Rapid acquisition of battery impedance across multiple scenarios using DRT analysis

Quick and accurate electrochemical impedance spectroscopy (EIS) measurement has always been challenging. This paper proposes a reliable and fast measurement method, which, based on battery characteristics, utilizes a distribution of relaxation times (DRT) extraction algorithm to construct a characteristic spectrum and map it to a binary sequence. The proposed method for constructing characteristic sequences is applicable to battery measurements in multiple scenarioes. The paper additionally introduces a hybrid reconstruction filtering (HRF) method that amalgamates the DRT reconstruction technique and non-parametric estimation techniques. The filtering method does not require preset parameters, resulting in high accuracy and applicability. Simultaneously, combining the characteristic sequence with HRF ultimately obtains a smooth and complete EIS curve under varying states of charges (SOCs), temperatures, and capacities. The proposed testing framework obtains impedance from 0.1 Hz to 2000 Hz <13.2 s. The overall measurement error for batteries with different internal resistances does not exceed 2 %. This work provides a reliable characteristic optimization sequence for large-scale battery impedance measurements, offering a method that is both simple and highly versatile.