By Hassan Shabbir, William Dunford and Tina Shoa
Research Paper
Abstract
Since batteries have a limited lifetime, repeated charge and discharge cycles deteriorate their electrochemical properties. With reduced capacity and other changes in state of health, an electronic device may malfunction during operation and cause serious repercussions in critical applications. This research upgrades Electrochemical Impedance Spectroscopy (EIS) technology to decipher the electrolytic properties and electrochemical health of a battery. A test bed gathered EIS scans from batteries with varying states of health. Based on those scan footprints, a classification algorithm categorized batteries according to their health. Hardware prototype tests showed state-of-health estimation accuracy of almost 90%. The proposed method removes the need for battery modeling and parameter estimation from a Nyquist plot, simplifying the computational algorithm and reducing processing time for rapid battery testing.