Title :
State Estimation of a Lithium-Ion Battery Through Kalman Filter
Author :
Urbain, M. ; Raël, S. ; Davat, B. ; Desprez, P.
Author_Institution :
GREEN-INPL-CNRS (UMR 7037) 2, Vandceuvre-les-Nancy
Abstract :
Online evaluation of operating conditions is crucial for battery management system. For this purpose, the resistance and the capacity best characterize the state-of-health of a lithium-ion cell, whereas the state-of-charge is a reliable information about its remaining stored energy. This paper describes the use of Kalman filter in order to estimate these parameters for photovoltaic applications, and hybrid electric vehicle applications. Rather than computing heavy models incompatible with embedded microcontroller capabilities, some assumptions associated to theses kinds of applications allow to implement a simple model to track parameters. Experimental validation of this process is fully depicted.
Keywords :
Kalman filters; hybrid electric vehicles; microcontrollers; power system state estimation; secondary cells; Kalman filter; battery management; embedded microcontroller; hybrid electric vehicle; lithium-ion battery; photovoltaics; state estimation; Battery management systems; Computer applications; Electric resistance; Embedded computing; Hybrid electric vehicles; Microcontrollers; Parameter estimation; Photovoltaic systems; Solar power generation; State estimation;
Conference_Titel :
Power Electronics Specialists Conference, 2007. PESC 2007. IEEE
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-0654-8
Electronic_ISBN :
0275-9306
DOI :
10.1109/PESC.2007.4342463