An adaptive physics-based reduced-order model of an aged lithium-ion cell, selected using an interacting multiple-model Kalman filter

To do so, we first present a method for calculating a priori the changes to cell parameter values that will be produced by aging due to side reactions and/or material loss. These aged parameter values are utilized to produce reduced-order physics-based models at different stages of cell life. The reduced-order models are then used within a nonlinear interacting multiple-model Kalman filter to select the pre-computed model whose voltage predictions most resembles present measured voltage, so providing an estimate of the aged parameter values of a cell via the parameter values of this model. The selected model may then be used for state-of-charge estimation, state-of-power estimation, state-of-energy estimation, and other model-based battery-management tasks.