As a paradigm of dynamic data-driven application systems (DDDAS), this paper addresses real-time identification of the State of Health (SOH) parameter over the life span of a battery that is subjected to approximately repeated cycles of discharging/recharging current. In the proposed method, finite-length data of interest are selected via wavelet-based segmentation from the time series of synchronized input–output (i.e., current–voltage) pairs in the respective two-dimensional space. Then, symbol strings are generated by partitioning the selected segments of the input–output time series to construct a special class of probabilistic finite state automata (PFSA), called D-Markov machines. Pertinent features of the statistics of battery dynamics are extracted as the state emission matrices of these PFSA. This real-time method of SOH parameter identification relies on the divergence between extracted features. The underlying concept has been validated on (approximately periodic) experimental data, generated from a commercial-scale lead-acid battery. It is demonstrated by real-time analysis of the acquired current–voltage data on in-situ computational platforms that the proposed method is capable of distinguishing battery current–voltage dynamics at different aging stages, as an alternative to computation-intensive and electrochemistry-dependent analysis via physics-based modeling.