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

Title	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
Publication Date:	2019
Publication Place	- IEEE
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-605011275-7
Record ID	f975c0b2-a3fd-4428-8da5-3e8dce5461d0
Library Location	Natural and Mathematical Sciences, Electrical & Electronics Engineering
Date	2019
Notes	TÜBİTAK
Sample Text	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

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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

Publication Date 2019

Publication Place - IEEE

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-605011275-7

Record ID f975c0b2-a3fd-4428-8da5-3e8dce5461d0

Library Location Natural and Mathematical Sciences, Electrical & Electronics Engineering

Date 2019

Notes TÜBİTAK

Sample Text 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