Batteries – Reaction Kinetics and Optimization
In our group, we are involved in research on lithium-ion and next-generation batteries. An essential part of our work is the deeper understanding of the underlying physical and chemical processes. To gain a deeper insight into these processes, we use synergies between mechanistic modelling and experimental investigations. This enables us to analyse and optimise batteries in terms of performance, ageing properties and safety.
One of our focal points is the investigation of reaction mechanisms and kinetics, starting from the structure of functional boundary layers up to energy conversion and degradation. This includes the description of a cell from its production to cell death.
Accurate chemical as well as electrochemical cell characterization is necessary for a detailed understanding of the underlying processes. Mass spectroscopic measurements, such as OEMS, allow deeper insights into ongoing reactions by determining reaction products. Furthermore, the electrochemical behaviour can be investigated in more detail by means of current-carrying capacity measurements, electrochemical impedance spectroscopy, nonlinear frequency response analysis or cyclic voltammetry. In combination with our mechanistic models, these measurement methods allow a holistic understanding of current and future energy storage and energy conversion technologies. Depending on the problem, we can use models with different levels of detail, ranging from atomistic reactions to coupled microscopic/macroscopic descriptions.
In addition, we apply rigorous mathematical optimizations in order to translate the results of these models into practical applications. In collaborations with experts in the field of battery production, process understanding and model-based design proposals can be combined to accelerate progress in both areas.
Our research on next generation batteries focuses on the development of sodium ion-based chemistries as part of the Post Lithium Storage Cluster of Excellence (POLiS). We place an emphasis on exploring the formation and degradation of the Solid Electrolyte Interphase on hard carbon anodes in the presence of carbonate electrolytes. To this end, we employ a combination of molecular and multiscale modeling, paired with electrochemical characterization and OEMS, in order to determine the underlying molecular mechanisms that drive cell behavior. This work is funded by the German Research Foundation (DFG) under Project ID 390874152 (POLiS Cluster of Excellence).
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Contact: Janika Wagner-Henke, Julian Ulrich
Title | Type | Supervisor |
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Multiscale modeling of the anode-electrolyte interface in next generation lithium metal batteries | Masterarbeit | |
Elektrochemische Charakterisierung an Natrium Ionen Batterien | Bachelor-/Masterthesis | |
Model-based Study of Electrolyte Decomposition and Interfacial Layer Growth in Sodium Ion Batteries | Bachelor-/Masterthesis | |
Model-based Validation of Reaction Mechanism in Lithium-Ion Batteries during Thermal Stress | Bachelor-/Master thesis | |
Thermische Degradationsanalyse von Lithium-Ionen Batterien | Bachelor- / Masterarbeit | Simone Lisa Holzmann, M. Sc. |
Degradation Mechanisms of Impurities in Lithium-ion Batteries during thermal runaway | Bachelor thesis |
Current research topics
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Topic | Contact |
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Thermodynamic and kinetic stability of the Lithium-Liquid Electrolyte Interface - Multiscale Modeling (Lillint2) | |
Investigation of solid electrolyte interphase (SEI) in Lithium-Ion batteries at elevated temperatures (SiMET) | Jorge Valenzuela |
Quantitative Analysis of the Nonlinear Frequency Response (also known as Nonlinear Electrochemical Impedance Spectroscopy) for Lithium-Ion Batteries | Julian Ulrich |
Model-based investigation of the chemical and electrochemical processes of lithium-sulfur batteries(Aero-LiS-SE) | Walter Cistjakov (KIT Partner) |
Simulation-based safety assessment of uncontrolled thermal runaway in aged battery cell (SimDural) | Leon Schmidt, Jorge Valenzuela |
Post Lithium Storage Cluster of Excellence (POLiS) | Dr. Kie Scott Hankins, Michelle Allion |
Holistic method of combined data- and model-based electrode design supported by artificial intelligence (HiBRAIN) |