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Impact of Inductive Charging Infrastructure at Intersections on Battery Electric Bus Operations
(2023)
Battery electric buses are the go-to solution for clean public transport at the moment. But they come with operational challenges. One technology that could potentially help in some of those is inductive in-motion charging, as it reduces additional dwell times and can help minimize battery size. In addition, the infrastructure can also be used by other road users. This paper presents a framework for evaluating the impact of intersection-based inductive charging infrastructure on battery electric bus operations using a traffic simulation and a physics-based energy demand model. The results are split into two categories: first, investigations of the charging lane lengths on a single intersection with increasing traffic volume, and second, implementation of charging infrastructure along a real bus line to better understand the implications of the charged energy in relation to the energy demand. The findings from the analysis reveal that the higher the traffic volume, the longer the charging lanes need to be to make use of the resulting delay times. The analysis indicates that, in our scenario, the bus can charge around 100% of the required energy with a charging lane length of around 80 meters and a charging power of 250kW. This work can inform decision-making for the deployment of charging infrastructure in urban environments and contribute to the development of sustainable urban transportation systems.
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.
Training communication with patients is an important aspect of medical education around the world. The general complexity of communication and specifically communication with patients can be practiced with actors. However, access to this training method is restricted by limited availability of actors, complex organization of time schedules, and the need of physical training space. Our work explores the benefits of using a serious game for training patient-physician communication. The game implements the SPIKES communication model to efficiently and effectively structure such conversations. SPIKES is used for example to deliver bad news or inform patients about medical treatment plans. This paper presents the results of a first study with medical students evaluating acceptance and the educational benefit of the game as part of the communication training.
As the adoption of battery electric buses (BEBs) in public transportation systems grows, the need for precise energy consumption forecasting becomes increasingly important. Accurate predictions are essential for optimizing routes, charging schedules, and ensuring adequate operational range. This paper introduces an innovative forecasting methodology that combines a propulsion and auxiliary energy model with a novel concept, the environment generator. This approach addresses the primary challenge in electric bus energy forecasting: estimating future environmental conditions, such as weather, passenger load, and traffic patterns, which significantly impact energy demand. The environment generator plays a crucial role by providing the energy models with realistic input data. This study validates various models with different levels of model complexity against real-world operational data from a case study of over one year with 16 electric buses in Göttingen, Germany. Our analysis thoroughly examines influencing factors on energy consumption, like altitude, temperature, passenger load, and driving patterns. In order to comprehensively understand energy demands under varying operational conditions, the methodology integrates data-driven models and physical simulations into a modular and highly accurate energy predictor. The results demonstrate the effectiveness of our approach in providing more accurate energy consumption forecasts, which is essential for efficient electric bus fleet management. This research contributes to the growing body of knowledge in electric vehicle energy prediction and offers practical insights for transit authorities and operators in optimizing electric bus operations.
Im vorliegenden Beitrag wird eine Methode zur subjektiven und objektiven Charakterisierung von aktiven Fahrstreifenwechselfunktionen sowie eine Korrelationsanalyse zur Ermittlung optimaler Funktionseigenschaften vorgestellt. Zur Quantifizierung maßgeblicher subjektiver Eigenschaften wurden Bewertungskategorien und -kriterien aus den Bereichen Fahrerkooperation, Funktionsperformance, Entlastungsgrad und Sicherheitsgefühl erarbeitet, deren Beurteilung im Rahmen einer umfassenden Fahrstudie erfolgte. Die beurteilten Fahrzeuge wurden hinsichtlich ihrer unterschiedlichen Funktionsausprägungen anschließend in einem neuartigen fahrmanöverbasierten Prüfverfahren vermessen. Das Verfahren umfasst hierbei drei Typen von Fahrstreifenwechselszenarien in welchen unter anderem die Eigen- und Relativbewegung von Ego- und Target-Fahrzeug sowie die Funktionsrückmeldung am Lenkrad und im Kombi-Instrument des Egofahrzeugs messtechnisch erfasst wurden. Die Auswertung des hiermit aufgezeichneten objektiven Funktionsverhaltens geschieht durch eine automatisierte KPI-basierte Softwareumgebung. Ausgehend von der korrelativen Gegenüberstellung aller Subjektivkriterien mit den ermittelten KPI-Kennwerten können wichtige Trends und Zusammenhänge geprüft, erkannt und nutzbringend in die Festlegung optimaler Wertbereiche eingearbeitet werden. Die vorgestellte Methodik ermöglicht somit eine zielgerichtete Auslegung und Abstimmung der Eigenschaften einer aktiven Fahrstreifenwechselfunktion.
Das Forschungsprojekt TwinMaP hat zum Ziel, eine effiziente Komplettbearbeitung von Bauteilen variabler Losgrößen in einem heterogenen Maschinenpark zu ermöglichen. Der wirtschaftlichste Produktionsablauf soll mittels digitalen Zwillingen, die mit den realen Anlagen und einer Integrationsplattform vernetzt sind, in einer Vorsimulation ermittelt werden.
Die Fertigungsindustrie orientiert sich derzeit in Richtung von Technologien, die Sprungbrettinnovationen in der Produktion ermöglichen. In diesem Zusammenhang werden häufig das Industrial Metaverse und die Künstliche Intelligenz (KI) genannt. In diesem Artikel werden erste Implementierungen dieser Technologien und aktuelle Forschungsaktivitäten im Produktionsumfeld vorgestellt.
The Industrial Metaverse (IM) is an upcoming topic for companies and offers new possibilities to digitalize and optimize their business processes together with AI capabilities. In the production domain, the Industrial Metaverse is a step towards the vision of predicting factory behavior for optimization purposes. A central challenge is a complete factory model necessary as the base to predict its behavior. Therefore, the IM approach is promising to build and contain this model out of available single Digital Twins of factory parts. Consequently, an IT target landscape is required to build an Industrial Metaverse for Digital Twins. This paper evaluates different design pattern options for an industrial IT architecture reference implementation of an IM that companies can use in current IT landscapes. It also proposes a high-level roadmap towards the proposed target IT architecture of an IM.
This paper reviews the recent literature on the “social ranking problem”, that is, the problem of converting group rankings into individual rankings. We introduce and categorize existing social ranking methods and we briefly explain their attributes. Three main categories of social ranking methods are identified: lexicographic social rankings, methods based on voting mechanisms, and those inspired by the theory of coalitional games. An open-source R package called socialranking for computing the majority of the existing social rankings is also presented and discussed.
Computing Shapley values for large cooperative games is an NP-hard problem. For practical applications, stochastic approximation via permutation sampling is widely used. In the context of machine learning applications of the Shapley value, the concept of antithetic sampling has become popular. The idea is to employ the reverse permutation of a sample in order to reduce variance and accelerate convergence of the algorithm. We study this approach for the Shapley and Banzhaf values, as well as for the Owen value which is a solution concept for games with precoalitions. We combine antithetic samples with established stratified sampling algorithms. Finally, we evaluate the performance of these algorithms on four different types of cooperative games.