State Estimation Approach of Lithium-Ion Batteries by Simplified Ultrasonic Time-of-Flight Measurement

In this work, we use ultrasonication and chemical etching agents to assist preparation of metal current collectors with nano-scale pores on the surface. Four different current collectors (copper foil, copper foam, aluminum foil, and aluminum foam) are prepared. The preparation parameters, ultrasonic time and etching agent concentration, are investigated and optimized accordingly. The morphologies of the as-prepared current collectors are observed under a scanning electronic microscope. Soft-packed lithium ion batteries with various current collectors are fabricated and tested. The prepared lithium ion batteries show good long-term cycle stability. The nanoporous structure of the current collector has little impact on the improvement of battery capacity under slow charging/discharging rates but has a positive impact on capacity retention under fast charging/discharging rates.

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Machine Learning Based Preprocessing to Ensure Validity of Cross-Correlated Ultrasound Signals for Time-of-Flight Measurements

Journal of Nondestructive Evaluation ◽

10.1007/s10921-020-00745-7 ◽

2021 ◽

Vol 40 (1) ◽

Author(s):

Simon Herter ◽

Sargon Youssef ◽

Michael M. Becker ◽

Sarah C. L. Fischer

Keyword(s):

Machine Learning ◽

High Precision ◽

Cross Correlation ◽

Complex Structure ◽

Time Of Flight ◽

Industrial Applications ◽

Ultrasonic Signals ◽

Ultrasonic Time ◽

Flight Measurement ◽

Flight Measurements

AbstractHigh precision ultrasonic time-of-flight measurement is a well known part of non-destructive evaluation used in many scientific and industrial applications, for example stress evaluation or defect detection. Although ultrasonic time-of-flight measurements are widely used there are some limitations where high noise and distorted ultrasonic signals are conflicting with the demand for high precision measurements. Cross-correlation based time-of-flight measurement is one strategy to increase reliability but also exhibits some ambiguous correlation states yielding to wrong time-of-flight results. To improve the reliability of these measurements a new machine learning based approach is presented based on experimental data collected on tightened bolts. Due to the complex structure of the bolts the ultrasonic signal is influenced by boundary conditions of the geometry which lead to high number of the ambiguous cross-correlation results in practice. In this particular application, bolts are in practice evaluated discontinuously and without knowledge of the time-of-flight in the unloaded condition which prevents the use of all other available comparative preprocessing techniques to detect time-of-flight shifts. Three different preprocessing strategies were investigated based on variations in the bolting configurations to ensure a machine learning based model capable of predicting the state of the cross-correlation function for different bolting parameters. With this approach, we achieve up to 100% classification accuracy for both longitudinal and transversal ultrasonic signals under laboratory conditions. In the future the method should be extended to become more robust and be applicable in real-time for industrial applications.

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