Fakultät Maschinenbau und Verfahrenstechnik (M+V)
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This master’s thesis conducts a detailed performance comparison of two advanced smart charging algorithms—ESG a swarm-based control algorithms and model predictive control (MPC) —within the Electric Vehicle (EV) charging park at the University of Applied Sciences, Offenburg. With the rapid increase in EV adoption, effective energy management strategies are essential to prevent grid overload, reduce operational costs, and promote sustainable energy use.
The comparison is centered around three key use cases: (1) grid stability, which focuses on maintaining a balanced load and preventing peaks in energy demand; (2) minimizing charging costs by optimizing energy usage during off-peak hours or when renewable energy supply is abundant; and (3) a combined scenario that seeks to balance both grid stability and cost minimization.
The performance results of both algorithms were closely aligned, demonstrating their effectiveness in managing charging park operations. However, the MPC algorithm, being a mathematical optimization framework, showed a slight edge over ESG algorithm. This advantage is particularly evident in scenarios where accurate forecasts of grid demand and energy pricing are available. With improved forecasting capabilities, MPC is expected to consistently outperform swarm-based algorithms, enhancing its effectiveness in overall system performance.
The research contributes valuable insights into the trade-offs between decentralized and predictive control strategies, offering recommendations for future implementations of intelligent charging systems that aim to balance grid stability and economic efficiency in large-scale electric mobility infrastructure.
A deep mutational scanning platform to characterize the fitness landscape of anti-CRISPR proteins
(2024)
Deep mutational scanning is a powerful method for exploring the mutational fitness landscape of proteins. Its adaptation to anti-CRISPR proteins, which are natural CRISPR-Cas inhibitors and key players in the co-evolution of microbes and phages, facilitates their characterization and optimization. Here, we developed a robust anti-CRISPR deep mutational scanning pipeline in Escherichia coli that combines synthetic gene circuits based on CRISPR interference with flow cytometry coupled sequencing and mathematical modeling. Using this pipeline, we characterized comprehensive single point mutation libraries for AcrIIA4 and AcrIIA5, two potent inhibitors of CRISPR-Cas9. The resulting mutational fitness landscapes revealed considerable mutational tolerance for both Acrs, suggesting an intrinsic redundancy with respect to Cas9 inhibitory features, and – for AcrIIA5 – indicated mutations that boost Cas9 inhibition. Subsequent in vitro characterization suggested that the observed differences in inhibitory potency between mutant inhibitors were mostly due to changes in binding affinity rather than protein expression levels. Finally, to demonstrate that our pipeline can inform Acrs-based genome editing applications, we employed a selected subset of mutant inhibitors to increase CRISPR-Cas9 target specificity by modulating Cas9 activity. Taken together, our work establishes deep mutational scanning as a powerful method for anti-CRISPR protein characterization and optimization.
Die Eigenschaften von Fluiden sind zur Beschreibung von Strömungsvorgängen mit den Erhaltungssätzen für Masse, Impuls und Energie notwendig. Für inkompressible Fluide wird die Grenze der Dichteänderung in Abhängigkeit der Machzahl angegeben. Die Rheologie behandelt die Fließeigenschaften der Fluide bei Deformationen in Strömungen. Die Viskosität tritt beim newtonschen Schubspannungsansatz auf. Das Verhalten von Druck und Dichte in der Hydro- und Aerostatik wird beschrieben.
Die Baubranche setzt sich seit mehreren Jahren mit dem Building Information Modeling (kurz BIM) auseinander. Während diese Methode in Architekturbüros vermehrt zur optimierten Planung eingesetzt wird, halten BIM-Methoden nur langsam Einzug in die TGA-Planung, die Ausführung und in die Betriebsphase von Gebäuden. Dieses Projekt entwickelt BIM-Methoden für die Inbetriebnahme und betriebsbegleitende Optimierung von TGA-Anlagen und demonstriert diese an einem Niedrigstenergiegebäude. Damit gibt das Projekt einen Impuls zur breiten Anwendung von BIM-Methoden von der Entwurfsplanung über die Ausführung bis hin zur Inbetriebnahme. So können BIMMethoden zielgerichtet genutzt werden, um Arbeitsplatzqualität und Energieeffizienz deutlich zu verbessern.
Diese Digitalisierung verändert die traditionelle Inbetriebnahme und Betriebsführung. Das einzelne Gebäude kann dabei zur zentralen Schnittstelle für die Erprobung und Umsetzungen digitaler Angebote werden und damit einen Beitrag zum effizienteren Energieverbrauch, sowie zum Klimaschutz leisten.
New Device for Accurate Measurement of Busbarless Bifacial Solar Cells by Using N.I.C.E.™ Technology
(2020)
In this work the construction and characterization of a new device for the accurate measurement of busbarless, bifacial solar cells is presented. The measuring apparatus was designed and built according to the principle of the N.I.C.E.™ (New Industrial Cells Encapsulation) technology. For reasons of comparability, the measurement setup was based as closely as possible on the setup of manufactured N.I.C.E.™ mini modules. This equipment is used to determine the series resistance as a function of the vacuum and the temperature prevailing in the module. The results obtained are then compared with the results of manufactured solar modules in order to assess how well the test setup predicts the performance of the cell in the subsequent module.
The invention relates to a method, a device and a computer program for carrying out a method for determining the capacity, internal resistance and open-circuit voltage curve of a chargeable battery. The method is based on measuring battery voltage (V mess) and battery current (I mess) over a time period. The measured voltage is applied to a voltage-regulated battery model that calculates a current intensity (I mod). The battery model is provided with arbitrarily assumed values for the parameters to be determined (capacity and/or internal resistance and/or open-circuit voltage curve). Since these do not as a rule correspond to the values of the real battery, there is a difference between simulated current intensity (I mod) and measured current intensity (I mess). From this difference, the deviation between assumed and real values for capacity, internal resistance and open-circuit voltage curve is determined by means of suitable calculation rules. From this, and from the assumed values, follow the real values for capacity, internal resistance and/or open-circuit voltage characteristic, which can be stored or displayed to a user. If one or more of the parameters are already known from the beginning, the known values are used in the model, and only the unknown values are determined. The determined values can also be used to update the model. Further measurement data, or a repetition using the same measurement data, then permit the measuring accuracy to be increased. A continuous measurement of capacity, internal resistance and/or open-circuit voltage curve over longer periods is also possible, which thus enables the ageing of the battery to be assessed.
With climate change and global rising temperatures heat health warning systems have become important in accurately predicting heat waves. However, most heat health warning systems rely on the ambient temperature forecast and do not take indoor building conditions into consideration. Moreover, a general heat warning system cannot accurately predict the heat stress conditions in individual buildings. To implement the prediction algorithms the study also proposes a Raspberry Pi based measurement system. Furthermore, to reduce the computational load on Raspberry Pi a Transfer learning technique is implemented from a pre trained Long Short-Term Memory (LSTM) neural network. The results show prediction accuracy of 97% with an RMSE of 0.218 for indoor temperature prediction.
Die Erfindung betrifft ein Verfahren sowie eine Vorrichtung und ein Computerprogramm zur Ausführung des Verfahrens zur Bestimmung der Kapazität, des Innenwiderstands und/oder der Leerlaufspannungskurve einer aufladbaren Batterie. Das Verfahren beruht auf der Messung von Batteriespannung Vmessund Batteriestrom Imessüber einen Zeitraum. Die gemessene Spannung wird auf ein spannungsgeführtes Batteriemodell aufgeprägt, das eine Stromstärke Imodberechnet. Das Batteriemodell wird mit beliebig angenommenen Werten für die zu bestimmenden Parameter (Kapazität und/oder Innenwiderstand und/oder Leerlaufspannungskurve) versehen. Da diese i.d.R. nicht den Werten der realen Batterie entsprechen, ergibt sich ein Unterschied zwischen simulierter Stromstärke Imodund gemessener Stromstärke Imess. Aus diesem Unterschied wird mittels geeigneter Rechenvorschriften die Abweichung zwischen angenommenen und realen Werten für Kapazität, Innenwiderstand und/oder Leerlaufspannungskurve ermittelt. Daraus und aus den angenommenen Werten folgen die realen Werte für Kapazität, Innenwiderstand und/oder Leerlaufspannungskennlinie, welche gespeichert oder einem Nutzer angezeigt werden können. Sind ein oder mehrere der Parameter bereits von Beginn an bekannt, werden die bekannten Werte im Modell verwendet, und nur die unbekannten ermittelt. Die ermittelten Werte können auch verwendet werden, um das Modell zu aktualisieren. Weitere Messdaten, oder eine Wiederholung mit den gleichen Messdaten, erlauben dann eine Erhöhung der Messgenauigkeit. Auch ist eine kontinuierliche Messung von Kapazität, Innenwiderstand und/oder Leerlaufspannungskurve über lange Zeiträume möglich und damit eine Bewertung der Alterung der Batterie.
Quantifying movement coordination in cross-country (XC) skiing, specifically the technique with its elemental forms, is challenging. Particularly, this applies when trying to establish a bidirectional transfer between scientific theory and practical experts' knowledge as expressed, for example, in ski instruction curricula. The objective of this study was to translate 14 curricula-informed distinct elements of the V2 ski-skating technique (horizontal and vertical posture, lateral tilt, head position, upper body rotation, arm swing, shoulder abduction, elbow flexion, hand and leg distance, plantar flexion, ski set-down, leg push-off, and gliding phase) into plausible, valid and applicable measures to make the technique training process more quantifiable and scientifically grounded. Inertial measurement unit (IMU) data of 10 highly experienced XC skiers who demonstrated the technique elements by two extreme forms each (e.g., anterior versus posterior positioning for the horizontal posture) were recorded. Element-specific principal component analyses (PCAs)—driven by the variance produced by the technique extremes—resulted in movement components that express quantifiable measures of the underlying technique elements. Ten measures were found to be sensitive in distinguishing between the inputted extreme variations using statistical parametric mapping (SPM), whereas for four elements the SPM did not detect differences (lateral tilt, plantar flexion, ski set-down, and leg push-off). Applicability of the established technique measures was determined based on quantifying individual techniques through them. The study introduces a novel approach to quantitatively assess V2 ski-skating technique, which might help to enhance technique feedback and bridge the communication gap that often exists between practitioners and scientists.