State-of-Charge Estimation of Lithium-Ion Batteries Subject to Random Sensor Data Unavailability: A Recursive Filtering Approach

• Published in: IEEE transactions on industrial electronics (1982) Vol. 69; no. 5; pp. 5175 - 5184
• Main Authors: Chen, Hui, Tian, Engang, Wang, Licheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Availability; Batteries; Battery charge measurement; Electronic countermeasures; Equivalent circuits; Estimation; Filtration; Integrated circuit modeling; Least squares method; Lithium; Lithium-ion batteries; Lithium-ion battery; Mathematical model; measurement unavailability; Optimization; Parameter identification; Rechargeable batteries; recursive filtering; Sensors; State of charge; state of charge (SOC); Upper bounds
• DOI: 10.1109/TIE.2021.3078376
• ISSN: 0278-0046

In this article, the estimation problem of the state of charge (SOC) of Lithium-ion batteries is investigated. In order to truly reflect the unreliability of the sensor measured data, the data missing phenomenon with respect to the sensor measurement (e.g., the terminal voltage) is taken into account for the addressed estimation issue. By introducing a stochastic variable obeying the Bernoulli distribution with a known probability, the random occurrence of the sensor measurement unavailability is well characterized. The second-order resistor-capacitor equivalent circuit model, where the model parameters are identified by the recursive least-squares method, is developed to govern the dynamical behaviors of a Lithium-ion battery. A data-unavailability-resistant nonlinear recursive filtering algorithm is proposed to estimate the real SOC in an unreliable industrial environment. An upper bound of the filtering error covariance is obtained, which is further minimized at each sampling instant. In addition, the filter gain is recursively parameterized by solving an optimization problem with respect to two coupled recursive Riccati-like equations, thereby being suitable for the online implementation. Finally, extensive experiments are conducted to demonstrate the validity of the proposed filtering approach.