A new diagnostic indicator for lithium-ion batteries via electrochemical impedance spectroscopy: Harnessing the highest frequency peak in distribution of relaxation times

This paper proposes a new diagnostic indicator derived from the distribution of relaxation times (DRT) analysis of electrochemical impedance spectroscopy (EIS) data for lithium-ion battery state estimation. The indicator is the area of the peak occurring within the highest frequency region of the DRT spectrum, exhibiting correlation with battery internal temperature, state of charge (SOC), and state of health (SOH). By focusing EIS measurements on a narrow high-frequency range and preprocessing data before DRT conversion, the overall time for impedance measurement and DRT calculation is significantly reduced, enabling practical onboard implementation in battery management systems (BMSs). Experimental analysis validates the proposed indicator's effectiveness and trends under varying temperature, SOC, and SOH conditions. A case study compares the proposed DRT-based method with an existing intercept frequency-based approach for internal temperature estimation, demonstrating the DRT method's superior robustness in the presence of noise. This suggests the potential for accurate battery state monitoring in noisy operating environments like electric vehicles. The proposed methodology paves the way for integrating advanced EIS-based diagnostic tools into real-time BMSs for enhanced battery performance and safety.