Research on an Online Identification Algorithm for a Thevenin Battery Model by an Experimental Approach

Abstract

To improve the estimation accuracy of battery’s inner state for battery management system, an online parameters identification algorithm for Thevenin battery model is researched. The Thevenin model and parameters identification algorithm based on recursive least square adaptive filter algorithm was built with the Simulink/xPC Target. The results of hardware-in-loop experiment, which uses Federal Urban Driving Schedule test to verify the parameters identification approach, show the proposed approach can accurately identify the model parameters within 1% maximum terminal voltage estimation error, and the State of Charge error which calculated by the open circuit voltage estimates can be efficiently reduced to 4%.