Investigation of the interaction of degradation processes in lithium-ion batteries with variation of aging sequences

• Applicant: Professor Dr.-Ing. Fridolin Röder
• Subject Area: Chemical and Thermal Process Engineering
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 527466263

Lithium-ion batteries are a key technology for the energy transition and in particular for the electrification of the mobility sector. A central challenge in the operation of batteries is a precise lifetime prognosis, which is currently associated with significant uncertainties. The reason is a complex aging behavior, which depends not only on the operating conditions, but also on their timing (sequence). This phenomenon is often referred to as path dependence and can be caused by the interaction of different degradation processes in the battery cell. The goal of this project is to understand interactions of degradation processes and their effect on the path dependence of battery aging. The project focuses on the interaction between the deposition of metallic lithium and the growth of the "solid-electrolyte interface", which both play a prominent role in the aging of lithium-ion batteries. For this purpose, aging data with varying sequences will be generated. It is expected that the complex aging behavior will be prominent when the sequences are varied, in contrast to classical parameter variations. The data will be analyzed using mechanistic degradation models. Different plausible variants for the interaction between degradation processes will be modeled and their predictive ability will be evaluated. This approach will allow to identify and interpretate the interaction. The project will reveal whether this interaction is a cause of path dependence and what the cause-effect relationships are. The project will also provide a fundamental understanding of such interaction dynamics and their importance for lifetime prediction. On this basis, currently used operational strategies and forecasting models can be systematically improved.

• DFG Programme: Research Grants