Noninvasive Battery-Health Diagnostics Using Retrospective-Cost Identification of Inaccessible Subsystems Available to Purchase

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.