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The performance of lithium-ion batteries is determined by the structural properties of the electrodes, e.g., the choice of an active material and porosity. An increase in performance is crucial for fulfilling the future requirements of various applications, such as electric vehicles and consumer electronics. Laser structuring with short and ultrashort pulses offers great potential, which has been demonstrated in many research studies. Based on already published preliminary work, which is primarily dedicated to the proof of electrochemical enhancements, requirements for the structuring process are examined. Starting from a realistic production scenario for lithium-ion electrode processing, calculations concerning the technical requirements for the laser scanner system are made. The results show high demands on usable control technology. For this reason, the development of a new type of control system is presented, which has significantly more temporal accuracy in signal transmission and thus provides the basis for fast and, at the same time, high-precision processing with pulsed laser systems. [ABSTRACT FROM AUTHOR]

Within this paper we report on a lithium-ion battery with laser-structured graphite anodes, alleviating current drawbacks of lithiumion batteries such as the reduced discharge capacity at high C-rates and the on-set of lithium-plating during fast charging. These issues are intensified at low temperatures, as reaction and diffusion kinetics decelerate, which is why a focus of the presented work lies on low temperature performance. Electrochemical impedance spectroscopy was used to show a reduction in the impedances of cells with laser-structured anodes in comparison to their conventional counterparts. The discharge capacity retention at high C-rates was enhanced by up to 27% compared to conventional cells, proving potential for high power applications. For the cells with laserstructured anodes, the on-set of lithium-plating at 0°C was observed at higher charging C-rates by analyzing the voltage relaxation after charging. At-15°C, a smaller amount of plated lithium was detected, even though lithium-plating could not be entirely avoided. Laser structuring also enabled shorter charging times, as the upper cutoff voltage was reached at a higher SOC. The results point out that laser structuring of the anode improves the fast charging capability of lithium-ion cells, especially under demanding operating conditions. [ABSTRACT FROM AUTHOR]

In the production process chain of lithium-ion battery cells, the filling, consisting of dosing and wetting steps, of the cell and its components with electrolyte liquid is eminent for the final product quality and costs. To reduce the unnecessary wetting duration between filling and formation, and thereby the production costs, a measurement method for the wetting progress is necessary. In this paper, electrochemical impedance spectroscopy (EIS) as a well-established technique is used for the first time to quantify the wetting degree of batteries during cell production. The experimental data of the EIS acquired during the dosing and subsequent wetting process is correlated to images recorded by in situ neutron radiography. Results show that the impedance of the battery cells strongly depends on the wetting degree of the cell assembly and can thus be used to determine the fully wetted state enabling faster processing. [ABSTRACT FROM AUTHOR]

Preliminary studies have shown the superiority of convolutional neural networks (CNNs) compared to other network architectures for determining the surface quality of friction stir welds. In this paper, CNNs were employed to detect cavities inside friction stir welds by evaluating inline measured process data. The aim was to determine whether CNNs are suitable for identifying surface defects exclusively, or if the approach is transferable to internal weld defects. For this purpose, 120 welds were produced and examined by ultrasonic testing, which was the basis for labeling the data as "good" or "defective." Different types of artificial neural network were tested for predicting the placement of the welds into the defined classes. It was found that the way of labeling the data is significant for the accuracy achievable. When the complete welds were uniformly labeled as "good" or "defective," an accuracy of 98.5% was achieved by a CNN, which was a significant improvement compared to the state of the art. When the welds were labeled segment-wise, an accuracy of 79.2% was obtained by using a CNN, showing that a segment-wise prediction of the cavities is also possible. The results confirm that CNNs are well suited for process monitoring in friction stir welding and their application enables the identification of various defect types. [ABSTRACT FROM AUTHOR]