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

Gerasimos Rigatos, Krishna Busawon, Pierluigi Siano, Masoud Abbaszadeh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2018 110th AEIT International Annual Conference, AEIT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9788887237405
ISBN (Print)9781538670712
DOIs
Publication statusPublished - 17 Dec 2018
Event110th AEIT International Annual Conference, AEIT 2018 - Bari, Italy
Duration: 3 Oct 20185 Oct 2018

Conference

Conference110th AEIT International Annual Conference, AEIT 2018
CountryItaly
CityBari
Period3/10/185/10/18

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