60 Technik
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Grid-connected multi-string battery energy storage systems (BESS) operating under sustained high-power conditions face heat accumulation, power derating, and cooling-related efficiency losses, leading to trade-offs among thermal management, efficiency, and system availability. To manage these competing objectives, this work proposes a mixed-integer nonlinear programming (MINLP)-based multi-objective optimization (MOO) framework for optimal power allocation across parallel strings under air cooling. The optimization incorporates an equivalent circuit model (ECM) with SOC-, temperature-, and C-rate-dependent internal resistance, temperature-driven derating, and an experimentally derived inverter loss model. High-fidelity control is achieved by co-simulating the optimizer with an electro-thermal BESS simulation that captures spatial thermal dynamics of the battery pack. The simulation provides online state of charge (SOC) and temperature feedback and is validated against industry-measured battery pack temperatures, revealing core temperatures up to 4.5 °C higher than those predicted by conventional 0D average thermal models calibrated to pack surface measurements. Simulation results show that the baseline MINLP controller with evenly weighted objectives achieves approximately 3% higher round-trip efficiency (RTE) and system availability compared to the industrial benchmark droop control while minimizing thermal derating. Linearized optimal control without thermal optimization results in higher maximum string temperatures up to 40 °C and 4% higher derating losses. A Pareto-based objective prioritization improves energy performance by up to 5% while keeping temperatures below the 35 °C derating threshold. System scalability analysis shows that operating an optimal subset of strings outperforms conventional full-string operation, achieving 5.5% higher RTE with comparable availability and thermal performance. The framework is released open-source for adaptation to custom BESS applications.
The utilization of uncrewed aerial vehicles (UAVs) in search and rescue (SAR) operations has become increasingly prevalent because the deployment of UAVs is expected to facilitate a higher degree of operational flexibility while simultaneously reducing costs. Currently, commercially available UAVs can be equipped with low-resolution thermal infrared (IR) cameras with typical resolutions of 640 × 512 pixels, which generally are evaluated manually by the SAR teams during an operation. Automatic person detection in IR images still remains a challenge. The objective of the proposed AIResQ dataset is to significantly enhance the performance of object detectors in the IR domain, employed in SAR operations for missing and potentially injured persons. AIResQ comprises 9,788 IR images with a resolution of up to 2048 × 1536 pixels captured from drone perspectives with a handheld camera under
varying weather conditions and in different terrains. Additionally, AIResQ displays persons in atypical poses. In order to test new object detectors in the context of SAR, we established a benchmark dataset stemming from exercises with real drone flights together with SAR organizations.
Aligning a lens system relative to an imager is a critical challenge in camera manufacturing. While optimal alignment can be mathematically computed under ideal conditions, real-world deviations caused by manufacturing tolerances often render this approach impractical. Measuring these tolerances can be costly or even infeasible, and neglecting them may result in suboptimal alignments. We propose a reinforcement learning (RL) approach that learns exclusively in the pixel space of the sensor output, eliminating the need to develop expert-designed alignment concepts. We conduct an extensive benchmark study and show that our approach surpasses other methods in speed, precision, and robustness. We further introduce relign, a realistic, freely explorable, open-source simulation utilizing physically based rendering that models optical systems with non-deterministic manufacturing tolerances and noise in robotic alignment movement. It provides an interface to popular machine learning frameworks, enabling seamless experimentation and development. Our work highlights the potential of RL in a manufacturing environment to enhance efficiency of optical alignments while minimizing the need for manual intervention.
Diese Arbeit beschreibt die analytische Berechnung der aus der Kinematik stammenden maximalen Vertikalkraft eines Deltaroboters, reduziert auf die Position im Arbeitsraum, das maximale Motormoment sowie die Basisgeometrie der Roboterarme und Gelenke. Über den Rechenweg der inversen Kinematik und über die direkte Jacobi-Matrix wird ein Zusammenhang entwickelt, welcher direkt und für die numerische Berechnung optimiert die gesuchte Kraft berechnen lässt. Gleichzeitig wird analog der komplementäre Ansatz zur Berechnung der maximalen Geschwindigkeit in Vertikalrichtung beschrieben. Über die inverse Jacobi-Matrix wird ein Zusammenhang entwickelt, welcher ebenfalls auf der Position im Arbeitsraum, der maximalen Winkelgeschwindigkeit der Aktuatoren sowie der Basisgeometrie der Roboterarme und Gelenke basiert. Zusätzlich wird die numerische Laufzeit der Zusammenhänge untersucht und mit den gängigen Berechnungsverfahren verglichen.
Safety is a fundamental requirement for the acceptance of modern vehicles. When considering autonomous driving functions, this factor is crucial for the spread of this technology in the market. Human behavior plays a key role in the analysis, as these systems must be adapted and optimized to meet human needs in order to achieve a high level of market penetration. Accordingly, effect chains must be known in detail to identify influencing factors and to be able to optimize them. This paper presents a theoretical model for perceived safety - analyzing the sub-criteria of humans, vehicles, and the environment also demonstrating and linking their influences on the basis of existing investigations. These findings are intended to contribute to the understanding of human behavior in order to better adapt systems to the needs of the users.
Machine Learning (ML)-based LiDAR 3D object detectors in automated driving produce false detections, missed detections, and localisation errors under adverse weather and reduced visibility. Detection errors arising without hardware or software faults constitute performance insufficiencies under ISO 21448, Safety of the Intended Functionality (SOTIF), and the standard requires identification of the triggering conditions responsible. The prescribed analysis methods assume a design specification, but ML-based LiDAR object detectors have no design specification because the mapping from point clouds to bounding boxes is learned from training data. This paper proposes an uncertainty evaluation methodology that uses disagreement among deep ensemble members to separate correct from incorrect detections. Ensemble disagreement and performance insufficiencies arise from insufficient training data coverage of the operating condition. The methodology evaluates whether three uncertainty indicators derived from ensemble disagreement (mean confidence, confidence variance, and geometric disagreement) separate correct from incorrect detections. The evaluation produces outputs mapped to ISO 21448 analysis activities: discrimination metrics, triggering condition rankings by false positive share, frames flagged for investigation, and acceptance gates reporting coverage and false acceptance rate. A case study using simulated ensemble predictions across 22 weather configurations shows that geometric disagreement achieves the strongest separation, with acceptance gates that retain only true detections at reduced coverage. The observed separation arises because false detections produce spatially inconsistent bounding boxes across ensemble members where no physical object constrains the predicted position, while true detections remain spatially consistent.
Uncertainty in LiDAR sensor-based object detection arises from environmental variability and sensor performance limitations. Representing these uncertainties is essential for ensuring the Safety of the Intended Functionality (SOTIF), which focuses on preventing hazards in automated driving scenarios. This paper presents a systematic approach to identifying, classifying, and representing uncertainties in LiDAR-based object detection within a SOTIF-related scenario. Dempster-Shafer Theory (DST) is employed to construct a Frame of Discernment (FoD) to represent detection outcomes. Conditional Basic Probability Assignments (BPAs) are applied based on dependencies among identified uncertainty sources. Yager's Rule of Combination is used to resolve conflicting evidence from multiple sources, providing a structured framework to evaluate uncertainties' effects on detection accuracy. The study applies variance-based sensitivity analysis (VBSA) to quantify and prioritize uncertainties, detailing their specific impact on detection performance.
Robots are often showcased as precise machines that seamlessly collaborate with humans. However, reality diverges significantly, as robots encounter failures that lead to suboptimum outcomes, misunderstandings and socially awkward situations. Conversely, human-induced errors in these interactions often go undetected by robots, amplifying the complexity of the interaction dynamics on top of the uncertainties in the environment and interaction contexts.
In human-robot interaction (HRI) research, errors–wrong actions that are made due to the lack of knowledge–and mistakes–actions that turn out to be wrong–are commonly viewed as impediments to achieving flawless collaboration. Scholars and practitioners aspire to meticulously control variables, creating environments with predictable storylines and outcomes. Nevertheless, the controlled setting of a laboratory rarely mirrors the unpredictable nature of real-world scenarios, contributing to a notable disparity between expectations and actual experiences. The ability of robots to navigate erroneous situations is paramount to the sustained success of HRI. These imperfections are also perfect learning opportunities for robots to continuously adapt to the ever-shifting complexity and dynamics in real-world HRI.
This Research Topic contains research that addresses the gap between anticipated perfection and the inherent uncertainties in a diverse range of real-world applications. The papers presented here shine light on a variety of aspects ranging from novel technical approaches to repair failures in HRI, to user studies that aim to understand social dimensions of errors in HRI.
Thermische Fehlerdiagnose der wassergekühlten Statorwicklung von Grenzleistungs-Turbogeneratoren
(2000)
In dieser Arbeit wird die thermische Fehlerdiagnose der wassergekühlten Statorwicklung von Grenzleistungs-Turbogeneratoren betrachtet. Die auftretenden Fehlerursachen sind Ablagerungen in den Hohlteilleitern durch Korrosionsprodukte bis hin zur völligen Teilleiterverstopfung (mittel- bis langfristig auftretende Fehler) sowie die teilweise Verstopfung von Wasserkammern durch im Kühlrohrsystem befindliches Dichtungsmaterial (kurzfristig auftretende Fehler). Die Messwerte der Warmwassertemperaturen aller Statorstäbe werden zur Fehlerdiagnose mit den entsprechenden, vom Diagnosesystem berechneten Referenzwerten verglichen. Bei bestehenden Diagnosesystemen werden diese Referenzwerte aus algebraischen Gleichungen der Einflussgrößen (Temperaturen von Kaltwasser und Kaltgas, Strangströme, Kühlwasser-Differenzdruck) mit empirisch ermittelten Koeffizienten bestimmt. Daher ist die Anwendung derartiger Verfahren nur in quasi-stationären Betriebspunkten und mit eingeschränkter Empfindlichkeit (Toleranz ± 2 K) möglich.
Basierend auf der vollständigen thermischen Modellierung von Grenzleistungs-Turbogeneratoren, unterteilt in Teilmodelle für die Statorwicklung. die Rotorwicklung und den Statorblechkörper, werden die für den Wärmeenergieaustausch mit den Statorstäben relevanten Temperaturen (Kühlgastemperaturen und Eisenrandtemperaturen im Nutbereich) durch einen Zustandsbeobachter für die Eisenkerntemperaturen rekonstruiert. Als Rückführungsgröße wird die Warmgastemperatur verwendet, da diese Größe die Erwärmungs- und Abkühlungsvorgänge des Statorblechkörpers wiedergibt. Die Bestimmung der Beobachter-Rückführungskoeffizienten erfolgt nach der Methode der Polfestlegung.
Die hydraulischen Durchmesser der Kühlkanäle der einzelnen Statorstäbe werden als zentrale Modellparameter für die thermische Fehlerdiagnose der wassergekühlten Statorwicklung in der Inbetriebnahmephase des Diagnosesystems als "Fingerprint", der den fehlerfreien Zustand repräsentiert, durch Parameteroptimierung bestimmt. Die Inbetriebnahme des Diagnosesystems kann durch die automatische Auswertung von Datensätzen, die dynamische Vorgänge wie Änderungen des Betriebspunktes enthalten, an Stelle des ansonsten erforderlichen, zeit- und kostenintensiven Anfahrens einer Reihe charakteristischer Betriebspunkte deutlich vereinfacht werden.
Für die Bewertung der Beobachterfehler ("Residuen") wird eine mit Fuzzy-Logik operierende Diagnosekomponente vorgeschlagen. Auf diese Weise kann auch zusätzliches Expertenwissen berücksichtigt werden. Die modellbasierte, beobachtergestützte Residuengenerierung wird an Messdaten, die im Kernkraftwerk Unterweser aufgenommen wurden, verifiziert. Es zeigt sich, dass das im Rahmen dieser Forschungsarbeit entwickelte Diagnosesystem kontinuierlich und mit verbesserter Empfindlichkeit (Toleranz ± 0,5 K) eingesetzt werden kann. Darüber hinaus werden interne, nicht oder nur mit großem messtechnischen Aufwand zugängliche Zustandsva-riablen für ein Monitoring-System zur Verfügung gestellt.
The global shift towards renewable energy sources is imperative to meet increasing energy demands while mitigating environmental impacts. Solar power, as a pivotal renewable resource, requires effective integration techniques to maximize utility and grid compatibility. This study investigates the integration of a 400 kW solar power generation system with the utility grid using a Modular Multilevel Converter (MMC) and Maximum Power Point Tracking (MPPT). Using the Perturb and Observe method, the MPPT effectively harnesses optimal power from photovoltaic (PV) arrays, ensuring efficient energy conversion. The MMC, composed of multiple submodules, generates a multilevel AC output with minimal harmonic distortion, achieving a Total Harmonic Distortion (THD) of less than 5%. The boost converter successfully steps up the PV array voltage to 2000 V, and the output voltage stabilizes within 0.8 seconds. The MMC ensures high-quality power delivery, and the system produces a consistent three-phase output with minimal distortion, improving overall power quality and grid stability. This scalable and reliable approach enhances the performance of solar power systems connected to the grid, maintaining stability even under fluctuating environmental conditions.
