Secure Estimation Against Malicious Attacks for Lithium-Ion Batteries Under Cloud Environments

• Published in: IEEE transactions on circuits and systems. I, Regular papers Vol. 69; no. 10; pp. 4237 - 4247
• Main Authors: Wang, Licheng, Tian, Engang, Wang, Changsong, Liu, Shuai
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: attack detection; Batteries; Data transmission; Equivalent circuits; Estimation; Integrated circuit modeling; Kalman filter; Kalman filters; Lithium; Lithium-ion batteries; malicious attacks; Parameter identification; Real-time systems; Rechargeable batteries; secure remote estimation; State of charge; Upper bounds
• ISSN: 1549-8328; 1558-0806
• DOI: 10.1109/TCSI.2022.3187725

This paper is concerned with the secure estimation problem for the state of charge of Lithium-ion batteries subject to malicious attacks during the data transmission from sensors to cloud-based battery management system terminal. First, the second-order resistance-capacitance equivalent circuit model, whose parameters are identified by Kalman filter in an off-line manner, is introduced to describe the internal dynamics of lithium-ion batteries. Then, by applying the <inline-formula> <tex-math notation="LaTeX">\chi ^{2} </tex-math></inline-formula> detection mechanism, real-time malicious attacks are first detected and then a secure estimator is designed to suppress the influence of attacks on the estimation performance. An upper bound of the filtering error covariance is determined by solving certain coupled Riccati-like equations, and the filter parameter is obtained by minimizing such an upper bound at each time step. Finally, the validity of the proposed attack detection approach and the effectiveness of the developed estimation scheme are verified by experiment results under Federal Urban Driving Schedule condition.