This paper presents real-time parameter identification in battery systems as a paradigm of dynamic data-driven application systems (DDDAS). In the proposed method, symbol sequences are generated by partitioning (finite-length) time series data of synchronized input-output (i.e., current-voltage) pairs in the respective two-dimensional space. Then, a special class of probabilistic finite state automata (PFSA), called D-Markov machine, is constructed to extract pertinent features from the statistics of time series as state probability vectors. The proposed method has been validated on (approximately periodic) experimental data of a lead-acid battery for real-time identification of its pertinent parameters: State-of-Charge (SOC) and State-of-Health (SOH). The results of experimentation show that the analysis of input-output-pair data exceeds the performance of output-only data analysis.