Identification of the battery state-of-health parameter from input-output pairs of time series data

Yue Li, Pritthi Chattopadhyay, Asok Ray, Christopher D. Rahn

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)235-246
Number of pages12
JournalJournal of Power Sources
Volume285
DOIs
StatePublished - Jul 1 2015

Fingerprint

health
electric batteries
Time series
Health
Finite automata
output
Electric potential
Lead acid batteries
Electrochemistry
electric potential
Identification (control systems)
lead acid batteries
recharging
Physics
life span
Aging of materials
parameter identification
Statistics
electrochemistry
divergence

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Physical and Theoretical Chemistry
  • Electrical and Electronic Engineering

Cite this

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title = "Identification of the battery state-of-health parameter from input-output pairs of time series data",
abstract = "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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Identification of the battery state-of-health parameter from input-output pairs of time series data. / Li, Yue; Chattopadhyay, Pritthi; Ray, Asok; Rahn, Christopher D.

In: Journal of Power Sources, Vol. 285, 01.07.2015, p. 235-246.

Research output: Contribution to journalArticle

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