Online model identification of lithium-ion battery for electric vehicles

Abstract

In order to characterize the voltage behavior of a lithium-ion battery for on-board electric vehicle battery management and control applications, a battery model with a moderate complexity was established. The battery open circuit voltage (OCV) as a function of state of charge (SOC) was depicted by the Nernst equation. An equivalent circuit network was adopted to describe the polarization effect of the lithium-ion battery. A linear identifiable formulation of the battery model was derived by discretizing the frequent-domain description of the battery model. The recursive least square algorithm with forgetting was applied to implement the on-line parameter calibration. The validation results show that the on-line calibrated model can accurately predict the dynamic voltage behavior of the lithium-ion battery. The maximum and mean relative errors are 1.666% and 0.01%, respectively, in a hybrid pulse test, while 1.933% and 0.062%, respectively, in a transient power test. The on-line parameter calibration method thereby can ensure that the model possesses an acceptable robustness to varied battery loading profiles.

Keywords

battery model / on-line parameter identification / lithium-ion battery / electric vehicle