Fast Impedance Measurement of Li-Ion Battery Using Discrete Random Binary Excitation and Wavelet Transform

Abstract

Electrochemical impedance spectroscopy (EIS) is a widely used means for characterization of the dynamics of batteries and electrochemical energy conversion systems in general. EIS is useful for on-line condition monitoring since it contains valuable information on the internal condition of the batteries. The conventional approach to the EIS of batteries relies on their successive excitation with mono-component sinusoidal signals at the pre-defined frequencies. When inferring about the battery's state-of-health, the low-frequency part of the impedance characteristic is of particular interest. Excitation in the low-frequency region can take an excessively long time. Moreover, maintaining stable experimental conditions over long time intervals i na way that the external disturbances will not affect the estimated impedance, might be demanding, especially in the in-field applications. To alleviate the said limitations, and to minimally intrude the process operation, in this paper we apply broadband electrochemical impedance spectroscopy based on a discrete random binary sequence for perturbation of the battery input. The impedance is evaluated by processing voltage and current signals with continuous Morse wavelet transform. The main contributions of the paper refer to (i) the accurate evaluation of the impedance spectra from µHz to kHz range with high-frequency resolution (more than 200 points per decade) and (ii) the evaluation of the uncertainty region of the impedance characteristic. The entire characterisation takes only a fraction of the time required by the classical sine-based electrochemical impedance spectroscopy. The algorithm is successfully demonstrated on a commercial Li-ion battery, which, together with the all datasets are available for download at https://repo.ijs.si/gnusev/supplementary_material.git.

Applications of the Generalized Morse Wavelets: A Review

“…This lower-bound is achieved by Gaussian functions of the form shown in (12) for α > 0, and ρ is a constant [33].…”

“…The analysis of power sources, such as battery state health, has also been studied through the CWT and GMWs. For example, Nusev et al [12] used a discrete random binary sequence-based broadband electrochemical impedance spectroscopy to measure changes in the battery input to make less intrusive the operation process of the battery and monitor its condition. The impedance was analyzed by processing current and voltage signals through the CWT and GMWs.…”

“…For example, biomedical signal processing has been one of the fields that have adopted extensively time-frequency analysis techniques for health monitoring applications or to perform diagnosis [9], [10]. Furthermore, in the case of power systems, time-frequency methods have also been used to monitor or detect faults in electrical components such as batteries, motors, capacitors, or power inverters [11], [12].…”

Capacity State-of-Health Estimation of Electric Vehicle Batteries Using Machine Learning and Impedance Measurements

“…In [4], the authors implemented two types of pseudo-random sequences, namely binary and ternary, to significantly reduce the execution time of the impedance measurements. A similar approach was applied in [5], but using the continuous Morse wavelet transform instead of the fast Fourier transform. Psuedo-random sequences were also employed in [6] with a focus on various implementation aspects in EV.…”

A Fast Impedance Measurement Method for Lithium-Ion Battery Using Power Spectrum Property

“…To alleviate the above issues, great efforts have been made on developing the excitation signals with a series of frequency superposition and related data processing methods [19], [20]. Recently, multi-steps [21], multi-sines [22]- [25], and Pseudo Random Sequence (PRS) [8], [19], [26]- [28], etc. have been investigated for battery EIS measurement.…”