Online electrochemical impedance spectroscopy measurement for lithium-ion batteries based on real-time operating data of interface converters

Purpose This study aims to develop an online electrochemical impedance spectroscopy (EIS) method for lithium-ion batteries (LIBs) using real-time operational data from interface converters. It addresses limitations of conventional EIS techniques – long measurement times, high costs and laboratory dependency – by enabling rapid, cost-effective impedance extraction during charging/discharging, suitable for real-time battery management systems (BMS). Design/methodology/approach A frequency-modulated square wave current, combining AC/DC components, is injected via a DC–DC converter. Voltage responses are processed using an adaptive Morlet wavelet transform with optimal time-frequency window matching. The method uses entropy minimization principles for wavelet basis optimization, validated experimentally on a custom EIS platform against commercial electrochemical workstations. Findings The proposed method achieves a root mean square error below 5% in mid-to-high frequencies (1–1,000 Hz) compared to laboratory equipment. Measurement time is reduced by 90% (from 20 to 120 s). Low-frequency impedance shifts due to bias current effects are observed, aligning with ion diffusion dynamics. Research limitations/implications Limitations include restricted validation under extreme temperatures and state of charge ranges. The method’s dependency on converter hardware may limit universal applicability. Further studies are needed to address nonlinear battery behaviors and validate scalability across diverse LIB chemistries and configurations. Practical implications This approach enables real-time EIS monitoring in electric vehicles and energy storage systems without hardware modifications. It enhances BMS by providing rapid impedance data for state estimation, fault diagnosis and dynamic adjustments, improving safety and operational efficiency. Originality/value The integration of binary multisine signals with adaptive wavelet transforms for online EIS extraction is novel. The method eliminates lab-grade instruments, leveraging existing converter infrastructure for cost-effective, high-speed impedance measurement. Its entropy-based optimization ensures precision across varying time-domain windows, advancing real-time battery diagnostics.