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 Scopus citations

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
ISBN (Electronic)9781467386821
DOIs
StatePublished - Jul 28 2016
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: Jul 6 2016Jul 8 2016

Publication series

NameProceedings of the American Control Conference
Volume2016-July
ISSN (Print)0743-1619

Other

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

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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 (pp. 4907-4912). [7526130] (Proceedings of the American Control Conference; Vol. 2016-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2016.7526130