In-depth study and analysis of lithium-ion battery states using battery degradation data from electrochemical impedance spectroscopy

Abstract

This thesis focuses on the study and analysis of lithium-ion battery (LIB) degradation mechanisms throughout the battery life. The performance of LIB deteriorates due to chemical and mechanical degradation that occurs during the battery operation affecting the health of the batteries. It is critical to understand degradation mechanisms for enhancing the performance, reliability, and safety of LIBs. This thesis studies the degradation mechanisms and degradation characterization techniques for designing an equivalent circuit model (ECM) of LIB. The thesis highlights the importance of considering the degradation effects into account for different LIB state estimations including state of charge (SOC), state of health (SOH), state of temperature (SOT), and state of power (SOP). The Electrochemical impedance spectroscopy (EIS) technique is a degradation diagnostic tool. The thesis covered the EIS principles, measurement techniques, and impedance data interpretation specific to LIB. A lithium nickel cobalt aluminium oxide cylindrical (NCA) battery is subjected to charge/discharge cycles to study the battery degradation mechanisms. The impedance spectrum from EIS tests is analyzed for ECM circuit parameterization. From the experimental data the degradation parameters are studied, and it is observed that the bulk resistance (𝑅􀯦), charge transfer resistance (𝑅􀯖􀯧 ), and mass transfer resistance (𝑅􀯪) showed an increasing trend with an increase in cycle life. Growth of the solid electrolyte interface (SEI) layer is observed after the 150th cycle with the evolution of two semi-circles in the Nyquist plots emphasizing the use of second-order ECM for LIB performance estimations. The changes in the double-layer capacitance of LIB are studied through the experimental results. The changes in the temperature behaviour of LIB through aging are highlighted and attributed to the changes in the degradation parameters. Furthermore, the thesis proposes an approach for estimating SOC using an adaptive extended Kalman filter (EKF) based on the parameters obtained from EIS. By updating the ECM parameters, the proposed SOC estimation technique can maintain accuracy throughout the battery life, as the ECM parameters are continuously updated based on feedback from the EIS data.