A Composite Single Particle Lithium-Ion Battery Model Through System Identification

In the realm of lithium-ion (Li-ion) battery modeling, owing to its simplicity, the single particle model (SPM) has long been considered to be a promising reduced-order model (ROM) candidate to usher in the era of physics-inspired models (PIMs) in embedded applications. However, at high load currents, the standard SPM exhibits poor accuracies in computing the cell’s terminal voltage, thereby rendering it unsuitable as the plant model in state-estimation tasks. A comprehensive evaluation of the salient electrolyte-enhanced SPMs from the literature reveals that current solutions are either mathematically intractable or overly simplistic. For the ionic concentration in the electrolyte, the well-known quadratic approximation model, which straddles the boundary of computational complexity and mathematical tractability, reveals poor temporal performance, particularly at the current collector interfaces. In this work, we retain the spatial dynamics of the quadratic approximation model while proposing a novel approach using system identification techniques for its temporal dynamics. By employing linear approximations for the relevant subsystems, we identify discrete-time transfer functions of the debiased number of moles per unit area of lithium ions in the electrolyte within each electrode region, that yield improved spatio-temporal accuracies for the electrolyte concentration profile. We then augment the standard SPM with the new system identification-based electrolyte dynamics to arrive at an electrolyte-enhanced composite SPM (EECSPM). Finally, we demonstrate the superior performance of the EECSPM compared with the incumbent state of the art, thereby representing a concrete advancement toward the goal of using PIMs in real-time applications.