Identification of battery parameters via symbolic input-output analysis

A dynamic data-driven approach

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

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

1 Citation (Scopus)

Abstract

This paper presents real-time parameter identification in battery systems as a paradigm of dynamic data-driven application systems (DDDAS). In the proposed method, symbol sequences are generated by partitioning (finite-length) time series data of synchronized input-output (i.e., 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 to extract pertinent features from the statistics of time series as state probability vectors. The proposed method has been validated on (approximately periodic) experimental data of a lead-acid battery for real-time identification of its pertinent parameters: State-of-Charge (SOC) and State-of-Health (SOH). The results of experimentation show that the analysis of input-output-pair data exceeds the performance of output-only data analysis.

Original languageEnglish (US)
Title of host publicationACC 2015 - 2015 American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5200-5205
Number of pages6
Volume2015-July
ISBN (Electronic)9781479986842
DOIs
StatePublished - Jan 1 2015
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: Jul 1 2015Jul 3 2015

Other

Other2015 American Control Conference, ACC 2015
CountryUnited States
CityChicago
Period7/1/157/3/15

Fingerprint

Time series
Lead acid batteries
Finite automata
Identification (control systems)
Health
Statistics
Electric potential

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Li, Y., Ray, A., Chattopadhyay, P., & Rahn, C. D. (2015). Identification of battery parameters via symbolic input-output analysis: A dynamic data-driven approach. In ACC 2015 - 2015 American Control Conference (Vol. 2015-July, pp. 5200-5205). [7172151] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2015.7172151
Li, Yue ; Ray, Asok ; Chattopadhyay, Pritthi ; Rahn, Christopher D. / Identification of battery parameters via symbolic input-output analysis : A dynamic data-driven approach. ACC 2015 - 2015 American Control Conference. Vol. 2015-July Institute of Electrical and Electronics Engineers Inc., 2015. pp. 5200-5205
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Li, Y, Ray, A, Chattopadhyay, P & Rahn, CD 2015, Identification of battery parameters via symbolic input-output analysis: A dynamic data-driven approach. in ACC 2015 - 2015 American Control Conference. vol. 2015-July, 7172151, Institute of Electrical and Electronics Engineers Inc., pp. 5200-5205, 2015 American Control Conference, ACC 2015, Chicago, United States, 7/1/15. https://doi.org/10.1109/ACC.2015.7172151

Identification of battery parameters via symbolic input-output analysis : A dynamic data-driven approach. / Li, Yue; Ray, Asok; Chattopadhyay, Pritthi; Rahn, Christopher D.

ACC 2015 - 2015 American Control Conference. Vol. 2015-July Institute of Electrical and Electronics Engineers Inc., 2015. p. 5200-5205 7172151.

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

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Li Y, Ray A, Chattopadhyay P, Rahn CD. Identification of battery parameters via symbolic input-output analysis: A dynamic data-driven approach. In ACC 2015 - 2015 American Control Conference. Vol. 2015-July. Institute of Electrical and Electronics Engineers Inc. 2015. p. 5200-5205. 7172151 https://doi.org/10.1109/ACC.2015.7172151