Secure state of charge estimation for lithium-ion batteries under deception attacks based on attack-resilient fractional-order extended Kalman filter

• Published in: Journal of energy storage Vol. 95; p. 112438
• Main Authors: Yang, Tong, Li, Yan, Zeng, Yi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2024
• Subjects: Attack-resilient fractional-order extended Kalman filter; Deception attacks; Fractional-order model; Lithium-ion batteries; State of charge; Lithium-ion batteries; State of charge; Deception attacks; Fractional-order model; Attack-resilient fractional-order extended Kalman filter
• ISSN: 2352-152X
• DOI: 10.1016/j.est.2024.112438

Under the framework of Internet of Vehicles, the insecure network environment will cause the cloud battery management system (CBMS) to receive the tampered measurement data, making it difficult to accurately acquire the state of charge (SOC) of lithium-ion batteries in electric vehicles. This paper investigates the secure SOC estimation problem of LIBs when the end-side measurement data is subject to deception attacks under CBMS. Firstly, a second-order fractional-order model is introduced, and the discrete form of dynamic equation is established by Grünwald–Letnikov definition. Subsequently, the nonlinear quantitative relationship between open circuit voltage and SOC at different temperatures is analyzed by the curve fitting method, and the particle swarm optimization algorithm is used to identify the model parameters. Then, an improved linearization method for measurement equation is developed to reduce linearization error, and a randomly generated Bernoulli distribution sequence is used to establish the deception attack model. Based on those, the attack-resilient fractional-order extended Kalman filter is proposed, which can minimize the upper bound of the estimation error covariance and provide reliable SOC estimation under deception attack environments. Lastly, the effectiveness and adaptability of the proposed method are verified under different working conditions and temperatures. •The attack-resilient fractional-order extended Kalman filter (ARFOEKF) is proposed for secure SOC estimation under deception attacks.•The upper bound of the estimation error covariance is derived, and the filter gain is obtained by minimizing this upper bound.•The linearization measurement equation process of FOEKF is improved, and the linearization error is reduced.•The deception attack model is established realistically by the randomly generated Bernoulli sequences.