D20.8 - Online Extraction of Electrochemical Impedance Spectroscopy Pattern based on EV Load Profile and Short Time Fourier Transform for Diagnosis of Lithium-Ion Battery Safety

Thursday, March 20, 2025

11:30 AM – 1:30 PM ET

Location: Level One, Hall A3

Technical Dialogue Presenter(s)

Miyoung Lee

Chungnam National University

Presentation Paper

Co-Author(s)

DL

Dongcheol Lee

Chungnam National University
YB

Youngmin Bae

Chungnam National University
JA

Jongchan An

Chungnam National University
GY

Garam Yang

Chungnam National University
JK

Jonghoon Kim

Chungnam National University
WN

Woonki Na

California State University Fresno

Batteries which that convert chemical energy into electrical energy and play an important role in our daily lives and across various industries. The chemical reaction that occurring within a battery during operation of an electric vehicle (EV) depends on environmental conditions such as temperature, load, and humidity, and these variables greatly affect battery performance and life. However, is challenging analyze these electrochemical phenomena with only basic electric signals. In this study, passive electrochemical impedance spectroscopy (PEIS) is derived by applying a signal processing algorithm that converts time series signals into frequency-domain signals. Based on this, we propose to learn a convolutional neural network (CNN) algorithm on the converted PEIS pattern and to effectively diagnose the state of the battery through similarity evaluation.