Interconnected Sigma-Point Kalman Filter Application for Electrochemical State Estimation of Lithium-Ion Batteries

Lithium-ion batteries (LIBs) are the energy-storage element of choice due to their high energy density and low self-discharge. Accurate estimation of battery state-of-charge (SOC), state-of-health (SOH) and state-of-power (SOP) depend on accurate mathematical models of underlying electrochemical behavior. High-accuracy models of LIBs requires knowledge of the potentials associated with both positive and negative electrodes. Viewing each electrode as a proper subsystem of a cell, it is natural to consider estimating the behavior of the interacting subsystems using a pair of interacting estimators. This paper introduces an interconnected sigma-point Kalman filter applied to a reduced-complexity single particle model with electrolyte dynamics (ESPM). The resultant estimator is able to calculate approximations of a variety of internal electrochemical variables (e.g., lithium concentrations and internal potentials) useful for advanced control applications.