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
N1 - Publisher Copyright:
© 2023, Beijing Paike Culture Commu. Co., Ltd.
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 - http://www.scopus.com/inward/record.url?scp=85161184542&partnerID=8YFLogxK
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 -
