Rapid Parameterization of Lithium-ion Batteries using Frequency Window Identification Technique for On-board Charge Control and Battery Management

According to current industry practice for an onboard charger power electronic control and ingenious battery management in an electric vehicle, primarily an equivalent circuit model and data-driven techniques are used for state estimations such as state of charge, health, power, and temperature. Electrochemical impedance spectroscopy (EIS) is a non-destructive and one of the most accurate methods that has been widely used in laboratory environments for lithium-ion battery characterization and estimating the degradation and aging mechanisms. Typically, performing one EIS test over a frequency ranging from 10 kHz to 10 mHz takes 1 to 2 hours, making the method impractical to use in an onboard battery management system. Time requirement increases exponentially due to the failure of a test and the requirement of a wider frequency band. Therefore, to leverage the attractive benefit of EIS for a practical battery management system and to optimize the requirement of test time a novel approach to EIS frequency window identification is proposed in this paper. Through the experimental data and analysis, it is evident that the entire EIS spectrum can be divided into small frequency windows and the proposed frequency window could infer sufficient information to depict battery aging and degradation profile.