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 Untersuchung der Zellausdehnung und des Gasinnendrucks zylindrischer Lithium-Ionen Zellen(2025) Hemmerling, Jessica; Birke, Kai Peter (Prof. Dr.-Ing)Item Open Access All-inorganic CsPbI2Br perovskite solar cells with thermal stability at 250 °C and moisture-resilience via polymeric protection layers(2025) Roy, Rajarshi; Byranvand, Mahdi Malekshahi; Zohdi, Mohamed Reza; Magorian Friedlmeier, Theresa; Das, Chittaranjan; Hempel, Wolfram; Zuo, Weiwei; Kedia, Mayank; Rendon, Jose Jeronimo; Boehringer, Stephan; Hailegnanw, Bekele; Vorochta, Michael; Mehl, Sascha; Rai, Monika; Kulkarni, Ashish; Mathur, Sanjay; Saliba, MichaelAll-inorganic perovskites, such as CsPbI2Br, have emerged as promising compositions due to their enhanced thermal stability. However, they face significant challenges due to their susceptibility to humidity. In this work, CsPbI2Br perovskite is treated with poly(3-hexylthiophen-2,5-diyl) (P3HT) during the crystallization resulting in significant stability improvements against thermal, moisture and steady-state operation stressors. The perovskite solar cell retains ∼90% of the initial efficiency under relative humidity (RH) at ∼60% for 30 min, which is among the most stable all-inorganic perovskite devices to date under such harsh conditions. Furthermore, the P3HT treatment ensures high thermal stress tolerance at 250 °C for over 5 h. In addition to the stability enhancements, the champion P3HT-treated device shows a higher power conversion efficiency (PCE) of 13.5% compared to 12.7% (reference) with the stabilized power output (SPO) for 300 s. In addition, the P3HT-protected perovskite layer in ambient conditions shows ∼75% of the initial efficiency compared to the unprotected devices with ∼28% of their initial efficiency after 7 days of shelf life.Item Open Access Mitigating the amorphization of perovskite layers by using atomic layer deposition of alumina(2025) Kedia, Mayank; Das, Chittaranjan; Kot, Malgorzata; Yalcinkaya, Yenal; Zuo, Weiwei; Tabah Tanko, Kenedy; Matvija, Peter; Ezquer, Mikel; Cornago, Iñaki; Hempel, Wolfram; Kauffmann, Florian; Plate, Paul; Lira-Cantu, Monica; Weber, Stefan A. L.; Saliba, MichaelAtomic layer deposition of aluminum oxide (ALD-Al2O3) layers has recently been studied for stabilizing perovskite solar cells (PSCs) against environmental stressors, such as humidity and oxygen. In addition, the ALD-Al2O3 layer acts as a protective barrier, mitigating pernicious halide ion migration from the perovskite towards the hole transport interface. However, its effectiveness in preventing the infiltration of ions and additives from the hole-transport layer into perovskites remains insufficiently understood. Herein, we demonstrate the deposition of a compact ultrathin (∼0.75 nm) ALD-Al2O3 layer that conformally coats the morphology of a triple-cation perovskite layer. This promotes an effective contact of the hole transporter layer on top of the perovskite, thereby improving the charge carrier collection between these two layers. Upon systematically investigating the layer-by-layer structure of the PSC, we discovered that ALD-Al2O3 also acts as a diffusion barrier for the degraded species from the adjacent transport layer into the perovskite. In addition to these protective considerations, ALD-Al2O3 impedes the transition of crystalline perovskites to an undesired amorphous phase. Consequently, the dual functionality (i.e., enhanced contact and diffusion barrier) of the ALD-Al2O3 protection enhanced the device performance from 19.1% to 20.5%, while retaining 98% of its initial performance compared to <10% for pristine devices after 1500 h of outdoor testing under ambient conditions. Finally, this study deepens our understanding of the mechanism of ALD-Al2O3 as a two-way diffusion barrier, highlighting the multifaceted role of buffer layers in interfacial engineering for the long-term stability of PSCs.Item Open Access Understanding the crystallization mechanism of organic-inorganic perovskite films(2025) Zuo, Weiwei; Saliba, Michael (Prof. Dr.)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 Structural changes and electrochemical characteristics during aging of electrodes in lithium-ion cells(2025) Ridder, Alexander; Birke, Kai Peter (Prof. Dr.-Ing.)Item Open Access Advancing battery digital twins through hybrid modelling approach(2025) Singh, Soumya; Birke, Kai Peter (Prof. Dr.-Ing.)Item Open Access Artificial neural network architectures for state estimation in lithium-ion batteries(2025) Kopp, Mike; Birke, Kai Peter (Prof. Dr.-Ing.)This dissertation investigates the application of artificial neural networks for predicting the state of charge, state of health, and temperature of lithium-ion battery cells. The study evaluates several model architectures, including encoder-based models, informer-based models, and transformer-based approaches (collectively referred to as attention-based models), as well as long short-term memory networks. The evaluation considers key aspects such as model size, complexity, and reproducibility. For state of charge and temperature predictions, the training data consists of charge cycles and worldwide harmonized light-duty vehicle test procedure cycles. A novel training algorithm was developed and consistently applied across all models to ensure comparability. Encoder-based models are systematically analyzed, focusing on architectural features such as positional encodings, normalization layers, and input scaling strategies, as well as autoregressive methods like artificial feature extraction and artificial recurrence. Similarly, both stateful and stateless long short-term memory models are evaluated for their robustness and predictive power. Among the attention-based models, the encoder-only architecture, incorporating positional encodings, post-normalization layers, and zero-importance scaling for current data, achieved notable results. However, long short-term memory models, particularly in stateless configurations, consistently outperformed encoder-based models in state of charge predictions, with the best-performing long short-term memory model achieving a root mean square error as low as 0.744 percent. This demonstrates that long short-term memory networks are more robust and effective for time series forecasting in the context of state estimation for lithium-ion battery cells. The challenges of temperature prediction are highlighted by limitations in the quality of the training data, which significantly impacted model performance. While increasing the number of trainable parameters improved model accuracy to some extent, these improvements eventually plateaued, emphasizing that data quality plays a more critical role than model complexity. Long short-term memory models exhibited more consistent performance, whereas encoder models were more variable, underscoring the importance of high-quality data in real-world applications. For state of health predictions, a novel classification model was developed by filtering out current pulses during complex operations, such as drive cycles, which were then analyzed by the artificial neural network to estimate the state of health. Long short-term memory models once again demonstrated superior performance and stability compared to encoder-based models. The findings reveal that long short-term memory networks remain a highly effective approach for state estimation, outperforming attention-based models in both state of charge and state of health predictions. These results underscore the potential of artificial neural networks in battery management systems while identifying key factors, such as data quality and architectural decisions, that significantly influence model performance.Item Open Access Temperaturbestimmung von Lithium Ionen Zellen mittels künstlicher neuronaler Netze basierend auf Daten der elektrochemischen Impedanzspektroskopie(2025) Ströbel, Marco; Birke, Kai Peter (Prof. Dr.-Ing.)Item Open Access Investigation and replication of the wetting process in battery materials(2025) Wanner, Johannes; Birke, Kai Peter (Prof. Dr.-Ing.)