Electrochemical impedance spectroscopy (EIS) has been a powerful tool for characterization, parameterization, and measurement of ohmic resistance, double layer capacitance, polarization, charge transfer, and diffusion processes in electrochemical and biomedical systems. State-of-charge (SOC) and state-of-health (SOH) of energy storage systems such as batteries, fuel cells, and super-capacitors, are estimated by using EIS. Known as electrical impedance tomography (EIT) in medicine, EIS is a proven technique for medical imaging, neural engineering, medical tissues characterization, and cancer detection.

We study and design EIS circuits, control systems and data analytics. We design multipurpose robust controllable EIS devices, which control the process of excitation signal despite circuit and batteries’ uncertainties using quantitative feedback theory (QFT) and H-infinity. Our EIS devices are capable of running offline and online tests, during charge and discharge for energy storage systems monitoring and diagnostics. Our EIS devices provide the user with an option to apply and study various types of excitation signals based on advanced system identification theories. We developed various system identification and Bayesian inference algorithms for the analysis and modeling of EIS data in both frequency and time domains. We worked on the identification of both fractional and ordinary Randles circuit models, which are widely used in the study of EIS data.