TY - JOUR
T1 - Identification of the battery state-of-health parameter from input-output pairs of time series data
AU - Li, Yue
AU - Chattopadhyay, Pritthi
AU - Ray, Asok
AU - Rahn, Christopher D.
N1 - Funding Information:
The work reported in this paper has been supported in part by the U.S. Air Force Office of Scientific Research under Grant No. FA9550-12-1-0270 . Any opinions, findings and conclusions or recommendations expressed in this publication are those of the authors.
Publisher Copyright:
© 2015 Elsevier B.V. All rights reserved.
PY - 2015/7/1
Y1 - 2015/7/1
N2 - 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.
AB - 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.
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U2 - 10.1016/j.jpowsour.2015.03.068
DO - 10.1016/j.jpowsour.2015.03.068
M3 - Article
AN - SCOPUS:84925303604
SN - 0378-7753
VL - 285
SP - 235
EP - 246
JO - Journal of Power Sources
JF - Journal of Power Sources
ER -