SOC estimation for li-Ion batteries using extended kalman filter with PID controlled process noise according to the voltage error

Name: SOC estimation for li-Ion batteries using extended kalman filter with PID controlled process noise according to the voltage error
Author: Çelik, Mert, Gözüküçük, Mehmet Ali, Akdoğan, Taylan, Uğurdağ, Hasan Fatih

İsim	SOC estimation for li-Ion batteries using extended kalman filter with PID controlled process noise according to the voltage error
Yazar	Çelik, Mert, Gözüküçük, Mehmet Ali, Akdoğan, Taylan, Uğurdağ, Hasan Fatih
Basım Tarihi:	2019
Basım Yeri	- IEEE
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-605011275-7
Kayıt Numarası	f975c0b2-a3fd-4428-8da5-3e8dce5461d0
Lokasyon	Natural and Mathematical Sciences, Electrical & Electronics Engineering
Tarih	2019
Notlar	TÜBİTAK
Örnek Metin	State of Charge (SOC) estimation is critical for battery powered devices in order to find out the remaining charge level. This process is relatively straightforward when the battery is in the resting state. However, it can be challenging while the device is operating, due to the process disturbances and model uncertainties. Various kinds of approaches have already been proposed in the literature like Neural Networks, Kalman Filtering, and Nonlinear Observers. Nevertheless, proposed methods in the literature do not have fast response for initial condition errors. This paper proposes a new implementation of Extended Kalman Filter, which improves the convergence characteristics of states for SOC estimation. The importance of initial condition errors is articulated in this paper, especially from an automotive perspective.
DOI	10.23919/ELECO47770.2019.8990538

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

SOC estimation for li-Ion batteries using extended kalman filter with PID controlled process noise according to the voltage error

Yazar Çelik, Mert, Gözüküçük, Mehmet Ali, Akdoğan, Taylan, Uğurdağ, Hasan Fatih

Basım Tarihi 2019

Basım Yeri - IEEE

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-605011275-7

Kayıt Numarası f975c0b2-a3fd-4428-8da5-3e8dce5461d0

Lokasyon Natural and Mathematical Sciences, Electrical & Electronics Engineering

Tarih 2019

Notlar TÜBİTAK

Örnek Metin State of Charge (SOC) estimation is critical for battery powered devices in order to find out the remaining charge level. This process is relatively straightforward when the battery is in the resting state. However, it can be challenging while the device is operating, due to the process disturbances and model uncertainties. Various kinds of approaches have already been proposed in the literature like Neural Networks, Kalman Filtering, and Nonlinear Observers. Nevertheless, proposed methods in the literature do not have fast response for initial condition errors. This paper proposes a new implementation of Extended Kalman Filter, which improves the convergence characteristics of states for SOC estimation. The importance of initial condition errors is articulated in this paper, especially from an automotive perspective.

DOI 10.23919/ELECO47770.2019.8990538