Huge efforts are performed to study energy related materials or devices with new techniques of in situ and in operando methods. In particular large scale facilities are more and more involved in providing unique experiment conditions to support this request e.g. for better understanding of electrochemistry in batteries. Neutrons with their properties of deep penetration in materials and very sensitive to light elements suit well as a probe to monitor full processes in cells as charging/discharging, electrolyte filling or gas formation etc..
The focus in this contribution is to introduce neutron techniques for battery research arising high attention in the last decade. The process of charging and discharging of NMC/graphite cells related to the intercalation of Li in the graphite layers can be observed in situ with neutron diffraction (ND) to monitor LiCx phases as LiC6 and LiC12 during the intercalation/de-intercalation process . Under fast charging conditions and low temperatures the phenomenon of Li plating can be detected. By means of voltage relaxation and in situ ND for different C-rates Li plating is investigated . Lithium iron phosphate (LFP), used for stationary energy storage systems, are studied with various types of graphites. Neutrons support the understanding why various losses of the storage capacity occur . Neutron imaging (radiography and tomography) enables a non-destructive view inside the cell to investigate objects on the length scale of >50 micrometer. An important application for industry is the observation of the electrolyte filling process. In the focus is the distribution of the electrolyte in the cell between the layer stacks in a pouch cell during the filling . Neutron induced prompt gamma activation analysis (PGAA) is a powerful tool to explain the capacity loss in NMC/graphite cells caused by tiny metal deposition on the graphite anode after charging/discharging processes . Finally, the method of neutron depth profiling (NDP) will be introduced for near surface studies.
The work was supported by the BMBF project ExZellTUM (grant number 03X4633A) and the Bavarian Ministry of Economic Affairs within the framework of the EEBatt project.
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