Security-Ensured State of Charge Estimation of Lithium-Ion Batteries Subject to Malicious Attacks

• Published in: IEEE transactions on smart grid Vol. 14; no. 3; pp. 2250 - 2261
• Main Authors: Tian, Engang, Chen, Hui, Wang, Changsong, Wang, Licheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; Computational modeling; Data transmission; Deception; deception attacks; Equivalent circuits; Estimation; Extended Kalman filter; Integrated circuit modeling; Linear matrix inequalities; linear matrix inequality; Lithium; Lithium-ion batteries; nonlinear observer; Open circuit voltage; Parameter identification; Polynomials; Rechargeable batteries; state estimation; State of charge; State of charge (SOC)
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2022.3202811

This paper is concerned with the secure estimation problem of the state of charge (SOC) for Lithium-ion batteries subject to deception attacks. A second-order resistor-capacitor (RC) equivalent circuit model (ECM), whose parameters are identified by means of the extended Kalman filter (EKF) algorithm, is applied to characterize the static and dynamic behaviors of a Lithium-ion battery. With the aid of the polynomial-function fitting technique, the nonlinear relationship between the open circuit voltage (OCV) and SOC of the ECM is established. The deception attacks launched by the malicious adversary are taken into consideration during the data transmission from the voltage/current sensor to the estimator. By employing the Lyapunov stability theory, a second-order RC-ECM-based nonlinear observer is designed in the presence of deception attacks. In addition, sufficient conditions are derived to guarantee the uniform boundedness of the estimation error and the estimator gain matrix is implicitly parameterized by solving finite linear matrix inequalities (LMIs) with certain constraints. Finally, the effectiveness of the proposed estimation method is verified through the experiments under the Dynamic Stress Test (DST) and Federal Urban Driving Schedule (FUDS) driving cycle.