Alumni

Title of thesis:

Elektrochemische Charakterisierung und systemtechnische Diagnose von Lithium-Ionen-Batterien

2025

Supervising Chair:

Electrical Energy Systems

In light of the growing prevalence of lithium-ion batteries, it is imperative to investigate alternative approaches to recycling and to enhance the longevity and performance of these batteries. To develop appropriate solutions, it's essential to have a thorough understanding of the electrical characteristics of both individual cells and modules. For this reason, a detailed method for process characterization, the Löwner-method, is used in this thesis. It is validated through the utilization of both equivalent circuit models and an established method for process characterization. It offers a promising and innovative approach for the analysis of a wide variety of electrochemical systems. Moreover, the Löwner-method is integrated with alternative techniques to enable a thorough characterization of 92 lithium-ion batteries, surpassing the limitations of traditional approaches like equivalent circuit parameter analysis or direct variable measurement.
The identified processes are examined for their distribution function, where the general assumption of a normal distribution can be declined for some processes. In addition, a correlation analysis reveals three discrete process correlation groups that are assigned to cell winding, surface processes, and diffusion processes. The results of the cell-to-cell-variation study are then used to model the energy and pulse power capability of battery modules in series and parallel connection. To account for parameter variation and correlation, multivariate normal distributions are used. A simulation is then employed to examine the impact of cell sorting and an inhomogeneously aged cell on the performance variables of energy and pulse power capability. The results show that cell sorting, despite its significant sorting effort, only marginally enhances performance.
In contrast, the performance exhibits a significant dependence on the inhomogeneity, particularly in the serial connection. Consequently, a method for detecting inhomogeneities in serially connected battery modules is being developed. For this purpose, the impedance of the entire module is simulated and analyzed using various impedance characteristics. It turns out that the low-frequency minimum is the most suitable feature for this purpose. It is therefore validated by measurements. This feature enables the detection of inhomogeneities in a series connection of up to ten cells.
This work therefore makes a significant contribution to the understanding of the electrical properties of battery modules and their performance. Moreover, it serves as a foundation for the future development of decision support algorithms in the field of circular economy.