Technology)

Technische Universität Dresden (Technical University of Dresden)

Aufwind GmbH

Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. (Fraunhofer Society for the Advancement of Applied Research, registered association)

Additive Drives GmbH

MT Aerospace AG

Contacts:

Project Manager: christian.weidermann@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMWE

BoLeRo (FKZ 03EI4041A) - Joint Project: BoLeRo - Monitoring the Aging of Stationary Battery Storage with Cloud-Based Methods to Extend Lifespan, Reduce Maintenance Efforts, and Optimize Operation Management; Sub-project: Digital Services for Battery Storage

Duration: October 1, 2021 - September 30, 2024

Large-scale stationary battery storage systems provide important system services for the energy grids of the future, characterized by electricity generation from renewable sources. We propose a cloud-based system for the monitoring and operational optimization of battery storage systems, which enables the following industrial application and answers to research questions:

• Data provision from battery cells during operation
• Cloud solution for monitoring, analysis, and modeling of stationary energy storage
• New and improved methods that extract time-varying battery parameters (e.g., capacity, Digital Twin Model) from real BMS data of regular battery operation
• Enabling an improved operating strategy through more accurate battery models and lower requirements for safety margins
• Provision of a general and automated solution for lower service costs
• Reduction of technological requirements for the operation of battery systems, especially considering project- and customer-specific requirements
• Method for ensuring the safe operation of batteries over their entire lifespan, including second life
• Analysis of the economic impact per operating strategy

The results of the project will be demonstrated during ongoing operation using the Freimann large-scale storage facility of Stadtwerke München. The demonstrator will show both the accuracy of parameter estimation and the improved battery operation. The results will be validated through practical tests with the battery storage system.

Consortium Partner:

Technische Universität München (Technical University of Munich)

Contacts:

Project Manager: andrei.szabo@siemens.com

Financial Manager: roland.deak@siemens.com

Funding Provider: BMWE

CBC-GREEN (FKZ 13N15451) - Use of a flexible green beam source within the industrial production of electrical components

Duration: July 1, 2021 - March 31, 2025

Within electronic products such as relays, contactors or power modules, components made of copper are increasingly being used. The reason for this is that the requirements for electrical and thermal conductivity are increasing (copper has approximately 1/3 better properties compared to aluminum). For the flexible processing (joining, separating, additive manufacturing, especially selective laser melting) of copper, laser processes are advantageous. Near-infrared (NIR) lasers with brilliant beam quality (fundamental mode or single-mode fiber lasers), which also offer high spatial resolution, are used. However, the high reflectivity of copper (97% at 1070 nm) complicates the use of laser technology, as there is a risk of component damage (absorption increases significantly with coupling into the melt pool), process times are too short (due to the low absorption rate), and qualitative defects can occur (melt spatters). The aim of the sub-project is to be able to use the laser developed and qualified by the consortium within Siemens' manufacturing landscape. Areas of application include automation technology, the production of electrical components, and the assembly of power electronics. The focus here is on welding and joining, additive manufacturing, and the cutting of Cu sheets. The focus is on demonstrating the increase in efficiency and reproducibility through the use of the new laser beam source.

Consortium Partners:

thyssenkrupp Automation Engineering GmbH

Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung eingetragener Verein (Fraunhofer Society for the Advancement of Applied Research, registered association)

Karl H. Arnold Maschinenfabrik GmbH & Co. KG

amcm GmbH

Contacts:

Project Manager: markus.lasch@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMFTR

DAFODIL (FKZ 03XP0389A) - DAFODIL - Data-based Manufacturing Optimization of Battery Cells based on End-of-line Data through Massive Use of Machine Learning Algorithms and Inline Analytics

Duration: September 1, 2021 - August 31, 2025

In the DAFODIL project, fundamentally new methods and techniques for cross-process manufacturing optimization of lithium-ion cells are planned to be developed.

The state of the art in production facilities for Li-ion cells is the optimization of each individual production step to a local optimum. Since some of the quality parameters can have a global optimum if individual production facilities are operated outside the local optimum, the DAFODIL project will develop the global optimization of production through the intermediate step of investigating new sensor systems. For this purpose, various sensor systems will be examined in preliminary studies for their suitability for data acquisition in cell production. For all measured values, it will be assessed which sensor information is directly related to a defined quality parameter.

DAFODIL (FKZ 03XP0389A) - DAFODIL - Data-based Manufacturing Optimization of Battery Cells based on End-of-line Data through Massive Use of Machine Learning Algorithms and Inline Analytics

Duration: September 1, 2021 - August 31, 2025

In the DAFODIL project, fundamentally new methods and techniques for cross-process manufacturing optimization of lithium-ion cells are planned to be developed.

The state of the art in production facilities for Li-ion cells is the optimization of each individual production step to a local optimum. Since some of the quality parameters can have a global optimum if individual production facilities are operated outside the local optimum, the DAFODIL project will develop the global optimization of production through the intermediate step of investigating new sensor systems. For this purpose, various sensor systems will be examined in preliminary studies for their suitability for data acquisition in cell production. For all measured values, it will be assessed which sensor information is directly related to a defined quality parameter.

Furthermore, the influence of correlating characteristic values on complex cause-and-effect patterns will be investigated. For this purpose, algorithms and methods are to be developed that identify these complex causal chains. A sensitivity analysis will investigate which sub-influences in these causal chains favor certain effects and how these can be quantitatively recorded. In the Siemens sub-project, a novel sensor system, a so-called hyperspectral camera, will be investigated for the first time for its possible applications in battery production. Furthermore, electrochemical measurement methods in conjunction with modifications to cell manufacturing will be carried out to investigate the influence of production parameters on quality and to develop optimization potential in the characterization of cell quality. For this purpose, Siemens will participate in the analysis of the production process. In addition, algorithms for analyzing sensor data and qualification measurements will also be programmed in the Siemens sub-project, with the help of which complex cause-and-effect patterns can be identified.

Consortium Partners:

Customcells Tübingen GmbH

Safion GmbH

SURAGUS GmbH

Rheinisch-Westfälische Technische Hochschule Aachen (RWTH Aachen University)

Universität Münster (University of Münster)

Contacts:

Project Manager: andreas-albrecht@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMFTR

DIAA-H2 (FKZ: 03EI3067A) - Digital Twins for H2 Production Plants

Duration: July 1, 2022 - June 30, 2025

Siemens Energy Global GmbH & Co. KG, Siemens AG, Technische Universität Dresden, and Josef Pfaffinger Bauunternehmung GmbH plan to research the foundation for a Digital Twin for process plants, usable throughout their entire lifecycle, using an electrolysis plant as an example. The methods for product creation and management established in mechanical engineering are to be made usable for the energy transition by adapting them to plant construction. The research project focuses on the development of methods and tools for deriving, instantiating, and maintaining the Digital Twin. These will be implemented concurrently with the construction and operation of the plant. In addition, various modularization approaches based on discipline-specific basic models, which are synchronized and harmonized with a cross-disciplinary plant model, will be developed. The plant model is also intended to enable a novel variant generation for plant construction. The described focus areas will be brought together in the prototype of a first Plant Lifecycle Management System (Plant-LM System) and validated through application in engineering, plant construction, and operation. As a result, the Plant-LM System is intended to significantly increase the economic efficiency of plants, reduce investment costs, and improve maintenance. This will result in a significant and long-term competitive advantage for users of the developed methods and tools for the Digital Twin of electrolysis plants.

Consortium Partners:

Pfaffinger Anlagenbau & Energietechnik GmbH

Technische Universität Dresden (Dresden University of Technology)

Siemens Energy Global GmbH & Co. KG

Contacts:

Project Manager: jonathan.leidich@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMWE

DigiZug3D (FKZ 13IK031A) - Joint Project: Digital Approval of 3D-Printed Components for Rail Vehicles (DigiZug3D) - Sub-project: Digitalization and Industrialization of the LPBF Process

Duration: October 1, 2023 - August 31, 2026

DigiZug3D (FKZ 13IK031A) - Joint Project: Digital Approval of 3D-Printed Components for Rail Vehicles (DigiZug3D) - Sub-project: Digitalization and Industrialization of the LPBF Process

Duration: October 1, 2023 - August 31, 2026

Based on many years of experience as a user and supplier for additive manufacturing, Siemens will support the industrialization of the LPBF process. This will be achieved in particular by promoting digitalization on several levels, namely the digital mapping of approval processes to enable an approval platform, participation in computational proof instead of destructive testing, and the evaluation of further digital methods to reduce the scope of testing in quality assurance. The digital methods include the combination of various process monitoring systems with an intended automated evaluation, as well as the use of selected methods on the growing data basis within the project. The result will be a concrete evaluation of selected process monitoring methods. This can serve as a basis for future developments of process monitoring or evaluation methodologies, as well as for assessing currently available systems. Ideally, concrete development goals can be formulated, and correlations to component quality can be shown. Overall, the digitalization of approval processes and proofs will remove a significant hurdle to the introduction of additive manufacturing. Furthermore, to support industrialization, experiences along the entire additive process chain will be bundled into concrete recommendations for action on how to use the process in the field of rail vehicles and beyond. Selected hypotheses and recommendations from a research report by the German Centre for Rail Transport Research will be examined and concretized, and their general validity beyond the material used in the project will be checked. Finally, knowledge transfer to support broad industrialization will be promoted through the development of regulations and standards.

Consortium Partners:

Mobility goes Additive e.V.

IMA Materialforschung und Anwendungstechnik GmbH (IMA Material Research and Application Technology GmbH)

TÜV NORD Systems GmbH & Co. KG

Additive Marking GmbH

Siemens Mobility GmbH

Universität Paderborn (University of Paderborn)

Contacts:

Project Manager: christoph.kiener@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMWE

DiMad (FKZ 13XP5188A) - Joint Project: Digital Material and Process Design for Wire-based Additive Manufacturing - DiMad - ; Sub-project: Digital Manufacturing Chain for the WAAM Process of High-alloy Steels

Duration: January 1, 2023 - December 31, 2025

Additive Manufacturing (AM) processes for metals involve considerable, predominantly experimental effort when it comes to identifying suitable process windows for material systems or transferring optimized process parameters from one plant type to another. Accelerated implementation from design to component is only possible if the process chain, from raw materials to the component, is defined and digitally constructed in its entirety. The DiMad project focuses on wire-based processes using arc and laser radiation as heat sources for an austenitic and a duplex steel. The following characteristics, which are essential from a user's perspective and influence the quality and properties of the component, are considered in depth: microstructure and phase formation as a result of local solidification and cooling conditions; residual stresses and distortion due to heating and cooling and heat treatment; critical defects such as lack of fusion and porosity. Microstructure, phases, and stresses are described by simulation. Machine learning (ML) is used for correlating process-defect-property and also for interpreting the totality of data (simulation, experiment, analysis). The digital work is accompanied by experiments, analyses, and material testing, which serve to determine input parameters for the simulation, validate the simulation, and train/validate the ML models. Based on the process parameters derived from the digital tools, two real components will be built. Regarding the two AM processes, a user ontology for wire materials will be developed. For the exchange and evaluation of data between partners and with the Material Digital platform, a uniform data structure will be developed, and suitable data formats will be used. The existing AixViPMaP solution will initially be used as a workflow framework, which is to be integrated into the PMD.

Consortium Partners:

OSCAR PLT GmbH

Icon Pro GmbH

FEF Forschungs- und Entwicklungsgesellschaft Fügetechnik GmbH (FEF Research and Development Company for Joining Technology GmbH)

Deutsches Zentrum für Luft- und Raumfahrt e.V. (German Aerospace Center)

Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung eingetragener Verein (Fraunhofer Society for the Promotion of Applied Research, registered association)

ACCESS e.V.

Contacts:

Project Manager: philip.howell@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMFTR

DORSMAN (FKZ 03XP0633A) - Joint Project: Data- and Sensor-based Manufacturing of Battery Cells - DORSMAN; Sub-project: Acceleration of Cell Finalization and Quality Improvement of Lithium-Ion Batteries through the Use of Innovative Sensor Technology and Measurement

Duration: January 1, 2025 - December 31, 2027

This research project, funded by the BMBF, aims to accelerate the production of lithium-ion batteries.

The joint project "Data- and Sensor-based Manufacturing of Battery Cells" (DORSMAN) investigates how the production of lithium-ion batteries can be made more resource-efficient and faster. The project is funded by the Federal Ministry of Education and Research (BMBF) (FKZ: 03XP0633A). In addition to Siemens as coordinator, the consortium includes BioLogic Science Instruments GmbH, the Institute for Machine Tools and Industrial Management (iwb) at the Technical University of Munich, and the Institute for Power Electronics and Electrical Drives (ISEA) at RWTH Aachen University. The overarching goal is to accelerate battery production through innovative sensor technology and digitalization.

Batteries are essential for the success of the energy and mobility transition. However, the production process is lengthy, the scrap rate is high, the quality of batteries varies, and can only be reliably determined with complex measurement procedures. Particularly the last phase of the manufacturing process, cell finalization, is very time-consuming. Finalization involves several process steps. Wetting, where the electrolyte distributes within the battery, formation, where the battery is charged and discharged for the first time, and the measurement of self-discharge are particularly time-intensive. This is because the progress of the process cannot be monitored with currently available means, and therefore blanket waiting times are necessary.

DORSMAN addresses precisely this aspect of the production process. Novel sensors will be integrated into the production plant, enabling deeper insights into the electrochemical processes within the cell. Furthermore, measurement procedures for determining the self-discharge rate will be further developed. According to the current state of the art, these measurements take several days to weeks. Research will be conducted on how the measurement duration can be reduced with suitable measurement technology and data evaluation.

Until now, the sensors and measurement procedures have only been demonstrated on a laboratory scale. Within the framework of DORSMAN, the results achieved here will be brought into industrial application through cooperation between research institutes and industry. A digital infrastructure will be established for the production plant, which will form the basis for online-capable process control based on measurement data. The project partners will implement the developed solutions in a pilot plant, where they will be tested. Subsequently, batteries will be produced in several production campaigns, and adjustments to the production process will be made based on the recorded measurements. Successful implementation will make it possible to integrate the sensor technology and measuring devices into industrial production facilities.

Consortium Partners:

Bio-Logic Science Instruments GmbH

Technische Universität München (TUM) (Technical University of Munich)

Rheinisch-Westfälische Technische Hochschule Aachen (RWTH Aachen University)

Contacts:

Project Manager: felix.katzer@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMFTR

ENable (FKZ 03EN3061A) - Joint Project EnEff:Stadt: Enable - VErtical INtegration of Communication and Control Systems for Activating Flexibility for District-wide Energy Optimization. Sub-project: Optimized Control of the Energy System (Siemens)

Duration: April 1, 2022 - March 31, 2025

A crucial prerequisite for sector-spanning optimized control of building/campus energy systems and the marketing of flexibility is the establishment of an Information and Communication Technology (ICT) for seamless interaction between sensors, individual components, the use of forecasting systems, and interfaces to the grid operator, energy supplier, or a trading platform. The aim of this project is to demonstrate this complete ICT chain in the field, using the largest district development in Bamberg (Lagarde Campus conversion area) as an example. A further project goal is the metrological and simulative evaluation of scalability and transferability to other districts, as well as the resulting savings potentials regarding CO2 and costs. The optimized, cross-sector control of controllable electrical and thermal loads, energy storage, and energy conversion units will be realized via an automated energy management system that works in conjunction with classic building management technology, external services such as weather services, AI-based forecasting techniques, and a trading platform. In doing so, the district will be supplied with energy in a cost and CO2-optimized manner, taking into account all technical boundary conditions of the individual components and networks.

Consortium Partners:

Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung eingetragener Verein (Fraunhofer Society for the Promotion of Applied Research, registered association)

Stadtwerke Bamberg Energie- und Wasserversorgungs GmbH (Bamberg Municipal Utilities Energy and Water Supply GmbH)

SEtrade GmbH

Contacts:

Project Manager: stefan.langemeyer@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMWE

ENSURE3 (FKZ 03SFK1A0-3) - Joint Project: New Energy Network Structures for the Energy Transition

Duration: August 1, 2023 - July 31, 2026

Siemens AG is participating as the consortium leader in the Copernicus Project ENSURE3.
In the project, Siemens AG will contribute to the four main goals and, together with its partners, derive recommendations for action for various stakeholders. Furthermore, Siemens AG is working on numerous technology advancements in the energy transition toolkit. As a contribution to the co-demonstration platform, the "Single Source of Truth" will be further developed, and work will be done on integrating flexible station control technology. Siemens AG has set up a showroom that serves to transfer the results.

Consortium Partners:

Karlsruher Institut für Technologie (Universitätsaufgabe) (Karlsruhe Institute of Technology (University Task))

Technische Universität Dortmund (Technical University of Dortmund)

DVGW Deutscher Verein des Gas- und Wasserfaches e.V. – Technisch-wissenschaftlicher Verein (DVGW German Technical and Scientific Association for Gas and Water)

Deutsche Umwelthilfe e.V. (Environmental Action Germany)

Hochschule für angewandte Wissenschaften München (Munich University of Applied Sciences)

PSI FLS Fuzzy Logik & Neuro Systeme GmbH

TenneT TSO GmbH

Germanwatch e.V.

OPAL-RT Germany GmbH

SWKiel Netz GmbH

AllgäuNetz GmbH & Co. KG

Christian-Albrechts-Universität zu Kiel (Kiel University)

Westfalen Weser Netz GmbH

LEW Verteilnetz GmbH

Technische Universität Ilmenau (Ilmenau University of Technology)

Öko-Institut. Institut für angewandte Ökologie e.V. (Öko-Institut. Institute for Applied Ecology e.V.)

Avacon Netz GmbH

Hitachi Energy Germany AG

Maschinenfabrik Reinhausen GmbH

Friedrich-Alexander-Universität Erlangen-Nürnberg (Friedrich-Alexander University Erlangen-Nuremberg)

Karlsruher Institut für Technologie (Großforschungsaufgabe) (Karlsruhe Institute of Technology (Large-Scale Research Task))

Rheinisch-Westfälische Technische Hochschule Aachen (RWTH Aachen University)

Energiewirtschaftliches Institut an der Universität zu Köln gGmbH (Institute of Energy Economics at the University of Cologne gGmbH)

Bergische Universität Wuppertal (University of Wuppertal)

Contacts:

Project Manager: annebauer@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMBF

F-HiL-Reloaded (FKZ 03EI6104A) - Joint Project: Development of a Test Infrastructure for the Normative Validation of Dynamic Processes of Grid-forming Decentralized Generation Units - F-HiL-Reloaded; Sub-project: Development of a Grid-forming Decentralized DC Wallbox to Increase Supply Security

Duration: October 1, 2023 - September 30, 2026

The goal of the F-HiL-Reloaded project is to establish a real-time simulation and test system that models a distribution grid section with strong converter interoperability and automatically executes test scenarios for grid-forming test objects. A hybrid amplifier controlled via a dynamic power interface and an adaptive X/R line impedance are used as interfaces to the real environment. The F-HiL-Reloaded project is a continuation of the FHiL research project; the insights gained and results achieved serve as a basis for the further development of the test and inspection system. Additionally, the project considers the grid-forming properties of the test objects: a DC wallbox and a grid-forming converter. Special attention is paid to the interoperability of the converters. In particular, resonance effects of charging stations for electric vehicles in conjunction with PV systems are simulated with the test and inspection system. The positive (and potentially negative) effects of grid-formers in the distribution grid should be demonstrable by the test and inspection system, which is tailored to the grid-forming properties of the test objects. The reproducible tests are intended to form the basis for standardized test sequences for grid-forming systems. The overarching goal of the F-HiL-Reloaded project is to positively influence the development of grid-forming and grid-following converters through standardized test procedures and the test system, thereby ensuring supply security in an energy supply based exclusively on converters. In society, the demonstrability of secure supply through grid-forming systems strengthens the sense of security and acceptance for the green energy transition.

Consortium Partners:

Hochschule Bonn-Rhein-Sieg (Bonn-Rhein-Sieg University of Applied Sciences)

Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung eingetragener Verein (Fraunhofer Society for the Promotion of Applied Research, registered association)

SUMIDA Components & Modules GmbH

OPAL-RT Germany GmbH

Contacts:

Project Manager: sebastian.nielebock@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMWE

3DGlassGuard (FKZ: 13N16851) - Joint Project: Security through Photonic System Integration in Glass Foils; Sub-project: Planar Integrated Optical Current Sensor

Duration: April 1, 2024 - March 31, 2027

3DGlassGuard (FKZ: 13N16851) - Joint Project: Security through Photonic System Integration in Glass Foils; Sub-project: Planar Integrated Optical Current Sensor

Duration: April 1, 2024 - March 31, 2027

The integration of three-dimensional optical microstructures and robust interfaces into display glass is to be technically developed using IOX (ion implantation) combined with fs-LS (femtosecond laser structuring) to enable miniaturized photonic system components. A novel design tool for IPD (inverse photonic design) efficiently allows for the necessary three-dimensional wave-optical and chemical simulation. Two new sensor principles, only possible on this basis, in the field of current measurement and environmental sensing, are to be pre-developed and functionally validated by the application partners. Methods of intelligent sensor data evaluation will be used for this purpose. The planned design-supported combination processes unite the advantages of IOX (low damping and large areas) with those of fs-LS (three-dimensionality). This allows, for the first time, three-dimensional optical structures to be integrated into large-area, very thin glass substrates (< 1 mm). This combination is only efficient in terms of design through AI-supported IPD, as designs based on classically experimentally determined regression models cannot be quickly simulated due to computational limitations. Today's optical sensors are often free-beam or fiber-based and therefore require considerable installation space. Furthermore, flexible optical fibers are costly to process and assemble. Photonic integration at the system level thus offers significant cost and miniaturization advantages. This is technically achieved in the proposed project by using display glass and panel-based batch processes for IOX and metallization processes, as well as the integration of 3-dimensional optical structures with the fs-laser into thin glass foils, which are integrated as highly efficient system components into optoelectronic systems for novel applications such as the optical current sensor.

Consortium Partners:

Sea & Sun Technology GmbH

Contag AG

Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung eingetragener Verein (Fraunhofer Society for the Promotion of Applied Research, registered association)

LightFab GmbH

Technische Universität Berlin (Technical University of Berlin)

Contacts:

Project Manager: stefan.nerreter@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMBF

GreenInverter (FKZ 03EN4079A) - Joint Project: Sustainable Power Electronics with Innovative Cooling to Increase Recyclability - GreenInverter; Sub-project: Technological Variants and Inverter System

Duration: March 1, 2024 - February 28, 2027

GreenInverter (FKZ 03EN4079A) - Joint Project: Sustainable Power Electronics with Innovative Cooling to Increase Recyclability - GreenInverter; Sub-project: Technological Variants and Inverter System

Duration: March 1, 2024 - February 28, 2027

The "GreenInverter" project aims to set new standards for recyclable, repairable, and upgradeable hardware in the field of electrical energy conversion. At the same time, energy efficiency is to be significantly improved. The benefits will be demonstrated using an inverter as an example. The current state of the art, which is based on the principle of individual component developments, is to be replaced by a new systemic approach. Through a disruptively novel approach for a common cooling and insulation strategy, component temperatures will be reduced, and the use of new materials will be enabled, thereby increasing energy efficiency. A significant increase in recyclability, a reduction in the product's CO2 footprint, and an increase in product lifespan due to a more homogeneous temperature distribution within the inverter will also be achieved. Furthermore, future, new business models will be included in the considerations, meaning that design and layout will be less driven by manufacturing costs. The project focuses on three key areas: "Energy Efficiency," "Recyclability, Circularity, and Dematerialization," and "Lifespan, Component Utilization, and Flexibility in Use." Disruptive solutions for significant improvements in all these areas will be researched and developed, which are to be demonstrated in the form of functional prototypes. The consortium estimates that, with full implementation of the results, an energy saving potential of more than 1.6 TWh annually can be achieved in Germany. Siemens contributes expertise in R&D in the field of electronics, cooling methods, and the design & manufacturing of inverters. This includes many years of experience in the design, construction, setup, and operation of experimental facilities and in leading large research and development projects.

Consortium Partners:

Rohstoffhandel Bernhard Westarp GmbH & Co. KG

Friedrich-Alexander-Universität Erlangen-Nürnberg (Friedrich-Alexander University Erlangen-Nuremberg)

Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung eingetragener Verein (Fraunhofer Society for the Promotion of Applied Research, registered association)

Sumida Components & Modules GmbH

Contacts:

Project Manager: joern.grundmann@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMWE

GridAssist (03EI6126A) - Joint Project: Assistance Systems for Optimized Automated System Operation in Distribution Grids - Sub-project Title: Automated Optimal System Operation

Duration: December 1, 2024 - November 30, 2027

GridAssist (03EI6126A) - Joint Project: Assistance Systems for Optimized Automated System Operation in Distribution Grids - Sub-project Title: Automated Optimal System Operation

Duration: December 1, 2024 - November 30, 2027

The GridAssist project aims to develop assistance systems for optimized automated system operation in distribution grids. This is intended to enable a higher integration of renewable energy generators into the power grid. Solutions individually tailored to the voltage levels will be developed. The transferability of these solutions and the field testing of the assistance systems are central components of the project. Sufficient observability forms the basis for any automation in distribution grids. The lower the voltage level, the less is currently known about the actual grid state. In GridAssist, therefore, the necessary observability will first be established. In addition to the installation of measurement technology, appropriate concepts need to be developed on how the measurement data can be most effectively integrated with the respective grid operators and their processes. With sufficiently installed measurement and communication technology, i.e., given observability, the respective grid state can then be analyzed. In addition to analytical methods, data-based methods will be used. Based on the analysis of the grid state, decisions will then be made by the assistance systems. These can range from recommendations for action for the system operator to fully automated flexibility dispatch. Siemens contributes its expertise in grid automation and software development, especially in the area of digital twins and AI. A central aspect of GridAssist is the automation of processes in the distribution grid. This is indispensable due to current developments. In addition to these technical aspects, the project aims to reduce the operating and investment costs of grid operators. This is achieved through improved asset management and a reduced need for grid expansion.

Consortium Partners:

LEW Verteilnetz GmbH

Energieversorgung Gera GmbH

OPAL-RT Germany GmbH

Friedrich-Alexander-Universität Erlangen-Nürnberg (Friedrich-Alexander University Erlangen-Nuremberg)

Rheinisch-Westfälische Technische Hochschule Aachen (RWTH Aachen University)

Contacts:

Project Manager: annebauer@siemens.com and christian.scheibe@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMWE

HyFlexBahn (FKZ 03ETE045A) - Joint Project: Hybrid and Flexible Battery Systems in Rail Transport (HyFlexBahn)

Duration: January 1, 2023 - December 31, 2025

The globally increasing demand for green train solutions for non-electrified railway lines will lead to a growing demand for vehicles with traction batteries instead of diesel engines. So far, battery-electric trains are primarily equipped with medium energy density lithium-ion cells, which are very powerful but also expensive compared to cells used in the automotive sector. The goal of HyFlexBahn is to enable the use of inexpensive, high energy density cells in combination with high-performance cells. This allows for high energy densities with simultaneously lower life cycle costs. In the project, simulation tools for hybrid battery storage systems with cells of different chemistries will be created and parameterized, system designs for various operating profiles will be simulated, and economic viability will be assessed. The flexibility achieved through hybridization contributes to making battery trains more competitive and to using the batteries efficiently and for as long as possible, both in their first use and in a potential second life.

Consortium Partners:

Siemens Mobility GmbH

Rheinisch-Westfälische Technische Hochschule Aachen (RWTH Aachen University)

Contacts:

Project Manager: michael-fischer@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMWE

KoLiBri (FKZ: 03LB3039A) - Joint Project: Complex Lightweight Structures for Electronic Applications within Mobility; Sub-project: Conception and Testing of Metal Foam Demonstrators for Power Electronics within Mobility

Duration: December 1, 2021 - February 28, 2025

The KoLiBri project addresses the cooling of e-motors and power electronics through the use of novel lightweight structures made of open-cell metal foams. These are expected to offer improved cooling performance and, crucially, enable new designs. By using open-cell metal foam structures, the cooling performance is intended to be increased by 20-50% within the same installation space, or, for the same performance, the weight is to be reduced by 50%. Additionally, the manufacturing methods addressed, such as investment casting and coating, are expected to lead to a cost reduction of 75% compared to purely additive manufacturing (e.g., Selective Laser Sintering). Furthermore, the structural-mechanical advantages of metal foams (high stiffness-to-volume ratio) allow for the integration of coolers into the design for the respective application, thereby achieving further weight reduction by leveraging mechanical load-bearing capacity in conjunction with cooling. Another research focus is the direct printing of metal foam structures onto power modules. This enhances thermal performance while simultaneously saving weight and resources, as a conventional base plate for the cooler can be omitted. Since only circular technical solutions in the life cycle of components have lower environmental impacts than standard solutions, the project also addresses the composite breakdown and the single-sort recovery of the materials used, although the handling of galvanized surfaces is considered challenging from a recycling perspective. The developed technical solutions are cross-sectoral and address the entire mobility spectrum (cars, buses, trucks, trains, electric flying, ...). Throughout the project, life cycle assessment data and software will be further developed to enable easy environmental assessment for designers in early design phases in the future.

Consortium Partners:

Valeo eAutomotive Germany GmbH

iPoint-systems GmbH

HSO Herbert Schmidt GmbH & Co. KG

NRU GmbH

Technische Universität Bergakademie Freiberg (Technical University Bergakademie Freiberg)

Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung eingetragener Verein (Fraunhofer Society for the Promotion of Applied Research, registered association)

Technische Universität Dresden (Technical University Dresden)

Contacts:

Project Manager: christian.baar@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMWE

LeiKo (FKZ 03EI6041A) - Joint Project: Reliable Film Capacitors with Increased Power Potential; Sub-project: Lifetime Models and Nano-Technology for Film Capacitors with Increased Power Potential as a Component for Improving Grid Infrastructure

Duration: February 1, 2021 - July 31, 2024

The overall objective of the OntOMat research project is to research data-based material development and optimization solutions for industrially used thermoplastic and thermoset fiber-reinforced components, as well as the materials and manufacturing processes used for their production, including the consideration of material cycles. A harmonized description of the cross-scale relationships between material, process, and component is currently not available for fiber-reinforced materials. Likewise, there is insufficient data integrity from material characterization through simulation methods to manufacturing processes. The research into a harmonized ontological description therefore unlocks previously unused potential for data-driven material development and process optimization. This sub-project specifically addresses the:

• Development of relevant causal relationships (digital material description),

• Elaboration, construction, and validation of the multiscale simulation process chain with process integration, and

• Analysis and incorporation of the material cycle into the ontological description.

Here, with a material focus on injection-compression moldable polymer composites such as SMCs, formalized descriptions of the "engineering workflow" at macro and micro-scale levels are to be developed. The resulting relevant causal relationships form the basis for the cross-scale ontology creation and the establishment of the bidirectional multiscale simulation process chain as the second focus. The promotion of material cycles forms the third focus. For this, it is necessary to determine the influences of potentially lower quality material properties and, on the other hand, to map the recycling processes in the ontological description, in order to promote sustainable developments already in the design phase.

Consortium Partners:

• Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung eingetragener Verein (Fraunhofer Society for the Promotion of Applied Research, registered association)

• FASERINSTITUT BREMEN e.V. (FIBRE Institute Bremen registered association)

• BIAS - Bremer Institut für angewandte Strahltechnik GmbH (BIAS - Bremen Institute for Applied Beam Technology GmbH)

• Nexpirit GmbH

• Deutsches Zentrum für Luft- und Raumfahrt e.V. (German Aerospace Center registered association)

• Hans Weber Maschinenfabrik GmbH

Contacts:

• Project Manager: martin.koerdel@siemens.com

• Financial Manager: bianca.miene@siemens.com

Funding Provider: BMFTR

PredictECO (FKZ 01MN23014A) - Joint Project: Product Development Considering Simulation-Supported Sustainability Assessment (PredictECO) - Sub-project: Continuous Green Digital Twin with Connection of Virtual and Physical Manufacturing Processes for Sustainable Design and Optimization

Duration: June 1, 2023 - May 31, 2026

The overall objective of the project is to provide a platform for assessing and optimizing the sustainability of resource-intensive manufacturing processes already at the design stage. The modeling of manufacturing processes serves as the basis. Thus, all expected data and their grouping are known early in the process and can be provided as a data structure in a green digital twin. Based on the data structure, interfaces for sustainability prediction using LCA software are provided. Due to the known information flows, efficient preliminary evaluations can be utilized via Edge Computing. Specifically, the following sub-goals are addressed:

• Development of quantifiable and measurable criteria and indicators for assessing the ecological footprint of composite manufacturing processes
• Integration of relevant criteria and indicators into simulation tools for designing factory layouts and process steps based on target values
• Integration of relevant criteria into product-specific manufacturing planning tools for designing product design (CAE/CAM) based on target values
• Automated and standardized real-time data acquisition, pre-processing, documentation, and comparison of the collected data with defined target values as a basis for truly implementable optimization solutions.

Building on this, the sub-project can be divided into 5 work objectives:

PredictECO (FKZ 01MN23014A) - Joint Project: Product Development Considering Simulation-Supported Sustainability Assessment (PredictECO) - Sub-project: Continuous Green Digital Twin with Connection of Virtual and Physical Manufacturing Processes for Sustainable Design and Optimization

Duration: June 1, 2023 - May 31, 2026

The overall objective of the project is to provide a platform for assessing and optimizing the sustainability of resource-intensive manufacturing processes already at the design stage. The modeling of manufacturing processes serves as the basis. Thus, all expected data and their grouping are known early in the process and can be provided as a data structure in a green digital twin. Based on the data structure, interfaces for sustainability prediction using LCA software are provided. Due to the known information flows, efficient preliminary evaluations can be utilized via Edge Computing. Specifically, the following sub-goals are addressed:

• Development of quantifiable and measurable criteria and indicators for assessing the ecological footprint of composite manufacturing processes
• Integration of relevant criteria and indicators into simulation tools for designing factory layouts and process steps based on target values
• Integration of relevant criteria into product-specific manufacturing planning tools for designing product design (CAE/CAM) based on target values
• Automated and standardized real-time data acquisition, pre-processing, documentation, and comparison of the collected data with defined target values as a basis for truly implementable optimization solutions.

Building on this, the sub-project can be divided into 5 work objectives:

• The development of overarching Cradle-to-Cradle LCA models for components based on composite materials
• The expansion of existing process simulation solutions at manufacturing and factory level to include the developed assessment criteria and indicators
• The integration of indicators and assessment criteria into product-specific manufacturing planning (CAM)
• The acquisition and processing of real process data using edge devices
• Demonstration and requirements gathering for transfer to broad manufacturing

Consortium Partners:

Grasse Zur Ingenieurgesellschaft mbH

INVENT Innovative Verbundwerkstoffe Realisation und Vermarktung neuer Technologien GmbH

SWMS Systemtechnik Ingenieurgesellschaft mbH

CONTACT Software GmbH

Deutsches Zentrum für Luft- und Raumfahrt e.V. (German Aerospace Center registered association)

Maschinenbau Scholz GmbH & Co. KG

Contacts:

Project Manager: oliver.lohse@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMFTR

PV4Life (FKZ 03EE1165A) - Joint Project: Increasing Converter Lifetime through Optimized Component Optimization using Digital AI Twin - PV4Life; Sub-project: Sensor-based Lifetime Prediction of Converter Components

Duration: August 1, 2023 - July 31, 2026

In this sub-project, Siemens AG is researching improved and more reliable utilization of converter-based systems and their critical components. More efficient use of power converters, along with the associated extension of their lifespan and improved reliability, can save natural resources. The goal of Siemens AG's sub-project is sensor-based condition monitoring, which allows the determined aging to be incorporated into an individual operating strategy that optimally utilizes the PV system without wasting power or lifetime. Additional sensors in the power module will be used to make lifetime predictions, which will serve as the basis for models of PV inverter systems. For this purpose, suitable sensors will be selected and experimentally evaluated with regard to their sensitivity for aging detection of converter components. Accelerated lifetime tests will generate various aging failures, which will be detected and evaluated early by sensors. The sensor data collected during the experiments will be compared with conventional data from accelerated lifetime tests to assess the added value of sensor-based lifetime monitoring. For this, specific test benches for semiconductors will be set up for metrologically supported optimizations, and the sensor technology in the power module will be expanded. Siemens will provide the sensor-based measurement data from accelerated lifetime tests to the project partners for the development of digital twin models that consider aging phenomena and individual loads occurring in the application. In particular, the generated sensor data will serve as input for AI-based life models at the project partners. These models optimize the utilization and lifetime of PV inverter systems by adjusting operating parameters according to increasing aging.

Consortium Partners:

Infineon Technologies AG

SUMIDA Components & Modules GmbH

Hochschule Bonn-Rhein-Sieg (Bonn-Rhein-Sieg University of Applied Sciences)

OPAL-RT Germany GmbH

Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung eingetragener Verein (Fraunhofer Society for the Promotion of Applied Research, registered association)

Contacts:

Project Manager: kai.kriegel@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMWE

REMEASURE (FKZ 03EN6022A) - Collaborative Project: REMEASURE Rollout-Capable Multimodal Energy Management for Supermarkets; Sub-project: Model Predictive Multimodal Load Shifting in a Pilot Supermarket

Duration: December 1, 2022 - November 30, 2025

REMEASURE (FKZ 03EN6022A) - Collaborative Project: REMEASURE Rollout-Capable Multimodal Energy Management for Supermarkets; Sub-project: Model Predictive Multimodal Load Shifting in a Pilot Supermarket

Duration: December 1, 2022 - November 30, 2025

To meet national and European climate targets, the expansion of renewable fluctuating electricity generation must be accelerated and efficiently utilized. In addition to the costly expansion of energy grids and storage capacities, existing local energy systems can be used to shift demand in relation to the supply of renewable energy through multimodal load shifting. A quickly implementable, scalable, and economical solution is needed. Supermarkets are of great interest because their energy systems are largely standardized. Furthermore, they are comprehensively equipped with measurement technology and have a high degree of automation. Load shifting potential exists due to the flexibility of the supermarket's local refrigeration network in conjunction with controllable refrigeration consumers, such as freezers or cold rooms. Energy systems in state-of-the-art supermarkets are currently typically operated purely demand-driven based on static rules. Such operation is not effective, as the supply of fluctuating, renewable energies is not taken into account and thus cannot be efficiently utilized. The project's goal is to investigate whether efficient utilization of fluctuating, renewable energies is possible through multimodal load shifting, particularly with regard to the refrigeration supply of supermarkets. This will consider both dynamic electricity prices and forecasts of dynamic boundary conditions, such as electrical and thermal base loads. The technical feasibility will be demonstrated in simulation and validated in the real operation of a pilot supermarket. The effort for installing the energy management system and any necessary upgrading of the energy system, e.g., through additional measuring devices or adjustments in automation, should be kept as low as possible.

Consortium Partners:

Rheinisch-Westfälische Technische Hochschule Aachen (RWTH Aachen University)

Contact Persons:

Project Manager: stefan.langemeyer@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMWE

RIESIQ (01IS24087A) - Collaborative Project: Research and Testing of New AI Methods for Robust, Intelligent, and Efficient Industrial-Grade Systems

Duration: December 1, 2024 - November 30, 2027

RIESIQ (01IS24087A) - Collaborative Project: Research and Testing of New AI Methods for Robust, Intelligent, and Efficient Industrial-Grade Systems

Duration: December 1, 2024 - November 30, 2027

The goal of this research project is to overcome the deficits of generative AI models or large language models (LLM) with regard to their applicability in industrial production processes. The focus is on researching and testing robust yet flexible learning methods that enable complex dynamic systems and physical processes to be modeled with sufficient accuracy, as well as controlled intelligently and interpretably. The necessary resource requirements during training and inference are to be minimized by data-efficient algorithms to ensure sustainable and environmentally friendly use on an industrial scale. To achieve these goals, emphasis will be placed on a scalable, synergistic coupling of ML algorithms and numerical simulation methods, as well as on resilient and practical RL methods for control optimization built upon them. The resulting methods must simultaneously meet high demands for transparency and explainability, which is why an additional focus is on researching new interpretation techniques for structured model architectures. The newly developed AI solution modules will be prototypically and practically tested using automated production and quality assurance processes in the manufacturing of lithium-ion battery cells, thereby demonstrating the added value for the AI-driven transformation process towards a scalable and flexible factory of the future.

Consortium Partners:

deepset GmbH

Technische Universität Berlin (TU Berlin)

Contact Persons:

Project Manager: marc.weber@siemens.com

Financial Manager: roland.deak@siemens.com

Funding Provider: BMWE

VALUE-M (FKZ 16BZF332A) - Collaborative Project: Extending the Usable Lifespan of Storage Systems through Microcycle Management with Continuous Condition Monitoring - VALUE-M

Duration: June 1, 2023 - May 31, 2026

Siemens AG is active in the market as a provider of solutions and components, as well as a development partner for projects. In this context, Siemens will primarily work on the technical implementation and potential commercial exploitation of the VALUE-M project results. In algorithm development, Siemens will ensure compatibility with ongoing third-party projects and feasibility in industrial systems. Besides the implementation of communication software and cloud systems, which is less critical for VALUE-M, the focus is on developing local control units that optimally operate the respective storage system to be controlled, using the developed algorithms in a multi-use structure for several management forms simultaneously. To practically verify the economic potential of an extended algorithm and control technology in the spirit of the project, individual demonstration systems will be managed by a subcontracted electricity trading company. On the hardware side, methods for the technical integration of second-life storage units will be developed, where systems supplied by third parties can be completed with suitable control technology. Specifically for multi-use management, including instantaneous reserve, sufficient power reserves and high reaction speed will be ensured in the selection of power electronics to compensate for very short-term grid disturbances.

Consortium Partners:

SWW Wunsiedel GmbH

Rheinisch-Westfälische Technische Hochschule Aachen (RWTH Aachen University)

Contact Persons:

Project Manager: heiko.winterstein@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMWE

VE4 (FKZ 16ME0735K) - Collaborative Project: Trustworthy Disturbance Mitigation in Energy Grids using Real-time Capable Edge Components

Duration: November 1, 2022 - October 31, 2025

Motivation: The quality of electrical supply in industrial grids has significantly deteriorated due to increasingly complex, highly dynamic consumers that cause grid disturbances. At the same time, the highly networked Industry 4.0 systems are very sensitive to these grid disturbances. This increasingly leads to plant downtimes with high costs. Currently, strong loads or unfavorable mutual interferences of grid components cannot be detected in advance. The main goals of VE4 are the continuous detection and intelligent active compensation of disturbances in electrical energy supply grids for industry. A significant increase in robustness, as well as energy and resource efficiency, is to be achieved. For this purpose, a distributed, novel digital compensation system is to be developed. This consists of a real-time capable heterogeneous sensor network and edge devices, which allow for ML-based fast prediction of disturbance signals and calculate counter-coupling signals to compensate for the disturbances. The counter-coupling signals are finally fed back into the grid by active filters. The foundations for flexible plant monitoring and real-time control through edge-device technologies are thus being created. The interfaces are to be standardized. This makes the system flexibly expandable for a wide range of control and diagnostic tasks in digital energy grids – far beyond the active disturbance compensation aimed for in the project. The edge-device-based diagnostic and disturbance compensation facility for modern digital industrial energy supply grids is to be developed in several steps. Hardware and software will be conceptualized and prototypically realized. This includes time-synchronized networked real-time capable measurement nodes, the edge devices with ML methods, a cloud computer, and the time-synchronized active filters. The performance will be evaluated in an overall system demonstrator in a real plant.

Consortium Partners:

Technische Universität Dortmund (Technical University of Dortmund)

Meanwave GmbH

Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung eingetragener Verein (Fraunhofer-Gesellschaft for the Advancement of Applied Research, registered association)

Bleenco GmbH

Contact Persons:

Project Manager: stefan.haensel@siemens.com

Financial Manager: bianca.miene@siemens.com

Funding Provider: BMFTR

AVEL – Development of an inorganic potting compound for electrical machines and power electronics
Duration: 1 Sep 2021 – 28 Feb 2025
Project Manager: Klaus Wilke
Financial Manager: Bianca Miene
Funding body: BMWE

Connect4HCA – Flexible drone-based communication network with sensor functionality for modern production facilities
Duration: 1 Jan 2024 – 31 Dec 2028
Project Manager: Volkmar Doericht
Financial Manager: Roland Deak
Funding body: BMFTR

CONTAIN – Threats from the digital domain
Duration: 1 Mar 2023 – 28 Feb 2025
Project Manager: Tiago Gasiba
Financial Manager: Roland Deak
Funding body: BMFTR

CTSimU2 – Realistic simulation of real X-ray computed tomography systems
Duration: 1 Oct 2022 – 31 Mar 2025
Project Manager: Christian Watzl
Financial Manager: Roland Deak
Funding body: BMWE

ECO:DIGIT (collaborative project) – Enabling green computing and digital transformation; sub-project: Enabling green edge computing
Duration: 1 Jun 2023 – 31 May 2026
Project Manager: Karsten Schoerner
Financial Manager: Roland Deak
Funding body: BMWE

EMDRIVE – Embedded computing performance and diagnosis hierarchy from sensors to edge
Duration: 1 Feb 2022 – 30 Apr 2025
Project Manager: Daniel Horcher
Financial Manager: Roland Deak
Funding body: BMFTR

EREP – Successful remanufacturing through data-based decision-making and intelligent process planning
Duration: 1 Oct 2022 – 30 Sep 2025
Project Manager: Tobias Kamps
Financial Manager: Bianca Miene
Funding body: BMFTR

EvoFrame – Development and validation of an evolutionary, human-centred framework for transformation-capable organisations
Duration: 1 Jan 2023 – 31 Dec 2025
Project Manager: Martin Kramer
Financial Manager: Roland Deak
Funding body: BMFTR

HEROES – Highly efficient and reliable smart power electronic systems
Duration: 1 Oct 2022 – 30 Sep 2025
Project Manager: Klaus Wilke
Financial Manager: Bianca Miene
Funding body: BMWE

HoverGaN – Development of innovative GaN power semiconductors for high-voltage applications
Duration: 1 Oct 2021 – 31 Mar 2025
Project Manager: Klaus Wilke
Financial Manager: Bianca Miene
Funding body: BMWE

HTA 2.0 – Hardware trust anchors for the next generation of secure embedded systems
Duration: 1 Sep 2022 – 31 Aug 2025
Project Manager: Harald Mueller
Financial Manager: Roland Deak
Funding body: BMFTR

SafeOps – Secure industrial operations through AI-based assistance systems
Duration: 1 Oct 2022 – 30 Sep 2025
Project Manager: Yushan Liu
Financial Manager: Roland Deak
Funding body: BMWE

VITAMIN – Trustworthy AI for industrial applications
Duration: 1 Oct 2022 – 30 Sep 2025
Project Manager: Markus Sauer
Financial Manager: Roland Deak
Funding body: BMWE