A New Health Assessment Approach of Lithium-Ion Batteries Under Variable Operation Conditions

Sheng Hong; Yining Zeng

doi:10.1007/978-3-030-62460-6_17

A New Health Assessment Approach of Lithium-Ion Batteries Under Variable Operation Conditions

Sheng Hong^*
, Yining Zeng

^*Corresponding author for this work

School of Cyber Science and Technology

Nanchang University

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

Abstract

The monitoring information of the Lithium-ion batteries is influenced by variable operation conditions. Many health assessment approaches acquire the battery monitoring information to assess the battery health status. However, these approaches have poor adaptability under variable operation conditions. This paper presents a new health assessment approach of the Lithium-ion batteries under variable operation conditions. Specifically, it extracts the geometrical characteristics of charging and discharging curves of the lithium-ion batteries. Furthermore, it adapts a multiple dimensionality reduction approach based on the locally linear embedding and Isomap. Moreover, the synthetical correlation coefficient is proposed to evaluate the ability of the method to be immune to variable operation conditions. Finally, the example illustrates the effectiveness of the proposed method.

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	190-200
Number of pages	11
ISBN (Print)	9783030624590
DOIs	https://doi.org/10.1007/978-3-030-62460-6_17
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 assessment
Isomap
Lithium-ion battery
Locally linear embedding

Access to Document

10.1007/978-3-030-62460-6_17

Cite this

Hong, S., & Zeng, Y. (2020). A New Health Assessment Approach of Lithium-Ion Batteries Under Variable Operation Conditions. In X. Chen, H. Yan, Q. Yan, & X. Zhang (Eds.), Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings (pp. 190-200). (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_17

Hong, Sheng ; Zeng, Yining. / A New Health Assessment Approach of Lithium-Ion Batteries Under Variable Operation Conditions. 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. 190-200 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{eef552134ebd4a93847ab00920451cb7,

title = "A New Health Assessment Approach of Lithium-Ion Batteries Under Variable Operation Conditions",

abstract = "The monitoring information of the Lithium-ion batteries is influenced by variable operation conditions. Many health assessment approaches acquire the battery monitoring information to assess the battery health status. However, these approaches have poor adaptability under variable operation conditions. This paper presents a new health assessment approach of the Lithium-ion batteries under variable operation conditions. Specifically, it extracts the geometrical characteristics of charging and discharging curves of the lithium-ion batteries. Furthermore, it adapts a multiple dimensionality reduction approach based on the locally linear embedding and Isomap. Moreover, the synthetical correlation coefficient is proposed to evaluate the ability of the method to be immune to variable operation conditions. Finally, the example illustrates the effectiveness of the proposed method.",

keywords = "Health assessment, Isomap, Lithium-ion battery, Locally linear embedding",

author = "Sheng Hong and Yining Zeng",

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\_17",

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 = "190--200",

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

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

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}

Hong, S & Zeng, Y 2020, A New Health Assessment Approach of Lithium-Ion Batteries Under Variable Operation Conditions. 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. 190-200, 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_17

A New Health Assessment Approach of Lithium-Ion Batteries Under Variable Operation Conditions. / Hong, Sheng; Zeng, Yining.
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. 190-200 (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 - A New Health Assessment Approach of Lithium-Ion Batteries Under Variable Operation Conditions

AU - Hong, Sheng

AU - Zeng, Yining

PY - 2020

Y1 - 2020

N2 - The monitoring information of the Lithium-ion batteries is influenced by variable operation conditions. Many health assessment approaches acquire the battery monitoring information to assess the battery health status. However, these approaches have poor adaptability under variable operation conditions. This paper presents a new health assessment approach of the Lithium-ion batteries under variable operation conditions. Specifically, it extracts the geometrical characteristics of charging and discharging curves of the lithium-ion batteries. Furthermore, it adapts a multiple dimensionality reduction approach based on the locally linear embedding and Isomap. Moreover, the synthetical correlation coefficient is proposed to evaluate the ability of the method to be immune to variable operation conditions. Finally, the example illustrates the effectiveness of the proposed method.

AB - The monitoring information of the Lithium-ion batteries is influenced by variable operation conditions. Many health assessment approaches acquire the battery monitoring information to assess the battery health status. However, these approaches have poor adaptability under variable operation conditions. This paper presents a new health assessment approach of the Lithium-ion batteries under variable operation conditions. Specifically, it extracts the geometrical characteristics of charging and discharging curves of the lithium-ion batteries. Furthermore, it adapts a multiple dimensionality reduction approach based on the locally linear embedding and Isomap. Moreover, the synthetical correlation coefficient is proposed to evaluate the ability of the method to be immune to variable operation conditions. Finally, the example illustrates the effectiveness of the proposed method.

KW - Health assessment

KW - Isomap

KW - Lithium-ion battery

KW - Locally linear embedding

UR - https://www.scopus.com/pages/publications/85097193071

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DO - 10.1007/978-3-030-62460-6_17

M3 - 会议稿件

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SN - 9783030624590

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

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EP - 200

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, Zeng Y. A New Health Assessment Approach of Lithium-Ion Batteries Under Variable Operation Conditions. 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. 190-200. (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_17

A New Health Assessment Approach of Lithium-Ion Batteries Under Variable Operation Conditions

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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