Regeneration of Lithium-ion Battery Impedance
The use of Electrochemical Impedance Spectroscopy on rechargeable Lithium-ion battery characterization is an extensively recognized non-destructive procedure for both in-situ and ex-situ analyses. In an impedance measurement for a rechargeable battery, the oscillating current with an accompanying phase angle is the response for a potential perturbation. The proper evaluation of phase angle as a crucial impedance parameter, provides critical understanding of the status of the battery. Although fast and simple, impedance data is difficult to interpret. Using a novel data-centric Machine Learning framework (co-modelling) we demonstrate how to impute experimental data quickly, precisely, and inexpensively that agrees with wholly experimentally generated data. In particular, we predict the phase angle by training emprical impedance data. This approach demonstrates a potentially burgeoning field of Machine Learning experimental data imputation and the consequence of faster diagnostic and study of batteries.