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Employing Real Automotive Driving Data for Electrochemical Impedance Spectroscopy on Lithium-Ion Cells
ISSN: 2167-4191, e-ISSN: 2167-4205
Published April 14, 2015 by SAE International in United States
Citation: Lohmann, N., Haussmann, P., Wesskamp, P., Melbert, J. et al., "Employing Real Automotive Driving Data for Electrochemical Impedance Spectroscopy on Lithium-Ion Cells," SAE Int. J. Alt. Power. 4(2):308-317, 2015, https://doi.org/10.4271/2015-01-1187.
Battery aging is a main concern within hybrid and electrical cars. Determining the current state-of-health (SOH) of the battery on board of a vehicle is still a challenging task. Electrochemical Impedance Spectroscopy (EIS) is an established laboratory method for the characterization of electrochemical energy storages such as Lithium-Ion (Li-Ion) cells. EIS provides a lot of information about electrochemical processes and their change due to aging. Therefore it can be used to estimate the current SOH of a cell. Standard EIS methods require the excitation of the cell with a certain waveform for obtaining the impedance spectrum. This waveform can be a series of monofrequent sinusoidal signals or a time-domain current pulse with a dedicated Fourier spectrum. However, any form of dedicated perturbation is not generally applicable on board of an electric vehicle.
This work presents a new passive spectroscopy method, which obtains the impedance spectrum directly out of real driving data. Results from an automotive 20Ah Li-Ion cell are compared with reference EIS measurements obtained from sinusoidal excitation. The main challenges for on-board measurements are identified, analyzed and their influences are separated. Findings of this analysis are used to optimize the measurement method. Results show good concordance with the spectrum obtained under laboratory conditions. Finally, equivalent resistances are calculated from the spectra and compared with time-domain reference measurements. Good concordance promises a possible implementation into an on-board state estimation algorithm.
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