Bayesian impedance deconvolution using timescale distribution for lithium-ion battery state estimation

The distribution of relaxation times (DRT) and distribution of diffusion times (DDT) are widely recognized as effective model-free methods for deconvolving the internal properties of complex electrochemical systems using electrochemical impedance spectroscopy (EIS) data. This study proposes an integrated framework that employs a Bayesian approach to accurately estimate both DRT and DDT and machine learning techniques to enhance capacity estimation, incorporating Gaussian process regression and transformer networks. These methods utilize the inferred DRT and DDT as inputs to predict the discharge capacity. In addition, we perform a peak analysis on the estimated DRT and DDT to extract additional physically meaningful features, which have also proven to be effective inputs for capacity prediction. The applicability of the proposed framework to the EIS experimental data of lithium-ion cells is demonstrated and compared with existing capacity estimation methods. The proposed framework demonstrates a mean absolute error in capacity prediction below 3.03% when using DRT and DDT directly and 2.71% when using extracted features, outperforming existing methods by approximately one to three percentage points. Our Bayesian approach, combined with peak analysis and the integration of machine learning techniques, enables a more robust diagnosis of the internal state of lithium-ion batteries through EIS measurements.