Electrochemical impedance spectroscopy (EIS) is a widely used method for battery tests in manufacturing and automotive field applications. As such, accurate measurement of the impedance over a broad frequency spectrum is of high relevance, also requiring specific calibration methods and evaluation of error bounds. Here, we report for the first time a comprehensive uncertainty analysis of calibrated EIS for batteries. We aim to identify two uncertainty sources, the fixture repeatability and measurement noise, and evaluate their effect on the measured impedances. To this end, an error model assigned to each of them and the real and imaginary parts of the model parameters are characterized in the frequency range of 100 mHz–5 kHz by performing specific impedance measurements on a shunt resistor and a short standard. We demonstrate how different uncertainties are combined with the measured impedances, incorporating also the impedance calibration procedure. The errors are propagated through the calibration and correction functions by applying the linear error propagation method provided by the UncLib package from METAS. The error bounds are derived from uncertainty propagation in the shunt standard and the prismatic battery cell EIS and verified by conducting a series of measurements. Thereby, the uncertainty is shown as ellipsoids in every frequency point of the Nyquist plot. For instance, at 1 Hz, the 34-Ah prismatic cell shows a calibrated real-part impedance and two standard deviation error bounds of 1.07 $\text{m}\Omega $ ± $26~ \mu \Omega $ .

This paper reports the development of a new composite material as a matching medium for medical microwave diagnostic systems, where maximizing the microwave energy that penetrates the interrogated tissue is critical for improving the quality of the diagnostic images. The proposed material has several advantages over what is commonly used in microwave diagnostic systems: it is semi-flexible and rigid, and it can maximize microwave energy coupling by matching the tissue’s dielectric constant without introducing high loss. The developed matching medium is a mirocomposite of barium titanate filler in polydimethylsiloxane (PDMS) in different weight-based mixing ratios. Dielectric properties of the material are measured using a Keysight open-ended coaxial slim probe from 0.5 to 10 GHz. To avoid systematic errors, a full dielectric properties calibration is performed before measurements of sample materials. Furthermore, the repeatability of the measurements and the homogeneity of the sample of interest are considered. Finally, to evaluate the proposed matching medium, its impact on a printed monopole antenna is studied. We demonstrate that the permittivity of the investigated mixtures can be increased in a controlled manner to reach values that have been previously shown to be optimal for medical microwave imaging (MWI) such as stroke and breast cancer diagnostic applications. As a result, the material is a good candidate for supporting antenna arrays designed for portable MWI scanners in applications such as stroke detection.

Electrochemical impedance spectroscopy (EIS) is widely used for battery cell testing in industrial production and R&D labs. This work addresses the use of EIS calibration and uncertainty analysis in cell classification. Five scenarios are investigated to discuss qualitatively the impact of calibration and uncertainty on classification. For an experimental demonstration, a cylindrical cell was measured with two mechanical fixtures of different qualities and characterized regarding random errors and calibrated impedances. The impact of uncertainty and impedance calibration on the cell classification is shown, and based on the uncertainty and corresponding error bounds, a confidence level was established for the classification results. Quantitative uncertainty bounds are presented for the full EIS frequency spectrum ranging from 150 mHz to 5 kHz.

Invited for this month′s cover picture is the group of researchers at Keysight Technologies Labs Austria. The front cover shows the error sources with different distribution functions affecting the battery impedance measurements. The uncertainties due to these errors are propagated in a metrology framework resulting in the confidence level of the impedance‐based cell classification. Read the full text of the article at 10.1002/batt.202200524

We present an interoperable hierarchical data representation for battery tests, leading to improved scalability of data transmission and enhanced data accessibility and comprehensibility for both human interpretation and machine processing. The hierarchical data format includes the raw trace electrical measurement data, the metrological calibration and uncertainty data, the metadata such as experimental settings, instruments and software versions, as well as post‐processed data such as electrochemical model fit parameters. This data representation allows repetition of the battery test under the exact same conditions such that identical results are achieved within defined error bounds. This is in line with the general F. A. I. R. data approach and provides repeatability and traceability in the battery value chain. As an application of the hierarchical data representation, we show the classification of cells as pass/fail being performed with quantitative confidence levels. We demonstrate the complete workflow of establishing the hierarchical data structure for electrochemical impedance spectroscopy (EIS), starting from metrological traceability of the calibration and uncertainty analysis towards the storage of the structured data as a single integrated file that preserves the hierarchical data format. The structured data file is provided in JSON format in the Zenodo repository, as well as the program scripts to generate and read the JSON EIS files.