Online Capacity Prediction of Lithium-Ion Batteries Based on Physics-Constrained Zonotopic Kalman Filter

Abstract

This article presents a novel physics-constrained zonotopic Kalman filter method for online capacity prediction of lithium-ion batteries. To describe capacity degradation, a state-space formulation is devised using the autoregressive model and an indirect representation of capacity. The approach consists of three steps: First, a zonotopic Kalman filter is proposed to estimate model parameters and parameter intervals. Subsequently, considering the capacity regeneration phenomenon, a physics-based constraint term is presented to optimize parameters, which updates the estimated model parameters obtained by the zonotopic Kalman filter. Finally, parameters and interval estimation are utilized to predict the future short-term capacity. The case study demonstrates the validity of our approach. Moreover, comparisons with the ellipsoid-based extended Kalman filter and predictive maintenance toolbox suggest that our approach can obtain more precise capacity prediction and tighter capacity interval results.