| Title |
Comparison of impedance characteristics under abnormal battery operating conditions using RLS voltage emulation and DRT analysis |
| Authors |
이재혁(Jae-Hyuk Lee) ; 김민혁(Min-Hyeok Kim) ; 이성준(Sung-Jun Lee) ; 이재아(Jae-A Lee) ; 김종훈(Jong-Hoon Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2026.75.9.2116 |
| Keywords |
Abnormal condition emulation; RLS voltage emulation; Electrochemical impedance spectroscopy(EIS); Short-time Fourier transform(STFT); Distribution of relaxation times(DRT) |
| Abstract |
This study proposes a unified methodology to consistently link electrochemical characteristics observed by electrochemical impedance spectroscopy (EIS) under normal and abnormal conditions to operating-condition voltage?current responses and the corresponding estimated impedance and distribution of relaxation times (DRT) features. Using current and voltage signals acquired under normal operation, an electrical equivalent circuit model (EECM) is developed in a Simulink environment, and its internal parameters are identified via recursive least squares (RLS). Parameter change rates derived from comparisons of EIS data measured under normal and abnormal conditions are then applied to the RLS-estimated parameters to simulate time-domain voltage responses representative of abnormal scenarios. Short-time Fourier transform (STFT) is performed on the simulated voltage?current signals to extract frequency components, and passive electrochemical impedance spectroscopy (PEIS)-based impedance is computed as the voltage-to-current ratio at identical frequency components. The resulting impedance is compared in Nyquist form to assess condition-dependent impedance-pattern differences. Furthermore, the PEIS results are transformed into the DRT domain to re-express impedance responses in the time-constant space and to quantitatively compare reaction characteristics under abnormal conditions relative to the normal reference. The proposed procedure bridges conventional EIS analysis and signal-based diagnostics applicable to real operating environments, providing a foundational framework for designing abnormal-state diagnostic indicators and conducting scenario-based evaluations. |