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Lithium Ion Batteries - Europe and Germany in general and BAM with safety aspects in particular
(2019)
The article introduces the BAM as well as the experimental possibilities at the BAM TTS in the first part. The second part explains general aspects of the electromobility strategy and the research landscape on lithium batteries in Germany and Europe. In the last part of the presentation current investigations on the safety of lithium batteries as well as perspectives planned investigations of the BAM in this area are presented.
Sicherheitskonzepte werden für die verschiedenen Szenarien für Lithiumakkus benötigt: Vor allem für den Bereich der Lagerung gibt es im Moment nur wenige Vorgaben. Im Vortrag werden verschiedene Brandschutzkonzepte vorgestellt. Für die Lagerung werden Empfehlungen ausgesprochen, die teilweise an die Vorgaben der Transportvorschriften anknüpfen.
Aging Mechanisms of Lithium Batteries: How can we make batteries more durable and more sustainable in the future?
Lithium batteries are particularly susceptible to ageing processes. During each charging and discharging process in an electric battery, lithium ions are deposited in the electrodes of the cell like in the pores of a sponge. Over time, however, fractures and cracks occur in the filigree structures. The result: more and more lithium ions no longer fit into the hollow spaces of the „sponge“, instead they accumulate in heaps around the electrodes and hinder the movement of other ions. The performance of the battery decreases.
We present our new project, in which we are developing a fast and cost-effective method with which companies that produce lithium batteries can already assess the ageing behaviour of their batteries in the laboratory.
Für den Transport von Lithium-Batterien sind verschiedene Vorschriften des Transportrechts zu beachten. Insbesondere für defekte und beschädigte Lithium-Batterien, die beim Transport gefährlich reagieren, in Brand geraten oder giftige Gase freisetzen können, werden in 2019 neue Regelungen anwendbar sein. Die BAM ist in Deutschland zuständige Behörde für deren Umsetzung.
Lithium exists in two stable isotopes, 6Li and 7Li. The ratio of these in every ore varies depending on the geological history of the sample, thus providing a tool for fingerprinting the distinct origin of Li containing samples. Determination of the exact isotope ratio for e.g. designation of provenance today relies on expensive and bulky instrumentation such as multi `collector inductively coupled plasma mass spectrometry` (MC-ICP-MS). These instruments, however, are known to bear pitfalls in the characterization of particular elements including Lithium. BAM recently developed two alternative analytical devices for this task, solely relying on inexpensive optical spectroscopy in combination with state-of-the-art multivariate data analysis such as Machine learning algorithms. Both techniques have been comprehensively studied using certified reference materials and comparing the results to MC-ICP-MS results and could be shown to result in comparable figures of merit, paving the way for a more general accessibility to provenance determination instrumentation. The results also pave the way towards even further simplification of the laboratory infrastructure demands and to further include additional elements into the isotopic fingerprinting methodology.
An alternative method for lithium isotope amount ratio analysis is proposed by combining atomic absorption spectrometry with spectra data analysis by machine leaning. It is based on the well-known isotope shift of around 15 pm for the electronic transition at wavelength 670.7845 nm which can be measured by a high-resolution continuum source atomic absorption spectrometer (HR-CS-AAS). For isotope amount ratio analysis, a scalable three boosting machine learning algorithm (XGBoost) was employed and calibrated with a set of samples with a 6Li isotope amount fraction ranging from 99% to 6%. The absolute Li isotope amount fractions of these calibration samples were previously measured by multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) and used as ab-initio data for the machine learning algorithm. Validation of the machine leaning model was performed with two standard reference materials (LSVEC and IRMM-016). The procedure was employed for the isotope amount ratio determination of a set of stock chemicals (Li2CO3, LiNO3, LiCl, LiOH, and LiF) as well as a BAM candidate LiMNC cathode reference material. Achieved uncertainties are one order of magnitude higher than those obtained by MC-ICP-MS. This precision and accuracy is nonetheless sufficient to resolve natural occurring variations in Lithium isotope ratios. Also, the LiMNC material was analyzed by HR-CS-AAS with and without matrix purification. The results are comparable within statistical error.