Dynamic battery remaining useful life estimation: An on-line data-driven approach

Jianbao Zhou; Datong Liu; Yu Peng; Xiyuan Peng

doi:10.1109/I2MTC.2012.6229280

Dynamic battery remaining useful life estimation: An on-line data-driven approach

Jianbao Zhou^*
, Datong Liu
, Yu Peng
, Xiyuan Peng

^*Corresponding author for this work

School of Electrical Engineering and Automation, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

47 Scopus citations

Abstract

Performance degradation and remaining useful life (RUL) estimation for lithium-ion battery has broad and practical applications in almost all industrial fields. The model-based prognostics is so complicated, moreover, they are not suitable for on-line application since that more parameters and modeling information should be obtained in advance. An on-line data-driven battery RUL prediction approach based on Online Support Vector Regression (Online SVR) is proposed. With Online SVR algorithm, the lithium-ion battery monitoring data series can be forecasted precisely, on the other hand, an ensemble approach is adopted to realize combined prediction with multi-models containing off-line and on-line algorithms to achieve better prediction capacity. Experimental results with the NASA battery data show that the proposed method can effectively predict the RUL of lithium battery.

Original language	English
Title of host publication	2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings
Pages	2196-2199
Number of pages	4
DOIs	https://doi.org/10.1109/I2MTC.2012.6229280
State	Published - 2012
Externally published	Yes
Event	2012 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2012 - Graz, Austria Duration: 13 May 2012 → 16 May 2012

Publication series

Name	2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings

Conference

Conference	2012 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2012
Country/Territory	Austria
City	Graz
Period	13/05/12 → 16/05/12

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Data-driven
Lithium-ion battery
On-line prediction
Prognostics and Heath Management
Remaining Useful Life

Access to Document

10.1109/I2MTC.2012.6229280

Cite this

Zhou, J., Liu, D., Peng, Y., & Peng, X. (2012). Dynamic battery remaining useful life estimation: An on-line data-driven approach. In 2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings (pp. 2196-2199). Article 6229280 (2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings). https://doi.org/10.1109/I2MTC.2012.6229280

@inproceedings{6c5ada2b54aa415f987c9c76b81ea029,

title = "Dynamic battery remaining useful life estimation: An on-line data-driven approach",

abstract = "Performance degradation and remaining useful life (RUL) estimation for lithium-ion battery has broad and practical applications in almost all industrial fields. The model-based prognostics is so complicated, moreover, they are not suitable for on-line application since that more parameters and modeling information should be obtained in advance. An on-line data-driven battery RUL prediction approach based on Online Support Vector Regression (Online SVR) is proposed. With Online SVR algorithm, the lithium-ion battery monitoring data series can be forecasted precisely, on the other hand, an ensemble approach is adopted to realize combined prediction with multi-models containing off-line and on-line algorithms to achieve better prediction capacity. Experimental results with the NASA battery data show that the proposed method can effectively predict the RUL of lithium battery.",

keywords = "Data-driven, Lithium-ion battery, On-line prediction, Prognostics and Heath Management, Remaining Useful Life",

author = "Jianbao Zhou and Datong Liu and Yu Peng and Xiyuan Peng",

year = "2012",

doi = "10.1109/I2MTC.2012.6229280",

language = "英语",

isbn = "9781457717710",

series = "2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings",

pages = "2196--2199",

booktitle = "2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings",

note = "2012 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2012 ; Conference date: 13-05-2012 Through 16-05-2012",

}

Zhou, J, Liu, D , Peng, Y & Peng, X 2012, Dynamic battery remaining useful life estimation: An on-line data-driven approach. in 2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings., 6229280, 2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings, pp. 2196-2199, 2012 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2012, Graz, Austria, 13/05/12. https://doi.org/10.1109/I2MTC.2012.6229280

Dynamic battery remaining useful life estimation: An on-line data-driven approach. / Zhou, Jianbao; Liu, Datong ; Peng, Yu et al.
2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings. 2012. p. 2196-2199 6229280 (2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Dynamic battery remaining useful life estimation

T2 - 2012 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2012

AU - Zhou, Jianbao

AU - Liu, Datong

AU - Peng, Yu

AU - Peng, Xiyuan

PY - 2012

Y1 - 2012

N2 - Performance degradation and remaining useful life (RUL) estimation for lithium-ion battery has broad and practical applications in almost all industrial fields. The model-based prognostics is so complicated, moreover, they are not suitable for on-line application since that more parameters and modeling information should be obtained in advance. An on-line data-driven battery RUL prediction approach based on Online Support Vector Regression (Online SVR) is proposed. With Online SVR algorithm, the lithium-ion battery monitoring data series can be forecasted precisely, on the other hand, an ensemble approach is adopted to realize combined prediction with multi-models containing off-line and on-line algorithms to achieve better prediction capacity. Experimental results with the NASA battery data show that the proposed method can effectively predict the RUL of lithium battery.

AB - Performance degradation and remaining useful life (RUL) estimation for lithium-ion battery has broad and practical applications in almost all industrial fields. The model-based prognostics is so complicated, moreover, they are not suitable for on-line application since that more parameters and modeling information should be obtained in advance. An on-line data-driven battery RUL prediction approach based on Online Support Vector Regression (Online SVR) is proposed. With Online SVR algorithm, the lithium-ion battery monitoring data series can be forecasted precisely, on the other hand, an ensemble approach is adopted to realize combined prediction with multi-models containing off-line and on-line algorithms to achieve better prediction capacity. Experimental results with the NASA battery data show that the proposed method can effectively predict the RUL of lithium battery.

KW - Data-driven

KW - Lithium-ion battery

KW - On-line prediction

KW - Prognostics and Heath Management

KW - Remaining Useful Life

UR - https://www.scopus.com/pages/publications/84864253846

U2 - 10.1109/I2MTC.2012.6229280

DO - 10.1109/I2MTC.2012.6229280

M3 - 会议稿件

AN - SCOPUS:84864253846

SN - 9781457717710

T3 - 2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings

SP - 2196

EP - 2199

BT - 2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings

Y2 - 13 May 2012 through 16 May 2012

ER -

Zhou J, Liu D , Peng Y , Peng X. Dynamic battery remaining useful life estimation: An on-line data-driven approach. In 2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings. 2012. p. 2196-2199. 6229280. (2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings). doi: 10.1109/I2MTC.2012.6229280

Dynamic battery remaining useful life estimation: An on-line data-driven approach

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this