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Stangendichtungen in Hydraulikanwendungen sind Maschinenelemente, die in sehr großer Menge produziert und genutzt werden. Ihre Aufgabe besteht im Wesentlichen darin, das Hydraulikfluid am Austritt zu hindern und somit den Betriebsdruck zu halten. Aufgrund der dynamischen Belastungen, denen diese Art von Dichtungen ausgesetzt ist, bestehen hohe Anforderungen bezüglich der Verschleißfestigkeit. Bei der Vielzahl an Bauformen und -größen sowie Herstellern kann es im Falle eines Schadens schwierig sein, zeitnah ein Ersatzteil zu beschaffen. Die Lieferzeiten können im Bereich von Wochen oder gar Monaten liegen. In dem Zusammenhang stellte sich die Frage, ob mit Methoden der additiven Fertigung, die in weiten Bereichen der Technik in den letzten Jahren Einzug gehalten haben, in kurzer Zeit und auch dezentral funktionsfähige, dynamische Dichtungen hergestellt werden können. Da zu dem Themengebiet keine Forschungsergebnisse vorlagen, wurde diese Möglichkeit im Rahmen der vorliegenden Arbeit untersucht. Diese zentrale Frage konnte positiv beantwortet werden. Auf dem hier dokumentierten Weg hin zur Beantwortung der Frage galt es zu klären, welche Verfahren geeignet sind und worin Unterschiede zwischen konventionell und additiv gefertigten Bauteilen bestehen. Hierzu wurden auch eine Reihe von Funktionstests mit Testteilen aus verschiedenen Materialien durchgeführt und dokumentiert. Darüber hinaus wird aufgezeigt, dass die zudem gestellte Frage, ob neben dem ökonomischen Nutzen, der sich aus einer potentiellen Vermeidung von Stillstandszeiten einer Maschine oder Anlage ergibt, auch ein ökologischer Nutzen stehen könnte, verneint werden muss. Abschließend werden, basierend auf dem positiven Ergebnis der Untersuchungen, Empfehlungen gegeben, wie diese Arbeit sinnvoll ergänzt und fortgeführt werden kann.

Energy storage systems (ESSs) are needed in the smart grids both at the generation, transmission, and distribution levels, and different types of ESSs have widely different characteristics and are suitable for different tasks and situations. In recent years, the smart grid concept has been dramatically developed in different applications, such as islands, shipboards, aircraft, microgrids and grid integration of renewables. With the wide application of ESS in smart grids, significant technical challenges remain along with the enhancement of smart grid operations and services. These challenges can be categorised as control of ESS, optimal operation, and energy management systems, optimal design of hybrid ESS, protection of ESS, and power electronics for ESS connections. This necessitates suitable design and control of the interfaces between ESSs in smart grids, as well as consideration of different applications of smart grid systems. This special issue of IET smart grid is focused on research ideas, articles, and experimental studies related to ‘Energy Storage for Green Transition of Electrical Grids’ from contributors in universities, industries, and research laboratories to develop and propose novel solutions on applications of ESS in smart grids.

Intelligent charging solutions facilitate mobility electrification. Mathematically, electric vehicle (EV) charging scheduling formulations are constrained optimization problems. Therefore, accurate constraint modeling is theoretically and practically relevant for scheduling. However, the current scheduling literature lacks an accurate problem formulation, including the joint modeling of the nonlinear battery charging profile and minimum charging power constraints. The minimum charging power constraint prevents allocating inexecutable charging profiles. Furthermore, if the problem formulation does not consider the battery charging profile, the scheduling execution may deviate from the allocated charging profile. An insignificant deviation indicates that simplified modeling is acceptable. After providing the problem formulation targeting the maximum possible vehicle battery state of charge (SoC) on departure, the numerical assessment shows how the constraint consideration impacts the scheduling performance in typical charging scenarios (weekday workplace and weekend public charging where the grid supplies up to forty vehicles). The simulation results show that the nonlinear battery charging constraint is practically negligible: For many connected EVs, the grid limit frequently overrules that constraint. The resulting difference between the final mean SoCs using and not using accurate modeling does not exceed 0.2%. Consequently, the results justify simplified modeling (excluding the nonlinear charging profile) for similar scenarios in future contributions.

The additive manufacturing (AM) of elastomeric parts based on high-viscosity reinforced rubbers has increasingly become a topic of scientific research in recent years. In addition to the viscosity, which is several decades higher during processing than the viscosities of thermoplastics, the flowability of the compound after the printing process and the necessary chemical crosslinking of the printed component play a decisive role in producing an elastic, high-quality, and geometrically stable part. After the first technological achievements using the so-called additive manufacturing of elastomers (AME) process, the knowledge gained has to be transferred first to concrete industrial parts. Therefore, in this study, the cure kinetics of a conventional rubber compound are tailored to match the specific requirements for scorch safety in the additive manufacturing of an industrial 2-component rod seal based on an acrylonitrile butadiene rubber O-ring in combination with a thermoplastic polyurethane as the base body. Experimental tests on a test rig for rod seals demonstrate the functionality of this additively manufactured 2-component rod seal.

With the trend in transportation electrification, electric vehicle (EV) charging/discharging scheduling has become an area of concern. Scheduling can help manage EV charging/discharging activities. Besides, it is also valuable for energy systems evaluation, in which case modeling the individual EV has high complexity and requires a long computation time due to too many EVs. The aggregated model significantly reduces computation time but may sacrifice accuracy. This work investigates the trade-off between accuracy and computational time when designing intelligent EV charging/discharging scheduling by comparing the individual and aggregated models. This work first provides a detailed problem formulation. The simulation results show that the aggregated model can achieve similar energy system performance estimations from the energy-matching perspective compared to the individual model, given that the system allows vehicle-to-grid (V2G). Otherwise, the aggregated model will overestimate the performance. Thus, this work, in the meantime, proposes an extra constraint to avoid such overestimation when V2G is not allowed. Given the validated accuracy of the aggregated model and its advantage of low complexity and computation time, the aggregated model is more suitable for assessing large (e.g., city-level) energy systems.