Health management of Li-ion batteries depends on knowledge of certain battery internal dynamics (e.g., lithium consumption and film growth at the solid-electrolyte interface) whose inputs and outputs are not directly measurable with noninvasive methods. This presents a problem of identification of inaccessible subsystems. To address this problem, we apply the retrospective-cost subsystem identification (RCSI) method. As a first step, this paper presents a simulation-based study that assumes as the truth model of the battery an electrochemistry-based battery charge/discharge model of Doyle, Fuller, and Newman, and later augmented with a battery-health model by Ramadass. First, this truth model is used to generate the data needed for the identification study. Next, the film-growth component of the battery-health model is assumed to be unknown, and the identification of this inaccessible subsystem is performed using RCSI. The results show that the subsystem identification method can identify the film growth quite accurately when the chemical reactions leading to film growth are consequential.
- Dynamic Systems and Control Division
Noninvasive Battery-Health Diagnostics Using Retrospective-Cost Identification of Inaccessible Subsystems
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D’Amato, AM, Forman, JC, Ersal, T, Ali, AA, Stein, JL, Peng, H, & Bernstein, DS. "Noninvasive Battery-Health Diagnostics Using Retrospective-Cost Identification of Inaccessible Subsystems." 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. 299-307. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8649
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