Flaw detection in the coating process of lithium-ion battery electrodes with acoustic guided waves

The energy requirements of current technologies, such as smartphones, drones, and electric vehicles, have forced lithium-ion cell manufactures to relentlessly increase the energy density of their cells. As a consequence, safety margins have been exhausted and cells operate near the edge of stability. In this configuration, minor inhomogeneities and flaws induced during the production process strongly affect cell performance and durability. Consequently, safety risks can emerge under demanding circumstances. The production of lithium-ion cells, especially of electrodes thereof, is a complex process. It requires continuous monitoring of process parameters and of output quality. Particularly, the detection and classification of adhesion losses at the coating-foil interface suffer under inefficient and high-cost monitoring systems. In this dissertation, a novel non-destructive evaluation (NDE) method is developed based on ultrasonic Lamb waves and horizontally polarized shear waves to overcome this shortcoming. In the proposed methodology, metal foils serving as substrates in lithium-ion battery (LIB) electrodes are used as wave guides for measuring ultrasonic signals. The theory of Lamb waves and horizontally polarized shear waves is discussed and extended for singe-layered and multi-layered structures. In particular, the excitation and reception of acoustic guided waves (AGW) are investigated theoretically and experimentally. The presence of inhomogeneities, such as flaws in the foil, adhesion flaws at the coating-foil interface, and gaps in the coating material, changes boundary conditions and influences guided wave propagation parameters. The correlation between flaw types and changes in the received signal amplitude, frequency, energy, and propagation velocity is investigated experimentally and later utilized for flaw identification and classification. The excitation and reception of both guided wave types are performed with optimized ultrasonic wedge transducers, which are positioned on both uncoated side areas of the foil. A mechanical construction was developed and manufactured to ensure valid acoustic contact at low contact pressure conditions, which in turn guarantees an undisturbed sliding of the foil under the sensors. The utilization of little or no couplant media also prevents any coating corruption.Advantages of multi-frequency wave excitation through the utilization of thickness and planar oscillation in piezoelectric ceramic PZT wedge transducers are discussed as well as theoretically and experimentally investigated. Flaw identification in blank foils is performed with the high frequency component of the PZT transducer oscillations. In this way, the high spatial sensitivity of short wavelengths can be exploited. The results prove that even 2 mm long scratches and cracks can be detected with this approach by analyzing the signal’s peak amplitude. In general, the sensing signals are highly damped and distorted by the coating material. Therefore, the low frequency component of the PZT transducer oscillations must be used to excite and to receive appropriate sensing amplitudes. The experimental findings show that signal changes depend on the coating material absorption rate. In coatings with lower attenuation, the received signal decreases, and in those with higher attenuation, the received signal increases in the presence of flaws. The slope and end-value of square signal integrals are identified as the preferred measurands for adhesion and gap flaw detection and classification in electrodes. Overall, the proof of concept for the proposed guided-wave-based monitoring system was successfully provided, and this supports the suitableness of the developed methodology as well as the mechanical setup

A Technology for Seismogenic Process Monitoring and Systematic Earthquake Forecasting

Earthquakes are a severe natural phenomenon that require continuous monitoring, analysis, and forecasting to mitigate their risks. Seismological data have long been used for this purpose, but geodynamic data from remote sensing of surface displacements have become available in recent decades. In this paper, we present a novel information technology for monitoring, analyzing seismogenic fields, and predicting earthquakes using Earth remote sensing data presented as a time series of surface displacement points for systematic regional earthquake prediction. We demonstrate, for the first time, the successful application of this technology and discuss the method of the minimum area of alarm, which was developed for machine learning and systematic earthquake prediction, as well as the architecture and tools of the GIS platform. Our technology is implemented as a network platform consisting of two GISs. The first GIS automatically loads earthquake catalog data and GPS time series, calculates spatiotemporal fields, performs systematic earthquake prediction in multiple seismically active regions, and provides intuitive mapping tools to analyze seismic processes. The second GIS is designed for scientific research of spatiotemporal processes, including those related to earthquake forecasting. We demonstrate the effectiveness of platform analysis tools that are intuitive and accessible to a wide range of users in solving problems of systematic earthquake prediction. Additionally, we provide examples of scientific research on earthquake prediction using the second GIS, including the effectiveness of using GPS data for forecasting earthquakes in California, estimating the density fields of earthquake epicenters using the adaptive weighted smoothing (AWS) method for predicting earthquakes in Kamchatka, and studying earthquake forecasts in the island part of the territory of Japan using the earthquake catalog and GPS. Our examples demonstrate that the method of the minimum area of alarm used for machine learning is effective for forecasting both catalog and remote sensing data

Analyzing the Performance of GPS Data for Earthquake Prediction

The results of earthquake prediction largely depend on the quality of data and the methods of their joint processing. At present, for a number of regions, it is possible, in addition to data from earthquake catalogs, to use space geodesy data obtained with the help of GPS. The purpose of our study is to evaluate the efficiency of using the time series of displacements of the Earth’s surface according to GPS data for the systematic prediction of earthquakes. The criterion of efficiency is the probability of successful prediction of an earthquake with a limited size of the alarm zone. We use a machine learning method, namely the method of the minimum area of alarm, to predict earthquakes with a magnitude greater than 6.0 and a hypocenter depth of up to 60 km, which occurred from 2016 to 2020 in Japan, and earthquakes with a magnitude greater than 5.5. and a hypocenter depth of up to 60 km, which happened from 2013 to 2020 in California. For each region, we compare the following results: random forecast of earthquakes, forecast obtained with the field of spatial density of earthquake epicenters, forecast obtained with spatio-temporal fields based on GPS data, based on seismological data, and based on combined GPS data and seismological data. The results confirm the effectiveness of using GPS data for the systematic prediction of earthquakes

In-Operando Impedance Spectroscopy and Ultrasonic Measurements during High-Temperature Abuse Experiments on Lithium-Ion Batteries

Lithium-Ion batteries are used in ever more demanding applications regarding operating range and safety requirements. This work presents a series of high-temperature abuse experiments on a nickel-manganese-cobalt oxide (NMC)/graphite lithium-ion battery cell, using advanced in-operando measurement techniques like fast impedance spectroscopy and ultrasonic waves, as well as strain-gauges. the presented results show, that by using these methods degradation effects at elevated temperature can be observed in real-time. These methods have the potential to be integrated into a battery management system in the future. Therefore they make it possible to achieve higher battery safety even under the most demanding operating conditions