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
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

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 (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
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. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 4907-4912 (Proceedings of the American Control Conference).
@inproceedings{9355b313b6e24384884f0c6c4c4bf1c9,
title = "Real-time identification of state-of-charge in battery systems: Dynamic data-driven estimation with limited window length",
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.",
author = "Pritthi Chattopadhyay and Yue Li and Asok Ray",
year = "2016",
month = "7",
day = "28",
doi = "10.1109/ACC.2016.7526130",
language = "English (US)",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4907--4912",
booktitle = "2016 American Control Conference, ACC 2016",
address = "United States",

}

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., 7526130, Proceedings of the American Control Conference, vol. 2016-July, 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. Institute of Electrical and Electronics Engineers Inc., 2016. p. 4907-4912 7526130 (Proceedings of the American Control Conference; Vol. 2016-July).

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

TY - GEN

T1 - Real-time identification of state-of-charge in battery systems

T2 - Dynamic data-driven estimation with limited window length

AU - Chattopadhyay, Pritthi

AU - Li, Yue

AU - Ray, Asok

PY - 2016/7/28

Y1 - 2016/7/28

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84992163539&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84992163539&partnerID=8YFLogxK

U2 - 10.1109/ACC.2016.7526130

DO - 10.1109/ACC.2016.7526130

M3 - Conference contribution

AN - SCOPUS:84992163539

T3 - Proceedings of the American Control Conference

SP - 4907

EP - 4912

BT - 2016 American Control Conference, ACC 2016

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

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