This work investigates the interpretability of autoencoder-derived latent spaces for characterizing battery aging dynamics. We propose a dual-path architecture where a primary network reconstructs the healthy voltage response from current inputs, while a secondary variational autoencoder (VAE) branch encodes aging-related deviations into a latent space. The latent variables, decoded into a residual signal, are combined with the healthy pathway output to reconstruct the observed voltage. We focus on an exploratory analysis of the latent space to answer: (1) How do latent dimensions encode aging-specific information? and (2) What relationships exist between latent variables and empirical aging parameters)? Using dimensionality reduction, clustering, and correlation analysis, we demonstrate that the VAE latent space organizes aging signatures into interpretable, low-dimensional structures. This analysis provides a framework for mapping latent representations to physically meaningful aging trends. The approach leverages xAI principles to uncover hidden insights and guide data-driven strategies for battery management.