Parameter identification and state estimation of Li-ion and NiMH batteries

Abstract

To optimize the efficiency of energy management algorithms in electric vehicles, good battery modeling and an accurate knowledge of the state of the battery is needed. A battery model is used to describe the behavior of the battery. Tests which identify model parameters are greatly dependent on sensor noise and on the chosen current profile. This paper first presents an improved (robust) parameter identification. Next, a state estimator (Kalman filter) is used to get an accurate knowledge on the State of Charge of the battery. This paper presents design guidelines on how to select appropriate weighting matrices for this state estimator.

Date of Award	2012
Original language	English
Supervisor	John T.B.A. Kessels (Supervisor 1) & W.H.A. Hendrix (Supervisor 1)