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

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

doi:10.1016/j.jpowsour.2014.06.099

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

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

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

27 Scopus citations

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 language	English (US)
Pages (from-to)	758-764
Number of pages	7
Journal	Journal of Power Sources
Volume	268
DOIs	https://doi.org/10.1016/j.jpowsour.2014.06.099
State	Published - Dec 5 2014

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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

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10.1016/j.jpowsour.2014.06.099

Cite this

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title = "Real-time estimation of lead-acid battery parameters: A dynamic data-driven approach",

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.",

author = "Yue Li and Zheng Shen and Asok Ray and Rahn, \{Christopher D.\}",

year = "2014",

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doi = "10.1016/j.jpowsour.2014.06.099",

language = "English (US)",

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T1 - Real-time estimation of lead-acid battery parameters

T2 - A dynamic data-driven approach

AU - Li, Yue

AU - Shen, Zheng

AU - Ray, Asok

AU - Rahn, Christopher D.

PY - 2014/12/5

Y1 - 2014/12/5

N2 - 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.

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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UR - https://www.scopus.com/pages/publications/84904335572#tab=citedBy

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SP - 758

EP - 764

JO - Journal of Power Sources

JF - Journal of Power Sources

ER -

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

Abstract

UN SDGs

All Science Journal Classification (ASJC) codes

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