Robust remaining useful life prediction for Li-ion batteries with a naïve Bayesian classifier

S. Y. S. Ng; K. L. Tsui

doi:10.1109/IEEM.2012.6838148

Robust remaining useful life prediction for Li-ion batteries with a naïve Bayesian classifier

S. Y. S. Ng^*
, K. L. Tsui

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

This paper presents an empirical procedure for predicting robust remaining useful life (RUL) using a naïve Bayesian classifier (NBC) with time as the response. The method is illustrated using public data for predicting Li-ion battery RUL to end-of-life (EOL). A battery life prediction is obtained using the capacity values up to the prediction time. The root mean squared error (RMSE) is used for performance evaluation. The predictions over time are compared with the actual time to EOL for each test battery and the RUL is calculated at four time intervals. The prediction performance of the NBC is compared with that of a support vector machine (SVM). The case study shows that the NBC generates competitive prediction performance, even though other factors contributing to Li-ion battery degradation are concealed. This method is also applicable to predicting RUL to end-of-discharge (EOD) and the failure prognostics for other components. © 2012 IEEE.

Original language	English
Title of host publication	IEEE International Conference on Industrial Engineering and Engineering Management
Publisher	IEEE
Pages	2254-2258
ISBN (Print)	9781467329453
DOIs	https://doi.org/10.1109/IEEM.2012.6838148
Publication status	Published - Dec 2012
Event	2012 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2012) - Hong Kong Convention and Exhibition Centre, Hong Kong, China Duration: 10 Dec 2012 → 13 Dec 2012

Publication series

Name	International Conference on Industrial Engineering and Engineering Management IEEM
ISSN (Print)	2157-3611
ISSN (Electronic)	2157-362X

Conference

Conference	2012 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2012)
Place	China
City	Hong Kong
Period	10/12/12 → 13/12/12

UN SDGs

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

SDG 7 Affordable and Clean Energy

Research Keywords

battery
naïve Bayesian classifier
remaining useful life

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10.1109/IEEM.2012.6838148

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@inproceedings{a5a423c01fa649fb8bf8746edb667086,

title = "Robust remaining useful life prediction for Li-ion batteries with a na{\"i}ve Bayesian classifier",

abstract = "This paper presents an empirical procedure for predicting robust remaining useful life (RUL) using a na{\"i}ve Bayesian classifier (NBC) with time as the response. The method is illustrated using public data for predicting Li-ion battery RUL to end-of-life (EOL). A battery life prediction is obtained using the capacity values up to the prediction time. The root mean squared error (RMSE) is used for performance evaluation. The predictions over time are compared with the actual time to EOL for each test battery and the RUL is calculated at four time intervals. The prediction performance of the NBC is compared with that of a support vector machine (SVM). The case study shows that the NBC generates competitive prediction performance, even though other factors contributing to Li-ion battery degradation are concealed. This method is also applicable to predicting RUL to end-of-discharge (EOD) and the failure prognostics for other components. {\textcopyright} 2012 IEEE.",

keywords = "battery, na{\"i}ve Bayesian classifier, remaining useful life",

author = "Ng, \{S. Y. S.\} and Tsui, \{K. L.\}",

year = "2012",

month = dec,

doi = "10.1109/IEEM.2012.6838148",

language = "English",

isbn = "9781467329453",

series = "International Conference on Industrial Engineering and Engineering Management IEEM",

publisher = "IEEE",

pages = "2254--2258",

booktitle = "IEEE International Conference on Industrial Engineering and Engineering Management",

address = "United States",

note = "2012 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2012) ; Conference date: 10-12-2012 Through 13-12-2012",

}

Ng, SYS & Tsui, KL 2012, Robust remaining useful life prediction for Li-ion batteries with a naïve Bayesian classifier. in IEEE International Conference on Industrial Engineering and Engineering Management. International Conference on Industrial Engineering and Engineering Management IEEM, IEEE, pp. 2254-2258, 2012 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2012), Hong Kong, China, 10/12/12. https://doi.org/10.1109/IEEM.2012.6838148

Robust remaining useful life prediction for Li-ion batteries with a naïve Bayesian classifier. / Ng, S. Y. S.; Tsui, K. L.
IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, 2012. p. 2254-2258 (International Conference on Industrial Engineering and Engineering Management IEEM).

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

TY - GEN

T1 - Robust remaining useful life prediction for Li-ion batteries with a naïve Bayesian classifier

AU - Ng, S. Y. S.

AU - Tsui, K. L.

PY - 2012/12

Y1 - 2012/12

N2 - This paper presents an empirical procedure for predicting robust remaining useful life (RUL) using a naïve Bayesian classifier (NBC) with time as the response. The method is illustrated using public data for predicting Li-ion battery RUL to end-of-life (EOL). A battery life prediction is obtained using the capacity values up to the prediction time. The root mean squared error (RMSE) is used for performance evaluation. The predictions over time are compared with the actual time to EOL for each test battery and the RUL is calculated at four time intervals. The prediction performance of the NBC is compared with that of a support vector machine (SVM). The case study shows that the NBC generates competitive prediction performance, even though other factors contributing to Li-ion battery degradation are concealed. This method is also applicable to predicting RUL to end-of-discharge (EOD) and the failure prognostics for other components. © 2012 IEEE.

AB - This paper presents an empirical procedure for predicting robust remaining useful life (RUL) using a naïve Bayesian classifier (NBC) with time as the response. The method is illustrated using public data for predicting Li-ion battery RUL to end-of-life (EOL). A battery life prediction is obtained using the capacity values up to the prediction time. The root mean squared error (RMSE) is used for performance evaluation. The predictions over time are compared with the actual time to EOL for each test battery and the RUL is calculated at four time intervals. The prediction performance of the NBC is compared with that of a support vector machine (SVM). The case study shows that the NBC generates competitive prediction performance, even though other factors contributing to Li-ion battery degradation are concealed. This method is also applicable to predicting RUL to end-of-discharge (EOD) and the failure prognostics for other components. © 2012 IEEE.

KW - battery

KW - naïve Bayesian classifier

KW - remaining useful life

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UR - https://www.scopus.com/pages/publications/84893873966#tab=impact

U2 - 10.1109/IEEM.2012.6838148

DO - 10.1109/IEEM.2012.6838148

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

SN - 9781467329453

T3 - International Conference on Industrial Engineering and Engineering Management IEEM

SP - 2254

EP - 2258

BT - IEEE International Conference on Industrial Engineering and Engineering Management

PB - IEEE

T2 - 2012 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2012)

Y2 - 10 December 2012 through 13 December 2012

ER -

Robust remaining useful life prediction for Li-ion batteries with a naïve Bayesian classifier

Abstract

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