On-line parameter estimation of a Lithium-Ion battery/supercapacitor storage system using filtering sliding mode differentiators

Abstract

This paper introduces a new approach to obtain precise on-line estimation of the internal parameters of a hybrid energy storage system based on Lithium-Ion Batteries and Supercapacitors. Filtering high-order sliding mode differentiators and a recursive least square estimation algorithm for time varying parameters are combined to obtain the parameters of an electric model of the storage devices. Complementary, a method is proposed to validate the estimated values, in accordance with a classic identifiability condition, i.e., the persistence of excitation. Representative simulation results to show the efficiency of the proposed on-line estimation methodology are presented, considering an electric vehicle subjected to realistic power demand. To this end, a standardised test that represents city driving conditions is used. Together with the on-line parameters estimation, it is allowed to infer the State-of-Charge and the State-of-Health during vehicle operation.