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„Hybride Montage“ als Antwort auf Modell Mix und Variantenvielfalt im produzierenden Mittelstand
(2024)
Die "Hybride Montage" als Kombination aus konventioneller Fließfertigung und innovativer Matrixproduktion offeriert vielfältige Potentiale, dem zunehmenden Maß an Variantenvielfalt in der produzierenden Industrie Rechnung zu tragen. Im Zeitalter des Industrial Metsverse und der damit verbundenen "Servitisierung" und des "Manufacturing as a Service" - d.h. der Güterproduktion als Dienstleistung - ermöglicht die Hybride Montage außerdem eine flexiblere Arbeitsplanung.
Die produzierende Industrie des DACH-Raumes erlebt im Jahr 2023 vielfältige Herausforderungen. Gerade scheint die Covid-Pandemie überwunden, welche die Vulnerabilität globaler Lieferketten schonungslos offenbart hat, treten neue Herausforderungen zutage. Veränderungen der gesetzlichen Anforderungen (wie EU Data Act), steigende Ansprüche an ökologische Nachhaltigkeit (z.B. Kreislaufwirtschaft) oder veränderte Kundenbedürfnisse (wie insbesondere Servitisierung) führen – bei einem konstant hohen Niveau an Variantenvielfalt – zu erheblichen technologischen Herausforderungen. Diese werden darüber vielerorts flankiert und im negativen Sinne überlagert durch einen erheblichen Mangel an Arbeits- und Fachkräften in der Produktion. Das Produktionssystem der Zukunft wird unserer Einschätzung nach daher nicht nur unternehmensübergreifenden Datenaustausch ermöglichen, Kreislaufwirtschaft befähigen und verstärkt kundenzentriert ausgerichtet sein. Es wird Arbeits- und Fachkräfte in der Produktion auf vielfältige Art und Weise „begeistern“. Auf diese Weise wird Fluktuation reduziert, generisches Wissen im Unternehmen gehalten und so die Grundlage für nachhaltigen Geschäftserfolg und technologischen Fortschritt sowie die langfristige Sicherung von attraktiven Arbeitsplätzen in der produzierenden Industrie geschaffen.
Nach unserer Überzeugung setzen sich in der produzierenden Industrie des DACH-Raumes diesbezüglich zwei Erkenntnisse durch:
1. Der „kritische Wettbewerb“ stammt selten aus Europa
2. Für die Bewerkstelligung dieser Herausforderungen sind authentische Impulse von außen – insbesondere von anderen, vergleichbaren Unternehmen – ein zentraler Erfolgsfaktor.
In dieser Gemengelage haben wir mit dem SUMMIT ALLGÄU eine Managementkonferenz „von der Industrie für die Industrie“ ins Leben gerufen. Im Zentrum des Veranstaltungskonzeptes stehen hierbei keine wissenschaftlichen Fachvorträge, sondern authentische Erfahrungsberichte hochkarätiger Referenten aus der industriellen Praxis. Beim ersten SUMMIT ALLGÄU Produktion am 23. und 24. Oktober 2023 in Marktoberdorf standen inhaltlich insbesondere die Themenkomplexe „Transformation & Nachhaltigkeit“, „Faktor Mensch in der Produktion“ sowie „Variantenvielfalt“ im Fokus. Auf überfachlicher Ebene wurden vor allem der persönliche Austausch und das Networking zwischen den zahlreichen Teilnehmern, Referenten und Ausstellern fokussiert.
Portable Emission Measurement Technology and RDE on Motorcycles as Instruments for Future Challenges
(2020)
The packaging of fresh meat has been studied for decades, leading to improved packaging types and conditions such as modified atmosphere packaging (MAP). While commonly used meat packaging uses fossil fuel-based materials, the use of biodegradable packaging materials for this application has not been studied widely. This study aimed at evaluating the sustainability of biodegradable packaging materials compared to established conventional packaging materials through analyses of the quality of freshly packaged pork. The quality was assessed by evaluating sensory aspects, meat color and microbiological attributes of the pork products. The results show no significant differences (p > 0.05) in ground pork and pork loin stored in biodegradable MAP (BioMAP) and conventional MAP for the evaluated sensory attributes, meat color or total bacterial count (TBC) over extended storage times. The data suggest that BioMAP could be a viable alternative to MAP using conventional, fossil fuel-based materials for the storage of fresh meats, while simultaneously fulfilling the customers’ wishes for a more environmentally friendly packaging alternative.
Die Rolle von Batteriespeichern im Kontext eines künftigen Energiesystems: Smart Energy Systems
(2023)
Welche Bedeutung haben Batteriespeicher für künftige Energiesysteme? Können wir mit Batteriespeichern eine sichere, stabile und bezahlbare Energieversorgung erreichen? Wie flexibel muss unser Stromsystem sein?
In seiner Präsentation im erläutert Prof. Dr. Holger Hesse anhand anschaulicher Beispiele und seiner jahrelangen Erfahrung, ob und wie Elektrofahrzeuge künftig auch durch "Vehicle-to-Grid" einen Beitrag leisten können, wann "Second-life" eine wirkliche Option sein kann und in welchen Bereichen es schon heute wirtschaftlich sein kann, einen Batteriespeicher zu betreiben.
Battery electric buses (BEBs) are gaining prominence in public transportation systems. In this paper, we investi-gate the impact of road grade, passenger load, and recuperation power limits on the energy consumption of BEBs using a physics-based model with tuned parameters. The model was employed to conduct a sensitivity analysis taking into account different altitude data sources, passenger load assumptions, and maximum recuperation power limits. The results highlight the importance of considering the route topology and its interaction with dynamic passenger loading for energy consumption predictions. Further-more, the results indicate that various altitude data sources are feasible to estimate the road grade for this purpose. Apart from that, the sensitivity for recuperation power limitations is shown and put into context. Within a broader framework, the findings suggest that physics-based energy consumption models with optimised parameters can serve as a powerful tool for enhanced operations and planning of BEBs.
AI in Battery Storage
(2023)
Energy storage is a crucial flexibility measure to temporally decouple power generation from power demand and is touted as the missing link in realizing a decarbonized energy system based on renewable energy. Energy storage capacity buildup at all levels of the global energy system is expected to accelerate the decarbonization process. To this end, a coherent mathematical framework to ascertain the carbon footprint of localized energy systems with energy storage is indispensable. This article presents an open-source energy system simulation program — Energy System Network (ESN). A variety of energy system configurations can be simulated with the Python program, which incorporates key energy system components such as generation, grid, storage, and loads. ESN features an integrated bottom-up approach that combines energy system modeling with streamlined life cycle assessment techniques to quantify the carbon footprint of all components in a localized energy system. The lifecycle phases of each component, including production, operation, and end-of-life treatment, can be considered. Carbon footprint values are obtained for two demonstrative case studies with lithium-ion battery applications: energy arbitrage and home energy systems. The metric Levelized Emissions of Energy Supply (LEES) has been used to evaluate the carbon footprint of each application. An unconventional energy arbitrage strategy designed to exploit the grid carbon intensity spreads instead of the energy price spreads manages to achieve a LEES value about 17% lower than the conventional variant. The influence of rooftop solar generation, battery energy storage system, and the energy management strategy on the LEES values for a home energy system is explored. A maximum LEES reduction of over 37% vis-á-vis the base scenario was observed with optimal energy management for the solar generation and the battery system. The open-source availability of ESN can contribute to transparency, comparability, and reproducibility in carbon footprint assessments of localized energy systems with energy storage.
Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.