05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Modeling and experimental investigation of the interaction between pressure-dependent aging and pressure development due to the aging of lithium-ion cells(2023) Avdyli, Arber; Fill, Alexander; Birke, Kai PeterIn order to meet the increasing demands of the battery in terms of range, safety and performance, it is necessary to ensure optimal operation conditions of a lithium-ion cell. In this thesis, the influence of mechanical boundary conditions on the cell is investigated theoretically and experimentally. First, fundamental equations are derived that lead to coupled models that can be parameterized based on specific cell measurements and predict the pressure evolution due to capacity aging and vice versa. The model is used to derive optimal operating points of the cell, which can be considered in the module design.Item Open Access Surface charge density and induced currents by self-charging sliding drops(2024) Bista, Pravash; Ratschow, Aaron D.; Stetten, Amy Z.; Butt, Hans-Jürgen; Weber, Stefan A. L.Spontaneous charge separation in drops sliding over a hydrophobized insulator surface is a well-known phenomenon and lots of efforts have been made to utilize this effect for energy harvesting. For maximizing the efficiency of such devices, a comprehensive understanding of the dewetted surface charge would be required to quantitatively predict the electric current signals, in particular for drop sequences. Here, we use a method based on mirror charge detection to locally measure the surface charge density after drops move over a hydrophobic surface. For this purpose, we position a metal electrode beneath the hydrophobic substrate to measure the capacitive current induced by the moving drop. Furthermore, we investigate drop-induced charging on different dielectric surfaces together with the surface neutralization processes. The surface neutralizes over a characteristic time, which is influenced by the substrate and the surrounding environment. We present an analytical model that describes the slide electrification using measurable parameters such as the surface charge density and its neutralization time. Understanding the model parameters and refining them will enable a targeted optimization of the efficiency in solid–liquid charge separation.Item Open Access A comprehensive model and experimental investigation of venting dynamics and mass loss in lithium-ion batteries under a thermal runaway(2025) Chen, Ai; Sahin, Resul; Ströbel, Marco; Kottke, Thomas; Hecker, Stefan; Fill, AlexanderThermal runaway (TR) has become a critical safety concern with the widespread use of lithium-ion batteries (LIBs) as an energy storage solution to meet the growing global energy demand. This issue has become a significant barrier to the expansion of LIB technologies. Addressing the urgent need for safer LIBs, this study developed a comprehensive model to simulate TR in cylindrical 18650 nickel cobalt manganese (NMC) LIBs. By incorporating experiments with LG ® -INR18650-MJ1 cells, the model specifically aimed to accurately predict critical TR parameters, including temperature evolution, internal pressure changes, venting phases, and mass loss dynamics. The simulation closely correlated with experimental outcomes, particularly in replicating double venting mechanisms, gas generation, and the characteristics of mass loss observed during TR events. This study confirmed the feasibility of assuming proportional relationships between gas generation and the cell capacity and between the mass loss from solid particle ejection and the total mass loss, thereby simplifying the modeling of both gas generation and mass loss behaviors in LIBs under TR. Conclusively, the findings advanced the understanding of TR mechanisms in LIBs, providing a solid foundation for future research aimed at mitigating risks and promoting the safe integration of LIBs into sustainable energy solutions.Item Open Access Analytic free-energy expression for the 2D-Ising model and perspectives for battery modeling(2023) Markthaler, Daniel; Birke, Kai PeterAlthough originally developed to describe the magnetic behavior of matter, the Ising model represents one of the most widely used physical models, with applications in almost all scientific areas. Even after 100 years, the model still poses challenges and is the subject of active research. In this work, we address the question of whether it is possible to describe the free energy A of a finite-size 2D-Ising model of arbitrary size, based on a couple of analytically solvable 1D-Ising chains. The presented novel approach is based on rigorous statistical-thermodynamic principles and involves modeling the free energy contribution of an added inter-chain bond DAbond(b, N) as function of inverse temperature b and lattice size N. The identified simple analytic expression for DAbond is fitted to exact results of a series of finite-size quadratic N N-systems and enables straightforward and instantaneous calculation of thermodynamic quantities of interest, such as free energy and heat capacity for systems of an arbitrary size. This approach is not only interesting from a fundamental perspective with respect to the possible transfer to a 3D-Ising model, but also from an application-driven viewpoint in the context of (Li-ion) batteries where it could be applied to describe intercalation mechanisms.Item Open Access Exploring different extrapolation approaches for the critical temperature of the 2D-Ising model based on exactly solvable finite-sized lattices(2025) Markthaler, Daniel; Birke, Kai PeterThe fact that the Ising model in higher dimensions than 1D features a phase transition at the critical temperature Tcdespite its apparent simplicity is one of the main reasons why it has lost none of its fascination and remains a central benchmark in modeling physical systems. Building on our previous work, where an approximative analytic free-energy expression for finite 2D-Ising lattices was introduced, we investigate different extrapolation strategies for estimating Tcof the infinite system from exactly solvable small lattices. Finite square lattices of linear dimension N with free and periodic boundary conditions were analyzed, exploiting their exactly accessible density of states to compute the heat capacity profiles C(T). Different approaches were compared, including scaling models for the peak temperature Tmax(N)and an envelope construction across the set of C(T)-profiles. We find that both approaches converge to the same asymptotic value and compare favorably to the established Binder cumulant method. Remarkably, a model for Tmaxwith a single model parameter following an N/(N+1)-law provides robust convergence, with a physical analogy motivating this proportionality. Our findings highlight that surprisingly few, but highly accurate, finite-size results are sufficient to obtain a precise extrapolation.Item Open Access A high frequency alternating current heater using the advantages of a damped oscillation circuit for low voltage Li-ion batteries(2024) Oehl, Joachim; Gleiter, Andreas; Manka, Daniel; Fill, Alexander; Birke, Kai PeterIn many cases, batteries used in light e-mobility vehicles such as e-bikes and e-scooters do not have an active thermal management system. This poses a challenge when these batteries are stored in sub-zero temperatures and need to be charged. In such cases, it becomes necessary to move the batteries to a warmer location and allow them to acclimatize before charging. However, this is not always feasible, especially for batteries installed permanently in vehicles. In this work, we present an internal high-frequency AC heater for a 48 V battery, which is used for light electric vehicles of EU vehicle classes L1e and L3e-A1 for a power supply of up to 11 kW. We have taken advantage of the features of a damped oscillating circuit to improve the performance of the heater. Additionally, only a small inductor was added to the main current path through a cable with three windings. Furthermore, as the power electronics of the heater is part of the battery main switch, fewer additional parts inside the battery are required and therefore a cost and space reduction compared to other heaters is possible. For the chosen setup we reached a heating rate of up to 2.13 K min -1 and it was possible to raise the battery temperature from -10 °C to 10 °C using only 3.1% of its own usable capacity.Item Open Access Closing the carbon cycle in plasma‐based CO2 splitting : a techno‐economic perspective(2025) Kaufmann, Samuel Jaro; Rößner, Paul; Birke, Kai PeterThis techno‐economic analysis examines the impact of CO₂ capture energy and capital costs on plasma‐based power‐to‐liquid (PtL) systems, identifying strategies to minimize the net production costs (NPC) for synthetic fuels. Three future development scenarios were defined. One, focusing on high efficiencies, reaches NPC of 2.9 EUR L-1 of marine diesel. The approach of prioritizing high CO2 conversion results in costs of 3.7 EUR L-1. A more balanced approach between efficiency and conversion achieves an NPC of 2.6 EUR L-1. Further NPC reductions are possible by sourcing lower‐cost renewable electricity, reducing the NPC further to a minimum of 1.6 EUR L-1. These findings underscore that direct air capture (DAC) cost‐efficiency and process integration improvements are essential for scalable, economically viable CO₂ utilization in PtL processes.Item Open Access Comparison of aqueous- and non-aqueous-based binder polymers and the mixing ratios for Zn//MnO2 batteries with mildly acidic aqueous electrolytes(2021) Fitz, Oliver; Ingenhoven, Stefan; Bischoff, Christian; Gentischer, Harald; Birke, Kai Peter; Saracsan, Dragos; Biro, DanielConsidering the literature for aqueous rechargeable Zn//MnO2 batteries with acidic electrolytes using the doctor blade coating of the active material (AM), carbon black (CB), and binder polymer (BP) for the positive electrode fabrication, different binder types with (non-)aqueous solvents were introduced so far. Furthermore, in most of the cases, relatively high passive material (CB+BP) shares ~30 wt% were applied. The first part of this work focuses on different selected BPs: polyacrylonitrile (PAN), carboxymethyl cellulose (CMC), styrene butadiene rubber (SBR), cellulose acetate (CA), and nitrile butadiene rubber (NBR). They were used together with (non-)aqueous solvents: DI-water, methyl ethyl ketone (MEK), and dimethyl sulfoxide (DMSO). By performing mechanical, electrochemical and optical characterizations, a better overall performance of the BPs using aqueous solvents was found in aqueous 2 M ZnSO4 + 0.1 M MnSO4 electrolyte (i.e., BP LA133: 150 mAh·g-1 and 189 mWh·g-1 @ 160 mA·g-1). The second part focuses on the mixing ratio of the electrode components, aiming at the decrease of the commonly used passive material share of ~30 wt% for an industrial-oriented electrode fabrication, while still maintaining the electrochemical performance. Here, the absolute CB share and the CB/BP ratio are found to be important parameters for an application-oriented electrode fabrication (i.e., high energy/power applications).Item Open Access Analyzing the influence of load current on the thermal RC network response of melting-type fuses used in battery electric vehicles(2025) Makan, Oliver; Birke, Kai-PeterHigh-voltage fuses are critical safety components in electric vehicle (EV) battery systems, yet their thermal behavior under charging currents remains insufficiently characterized. This study develops and validates a physics-based thermal resistor-capacitor (RC) network model of a high-voltage melting fuse, accounting for copper elements, quartz sand filling, and polyester casing. Experimental accelerated life tests and current step load profiles were performed in a climate chamber at 70 °C, with temperature measurements at the fuse terminals. The RC model was constructed using material properties and geometry-derived parameters, including three copper element sections, one quartz sand node, and one case node. A discretized state–space formulation was implemented to simulate the transient thermal behavior. The results reveal distinct dynamic and stationary characteristics, with thermal time constants varying strongly between fuse sections. Comparisons with experimental data demonstrate that the proposed model captures both rise time and steady-state behavior, with deviations attributable to contact resistances and parasitic effects. The findings highlight that charging currents in practical profiles typically remain below 50% of fuse current ratings, leaving optimization potential for higher permissible currents, faster charging, and reduced downtime while maintaining safety. The outcome of this model is highly relevant for lifetime prediction models.Item Open Access Hybrid modeling of lithium-ion battery : physics-informed neural network for battery state estimation(2023) Singh, Soumya; Ebongue, Yvonne Eboumbou; Rezaei, Shahed; Birke, Kai PeterAccurate forecasting of the lifetime and degradation mechanisms of lithium-ion batteries is crucial for their optimization, management, and safety while preventing latent failures. However, the typical state estimations are challenging due to complex and dynamic cell parameters and wide variations in usage conditions. Physics-based models need a tradeoff between accuracy and complexity due to vast parameter requirements, while machine-learning models require large training datasets and may fail when generalized to unseen scenarios. To address this issue, this paper aims to integrate the physics-based battery model and the machine learning model to leverage their respective strengths. This is achieved by applying the deep learning framework called physics-informed neural networks (PINN) to electrochemical battery modeling. The state of charge and state of health of lithium-ion cells are predicted by integrating the partial differential equation of Fick’s law of diffusion from a single particle model into the neural network training process. The results indicate that PINN can estimate the state of charge with a root mean square error in the range of 0.014% to 0.2%, while the state of health has a range of 1.1% to 2.3%, even with limited training data. Compared to conventional approaches, PINN is less complex while still incorporating the laws of physics into the training process, resulting in adequate predictions, even for unseen situations.