Project Details
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Modelling of a Lithium-Air-Battery using three-dimensional high resolution in-operando X-ray tomography and impedance spectroscopy

Subject Area Electrical Energy Systems, Power Management, Power Electronics, Electrical Machines and Drives
Term from 2017 to 2023
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 357753796
 
Final Report Year 2025

Final Report Abstract

Lithium-air batteries are considered a promising energy storage technology for the future, as they theoretically offer very high energy density. However, their practical implementation still faces significant challenges. This project combined material screening, electrical measurements as well as state-of-the-art imaging techniques with machine learning to gain a deeper understanding of the processes inside these batteries. Using in-situ synchrotron tomography and ex-situ FIB tomography, the structure and aging mechanisms of battery cells were analyzed in detail. An amide-based electrolyte mixture proved to be particularly beneficial for discharge capacity. The choice of carbon materials also had a major influence, where not just a high surface area or a large pore volume alone were decisive, but rather the optimal combination of both properties. Manganese mixed oxide and iron oxide were identified as the most promising catalysts. Another key finding concerns the aging of the lithium anode. It was shown that current density affects the mechanical degradation of the anode, which in turn reduces the battery’s lifespan. Thanks to high-resolution imaging, both the degradation of the anode and deposits within the gas diffusion electrode could be visualized. A major technological advancement of this project was the use of machine learning. In particular, the application of Random Forest algorithms enabled efficient analysis of complex imaging data—an essential factor given the highly intricate structures of aged electrodes. Additionally, combining different FIB detectors allowed for a detailed examination of highly porous nanostructures. The imaging analytics developed in this project have already proven to be highly valuable for further research, as reflected in numerous scientific publications. Moreover, new insights were gained into which materials are particularly promising for future lithium-air batteries. This project thus makes an important contribution to the advancement of this innovative battery technology.

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