A case study on battery life prediction using particle filtering

Yinjiao Xing; Eden W. M. Ma; K. L. Tsui; Michael Pecht

doi:10.1109/PHM.2012.6228847

A case study on battery life prediction using particle filtering

Yinjiao Xing, Eden W. M. Ma, K. L. Tsui, Michael Pecht^*

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

21 Citations (Scopus)

Abstract

Batteries play a critical role for the reliability of battery-powered systems. The prognostics in batteries provide warning to the advent of failure, which requires timely maintenance and replacement of batteries. This paper reviews current research on battery degradation models and focuses on the online implementation of prognostic algorithms. The particle filtering approach is utilized to track battery performance based on two degradation models that are highly efficient for online applications. An experimental demonstration of this method is provided. Through a comparison of the prognostic results, the problems of the models and the algorithm are discussed. © 2012 IEEE.

Original language	English
Title of host publication	Proceedings of IEEE 2012 Prognostics and System Health Management Conference, PHM-2012
DOIs	https://doi.org/10.1109/PHM.2012.6228847
Publication status	Published - 2012
Event	2012 3rd Annual IEEE Prognostics and System Health Management Conference, PHM-2012 - Beijing, China Duration: 23 May 2012 → 25 May 2012

Conference

Conference	2012 3rd Annual IEEE Prognostics and System Health Management Conference, PHM-2012
Place	China
City	Beijing
Period	23/05/12 → 25/05/12

Research Keywords

degradation model
lithium-ion battery
particle filtering
prognostics
RUL
SOH

UN SDGs

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

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10.1109/PHM.2012.6228847

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title = "A case study on battery life prediction using particle filtering",

abstract = "Batteries play a critical role for the reliability of battery-powered systems. The prognostics in batteries provide warning to the advent of failure, which requires timely maintenance and replacement of batteries. This paper reviews current research on battery degradation models and focuses on the online implementation of prognostic algorithms. The particle filtering approach is utilized to track battery performance based on two degradation models that are highly efficient for online applications. An experimental demonstration of this method is provided. Through a comparison of the prognostic results, the problems of the models and the algorithm are discussed. {\textcopyright} 2012 IEEE.",

keywords = "degradation model, lithium-ion battery, particle filtering, prognostics, RUL, SOH",

author = "Yinjiao Xing and Ma, {Eden W. M.} and Tsui, {K. L.} and Michael Pecht",

year = "2012",

doi = "10.1109/PHM.2012.6228847",

language = "English",

booktitle = "Proceedings of IEEE 2012 Prognostics and System Health Management Conference, PHM-2012",

note = "2012 3rd Annual IEEE Prognostics and System Health Management Conference, PHM-2012 ; Conference date: 23-05-2012 Through 25-05-2012",

}

A case study on battery life prediction using particle filtering. / Xing, Yinjiao; Ma, Eden W. M.; Tsui, K. L. et al.
Proceedings of IEEE 2012 Prognostics and System Health Management Conference, PHM-2012. 2012. 6228847.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A case study on battery life prediction using particle filtering

AU - Xing, Yinjiao

AU - Ma, Eden W. M.

AU - Tsui, K. L.

AU - Pecht, Michael

PY - 2012

Y1 - 2012

N2 - Batteries play a critical role for the reliability of battery-powered systems. The prognostics in batteries provide warning to the advent of failure, which requires timely maintenance and replacement of batteries. This paper reviews current research on battery degradation models and focuses on the online implementation of prognostic algorithms. The particle filtering approach is utilized to track battery performance based on two degradation models that are highly efficient for online applications. An experimental demonstration of this method is provided. Through a comparison of the prognostic results, the problems of the models and the algorithm are discussed. © 2012 IEEE.

AB - Batteries play a critical role for the reliability of battery-powered systems. The prognostics in batteries provide warning to the advent of failure, which requires timely maintenance and replacement of batteries. This paper reviews current research on battery degradation models and focuses on the online implementation of prognostic algorithms. The particle filtering approach is utilized to track battery performance based on two degradation models that are highly efficient for online applications. An experimental demonstration of this method is provided. Through a comparison of the prognostic results, the problems of the models and the algorithm are discussed. © 2012 IEEE.

KW - degradation model

KW - lithium-ion battery

KW - particle filtering

KW - prognostics

KW - RUL

KW - SOH

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84866145048&origin=recordpage

U2 - 10.1109/PHM.2012.6228847

DO - 10.1109/PHM.2012.6228847

M3 - RGC 32 - Refereed conference paper (with host publication)

BT - Proceedings of IEEE 2012 Prognostics and System Health Management Conference, PHM-2012

T2 - 2012 3rd Annual IEEE Prognostics and System Health Management Conference, PHM-2012

Y2 - 23 May 2012 through 25 May 2012

ER -

A case study on battery life prediction using particle filtering

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Research Keywords

UN SDGs

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