A Study of Battery EIS Measurement and AI-Based Battery State Classification under Variable Switching Discharge Conditions

Lithium-ion batteries are core components of electric vehicle (EV) and energy storage system (ESS). As their deployment expands, fire incidents have also increased, highlighting the growing importance of state diagnosis technologies. Electrochemical Impedance Spectroscopy (EIS) is a representative method for quantitatively evaluating the degradation state and electrochemical reactions of batteries by analyzing internal impedance characteristics across different frequencies. However, commercial EIS measurement equipment is designed for testing individual cells rather than real-time monitoring, making it difficult to implement in on-site battery systems for real-time data acquisition.<br/>In this paper, we propose a method for repeatedly acquiring online EIS data while integrated with the battery system, implement it into an actual device, and verify the validity of the EIS measurements. Furthermore, we present an artificial intelligence (AI) learning model to utilize the acquired EIS data for battery state classification. By using the measured EIS data as input, we demonstrate that state classification and evaluation are feasible through the proposed model.