A health assessment framework of lithium-ion batteries for cyber defense

被引:26
作者
Hong, Sheng [1 ]
Zeng, Yining [2 ]
机构
[1] Beihang Univ, Sch Cyber Sci & Technol, 37 Xue Yuan Rd, Beijing 100191, Peoples R China
[2] Nanchang Univ, Nanchang, Jiangxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Health assessment; Locally linear embedding; Isomap; Lithium-ion battery;
D O I
10.1016/j.asoc.2020.107067
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Due to the existence of cascading failures in the vehicle system, the vehicle telematics system would cause the failure of lithium-ion batteries under the threats of cyber-attacks. This paper presents a new health assessment framework for lithium-ion batteries to construct an efficient defense mechanism. The framework could mitigate the effects of variable operation conditions to the evaluating process. Specifically, it extracts the geometrical characteristics of charging and discharging curves of the lithium-ion batteries. Furthermore, it adopts a multiple dimensionality reduction method to assess the state of health of lithium-ion batteries. Moreover, the long short-term memory network is introduced to predict the state of health. Finally, the example illustrates the effectiveness of the proposed framework. (C) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页数:8
相关论文
共 36 条
[1]   Quantifying Colocalization by Correlation: The Pearson Correlation Coefficient is Superior to the Mander's Overlap Coefficient [J].
Adler, Jeremy ;
Parmryd, Ingela .
CYTOMETRY PART A, 2010, 77A (08) :733-742
[2]   Extending the Classical Multidimensional Scaling Algorithm Given Partial Pairwise Distance Measurements [J].
Amar, Alon ;
Wang, Yiyin ;
Leus, Geert .
IEEE SIGNAL PROCESSING LETTERS, 2010, 17 (05) :473-476
[3]   Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries [J].
Andre, Dave ;
Appel, Christian ;
Soczka-Guth, Thomas ;
Sauer, Dirk Uwe .
JOURNAL OF POWER SOURCES, 2013, 224 :20-27
[4]  
Bao T., 2017, J SHANDONG U ENG SCI, V47, P157
[5]   An LSTM-based aggregated model for air pollution forecasting [J].
Chang, Yue-Shan ;
Chiao, Hsin-Ta ;
Abimannan, Satheesh ;
Huang, Yo-Ping ;
Tsai, Yi-Ting ;
Lin, Kuan-Ming .
ATMOSPHERIC POLLUTION RESEARCH, 2020, 11 (08) :1451-1463
[6]   A Supplement to Multidimensional Scaling Framework for Mobile Location: A Unified View [J].
Chen, Zhang-Xin ;
Wei, He-Wen ;
Wan, Qun ;
Ye, Shang-Fu ;
Yang, Wan-Lin .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2009, 57 (05) :2030-2034
[7]   Deep learning with long short-term memory networks for financial market predictions [J].
Fischer, Thomas ;
Krauss, Christopher .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2018, 270 (02) :654-669
[8]   Model-Based Lithium-Ion Battery Resistance Estimation From Electric Vehicle Operating Data [J].
Giordano, Giuseppe ;
Klass, Verena ;
Behm, Marten ;
Lindbergh, Goran ;
Sjoberg, Jonas .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2018, 67 (05) :3720-3728
[9]   Vehicle energy system active defense: A health assessment of lithium-ion batteries [J].
Hong, Sheng ;
Yue, Tianyu ;
Liu, Hao .
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2022, 37 (12) :10081-10099
[10]   Cascading failure and recovery of spatially interdependent networks [J].
Hong, Sheng ;
Zhu, Juxing ;
Braunstein, Lidia A. ;
Zhao, Tingdi ;
You, Qiuju .
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2017,