| Title |
Fault Diagnosis of Lithium-Ion Batteries using EIS-Derived Multi-Channel GAF-DTW Images and SCNN |
| Authors |
Jaea Lee ; Eunjin Kang ; Minwoo Song ; Jonghoon Kim |
| DOI |
https://doi.org/10.6113/TKPE.2026.31.1.49 |
| ISSN |
1229-2214(pISSN), 2288-6281(eISSN) |
| Keywords |
Lithium-ion battery; EIS(Electrochemical impedance spectroscopy); GAF(Gaussian angular field); DTW(Dynamic time warping); Image transformation conversion |
| Abstract |
Electrochemical impedance spectroscopy (EIS) separates physicochemical effects across frequencies and enables nonintrusive diagnosis. However, one-dimensional spectra and equivalent-circuit parameters are insufficient for early anomaly detection under high-temperature operation. Cyclewise EIS residuals relative to a reference are derived and encoded into two complementary images. Gramian angular field (GAF) capturing the global angular structure and a dynamic time warping (DTW) cumulative cost matrix encoding nonlinear shape and timing differences. These images are fused as a multi-channel input to a weight-sharing Siamese convolutional neural network (SCNN), which determines anomalies from the learned similarity score. Experiments under ambient and high-temperature conditions show that the multi-channel image representation detects anomalies more reliably than raw data or single-channel images. Combining angle-based and distance-based information improves the sensitivity to subtle degradation and supports timely risk identification in battery management systems (BMSs). |