Health Evaluation of Lithium Ion Battery Based on Weighted Kalman Filter Algorithm

Sheng Hong; Tianyu Yue

doi:10.1007/978-3-030-62460-6_18

Health Evaluation of Lithium Ion Battery Based on Weighted Kalman Filter Algorithm

Sheng Hong^*
, Tianyu Yue

^*Corresponding author for this work

School of Cyber Science and Technology

Beihang University

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

Abstract

With the wide application of lithium ion batteries in various fields, the safety and reliability of lithium ion batteries have been put forward higher requirements, and the health evaluation of lithium ion batteries is very important. In this paper, a new health evaluation method for lithium ion batteries based on weighted kalman filter algorithm is proposed by investigating and analyzing the existing health evaluation methods for lithium ion batteries. Based on the general kalman filter, the weighted kalman filter algorithm was proposed to evaluate the health of lithium ion batteries by constructing the battery SOH double-exponential recession model and the gaussian-type feature correlation mapping model for the health characteristics of lithium ion batteries. Four lithium ion battery data sets provided by NASA were used to simulate and verify the proposed health evaluation method. The verification results show that the health evaluation method of lithium ion battery based on weighted kalman filter proposed in this paper has better evaluation accuracy than the ordinary kalman filter method, with an average percentage error of 0.61%. Moreover, the average absolute percentage error of the health evaluation method for different types of batteries was less than 0.9%, and the method was applicable to all types of lithium ion batteries.

Original language	English
Title of host publication	Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings
Editors	Xiaofeng Chen, Hongyang Yan, Qiben Yan, Xiangliang Zhang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	201-213
Number of pages	13
ISBN (Print)	9783030624590
DOIs	https://doi.org/10.1007/978-3-030-62460-6_18
State	Published - 2020
Event	3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020 - Guangzhou, China Duration: 8 Oct 2020 → 10 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12487 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020
Country/Territory	China
City	Guangzhou
Period	8/10/20 → 10/10/20

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Health evaluation
Lithium ion battery
Weighted kalman filtering

Access to Document

10.1007/978-3-030-62460-6_18

Cite this

Hong, S., & Yue, T. (2020). Health Evaluation of Lithium Ion Battery Based on Weighted Kalman Filter Algorithm. In X. Chen, H. Yan, Q. Yan, & X. Zhang (Eds.), Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings (pp. 201-213). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12487 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-62460-6_18

Hong, Sheng ; Yue, Tianyu. / Health Evaluation of Lithium Ion Battery Based on Weighted Kalman Filter Algorithm. Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. editor / Xiaofeng Chen ; Hongyang Yan ; Qiben Yan ; Xiangliang Zhang. Springer Science and Business Media Deutschland GmbH, 2020. pp. 201-213 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b3eaff5241804c9d95c3d38bd18fe720,

title = "Health Evaluation of Lithium Ion Battery Based on Weighted Kalman Filter Algorithm",

abstract = "With the wide application of lithium ion batteries in various fields, the safety and reliability of lithium ion batteries have been put forward higher requirements, and the health evaluation of lithium ion batteries is very important. In this paper, a new health evaluation method for lithium ion batteries based on weighted kalman filter algorithm is proposed by investigating and analyzing the existing health evaluation methods for lithium ion batteries. Based on the general kalman filter, the weighted kalman filter algorithm was proposed to evaluate the health of lithium ion batteries by constructing the battery SOH double-exponential recession model and the gaussian-type feature correlation mapping model for the health characteristics of lithium ion batteries. Four lithium ion battery data sets provided by NASA were used to simulate and verify the proposed health evaluation method. The verification results show that the health evaluation method of lithium ion battery based on weighted kalman filter proposed in this paper has better evaluation accuracy than the ordinary kalman filter method, with an average percentage error of 0.61\%. Moreover, the average absolute percentage error of the health evaluation method for different types of batteries was less than 0.9\%, and the method was applicable to all types of lithium ion batteries.",

keywords = "Health evaluation, Lithium ion battery, Weighted kalman filtering",

author = "Sheng Hong and Tianyu Yue",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020 ; Conference date: 08-10-2020 Through 10-10-2020",

year = "2020",

doi = "10.1007/978-3-030-62460-6\_18",

language = "英语",

isbn = "9783030624590",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "201--213",

editor = "Xiaofeng Chen and Hongyang Yan and Qiben Yan and Xiangliang Zhang",

booktitle = "Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings",

address = "德国",

}

Hong, S & Yue, T 2020, Health Evaluation of Lithium Ion Battery Based on Weighted Kalman Filter Algorithm. in X Chen, H Yan, Q Yan & X Zhang (eds), Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12487 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 201-213, 3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020, Guangzhou, China, 8/10/20. https://doi.org/10.1007/978-3-030-62460-6_18

Health Evaluation of Lithium Ion Battery Based on Weighted Kalman Filter Algorithm. / Hong, Sheng; Yue, Tianyu.
Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. ed. / Xiaofeng Chen; Hongyang Yan; Qiben Yan; Xiangliang Zhang. Springer Science and Business Media Deutschland GmbH, 2020. p. 201-213 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12487 LNCS).

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

TY - GEN

T1 - Health Evaluation of Lithium Ion Battery Based on Weighted Kalman Filter Algorithm

AU - Hong, Sheng

AU - Yue, Tianyu

PY - 2020

Y1 - 2020

N2 - With the wide application of lithium ion batteries in various fields, the safety and reliability of lithium ion batteries have been put forward higher requirements, and the health evaluation of lithium ion batteries is very important. In this paper, a new health evaluation method for lithium ion batteries based on weighted kalman filter algorithm is proposed by investigating and analyzing the existing health evaluation methods for lithium ion batteries. Based on the general kalman filter, the weighted kalman filter algorithm was proposed to evaluate the health of lithium ion batteries by constructing the battery SOH double-exponential recession model and the gaussian-type feature correlation mapping model for the health characteristics of lithium ion batteries. Four lithium ion battery data sets provided by NASA were used to simulate and verify the proposed health evaluation method. The verification results show that the health evaluation method of lithium ion battery based on weighted kalman filter proposed in this paper has better evaluation accuracy than the ordinary kalman filter method, with an average percentage error of 0.61%. Moreover, the average absolute percentage error of the health evaluation method for different types of batteries was less than 0.9%, and the method was applicable to all types of lithium ion batteries.

AB - With the wide application of lithium ion batteries in various fields, the safety and reliability of lithium ion batteries have been put forward higher requirements, and the health evaluation of lithium ion batteries is very important. In this paper, a new health evaluation method for lithium ion batteries based on weighted kalman filter algorithm is proposed by investigating and analyzing the existing health evaluation methods for lithium ion batteries. Based on the general kalman filter, the weighted kalman filter algorithm was proposed to evaluate the health of lithium ion batteries by constructing the battery SOH double-exponential recession model and the gaussian-type feature correlation mapping model for the health characteristics of lithium ion batteries. Four lithium ion battery data sets provided by NASA were used to simulate and verify the proposed health evaluation method. The verification results show that the health evaluation method of lithium ion battery based on weighted kalman filter proposed in this paper has better evaluation accuracy than the ordinary kalman filter method, with an average percentage error of 0.61%. Moreover, the average absolute percentage error of the health evaluation method for different types of batteries was less than 0.9%, and the method was applicable to all types of lithium ion batteries.

KW - Health evaluation

KW - Lithium ion battery

KW - Weighted kalman filtering

UR - https://www.scopus.com/pages/publications/85097145964

U2 - 10.1007/978-3-030-62460-6_18

DO - 10.1007/978-3-030-62460-6_18

M3 - 会议稿件

AN - SCOPUS:85097145964

SN - 9783030624590

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 201

EP - 213

BT - Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings

A2 - Chen, Xiaofeng

A2 - Yan, Hongyang

A2 - Yan, Qiben

A2 - Zhang, Xiangliang

PB - Springer Science and Business Media Deutschland GmbH

T2 - 3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

Y2 - 8 October 2020 through 10 October 2020

ER -

Hong S, Yue T. Health Evaluation of Lithium Ion Battery Based on Weighted Kalman Filter Algorithm. In Chen X, Yan H, Yan Q, Zhang X, editors, Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 201-213. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-62460-6_18

Health Evaluation of Lithium Ion Battery Based on Weighted Kalman Filter Algorithm

Abstract

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UN SDGs

Keywords

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