Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
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An intelligent battery management system (BMS) with end-edge-cloud connectivity – a perspective
(2025)
The widespread adoption of electric vehicles (EVs) and large-scale energy storage has necessitated advancements in battery management systems (BMSs) so that the complex dynamics of batteries under various operational conditions are optimised for their efficiency, safety, and reliability. This paper addresses the challenges and drawbacks of conventional BMS architectures and proposes an intelligent battery management system (IBMS). Leveraging cutting-edge technologies such as cloud computing, digital twin, blockchain, and internet-of-things (IoT), the proposed IBMS integrates complex sensing, advanced embedded systems, and robust communication protocols. The IBMS adopts a multilayer parallel computing architecture, incorporating end-edge-cloud platforms, each dedicated to specific vital functions. Furthermore, the scalable and commercially viable nature of the IBMS technology makes it a promising solution for ensuring the safety and reliability of lithium-ion batteries in EVs. This paper also identifies and discusses crucial challenges and complexities across technical, commercial, and social domains inherent in the transition to advanced end-edge-cloud-based technology.
This paper presents a new numerically efficient implementation of flow mixing algorithms in dynamic simulation of pipeline fluid transport. Mixed characteristics include molar mass, heat value, chemical composition and the temperature of the transported fluids. In the absence of chemical reactions, the modeling is based on the universal conservation laws for molar flows and total energy. The modeling formulates a sequence of linear systems, solved by a sparse linear solver, typically in one iteration per integration step. The functionality and stability of the developed simulation methods have been tested on a number of realistic network scenarios. The main output of the paper is a functioning and stable implementation of flow mixing algorithms for dynamic simulation of fluid transport networks.
Because of their resilience, Time-of-Flight (ToF) cameras are now essential components in scientific and industrial settings. This paper outlines the essential factors for modeling 3D ToF cameras, with specific emphasis on analyzing the phenomenon known as “wiggling”. Through our investigation, we demonstrate that wiggling not only causes systematic errors in distance measurements, but also introduces periodic fluctuations in statistical measurement uncertainty, which compounds the dependence on the signal-to-noise ratio (SNR). Armed with this knowledge, we developed a new 3D camera model, which we then made computationally tractable. To illustrate and evaluate the model, we compared measurement data with simulated data of the same scene. This allowed us to individually demonstrate various effects on the signal-to-noise ratio, reflectivity, and distance.
Visuelle Darstellungen von MINT-Berufen durch Bildgeneratoren: Wie viel Vielfalt ist möglich?
(2024)
In den vergangenen Jahren haben sich Text-zu-Bild-Transformer-Modelle wie DALL·E, Stable Diffusion und Midjourney etabliert, die realitätsnahe Bilder generieren. So wurden zwischen 2022 und 2023 über 15 Milliarden KI-Bilder produziert, Midjourney alleine zeigt eine Nutzendenbasis von 16 Millionen (Broz 2023; Valyaeva 2023; Zhou et al. 2024). Diese kritische retrospektive Analyse beschäftigt sich mit DALL·E Mini, einem der ersten öffentlich weit verbreiteten schwächeren Modelle, das für viele Nutzende den initialen Kontaktpunkt mit dieser Technologie darstellte.
Bei der Entwicklung von Kunststoffbauteilen kommen in kontinuierlich zunehmendem Maße Simulationen zum Einsatz. Vor dem Hintergrund von steigenden Produktanforderungen als auch dem unausweichlichen Zwang zur Schonung von Ressourcen ist der erweiterte Einsatz von Simulationswerkzeugen wichtiger Teil des Lösungsweges. Zu den nutzbaren, aber in Bezug zu Realprozessen bisher wenig eingesetzten Methoden gehört die Molekulardynamik Simulation. Auf Grundlage dieser Methode können auf mikroskopischer Ebene die tatsächlichen physikalischen Abläufe, die bei der Verarbeitung von Kunststoffen im Prozess auftreten, sichtbar gemacht werden. In dieser Arbeit wird beleuchtet, wie Randbedingungen in Anlehnung an den Extrusionsblasformprozess den Werkstoff Polyethylen auf mikroskopischer Ebene beeinflussen. Hierzu wird ein mesoskopisches Modell (Coarse-Graining) zur Beschreibung des Polymers genutzt. Dieses Modell wird durch die Bestimmung von Materialkennwerten verifiziert. Es wird der uniaxiale Zugversuch auf der Mikroskala modelliert, um Größen wie beispielsweise Elastizitätsmodul, Streckspannung oder Querkontraktionszahl zu ermitteln. Ebenso werden thermische Kenngrößen, insbesondere zur Charakterisierung des Kristallisationsverhaltens, bestimmt. Ziel dieser Untersuchungen ist, Effekte, die bei dynamisch ablaufenden Dehnungs- bzw. Kristallisationsvorgängen stattfinden, mikroskopisch zu beobachten und zu quantifizieren. Die ermittelten Kennwerte liegen insbesondere für die thermischen Größen in dichter Nähe zu experimentellen Daten. Das Spannungs-Dehnungs Verhalten wird qualitativ mit guter Übereinstimmung mit dem realen Verhalten wiedergegeben. Die kurze Zeitskala, auf der sich die Simulationsmodelle befinden, hat jedoch mikromechanisch extremeres Verhalten zur Folge, als makroskopisch beobachtet wird. Durch Erweiterung der Modelle werden biaxiale Verstreckvorgänge, wie sie im Extrusionsblasformprozess beispielsweise während des Aufblasens des Vorformlings auftreten, nachgebildet. Die Betrachtung verschiedener Abkühlbedingungen, insbesondere unter Formzwang, ist in Anlehnung an den Realprozess weiterer Schwerpunkt der Untersuchungen. Die Analyse der biaxial verstreckten Modelle offenbart, dass Entschlaufungsvorgänge während des Verstreckens die weitere Entwicklung der Polymersysteme dominieren. Es gelingt, die Dynamik von Kristallisationsvorgängen in Abhängigkeit von Verstreckgrad und Abkühlbedingungen durch unterschiedliche Größen (Verteilung von Verschlaufungspunkten, lokale Orientierungen) zu quantifizieren. Die erzielten Resultate zeigen auf, dass es mittels vergröberten Molekulardynamik Simulationen möglich ist, das mikromechanische Verständnis von Vorgängen, die bei der Verarbeitung von Kunststoffen auftreten, signifikant zu erweitern.
During the development phase of plastic components, simulations are being used to an increasing extent. Against the background of product requirements and the inevitable necessity of conserving resources, the expanded use of simulation tools is an essential part of the solution. Among available methods, but so far underutilized with respect to real-life processes, is the molecular dynamics simulation. By the use of this method it is possible to visualize the physical processes occurring on the microscopic level, as e.g. those that arise during plastics processing. This thesis examines how boundary conditions, which mimic the extrusion blow molding process, affect the behavior of polyethylene on the microscopic level. A mesoscopic model (coarse-graining) is applied to describe the polymer. Initially, this model is verified by determining material properties. The uniaxial tensile test is modeled on the micro-scale to identify parameters such as the elastic modulus, yield stress, and Poisson’s ratio. Additionally, thermal properties, particularly those characterizing the crystallization behavior, are identified. The objective of these investigations is the microscopic observation and quantification of effects that occur during dynamic stretching and crystallization processes. The calculated properties show good agreement with the experimental data, especially regarding the thermal parameters. Qualitatively, the stress-strain behavior is reproduced in alignment with experimentally observed results. However, the short time scale of the simulation models leads to micromechanical behavior that is more extreme than what is monitored on a macroscopic level. By extending the simulation models, biaxial stretching processes are simulated. These stretching processes resemble the situation during the inflation of the parison in the extrusion blow molding process. The examination of various cooling conditions, particularly by the use of mold constraints, is another focus of the investigations. The analysis of the biaxially stretched simulations reveals that disentanglement processes during stretching dominate the further development of polymer systems. It is possible to quantify the dynamics of crystallization processes depending on the degree of stretching and cooling conditions through various parameters (distribution of entanglement points, local orientations). The results indicate that coarse-grained molecular dynamics simulations are able to significantly enhance the micromechanical understanding of local events occurring during plastic processing.
Energy meteorology is an applied research field of meteorology that focuses on the study and prediction of weather conditions and events that affect energy production and use. This field has become increasingly important as the energy industry has become more dependent on weather conditions, especially in the areas of renewable energy sources such as wind energy, solar energy, and hydropower. The following paper has been written by experts of the Committee on Energy Meteorology of the German Meteorological Society summarizing their more than 30 years of experience and lessons learnt. It gives an overview of activities in energy meteorology that are already essential for the transformation of energy systems to systems with high shares of renewable energies. Building on this, the experts have created a vision of future topics that describe the future research landscape of energy meteorology. The authors explain that work in energy meteorology in recent years has primarily been concerned with the physically based modeling of wind and solar power generation and the development of short-term forecasting systems. In future years, a significant expansion of work in the areas of energy system modeling, digitalization, and climate change is expected. This includes the detailed consideration of regionally specified spatiotemporal variability for system design, the integration of artificial intelligence skills, the development of weather-related consumption based on smart meters, and the mapping of the effects of climate change on the energy system in planning and operating processes.
Optimal placement and upgrade of solar PV integration in a grid-connected solar photovoltaic system
(2024)
The shift towards renewable energy sources has heightened the interest in solar photovoltaic (SPV) systems, particularly in grid-connected configurations, to enhance energy security and reduce carbon emissions. Grid-tied SPVs face power quality challenges when specific grid codes are compromised. This study investigates and upgrades an integrated 90 kWp solar plant within a distribution network, leveraging data from Ghana's Energy Self-Sufficiency for Health Facilities (EnerSHelF) project. The research explores four scenarios for SPV placement optimization using dynamic programming and the Conditional New Adaptive Foraging Tree Squirrel Search Algorithm (CNAFTSSA). A Python-based simulation identifies three scenarios, high load nodes, voltage drop nodes, and system loss nodes, as the points for placing PV for better performance. The analysis revealed 85 %, 82.88 %, and 100 % optimal SPV penetration levels for placing the SPV at high load, voltage drop, and loss nodes. System active power losses were reduced by 72.97 %, 71.52 %, and 70.15 %, and reactive power losses by 73.12 %, 71.86 %, and 68.11 %, respectively, by placing the SPV at the above three categories of nodes. The fourth scenario applies to CNAFTSSA, achieving 100 % SPV penetration and reducing active and reactive power losses by 72.33 % and 72.55 %, respectively. This approach optimizes the voltage regulation (VR) from 24.92 % to 4.16 %, outperforming the VR of PV placement at high load nodes, voltage drop nodes, and loss nodes, where the voltage regulations are 5.25 %, 9.36 %, and 9.64 %, respectively. The novel CNAFTSSA for optimal SPV placement demonstrates its effectiveness in achieving higher penetration levels and improving system losses and VR. The findings highlight the effectiveness of strategic SPV placement and offer a comprehensive methodology that can be adapted for similar power distribution systems.
Lattice Boltzmann method (LBM) simulations of incompressible flows are nowadays common and well-established. However, for compressible turbulent flows with strong variable density and intrinsic compressibility effects, results are relatively scarce. Only recently, progress was made regarding compressible LBM, usually applied to simple one and two-dimensional test cases due to the increased computational expense. The recently developed semi-Lagrangian lattice Boltzmann method (SLLBM) is capable of simulating two- and three-dimensional viscous compressible flows. This paper presents bounce-back, thermal, inlet, and outlet boundary conditions new to the method and their application to problems including heated or cooled walls, often required for supersonic flow cases. Using these boundary conditions, the SLLBM's capabilities are demonstrated in various test cases, including a supersonic 2D NACA-0012 airfoil, flow around a 3D sphere, and, to the best of our knowledge, for the first time, the 3D simulation of a supersonic turbulent channel flow at a bulk Mach number of Ma=1.5 and a 3D temporal supersonic compressible mixing layer at convective Mach numbers ranging from Ma=0.3 to Ma=1.2. The results show that the compressible SLLBM is able to adequately capture intrinsic and variable density compressibility effects.
Design Strategies for an AC Loss Minimized Winding for a Fully Superconducting Wind Generator
(2025)
Pollution with anthropogenic waste, particularly persistent plastic, has now reached every remote corner of the world. The French Atlantic coast, given its extensive coastline, is particularly affected. To gain an overview of current plastic pollution, this study examined a stretch of 250 km along the Silver Coast of France. Sampling was conducted at a total of 14 beach sections, each with five sampling sites in a transect. At each collection site, a square of 0.25 m2 was marked. The top 5 cm of beach sediment was collected and sieved on-site using an analysis sieve (mesh size 1 mm), resulting in a total of approximately 0.8 m3 of sediment, corresponding to a total weight of 1300 kg of examined beach sediment. A total of 1972 plastic particles were extracted and analysed using infrared spectroscopy, corresponding to 1.5 particles kg−1 of beach sediment. Pellets (885 particles), polyethylene as the polymer type (1349 particles), and particles in the size range of microplastics (943 particles) were most frequently found. The significant pollution by pellets suggests that the spread of plastic waste is not primarily attributable to tourism (in February/March 2023). The substantial accumulation of meso- and macro-waste (with 863 and 166 particles) also indicates that research focusing on microplastics should be expanded to include these size categories, as microplastics can develop from them over time.
Experimental and Simulation based Analysis of an Active EMI Filter for automotive PFC Applications
(2024)
Aberrant Ras homologous (Rho) GTPase signalling is a major driver of cancer metastasis, and GTPase-activating proteins (GAPs), the negative regulators of RhoGTPases, are considered promising targets for suppressing metastasis, yet drug discovery efforts have remained elusive. Here, we report the identification and characterization of adhibin, a synthetic allosteric inhibitor of RhoGAP class-IX myosins that abrogates ATPase and motor function, suppressing RhoGTPase-mediated modes of cancer cell metastasis. In human and murine adenocarcinoma and melanoma cell models, including three-dimensional spheroid cultures, we reveal anti-migratory and anti-adhesive properties of adhibin that originate from local disturbances in RhoA/ROCK-regulated signalling, affecting actin-dynamics and actomyosin-based cell-contractility. Adhibin blocks membrane protrusion formation, disturbs remodelling of cell-matrix adhesions, affects contractile ring formation, and disrupts epithelial junction stability; processes severely impairing single/collective cell migration and cytokinesis. Combined with the non-toxic, non-pathological signatures of adhibin validated in organoids, mouse and Drosophila models, this mechanism of action provides the basis for developing anti-metastatic cancer therapies.
Grading student answers and providing feedback are essential yet time-consuming tasks for educators. Recent advancements in Large Language Models (LLMs), including ChatGPT, Llama, and Mistral, have paved the way for automated support in this domain. This paper investigates the efficacy of instruction-following LLMs in adhering to predefined rubrics for evaluating student answers and delivering meaningful feedback. Leveraging the Mohler dataset and a custom German dataset, we evaluate various models, from commercial ones like ChatGPT to smaller open-source options like Llama, Mistral, and Command R. Additionally, we explore the impact of temperature parameters and techniques such as few-shot prompting. Surprisingly, while few-shot prompting enhances grading accuracy closer to ground truth, it introduces model inconsistency. Furthermore, some models exhibit non-deterministic behavior even at near-zero temperature settings. Our findings highlight the importance of rubrics in enhancing the interpretability of model outputs and fostering consistency in grading practices.
Von der ersten Hausarbeit bis zum Examen: Wissenschaftliches Arbeiten ist eine Kernkompetenz in jedem Studium. Zum Erlernen der wichtigsten Methoden und Regeln des wissenschaftlichen Arbeitens geben Ihnen Martin Wördenweber und Paul R. Melcher einen prägnanten Leitfaden mit vielen Praxisbeispielen an die Hand. (Verlagsangaben)
Influence of Initialisation Parameter in Extended Kalman Filter on State of Charge Estimation
(2024)
Condition Monitoring of Power Modules for SiC and GaN Semicon- ductors by Piezoelectric Effect
(2024)
A novel multidimensional index modulation-based differential chaos shift keying (DCSK) technique, designated as Joint Subcarrier Time Reference Index Modulation-aided Differential Chaos Shift Keying (JSTRIM-DCSK), is proposed for efficient data transmission in chaotic communication systems. The JSTRIM-DCSK system integrates subcarrier, time slot, and reference signal indexing to transmit information and offers two variants: JSTRIM-DCSK-I and JSTRIM-DCSK-II. The data is organized into L subblocks, each containing Ns subcarrier index bits ps and Nt time slot index bits pt , reference index bits pr , and modulated bits pm . The subcarrier and time slot index bits jointly select an active or inactive subcarrier time slot combination from a total of Ns ⋅ Nt possibilities, categorizing the system as either JSTRIM-DCSK-I (active) or JSTRIM-DCSK-II (inactive). The reference indexed bits select a single chaotic reference signal from Nr orthogonal chaotic vectors generated using the Gram-Schmidt orthogonalization process. The modulated bits are transmitted using a DCSK modulation scheme. Analytical expressions for the bit error rate (BER) performance of the JSTRIM-DCSK system are derived under both additive white Gaussian noise (AWGN) and multipath Rayleigh fading channel (MRFC) conditions. Furthermore, the potential for energy savings, bandwidth efficiency, and system complexity of the JSTRIM-DCSK system are thoroughly analyzed and compared with those of the existing techniques. The simulation results validate the analytical expressions and demonstrate the potential of JSTRIM-DCSK to achieve high data rates, efficient energy savings, and a competitive BER performance.
Effects of Stretch-Bending Straightening on the Tensile Properties of Cold Rolled Packaging Steel
(2024)
Achieving perfect flatness, tension-free surfaces, and exceptional resistance to spring back are important characteristics of packaging steel, setting the standard for high-quality material performance. To guarantee these crucial parameters, the implementation of the stretch-bending process at the final stage of the production route is indispensable. Besides improving the flatness properties, the induced plastic deformation results in an accompanying change in the mechanical properties. This investigation focuses on understanding this change in mechanical properties due to different stretch-bending straightening process parameters. A multivariate predictive model is created to calculate a process window for achieving the desired flatness and also mechanical properties in the production of packaging steel. This model is validated by experiments with a laboratory facility.
Estimation of Time Series Databases Performance in Cloud Applications for Plastic Moulding Industry
(2024)
Analyzing the consequences of power factor degradation in grid-connected solar photovoltaic systems
(2024)
This study examines the impact of integrating solar photovoltaic (PV) systems on power factor (PF) within low-voltage radial distribution networks, using empirical data from the Energy Self-Sufficiency for Health Facilities in Ghana (EnerSHelF) project sites in Ghana. The research included simulations focusing on optimal PV integration, with and without PF considerations, and the strategic placement of PV and shunt capacitors (SC). Three scenarios evaluated PV injection at high-load demand nodes, achieving penetration levels of 85.00 percent, 82.88 percent with high voltage drop, and 100.00 percent with high loss nodes. Additionally, three scenarios assessed SC allocation methods: proportional to the node's reactive power demand (Scenario I), even distribution (Scenario II), and proportional to installed PV capacity at PV nodes (Scenario III).
The analysis used a twin-objective index (TOI), combining voltage deviations and power factor degradation. Results showed significant PV curtailment was necessary to achieve standard PF. Optimal penetration levels, considering TOI, reduced PV penetration from 85.00 percent to 63.75 percent, 82.88 percent to 57.38 percent, and 100.00 percent to 72.50 percent for high load, high voltage drops, and high loss nodes, respectively. Notably, all scenarios showed a concerning PF of 0.00 at dead-end nodes (P20, P21, P22).
Scenario I achieved PF ranges of -0.26 to 1.00 with PV at high load, -0.69 to 1.00 with PV at high voltage drop, and 0.95 to 1.00 with PV at high loss nodes. Scenario II produced similar ranges, -0.48 to 1.00, -1.00 to 0.99, and 0.30 to 0.96, with PV placement at high load, voltage drops, and loss nodes, respectively. Scenario III yielded ranges of -0.19 to 0.97 (high load), -0.23 to 1.00 (high voltage drop), and 0.86 to 0.96 (high losses).
The study concluded that the most effective strategy involves installing PVs at high-loss nodes and distributing SCs proportionally to the node's reactive power demand (Scenario I). This approach achieved a more uniform PF pattern throughout the network, highlighting the practical implications of strategic PV placement and targeted reactive power compensation for maintaining a healthy and efficient distribution system with solar PV integration.
This work considers a stationary simulation of pipeline fluid transport, in the presence of impurities and phase transitions. This simulation finds applications in diverse areas such as energy carrier transportation, including natural gas and hydrogen, as well as the efficient transport of carbon dioxide from emission sources to designated storage sites. Particularly for the transport of carbon dioxide, which is preferably carried out in a liquid or supercritical state, the accurate detection of phase transitions is of utmost importance. Additionally, evaluating the simulation precision based on the selected pipe subdivision is crucial for transporting fluids of any kind. Our implementation includes an algorithm that utilizes the Homogeneous Equilibrium Model and the GERG-2008 thermodynamic equation of state for phase transition detection. We have also developed an optimal pipe subdivision algorithm using empirical formulas derived from extensive numerical experiments. Rigorous testing of the algorithms has been conducted on realistic fluid transport scenarios, confirming their effectiveness in addressing the stated technical challenges.
The recent transformation of the energy sector brings new challenges in areas such as supply security, efficiency, and reliability. Especially the increase of decentralized power plants leads to a more complex energy system and an increasing complexity. This requires expansion and digitization of the power grid as well as an initiative-taking operation of the grid operator. To investigate such complex systems and its phenomena, modern development methods such as real-time simulation or digital twins (DT) can be used. In this approach a digital replica of the real-world system, a grid section, is developed, which can represent or predict the behavior of the real distribution grid. For this, a model of the real-world system is derived and implemented in a co-simulation environment, in which it receives data via an analyzer or measurement system from the grid model. This paper focuses on the development of the digital twin of a testing grid and a grid analyzer for the measurement. With the digital twin of the testing grid, a first approach is achieved in a real-time capable environment showing the functionalities and interactions of a digital twin. Subsequently the development of the digital twin model is explained, and the results are discussed.
This dataset contains questions and answers from an introductory computer science bachelor course on statistics and probability theory at Hochschule Bonn-Rhein-Sieg. The dataset includes three questions and a total of 90 answers, each evaluated using binary rubrics (yes/no) associated with specific scores.
Highly varying process conditions drive polymers into nonequilibrium molecular conformations. This has direct implications for the resulting structural and mechanical properties. This study rigorously investigated processing-property relations from a microscopic perspective. The corresponding models use a mesoscale molecular dynamics (MD) approach. Different loading conditions, including uniaxial and biaxial stretching, along with various cooling conditions, were employed to mimic process conditions on the micro-scale. The resulting intricate interplay between equi-biaxial stretching, orientation, and crystallization behavior in long polyethylene chains was reviewed. The study reveals notable effects depending on different cooling and biaxial stretching procedures. The findings emphasize the significance of considering distributions and directions of chain ordering. Local inspections of trajectories unveil that crystal growth predominantly occurs in regions devoid of entanglements.
Noncooperative Game Theory
(2024)
The goal of this study was to explore a route for introducing functionalities into agarose-based hydrogels to tune the physical, chemical, and biological properties. Several agarose derivatives were prepared by homogeneous synthesis, including anionic agarose sulfates (ASs), reactive azido agaroses (AZAs), and cationic agarose carbamates (ACs), as well as agarose tosylates (ATOSs) and agarose phenyl carbonates (APhCs). The products were characterized in terms of their molecular structure and solubility behavior. The results suggest that the native gel-forming ability of agarose is retained if the introduced functionalities are hydrophilic, and the overall degree of substitution is low (DS < 0.5). Thus, functional hydrogels from several agarose derivatives could be obtained. The mechanical stability of the functional hydrogels was decreased compared to native agarose gels but was still in a range that enables safe handling. An increase in mechanical strength could be achieved by blending functional agarose derivatives and agarose into composite hydrogels. Finally, it was demonstrated that the novel functional agarose hydrogels are biocompatible and can potentially stimulate interactions with cells and tissue.
Power-to-gas-to-X systems consisting of photovoltaic cells, proton-exchange membrane electrolysis, hydrogen storage based on metal hydrides, proton-exchange membrane fuel cells and buffer batteries could be used to meet heat and electricity demands of homes, businesses, or small districts. The actual size of the individual components and their interplay have to be optimized for the technical and economic feasibility of the overall system. A simulation-based optimization workflow would be a suitable way to accomplish this task, but there are hardly any tools that can simultaneously simulate power, fluid and heat flows of such systems and efficiently perform their optimization. In this paper, a multiphysical energy system simulation and optimization tool is introduced which models electrochemical and thermodynamic processes simultaneously, including modern equations of state and an own numerical solver for the arising differential–algebraic system of equations, and provides new methods for the calibration of parameters of the metal hydride storage, proton-exchange membrane electrolyzer and fuel cell as well as a metamodel-based approach for sizing optimization. As a demonstrator for the novel tool, a simulation model of a hydrogen lab is successfully set up based on experimental results. The novel tool is able to extract polarization and jump curves of the fuel cell, determine a first temperature and pressure dependency of the efficiency of the electrolysis coupled with the metal hydride storage and speed up sizing optimization through metamodeling by a factor 262.1 at 4.9% and 32.7 at 3.3% accuracy.