An adaptive strategy for Li-ion battery internal state estimation

Further developing a study presented in Di Domenico, Prada, and Creff (2011), this paper presents an extended Kalman filter (EKF) based on an electro-thermal model for the estimation of the internal state of a lithium-ion battery, i.e. state of charge and the cell overpotential. In order to compensate for uncertainties in the model parameters and in the measurements, it is first shown that the filter robustness strongly depends on the State of Charge (SOC) range. Then the filter weights are adapted according to the estimated SOC value. This estimation technique is tested using experimental data collected from a commercial A123 Systems lithium iron phosphate/graphite (LiFePO4/graphite) cell. The filter shows good performance. The estimation of SOC exhibits an average error within 3% range and the overpotential is estimated with a precision higher than 5mV.