Real-time estimation of lead-acid battery parameters: A dynamic data-driven approach

Yue Li, Zheng Shen, Asok Ray, Christopher D. Rahn

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

This short paper presents a recently reported dynamic data-driven method, Symbolic Dynamic Filtering (SDF), for real-time estimation of the state-of-health (SOH) and state-of-charge (SOC) in lead-acid batteries, as an alternative to model-based analysis techniques. In particular, SOC estimation relies on a k-NN regression algorithm while SOH estimation is obtained from the divergence between extracted features. The results show that the proposed data-driven method successfully distinguishes battery voltage responses under different SOC and SOH situations.

Original languageEnglish (US)
Pages (from-to)758-764
Number of pages7
JournalJournal of Power Sources
Volume268
DOIs
StatePublished - Dec 5 2014

Fingerprint

lead acid batteries
Lead acid batteries
health
Health
electric batteries
regression analysis
divergence
Electric potential
electric potential

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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Real-time estimation of lead-acid battery parameters : A dynamic data-driven approach. / Li, Yue; Shen, Zheng; Ray, Asok; Rahn, Christopher D.

In: Journal of Power Sources, Vol. 268, 05.12.2014, p. 758-764.

Research output: Contribution to journalArticle

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AU - Ray, Asok

AU - Rahn, Christopher D.

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AB - This short paper presents a recently reported dynamic data-driven method, Symbolic Dynamic Filtering (SDF), for real-time estimation of the state-of-health (SOH) and state-of-charge (SOC) in lead-acid batteries, as an alternative to model-based analysis techniques. In particular, SOC estimation relies on a k-NN regression algorithm while SOH estimation is obtained from the divergence between extracted features. The results show that the proposed data-driven method successfully distinguishes battery voltage responses under different SOC and SOH situations.

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