Publikationen

Lithium-ion battery energy storage systems are rapidly gaining widespread adoption in power systems across the globe. This trend is primarily driven by their recognition as a key enabler for reducing carbon emissions, advancing digitalization, and making electricity grids more accessible to a broader population. In the present study, we investigated the dynamic behavior of lithium-ion batteries during the charging and discharging processes, with a focus on the impact of terminal voltages and rate parameters on the state of charge (SOC). Through modeling and simulations, the results show that higher terminal charging voltages lead to a faster SOC increase, making them advantageous for applications requiring rapid charging. However, large values of voltage-sensitive coefficients and energy transfer coefficients were found to have drawbacks, including increased battery degradation, overheating, and wasted energy. Moreover, practical considerations highlighted the trade-off between fast charging and time efficiency, with charging times ranging from 8 to 16 min for different rates and SOC levels. On the discharging side, we found that varying the terminal discharging voltage allowed for controlled discharging rates and adjustments to SOC levels. Lower sensitivity coefficients resulted in more stable voltage during discharging, which is beneficial for applications requiring a steady power supply. However, high discharging rates and sensitivity coefficients led to over-discharging, reducing battery life and causing damage. These new findings could provide valuable insights for optimizing the performance of lithium-ion batteries in various applications.

Petri Net models are widely recognized for their ability to analyze concurrent, stochastic processes based on a solid mathematical foundation. However, one drawback of Petri Nets is their low-level abstraction: they offer only a few basic elements like places and transitions to represent all system components. While this limitation may not be an issue when working with small models, it becomes challenging when attempting to model larger processes or systems. As the complexity increases, the number of elements in the Petri Net also grows, making it difficult to distinguish and maintain them effectively. Furthermore, Petri Nets require verification to ensure that they accurately represent the behavior of the system they are intended to model. This verification process must be repeated whenever a model is created or modified. To address these challenges, this paper describes a Stochastic colored Petri Net semantics of a domain-specific language that allows modeling time-based hardware and software systems. We have developed a custom Eclipse-based framework that allows for both graphical and textual modeling, providing editors with useful features such as real-time validation of model constraints, which is not feasible at the low-level Petri Net abstraction due to the lack of contextual information. The DSL also offers the advantage of easy conversion from other modeling languages thanks to an intermediate language. From the model, valid Stochastic Colored Petri Nets (SCPNs) can be generated, which can automatically simulate certain system properties consistently. This approach aims to enhance modeling capabilities and alleviate some of the limitations associated with traditional Petri Nets.

Phenological shifts across plant species is a powerful indicator to quantify the effects of climate change. Today, mobile applications with automated species identification open new possibilities for phenological monitoring across space and time. Here, we introduce an innovative spatio-temporal machine learning methodology that harnesses such crowd-sourced data to quantify phenological dynamics across taxa, space and time. Our algorithm links individual phenological responses across thousands of species and geographical locations, using a similarity measure. The analysis draws on nearly ten million plant observations collected through the AI-based plant identification app Flora Incognita in Germany from 2018 to 2021. Our method quantifies changes in synchronisation across the annual cycle. During the growing season, synchronised behaviour can be encoded by a few characteristic macrophenological patterns. Nonlinear spatio-temporal changes of these patterns can be efficiently quantified using a data compressibility measure. Outside the growing season, the phenological synchronisation diminishes introducing noise into the patterns. Despite biases and uncertainties associated with crowd-sourced data, for example due to human data collection behaviour, our study demonstrates the feasibility of deriving meaningful indicators for monitoring plant macrophenology from individual plant observations. As crowd-sourced databases continue to expand, our approach holds promise to study climate-induced phenological shifts and feedback loops.

Using magnetic nanoparticles (MNPs) for extracorporeal heating applications results in higher field strength and, therefore, particles of higher coercivity can be used, compared to intracorporeal applications. In this study, we report the synthesis and characterization of barium hexa-ferrite (BaFe12O19) nanoparticles as potential particles for magnetic heating. Using a precipitation method followed by high-temperature calcination, we first studied the influence of varied synthesis parameters on the particles’ properties. Second, the iron-to-barium ratio (Fe/Ba = r) was varied between 2 and 12. Vibrating sample magnetometry, scanning electron microscopy and X-ray diffraction were used for characterization. A considerable influence of the calcination temperature (Tcal) was found on the resulting magnetic properties, with a decrease in coercivity (HC) from values above 370 kA/m for Tcal = 800-1000 ˚C to HC = 45-70 kA/m for Tcal = 1200 ˚C. We attribute this drop in HC mainly to the formation of entirely multi-domain particles at high Tcal. For the varying Fe/Ba ratios, increasing amounts of BaFe2O4 as an additional phase were detected by XRD in the small r (barium surplus) samples, lowering the particles’ magnetization. A decrease in HC was found in the increased r samples. Crystal size ranged from 47 nm to 240 nm and large agglomerates were seen in SEM images. The reported particles, due to their controllable coercivity, can be a candidate for extracorporeal heating applications in the biomedical or biotechnological field.

Spatial analysis of EEG data, e.g. using short-term stable microstates, is increasingly used in neuroscience and clinical applications. At the same time, scenarios involving mobility, sports, and home-based activities are becoming more prevalent in EEG studies. For this purpose, dry EEG electrodes are more and more commonly used. Thus, our objective was a comparison of microstates analysis between dry and gel-based EEGs. 256-channel EEGs were recorded from 30 volunteers using dry and gel-based electrodes during resting state eyes closed and eyes open. Microstates were extracted for each measurement and time-domain parameters were calculated. We found a high degree of consistency between the microstate maps extracted from dry and gel-based measurements for both eyes closed and eyes open conditions. The topographic similarities between the average maps for dry and gel-based recordings were above 81.5% for each of the seven extracted maps. We conclude that topographic microstate analyses are feasible using multichannel EEG setups with new dry EEG electrodes.