Fast acquisition method of battery electrochemical impedance spectra based on impedance fragments

Abstract

Electrochemical impedance spectroscopy (EIS) is often used for battery characterization and monitoring. However, there are still difficulties in acquiring battery impedance spectra in large quantities and wide frequency bands in practical applications. The lower the frequency range of acquisition, the longer the measurement time spent, and the higher the frequency range of measurement, the more expensive the measurement equipment, and these problems seriously limit the wide application of impedance information. To address this problem, this study proposes a method for fast acquisition of electrochemical impedance spectra of lithium-ion batteries based on impedance fragments. Firstly, impedance segments at specific frequencies in the EIS are selected as features based on the distribution of relaxation times (DRT). Then, a Long Short-Term Memory (LSTM) neural network model is used to reconstruct the complete impedance spectrum, and a population optimization algorithm is used to optimize the hyperparameters in the model. The validation results indicate that the reconstructed RMSE error is only 14.26 mΩ, corresponding to a MAPE of 1.65 %, while the measurement time is reduced to 27.01 % of the original measurement time. Finally, the performance of this method in identifying fractional-order model parameters is further discussed.