Low-Order Electrochemical State Estimation for Li-Ion Batteries

Batteries are complex systems involving spatially distributed microscopic mechanisms on different time scales whose adequate interplay is essential to ensure a desired functioning. Describing these phenomena yields nonlinearly coupled partial differential equations numerical solution requires considerable effort and computation time, making it an infeasible for real-time applications. Anyway, having information about the internal electrochemical states of battery can pave way many advanced monitoring control strategies with big potential improving efficiency longevity. For such purposes, in present paper, combination low-order representation dynamics associated based Dynamic Mode Decomposition (DMDc) proposed obtain improved equivalent circuit model (ECM) continuously updated parameters combined extended Kalman Filter (EKF). The model-order reduction step extensively exploits structure, yielding well structured without artificial correlations. performance method illustrated simulations well-established reference model, showing its usefulness applications requiring knowledge besides state-of-charge.