A Kalman Filter-based disturbance observer for state-of-charge estimation in EV batteries

Rigatos, Gerasimos, Busawon, Krishna, Siano, Pierluigi and Abbaszadeh, Masoud (2018) A Kalman Filter-based disturbance observer for state-of-charge estimation in EV batteries. In: 2018 AEIT International Annual Conference. IEEE. ISBN 978-1-5386-7071-2

Full text not available from this repository.
Official URL: http://dx.doi.org/10.23919/AEIT.2018.8577248

Abstract

A method for estimating the state-of-charge in batteries of electric vehicles is developed using a Kalman Filter-based disturbance observer. Estimation of the state-of-charge is important for the safe functioning of electric vehicles and for minimizing their charging time. The computation of the state-of-charge of the battery at each time instant becomes a non-trivial problem because one can measure only an output voltage. In the present article the equations of Kirchhoff's voltage and current laws are used first to obtain the electric dynamics of the battery and to formulate the associated state-space model. Next, the Kalman Filter is redesigned as a disturbance observer, so as to estimate the state-of-charge despite the effects of model uncertainty terms, The proposed method allows for computing not only the battery's state-of-charge but also for identifying perturbations and model uncertainty about the charging process.

Item Type: Book Section
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Becky Skoyles
Date Deposited: 06 Jun 2019 11:21
Last Modified: 10 Oct 2019 18:31
URI: http://nrl.northumbria.ac.uk/id/eprint/39531

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence