Future Mobility
Short description – Rapid urbanization and globalization, along with the associated environmental impacts, are giving rise to new challenges and goals, such as the push for greater decarbonization—particularly in mobility applications—and digitalization. The Future Mobility consortium project, led by Infineon Technologies Dresden GmbH & Co. KG, therefore aims to develop innovative solutions along the entire value chain—from product design and development to technology development, process innovation, and high-volume manufacturing—to enable novel power products and systems for future automotive electronics solutions.
General Information about the Project
Project Duration:
Jul 1, 2023 to Jun 30, 2026
Project Management:
Prof. Dr. Christoph Laroque (WHZ)
Partner:
Infineon Technologies AG, Fabmatics, SYSTEMA Systementwicklung, LEC, Technische Universität Chemnitz, Technische Universität Dresden, Hochschule für Technik und Wirtschaft Dresden, Westsächsische Hochschule Zwickau (Fakultät PTI), Helmholtz-Zentrum Dresden-Rossendorf, Fraunhofer-Institut für Photonische Mikrosysteme IPMS und Fraunhofer-Institut für Werkstoff- und Strahltechnik IWS
Project Description
Under the leadership of Infineon Dresden, the project partners are developing innovative solutions for microcontrollers and power semiconductors, from initial product concepts through to high-volume production. The work within the R&D consortium includes, among other things, the development of efficient methods for microcontroller design, new product and technology developments for power semiconductors, and process innovations for modern, efficient high-volume production. In addition, the project partners are addressing topics such as digital transformation and the future design of people-centered workplaces in high-volume manufacturing. The results for green mobility “made in Saxony” are intended to be incorporated into future automotive and industrial applications.
Product Design and Development: The disruptive changes in the development of new automobiles extend beyond the shift from internal combustion engines to CO2-neutral electric powertrains. The vehicle’s electrical and electronic systems are also undergoing a fundamental transformation—moving away from decentralized microcontrollers toward a networked architecture for onboard electronics. The focus here is on developing microcontrollers with embedded high-voltage circuit elements, which are used in many locations throughout the vehicle for the energy-efficient control of electric motors for a wide variety of applications (pumps, ventilation flaps, seat adjustment, brakes, electronic steering, etc.). A key objective of the project is to structure product and test development activities in a modular fashion, which will enable future products to be generated more quickly and with less effort using pre-built functional blocks. The partners IFD and TUC will collaborate in this area.
Technology Development and Process Innovations: In addition to high reliability, current and future automotive technologies focus on increasingly complex board architecture to meet requirements for environmental protection, functionality, and ease of use. As part of this project, partners IFD and IPMS are developing quality improvement concepts for technology designs that enable performance gains through reduced power loss and improved battery life. The next key focus is on ensuring quality and reliability, starting during product and process development and continuing through production and across the entire product lifecycle. To ensure and expand these standards for both existing and new products and processes, the development of appropriate methods and procedures for process analysis, design, and monitoring is both necessary and essential. The closely linked consortium partners TUC, HZDR, WHZ, IWS, and IPMS play a key role in this regard.
High-Volume Manufacturing: To transition new products and technologies into volume production at the Dresden site in a cost-effective and sustainable manner in the future, IFD will work with its consortium partners—WHZ, TUD, HTW, SYS, and FMX—to establish efficient processes and manufacturing logistics within the existing production lines. A key focus of the work is on further automating and digitizing the production lines. Using innovative factory simulation applications and visualizations of material flows, the project will ensure that no unforeseen, critical situations—such as bottlenecks or delivery delays—occur in production. At the same time, key decision-making processes for existing office workflows are being digitized, leading to simpler and more transparent office procedures. Another focus is on establishing human-centered workplaces. In addition to introducing further Industry 4.0 applications, research is being conducted to determine to what extent these applications create better working conditions for production employees. For tasks involving heavy loads, research is being conducted to determine which methods can be used to reduce the physical strain. In addition, innovative methods are being developed to reduce stress, improve time-consuming training methods, shorten onboarding times, and address the issue of the skilled labor shortage.
Our Contribution
In this project, the Industry Analytics research group and Professor Laroque aim to further develop backward simulation—originally used to generate feed-in planning—into a targeted approach for generating and evaluating data from large-scale simulation experiments. Such an application can be understood as “data farming” and is intended to efficiently and effectively increase both the volume of data and the information relevant to a given decision-making and planning problem.
This involves generating, processing, and analyzing very large amounts of simulation data in order to draw high-quality conclusions about the modeled production system through a sufficiently rigorous experimental design. The extension of previous work on backward simulation using the data farming approach is intended to significantly increase the informative value of simulation studies while simultaneously addressing the challenge of incorporating the dynamics and stochasticity of production systems and processes with sufficient accuracy. Ultimately, the data set is to be utilized in the context of machine learning methods.
Contact Persons
Prof. Dr. Christoph Laroque
Professor of Business Analytics
christoph.laroque[at]whz.de
Madlene Leißau
Research Assistant and Ph.D. Student
madlene.leissau[at]whz.de