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 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 Optimization of disassembly strategies for electric vehicle batteries(2021) Baazouzi, Sabri; Rist, Felix Paul; Weeber, Max; Birke, Kai PeterVarious studies show that electrification, integrated into a circular economy, is crucial to reach sustainable mobility solutions. In this context, the circular use of electric vehicle batteries (EVBs) is particularly relevant because of the resource intensity during manufacturing. After reaching the end-of-life phase, EVBs can be subjected to various circular economy strategies, all of which require the previous disassembly. Today, disassembly is carried out manually and represents a bottleneck process. At the same time, extremely high return volumes have been forecast for the next few years, and manual disassembly is associated with safety risks. That is why automated disassembly is identified as being a key enabler of highly efficient circularity. However, several challenges need to be addressed to ensure secure, economic, and ecological disassembly processes. One of these is ensuring that optimal disassembly strategies are determined, considering the uncertainties during disassembly. This paper introduces our design for an adaptive disassembly planner with an integrated disassembly strategy optimizer. Furthermore, we present our optimization method for obtaining optimal disassembly strategies as a combination of three decisions: (1) the optimal disassembly sequence, (2) the optimal disassembly depth, and (3) the optimal circular economy strategy at the component level. Finally, we apply the proposed method to derive optimal disassembly strategies for one selected battery system for two condition scenarios. The results show that the optimization of disassembly strategies must also be used as a tool in the design phase of battery systems to boost the disassembly automation and thus contribute to achieving profitable circular economy solutions for EVBs.Item Open Access High‐stable lead‐free solar cells achieved by surface reconstruction of quasi‐2D tin‐based perovskites(2023) Yang, Feng; Zhu, Rui; Zhang, Zuhong; Su, Zhenhuang; Zuo, Weiwei; He, Bingchen; Aldamasy, Mahmoud Hussein; Jia, Yu; Li, Guixiang; Gao, Xingyu; Li, Zhe; Saliba, Michael; Abate, Antonio; Li, MengTin halide perovskites are an appealing alternative to lead perovskites. However, owing to the lower redox potential of Sn(II)/Sn(IV), particularly under the presence of oxygen and water, the accumulation of Sn(IV) at the surface layer will negatively impact the device's performance and stability. To this end, this work has introduced a novel multifunctional molecule, 1,4‐phenyldimethylammonium dibromide diamine (phDMADBr), to form a protective layer on the surface of Sn‐based perovskite films. Strong interactions between phDMADBr and the perovskite surface improve electron transfer, passivating uncoordinated Sn(II), and fortify against water and oxygen. In situ grazing incidence wide‐angle X‐ray scattering (GIWAXS) analysis confirms the enhanced thermal stability of the quasi‐2D phase, and hence the overall enhanced stability of the perovskite. Long‐term stability in devices is achieved, retaining over 90% of the original efficiency for more than 200 hours in a 10% RH moisture N2 environment. These findings propose a new approach to enhance the operational stability of Sn‐based perovskite devices, offering a strategy in advancing lead‐free optoelectronic applications.Item Open Access Proof of concept : the GREENcell : a lithium cell with a F-, Ni- and Co-Free cathode and stabilized in-situ LiAl alloy anode(2023) Schad, Kathrin; Welti, Dominic; Birke, Kai PeterGiven the rising upscaling trend in lithium-ion battery (LiB) production, there is a growing emphasis on the environmental and economic impacts alongside the high energy density demands. The cost and environmental impact of battery production primarily arise from the critical elements Ni, Co, and F. This drives the exploration of Ni-free and Co-free cathode alternatives such as LiMn 2O 4 (LMO) and LiFePO 4 (LFP). However, the absence of Ni and Co results in reduced capacity and insufficient cyclic stability, particularly in the case of LMO due to Mn dissolution. To compensate for both low cathode capacitance and low cycle stability, we propose the GREENcell, a lithium cell combining a F-free polyisobutene (PIB) binder-based LMO cathode with a stabilized in -situ LiAL alloy anode. A LiAl alloy anode with the chemical composition of LiAl already shows a theoretical capacity of 993 Ah·kg−1. Therefore, it promises extraordinarily higher energy densities compared to a commercial graphite anode with a capacity of 372 Ah·kg−1. Following an iterative development process, different optimization strategies, especially those targeting the stability of the Al-based anode, were evaluated. During Al foil selection, foil purity and thickness could be identified as two of the dominant influencing parameters. A pressed-in stainless steel mesh provides both mechanical stability to the anode and facilitates alloy formation by breaking up the Al oxide layer beforehand. Additionally, a binder-stabilized Al oxide or silicate layer is pre-coated on the Al surface, posing as a SEI-precursor and ensuring a uniform liquid electrolyte distribution at the phase boundary. Employing a commercially available Si-containing Al alloy mitigated the mechanical degradation of the anode, yielding a favorable impact on long-term stability. The applicability of the novel optimized GREENcell is demonstrated using laboratory coin cells with LMO and LFP as the cathode. As a result, the functionality of the GREENcell was demonstrated for the first time, and thanks to the anode stabilization strategies, a capacity retention of >70% after 200 was achieved, representing an increase of 32.6% compared to the initial Al foil.Item Open Access A novel long short-term memory approach for online state-of-health identification in lithium-ion battery cells(2024) Kopp, Mike; Fill, Alexander; Ströbel, Marco; Birke, Kai PeterRevolutionary and cost-effective state estimation techniques are crucial for advancing lithium-ion battery technology, especially in mobile applications. Accurate prediction of battery state-of-health (SoH) enhances state-of-charge estimation while providing valuable insights into performance, second-life utility, and safety. While recent machine learning developments show promise in SoH estimation, this paper addresses two challenges. First, many existing approaches depend on predefined charge/discharge cycles with constant current/constant voltage profiles, which limits their suitability for real-world scenarios. Second, pure time series forecasting methods require prior knowledge of the battery’s lifespan in order to formulate predictions within the time series. Our novel hybrid approach overcomes these limitations by classifying the current aging state of the cell rather than tracking the SoH. This is accomplished by analyzing current pulses filtered from authentic drive cycles. Our innovative solution employs a Long Short-Term Memory-based neural network for SoH prediction based on residual capacity, making it well suited for online electric vehicle applications. By overcoming these challenges, our hybrid approach emerges as a reliable alternative for precise SoH estimation in electric vehicle batteries, marking a significant advancement in machine learning-based SoH estimation.