Broadband Impedance Measurement of Lithium-Ion Battery in the Presence of Nonlinear Distortions

Abstract

The impedance of a Lithium-ion (Li-ion) battery has been shown to be a valuable tool in evaluating the battery characteristics such as the state-of-charge (SOC) and state-of-health (SOH). Recent studies have shown impedance-measurement methods based on broadband pseudo-random sequences (PRS) and Fourier techniques. The methods can be efficiently applied in real-time applications where the conventional electrochemical-impedance spectroscopy (EIS) is not well suited to measure the impedance. The techniques based on the PRS are, however, strongly affected by the battery nonlinearities. This paper presents the use of a direct-synthesis ternary (DST) signal to minimize the effect caused by the nonlinearities. In such a signal, the second- and third-order harmonics are suppressed from the signal energy spectrum. As a result, the effect of the second- and third-order nonlinearities are suppressed from the impedance measurements. The impedance measurements are carried out for a nickel manganese cobalt Li-ion battery cell. The performance of the method is compared to the conventional EIS, as well as to other PRS signals which are more prone to battery nonlinearities. The Kronig–Kramers (K–K) transformation test is used to validate the uniqueness of the measured impedance spectra. It is shown that the measurement method based on the DST produces highly accurate impedance measurements under nonlinear distortions of the battery. The method shows a good K–K test behavior indicating that the measured impedance complies well to a linearized equivalent circuit model that can be used for the SOC and SOH estimation of the battery. Due to the good performance, low measurement time, and simplicity of the DST, the method is well suited for practical battery applications.

Second-Life of Lithium-Ion Batteries from Electric Vehicles: Concept, Aging, Testing, and Applications

“…Consequently, there is an opportunity to combine these methods and to optimize them in order to reduce their testing times and to complement the obtained information. This can lead, for example, to the utilization of a reduced charging interval used for capacity/ICA testing [38], followed by a set of pulses, such as directsynthesis-ternary sequence [39], providing EIS-like information in a fraction of the time and without such high demand on hardware. Moreover, the aforementioned markers from the study investigating the effects of first-life history on second-life use can be utilized via machine learning to effectively sort and predict the lifetime trajectories of the tested SLBs.…”