A Fault Diagnosis Method for Lithium-Ion Battery Based on Kolmogorov Complexity

Shengxu Huang; Ni Lin; Zhaosheng Zhang; Jinghan Zhang

doi:10.1007/978-981-99-1027-4_127

A Fault Diagnosis Method for Lithium-Ion Battery Based on Kolmogorov Complexity

Shengxu Huang
, Ni Lin^*
, Zhaosheng Zhang
, Jinghan Zhang

^*Corresponding author for this work

Beijing Institute of Technology

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

2 Citations (Scopus)

Abstract

Battery is the key component and main trouble source in an electric vehicle (EV). With rapid growth of market share, thermal runaway caused by malfunction of batteries have been frequently reported, making fault diagnosis essential to ensure safety as well as to promote performance. Unfortunately, most of existing fault diagnosis methods focus solely on identification of abnormal single cell while ignoring characteristics of battery macro system, failing to catch error of certain types. In this paper, a novel fault diagnosis method based on Kolmogorov complexity theory is proposed and verified by the real vehicle fault data from China’s national big data monitoring platform for electric vehicles. On top of Kolmogorov complexity, degree of confusion inside a battery pack can be quantitatively described, where analysis results clearly show the correlation between increase of confusion and thermal runaway accident. As a brief conclusion, the proposed method can be an important complement to traditional methods.

Original language	English
Title of host publication	The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022
Editors	Fengchun Sun, Qingxin Yang, Erik Dahlquist, Rui Xiong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	1217-1223
Number of pages	7
ISBN (Print)	9789819910267
DOIs	https://doi.org/10.1007/978-981-99-1027-4_127
Publication status	Published - 2023
Externally published	Yes
Event	5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022 - Virtual, Online Duration: 3 Dec 2022 → 4 Dec 2022

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	1016 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022
City	Virtual, Online
Period	3/12/22 → 4/12/22

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Electric Vehicle
Fault Diagnosis
Kolmogorov Complexity
Lithium Battery

Access to Document

10.1007/978-981-99-1027-4_127

Cite this

Huang, S., Lin, N., Zhang, Z., & Zhang, J. (2023). A Fault Diagnosis Method for Lithium-Ion Battery Based on Kolmogorov Complexity. In F. Sun, Q. Yang, E. Dahlquist, & R. Xiong (Eds.), The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022 (pp. 1217-1223). (Lecture Notes in Electrical Engineering; Vol. 1016 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-1027-4_127

Huang, Shengxu ; Lin, Ni ; Zhang, Zhaosheng et al. / A Fault Diagnosis Method for Lithium-Ion Battery Based on Kolmogorov Complexity. The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022. editor / Fengchun Sun ; Qingxin Yang ; Erik Dahlquist ; Rui Xiong. Springer Science and Business Media Deutschland GmbH, 2023. pp. 1217-1223 (Lecture Notes in Electrical Engineering).

@inproceedings{5d6638a6928e4cbf88d0df269403c2d7,

title = "A Fault Diagnosis Method for Lithium-Ion Battery Based on Kolmogorov Complexity",

abstract = "Battery is the key component and main trouble source in an electric vehicle (EV). With rapid growth of market share, thermal runaway caused by malfunction of batteries have been frequently reported, making fault diagnosis essential to ensure safety as well as to promote performance. Unfortunately, most of existing fault diagnosis methods focus solely on identification of abnormal single cell while ignoring characteristics of battery macro system, failing to catch error of certain types. In this paper, a novel fault diagnosis method based on Kolmogorov complexity theory is proposed and verified by the real vehicle fault data from China{\textquoteright}s national big data monitoring platform for electric vehicles. On top of Kolmogorov complexity, degree of confusion inside a battery pack can be quantitatively described, where analysis results clearly show the correlation between increase of confusion and thermal runaway accident. As a brief conclusion, the proposed method can be an important complement to traditional methods.",

keywords = "Electric Vehicle, Fault Diagnosis, Kolmogorov Complexity, Lithium Battery",

author = "Shengxu Huang and Ni Lin and Zhaosheng Zhang and Jinghan Zhang",

note = "Publisher Copyright: {\textcopyright} 2023, Beijing Paike Culture Commu. Co., Ltd.; 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022 ; Conference date: 03-12-2022 Through 04-12-2022",

year = "2023",

doi = "10.1007/978-981-99-1027-4\_127",

language = "English",

isbn = "9789819910267",

series = "Lecture Notes in Electrical Engineering",

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

pages = "1217--1223",

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booktitle = "The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022",

address = "Germany",

}

Huang, S, Lin, N , Zhang, Z & Zhang, J 2023, A Fault Diagnosis Method for Lithium-Ion Battery Based on Kolmogorov Complexity. in F Sun, Q Yang, E Dahlquist & R Xiong (eds), The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022. Lecture Notes in Electrical Engineering, vol. 1016 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 1217-1223, 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022, Virtual, Online, 3/12/22. https://doi.org/10.1007/978-981-99-1027-4_127

A Fault Diagnosis Method for Lithium-Ion Battery Based on Kolmogorov Complexity. / Huang, Shengxu; Lin, Ni ; Zhang, Zhaosheng et al.
The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022. ed. / Fengchun Sun; Qingxin Yang; Erik Dahlquist; Rui Xiong. Springer Science and Business Media Deutschland GmbH, 2023. p. 1217-1223 (Lecture Notes in Electrical Engineering; Vol. 1016 LNEE).

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

TY - GEN

T1 - A Fault Diagnosis Method for Lithium-Ion Battery Based on Kolmogorov Complexity

AU - Huang, Shengxu

AU - Lin, Ni

AU - Zhang, Zhaosheng

AU - Zhang, Jinghan

PY - 2023

Y1 - 2023

N2 - Battery is the key component and main trouble source in an electric vehicle (EV). With rapid growth of market share, thermal runaway caused by malfunction of batteries have been frequently reported, making fault diagnosis essential to ensure safety as well as to promote performance. Unfortunately, most of existing fault diagnosis methods focus solely on identification of abnormal single cell while ignoring characteristics of battery macro system, failing to catch error of certain types. In this paper, a novel fault diagnosis method based on Kolmogorov complexity theory is proposed and verified by the real vehicle fault data from China’s national big data monitoring platform for electric vehicles. On top of Kolmogorov complexity, degree of confusion inside a battery pack can be quantitatively described, where analysis results clearly show the correlation between increase of confusion and thermal runaway accident. As a brief conclusion, the proposed method can be an important complement to traditional methods.

AB - Battery is the key component and main trouble source in an electric vehicle (EV). With rapid growth of market share, thermal runaway caused by malfunction of batteries have been frequently reported, making fault diagnosis essential to ensure safety as well as to promote performance. Unfortunately, most of existing fault diagnosis methods focus solely on identification of abnormal single cell while ignoring characteristics of battery macro system, failing to catch error of certain types. In this paper, a novel fault diagnosis method based on Kolmogorov complexity theory is proposed and verified by the real vehicle fault data from China’s national big data monitoring platform for electric vehicles. On top of Kolmogorov complexity, degree of confusion inside a battery pack can be quantitatively described, where analysis results clearly show the correlation between increase of confusion and thermal runaway accident. As a brief conclusion, the proposed method can be an important complement to traditional methods.

KW - Electric Vehicle

KW - Fault Diagnosis

KW - Kolmogorov Complexity

KW - Lithium Battery

UR - https://www.scopus.com/pages/publications/85161184542

U2 - 10.1007/978-981-99-1027-4_127

DO - 10.1007/978-981-99-1027-4_127

M3 - Conference contribution

AN - SCOPUS:85161184542

SN - 9789819910267

T3 - Lecture Notes in Electrical Engineering

SP - 1217

EP - 1223

BT - The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022

A2 - Sun, Fengchun

A2 - Yang, Qingxin

A2 - Dahlquist, Erik

A2 - Xiong, Rui

PB - Springer Science and Business Media Deutschland GmbH

T2 - 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022

Y2 - 3 December 2022 through 4 December 2022

ER -

Huang S, Lin N , Zhang Z, Zhang J. A Fault Diagnosis Method for Lithium-Ion Battery Based on Kolmogorov Complexity. In Sun F, Yang Q, Dahlquist E, Xiong R, editors, The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022. Springer Science and Business Media Deutschland GmbH. 2023. p. 1217-1223. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-99-1027-4_127

A Fault Diagnosis Method for Lithium-Ion Battery Based on Kolmogorov Complexity

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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