In this paper, a robust nonlinear observer is proposed to estimate the State of Charge (SOC) of a Li-ion battery, a problem which is critical in designing efficient Li-ion battery management systems and energy management systems in battery-powered applications. An equivalent circuit is used to model the battery behavior. The advantage of this model is that a straightforward identification process can be utilized for parameter identification. Although this model can capture battery dynamics very well for various operating conditions, modeling errors and also unknown disturbances will still be present; therefore, the battery management system should be able to take these uncertainties into consideration. To this end, the proposed estimation algorithm is designed to be robust against uncertainties. Furthermore, the observer does not impose any constraints on the battery current or the SOC relationship with Open Circuit Voltage (OCV). In other words, this algorithm does not require the battery current to be constant or the SOC-OCV relationship to be linear. Global asymptotic convergence of the estimated SOC to its true value is proved via the Lyapanov Stability Theorem. Simulation and experimental results demonstrate the effectiveness of the proposed method.
- Dynamic Systems and Control Division
Robust Nonlinear Observer for State of Charge Estimation of Li-Ion Batteries
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Lotfi, N, & Landers, RG. "Robust Nonlinear Observer for State of Charge Estimation of Li-Ion Batteries." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperative and Decentralized Control; Dynamic System Modeling; Dynamical Modeling and Diagnostics in Biomedical Systems; Dynamics and Control in Medicine and Biology; Estimation and Fault Detection; Estimation and Fault Detection for Vehicle Applications; Fluid Power Systems; Human Assistive Systems and Wearable Robots; Human-in-the-Loop Systems; Intelligent Transportation Systems; Learning Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 641-648. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8743
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