LiFePO4 battery capacity prediction based on support vector machine

D. Liu; X. Liu; Z. Zhu; J. W. Sun; Y. Zhang

doi:10.1049/cp.2012.1218

LiFePO₄ battery capacity prediction based on support vector machine

D. Liu
, X. Liu
, Z. Zhu
, J. W. Sun
, Y. Zhang

School of Electrical Engineering and Automation, Harbin Institute of Technology
School of Civil Engineering, Harbin Institute of Technology
School of Transportation Science and Engineering, Harbin Institute of Technology

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

1 Scopus citations

Abstract

Capacity character is one of the most important parameters to reflect the basis performance of secondary battery, whose precise measurement is of great significance in the aspects of safety and efficiency of battery usage. The law between capacity, ambient temperature and charge-discharge rate are studied in this paper, and a novel method of capacity prediction is presented to apply to LiFePO₄ battery. Furthermore, battery capacity prediction experiments are respectively carried out for charging and discharging process in case of small sample, and the corresponding relative errors are lesser than 4.45% and 3.72%. In addition, this paper also conducts a subdivided prediction of battery capacity, which elaborates the validity of the proposed method from the global perspective.

Original language	English
Title of host publication	International Conference on Automatic Control and Artificial Intelligence, ACAI 2012
Pages	1302-1305
Number of pages	4
Edition	598 CP
DOIs	https://doi.org/10.1049/cp.2012.1218
State	Published - 2012
Externally published	Yes
Event	International Conference on Automatic Control and Artificial Intelligence, ACAI 2012 - Xiamen, China Duration: 3 Mar 2012 → 5 Mar 2012

Publication series

Name	IET Conference Publications
Number	598 CP
Volume	2012

Conference

Conference	International Conference on Automatic Control and Artificial Intelligence, ACAI 2012
Country/Territory	China
City	Xiamen
Period	3/03/12 → 5/03/12

Keywords

Capacity prediction
LiFePO battery
Support Vector Machine

Access to Document

10.1049/cp.2012.1218

Cite this

@inproceedings{3861c5f1ae1a4d13888d0b6da0554ffe,

title = "LiFePO4 battery capacity prediction based on support vector machine",

abstract = "Capacity character is one of the most important parameters to reflect the basis performance of secondary battery, whose precise measurement is of great significance in the aspects of safety and efficiency of battery usage. The law between capacity, ambient temperature and charge-discharge rate are studied in this paper, and a novel method of capacity prediction is presented to apply to LiFePO4 battery. Furthermore, battery capacity prediction experiments are respectively carried out for charging and discharging process in case of small sample, and the corresponding relative errors are lesser than 4.45\% and 3.72\%. In addition, this paper also conducts a subdivided prediction of battery capacity, which elaborates the validity of the proposed method from the global perspective.",

keywords = "Capacity prediction, LiFePO battery, Support Vector Machine",

author = "D. Liu and X. Liu and Z. Zhu and Sun, \{J. W.\} and Y. Zhang",

year = "2012",

doi = "10.1049/cp.2012.1218",

language = "英语",

isbn = "9781849195379",

series = "IET Conference Publications",

number = "598 CP",

pages = "1302--1305",

booktitle = "International Conference on Automatic Control and Artificial Intelligence, ACAI 2012",

edition = "598 CP",

note = "International Conference on Automatic Control and Artificial Intelligence, ACAI 2012 ; Conference date: 03-03-2012 Through 05-03-2012",

}

Liu, D, Liu, X, Zhu, Z, Sun, JW & Zhang, Y 2012, LiFePO₄ battery capacity prediction based on support vector machine. in International Conference on Automatic Control and Artificial Intelligence, ACAI 2012. 598 CP edn, IET Conference Publications, no. 598 CP, vol. 2012, pp. 1302-1305, International Conference on Automatic Control and Artificial Intelligence, ACAI 2012, Xiamen, China, 3/03/12. https://doi.org/10.1049/cp.2012.1218

LiFePO₄ battery capacity prediction based on support vector machine. / Liu, D.; Liu, X.; Zhu, Z. et al.
International Conference on Automatic Control and Artificial Intelligence, ACAI 2012. 598 CP. ed. 2012. p. 1302-1305 (IET Conference Publications; Vol. 2012, No. 598 CP).

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

TY - GEN

T1 - LiFePO4 battery capacity prediction based on support vector machine

AU - Liu, D.

AU - Liu, X.

AU - Zhu, Z.

AU - Sun, J. W.

AU - Zhang, Y.

PY - 2012

Y1 - 2012

N2 - Capacity character is one of the most important parameters to reflect the basis performance of secondary battery, whose precise measurement is of great significance in the aspects of safety and efficiency of battery usage. The law between capacity, ambient temperature and charge-discharge rate are studied in this paper, and a novel method of capacity prediction is presented to apply to LiFePO4 battery. Furthermore, battery capacity prediction experiments are respectively carried out for charging and discharging process in case of small sample, and the corresponding relative errors are lesser than 4.45% and 3.72%. In addition, this paper also conducts a subdivided prediction of battery capacity, which elaborates the validity of the proposed method from the global perspective.

AB - Capacity character is one of the most important parameters to reflect the basis performance of secondary battery, whose precise measurement is of great significance in the aspects of safety and efficiency of battery usage. The law between capacity, ambient temperature and charge-discharge rate are studied in this paper, and a novel method of capacity prediction is presented to apply to LiFePO4 battery. Furthermore, battery capacity prediction experiments are respectively carried out for charging and discharging process in case of small sample, and the corresponding relative errors are lesser than 4.45% and 3.72%. In addition, this paper also conducts a subdivided prediction of battery capacity, which elaborates the validity of the proposed method from the global perspective.

KW - Capacity prediction

KW - LiFePO battery

KW - Support Vector Machine

UR - https://www.scopus.com/pages/publications/84877778112

U2 - 10.1049/cp.2012.1218

DO - 10.1049/cp.2012.1218

M3 - 会议稿件

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SN - 9781849195379

T3 - IET Conference Publications

SP - 1302

EP - 1305

BT - International Conference on Automatic Control and Artificial Intelligence, ACAI 2012

T2 - International Conference on Automatic Control and Artificial Intelligence, ACAI 2012

Y2 - 3 March 2012 through 5 March 2012

ER -