Real-time identification of state-of-charge in battery systems: Dynamic data-driven estimation with limited window length

Pritthi Chattopadhyay, Yue Li, Asok Ray

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    This paper presents a symbolic dynamic method for real-time estimation of battery state-of-charge (SOC). In the proposed method, symbol strings are generated by partitioning (finite-length) time windows of synchronized input-output (e.g., 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 from the symbol strings to extract pertinent features. The SOC estimation is formulated as a sequential estimation scheme with adaptive acceptance of new features to circumvent the problem of having potential outliers. A major challenge is that SOC value is continuously varying during the operation. While modeling and analysis of such time-varying problems is computationally intensive, the data-driven approach requires adequate length of time series data for statistically significant analysis. From these perspectives, a critical aspect is to determine an optimal (or suboptimal) length of the analysis window to make a tradeoff between estimation accuracy and dynamic sensitivity. The proposed method has been validated on experimental data of a commercial-scale lead-acid battery.

    Original languageEnglish (US)
    Title of host publication2016 American Control Conference, ACC 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages4907-4912
    Number of pages6
    Volume2016-July
    ISBN (Electronic)9781467386821
    DOIs
    StatePublished - Jul 28 2016
    Event2016 American Control Conference, ACC 2016 - Boston, United States
    Duration: Jul 6 2016Jul 8 2016

    Other

    Other2016 American Control Conference, ACC 2016
    CountryUnited States
    CityBoston
    Period7/6/167/8/16

    Fingerprint

    Dynamical systems
    Lead acid batteries
    Finite automata
    Time series
    Electric potential

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

    Cite this

    Chattopadhyay, P., Li, Y., & Ray, A. (2016). Real-time identification of state-of-charge in battery systems: Dynamic data-driven estimation with limited window length. In 2016 American Control Conference, ACC 2016 (Vol. 2016-July, pp. 4907-4912). [7526130] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2016.7526130
    Chattopadhyay, Pritthi ; Li, Yue ; Ray, Asok. / Real-time identification of state-of-charge in battery systems : Dynamic data-driven estimation with limited window length. 2016 American Control Conference, ACC 2016. Vol. 2016-July Institute of Electrical and Electronics Engineers Inc., 2016. pp. 4907-4912
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    Chattopadhyay, P, Li, Y & Ray, A 2016, Real-time identification of state-of-charge in battery systems: Dynamic data-driven estimation with limited window length. in 2016 American Control Conference, ACC 2016. vol. 2016-July, 7526130, Institute of Electrical and Electronics Engineers Inc., pp. 4907-4912, 2016 American Control Conference, ACC 2016, Boston, United States, 7/6/16. https://doi.org/10.1109/ACC.2016.7526130

    Real-time identification of state-of-charge in battery systems : Dynamic data-driven estimation with limited window length. / Chattopadhyay, Pritthi; Li, Yue; Ray, Asok.

    2016 American Control Conference, ACC 2016. Vol. 2016-July Institute of Electrical and Electronics Engineers Inc., 2016. p. 4907-4912 7526130.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    Chattopadhyay P, Li Y, Ray A. Real-time identification of state-of-charge in battery systems: Dynamic data-driven estimation with limited window length. In 2016 American Control Conference, ACC 2016. Vol. 2016-July. Institute of Electrical and Electronics Engineers Inc. 2016. p. 4907-4912. 7526130 https://doi.org/10.1109/ACC.2016.7526130