Abstract
Since, batteries have a limited lifetime, repeated charge and discharge cycles quickly deteriorate the electrochemical properties of a battery. With the reduced capacity and several other changes in the state of health of a battery, the electronic device under operation might malfunction. The failure of electronic device during the operation can cause serious repercussions to the users in certain applications. This research was aimed to provide upgrade on Electrochemical Impedance Spectroscopy (EIS) technology to decipher the electrolytic properties and electrochemical health of the battery. The focus of this research was to design a test bet to gather experimental data of EIS scans of batteries with varying State of Health. Based on the footprints of scans, a state of health classification algorithm was proposed which categorized batteries according to the corresponding health of the battery. Tests were performed on hardware prototype to validate the designed algorithm that showed State of Health estimation accuracy of almost 90%. The main contribution of this project to existing EIS technology is the eradication of the requirement of battery modeling and parameter estimations from Nyquist plot to find the state of health of a battery (the remaining capacity of the battery). The proposed method simplifies the computational algorithm and reduces the processing time for rapid battery tester.
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State of Health Estimation of Li-Ion batteries using Electrochemical Impedance Spectroscopy (pdf)
