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To make the full performance of the intrinsic 100 V, 5 mΩ gallium nitride transistors available on system level, in this work an asymmetrical & thermally optimized PCB embedded single chip package with integrated resistance thermometer, high temperature capability and a thermal resistance Rth,j−hs of 3.3 K W−1 is characterized in a 48 V to 24 V 300 kHz mild-hybrid DC/DC operation with two paralleled chips in each low- (LS) and high-side (HS). The transistors are mounted on a 4-layer multilayer PCB with 1 mm copper inlays to achieve a high current capability, while allowing narrow logic traces on the same PCB. The designed converter is achieving a light load efficiency of ≥99 % and an efficiency of 97 % at 60 A output current and ≈1.3 kW output power in a 48 V to 24 V 300 kHz buck-converter operation. The on-board temperature readout circuit and the phase output current sensor offer the possibility to extend the GaN transistors to an intelligent power module by the compact and simple sensors.
Precise forecasting of thermal loads is a critical factor for economic and efficient operation of district heating and cooling networks. If thermal loads are known with high accuracy in advance, use of renewable energies can be maximized, and – in combination with thermal storage units – fossil generation, in particular in peaking units, can be avoided. Machine learning has proven to be a powerful tool for time series forecasting, and has demonstrated significant advancements in recent years. This paper presents the scientific methodology and first results of the publicly funded research project “deepDHC”, which aims at a broad benchmarking of traditional and advanced machine learning methods for thermal load forecasting in district heating and cooling applications. The analysis covers autoregressive forecasting approaches, decision trees such as “adaptive boosting”, but also latest “deep learning” techniques such as the “long short-term memory” (LSTM) neural network. This work is based on data from the district heating network of the city of Ulm in Germany. First, different performance metrics for evaluating forecasting qualities are introduced. Second, approaches for data screening and results of a linear and non-linear correlation analysis are presented. Third, the machine learning tuning process is described. For thermal load forecasting, weather data are key input parameters. This work uses hourly weather forecasts from weather models provided by the German meteorological service. These weather data are updated automatically, and have been statistically corrected in order to represent very accurate point forecasts for up to ten days ahead. In addition, a user-friendly web interface has been developed for use by the district heating network operator. The performance of different machine-learning algorithms is compared based on 72 h heating load forecasts.
A major concern about advanced motion-based simulators is their level of fidelity i.e., how close the motion sensation in a simulator is to the one perceived in a real vehicle. In this study, we collect the assessment from an exceptional sample composed by n = 33 automotive industry experts who were asked to evaluate the fidelity in terms of steering, braking and speed. Given the subjective nature of our measure, we propose a censored-data Tobit regression model that accounts for this issue, thus providing more accurate estimations. Our results show that, on average, experts evaluated the steering actions close to the maximum level of fidelity. However, braking and speed were evaluated lower in realism, and in fact both diminished the overall fidelity judgement by up to 50%. Moreover, coefficients indicate that steering contributes more to the judgement of fidelity than braking and speed actions. Heterogeneity in the experts' responses and general implications are discussed.
In recent decades, research in both academic and industry has focused on the area of highly automated driving systems and is expected to do so in the near future. The scenario-based analysis is widely used in automated vehicles that requires simulation-based vehicle control estimation. One of the assist system is transverse guidance that is inescapable for highly automated driving system. The transverse guidance assist system deals with the longitudinal and lateral behavior of the vehicle. For a specific use case, merging functions like adaptive cruise control and lane keeping assist system are considered. The paper is focused on the development of transverse guidance that forms the combined lateral and longitudinal control of highly automated driving system like Lane Change maneuver planning in highway scenario. Moreover, the parameters are demonstrated for the model and performance of the model has been shown for the use case scenario. The proposed approach is then evaluated for a scenario in a simulation environment modeled using MATLAB/Simulink.
The increasing share of distributed energy resources gives rise to new opportunities for deploying innovative business models and coordination schemes within sustainable energy systems. Different concepts entail different implications at socioeconomic, technical and institutional level. Hence, their thorough assessment is key to understanding their actual potential as enablers of the energy transition. Considering this background, we focus on local energy markets as an increasingly discussed approach for coordinating distributed energy systems and introduce a simulative framework for enabling a multi-regional assessment of this concept. Local energy markets bear the potential for increasing the active participation of end consumers, which could increase their acceptance for energy projects in general and their returns on investment, as well as for reducing the peak load on increasingly congested electrical grids by enhancing local energy balancing. We evaluate these hypotheses for twelve representative German regions, for which we formulate assumptions regarding the energy demand as well as the shares of distributed energy resources that are consistently aligned with an overall European energy scenario envisaging a rapid growth of electric vehicles in Germany. For this purpose, we enhance an existing framework for the assessment of local energy markets in order to be able to include the flexibility of the electric mobility sector in local trade activities. The simulation results show that local energy markets have a significant impact on energy systems: First, local trading increases the economic benefits over all participants, who would otherwise only be able to use their generation for self-consumption or direct marketing in central energy markets. Second, local energy balancing increases on average by 60%over all regions. Third, infrastructural relief of the overlaying transmission grids can be accomplished by reducing the yearly peak load at the point of common coupling by 39%on average and at the most by 97%. Furthermore, we find that including electric vehicles in local market activities does not alter but rather reinforces these effects.
This paper investigates the scenario catalog generation and scenario reduction approaches for a complete Highly Automated Driving Function (HADF). Such approaches focus on the clustering and/or grouping of scenarios by applying a simple stochastic process at an early stage of development. Dealing with an enormous number of scenarios considering Functional Safety (FuSa), Safety Of The Intended Functionality (SOTIF)including cybersecurity desires intelligent approaches for HADF’s scenario reduction. The reduction of scenarios in HADF is a challenge for automotive researchers since it relates to a large number of parameters (like environmental aspects). The main contributions of the scenario generation and reduction approach proposed in this work are the following: (1) contribution to a complete scenario catalog for a dedicated HADF, (2) logical scenario optimization with parameter distribution, and (3) optimize discretization step for finding semiconcrete scenarios that can be executed. Furthermore, the optimization method incorporating the Monte-Carlo(MC) experiment with the CarMaker simulation yields a systematic approach to modeling reduced scenarios without redundancy to support safety.
This volume of the series ARENA2036 compiles the outcomes of the first Stuttgart Conference on Automotive Production (SCAP2020).
It contains peer-reviewed contributions from a theoretical as well as practical vantage point and is topically structured according to the following four sections: It discusses (I) Novel Approaches for Efficient Production and Assembly Planning, (II) Smart Production Systems and Data Services, (III) Advances in Manufacturing Processes and Materials, and (IV) New Concepts for Autonomous, Collaborative Intralogistics.
Given the restrictive circumstances of 2020, the conference was held as a fully digital event divided into two parts. It opened with a pre-week, allowing everyone to peruse the scientific contributions at their own pace, followed by a two-day live event that enabled experts from the sciences and the industry to engage in various discussions. The conference has proven itself as an insightful forum that allowed for an expertly exchange regarding the pivotal Advances in Automotive Production and Technology.
This work describes why additive manufacturing is a key technology for efficient design iterations and rapid production ramp-up with large-scale manufacturing technologies. Laser cutting, injection moulding and folding were used to increase the production capacity of face shields for health care workers during the COVID-19 pandemic. We applied systematic learnings from the iterative processes used for additive manufacturing to these large-scale manufacturing technologies and the respective face shield designs. In cooperation with medical experts, structural and functional design requirements of face shields were identified and are described in detail in this work. The regulatory design requirements according to EN 166 are introduced, which were considered to receive a CE certification for three of the presented designs. The employed manufacturing techniques are specified and the respective implications on the design solutions are discussed. The paper concludes with a summary of the production initiative at the research campus ARENA2036 with a total output of over 13 000 face shields from April to June 2020, which were distributed internationally.
The widespread adoption of electric vehicles makes investments in charging parks both immediate and necessary to lower range anxiety and allow longer trips. However, many charging park operators struggle with sustainable and profitable operation due to high fees on peak loads and volatile availability of renewable energy. Smart charging strategies may enable such operation, but the computational complexity of most available algorithms increases significantly with the number of charging points. Thus, operators of larger charging parks need information systems that provide real-time decision support without immense cost for computation. This paper presents a model that uses recent methods from the field of Reinforcement Learning. Our model is trained on a charging park simulation with realworld data on highway traffic and day ahead energy prices. The results indicate that Reinforcement Learning is a feasible solution to improve the sustainable and profitable operation of large electric vehicle charging parks.
Aufgrund der hohen Beanspruchungen, denen Kirchenglocken beim Läuten ausgesetzt sind, entstehen nicht selten Ermüdungsrisse im Klangkörper von Glocken, die durch Schweißen repariert werden müssen. Das Schweißen von Glocken ist jedoch mit zahlreichen Unsicherheiten behaftet, so dass sich bereits nach wenigen Jahren erneut Risse ausbilden können. Die bisher eingesetzten Methoden zur Qualitätssicherung reparierter Glocken beschränken sich im Wesentlichen auf eine Sichtprüfung bezüglich der Oberfläche und einer Klangprüfung mit besonderem Fokus auf das Abklingverhalten. Eine deutlich weitreichendere Analyse bietet die akustische Bewertung des Schwingverhaltens von Glocken mit der Methode des musikalischen Fingerabdrucks. Diese nutzt die Zwillingstonbildung rotationssymmetrischer Körper aus, um Fehlstellen im Klangkörper von Glocken anhand von Klangaufnahmen zu ermitteln. Abhängig von der Lage, Ausprägung und Art vorhandener Fehlstellen können so charakteristische Abweichungen vom typischen Schwingverhalten von Glocken festgestellt werden, die eine Identifikation der verursachenden Fehlstelle ermöglicht. Auf diese Weise können die für Schweißreparaturen üblichen Klangveränderungen aufgrund abweichender Materialeigenschaften oder fehlerhafter Materialbindungen an Modellen simuliert und mit dem Schwingverhalten reparierter Glocken verglichen werden. Die für die Bewertung erforderlichen Klangaufnahmen können mit geringem Aufwand erfasst werden, so dass dies Verfahren ein kostengünstiges Monitoring von Glocken ermöglicht. Nach Reparaturen kann so der Zustand der Glocken zuverlässig überwacht werden.