A comprehensive electrochemical impedance study is conducted on LiNi 0.80 Co 0.15 Al 0.05 O 2 electrode material as a function of state-of-charge and aging. Electrodes are harvested from four commercial batteries with different state-of-health conditions. Odd random phase electrochemical impedance spectroscopy and the symmetric cell approach are applied in this work in order to obtain reliable impedance results. An equivalent electrical circuit model is constructed. The parameters related to the generalized finite-space Warburg element, the CPE element and the charge transfer resistance are further interpreted. Valuable information is obtained and closely linked to the physical phenomena. The charge transfer resistance has been proved to be the most reliable parameter for the estimation of state-of-health.

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Electrochemical impedance spectroscopy (EIS) is a very popular technique to investigate the corrosion behavior of metals and alloys in electrolytes. To interpret the EIS data, an equivalent electrical circuit (EEC) is built and the corrosion behavior can be explained on the basis of the values obtained for each element of the EEC after fitting the impedance data. Hence, the reliability of the interpretation greatly depends on the selection of the EEC for the impedance modeling. In this work, odd random phase electrochemical impedance spectroscopy (ORP-EIS) is used to investigate the corrosion behavior of Zn, Zn-Al and Zn-Al-Mg hot dip coated steel wires in 0.1 M NaCl. ORP-EIS uses a multisine excitation signal to obtain the impedance response of the electrochemical system. It is faster than conventional EIS as well as provides accurate information about linearity, stationarity and noise distortions of the system during the measurement. This is used to optimize the experimental conditions and to evaluate the correctness of the data modeling. The corrosion behavior of the metallic coated steel wires is quantified by EIS modeling. The validity of the proposed EEC is assessed based on the physical phenomena as well as the statistical evaluation of the modeling results.

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