  This paper proposes a fully unsupervised methodology for the reliable
extraction of latent variables representing the characteristics of lithium-ion
batteries (LIBs) from electrochemical impedance spectroscopy (EIS) data using
information maximizing generative adversarial networks. Meaningful
representations can be obtained from EIS data even when measured with direct
current and without relaxation, which are difficult to express when using
circuit models. The extracted latent variables were investigated as capacity
degradation progressed and were used to estimate the discharge capacity of the
batteries by employing Gaussian process regression. The proposed method was
validated under various conditions of EIS data during charging and discharging.
The results indicate that the proposed model provides more robust capacity
estimations than the direct capacity estimations obtained from EIS. We
demonstrate that the latent variables extracted from the EIS data measured with
direct current and without relaxation reliably represent the degradation
characteristics of LIBs.
