Identification of battery parameters via symbolic input-output analysis: A dynamic data-driven approach | IEEE Conference Publication | IEEE Xplore

Identification of battery parameters via symbolic input-output analysis: A dynamic data-driven approach


Abstract:

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...Show More

Abstract:

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.
Date of Conference: 01-03 July 2015
Date Added to IEEE Xplore: 30 July 2015
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Conference Location: Chicago, IL, USA

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