arXiv:2611.13202v1 Announce Type: new Abstract: We propose a scheme for estimating the full states and parameters of a second-order Th\'evenin battery model, using distinct features of the measured current. An identification step, run during suitable rest intervals, identifies the resistances and capacitances of the two RC branches from the voltage relaxation by linear least squares, without knowledge of the open-circuit voltage (OCV) map. An online estimator then runs continuously: the polarization voltages are estimated by open-loop observers; the series resistance is updated at current jumps using least squares; the state of charge (SOC) is propagated by Coulomb counting using the current capacity estimate, and both estimates are updated periodically by a sliding-window regression. We show that the identification is exact under a mild rank condition, that the series resistance is recovered at the first current jump, and that the online state and capacity estimates converge exponentially under verifiable excitation conditions. We also establish robustness of the online estimator under bounded process disturbances and measurement noise.