Impedance spectroscopy is a powerful technique and broadly used for battery characterization. In this study, we introduce a novel machine framework we call the duplex (for paired outputs) that constructs a linear ensemble of the best k models. Several impedance spectra of commercial lithium-ion battery coin cells at various states of charge and ambient temperatures are measured sensitively and compared with the spectra predicted by duplex learning modeling. The average difference between overall experimental and duplex estimated impedance data is approximately 3.374 %. The reliable predictions of battery impedance corresponding to a wide range of fre-quency are made over several operating conditions. The duplex can also be applied to different types of batteries at diverse operating conditions. Critical advantages of this approach are the speed of predicting the experimental space (the training/testing time for duplex is minimal), the small amount of data required to build the duplex, its performance compared to conventional machine learning techniques, as a new, integral component to battery management systems. All code/data are made freely available.