State-of-charge and state-of-health prediction of lead-acid batteries for hybrid electric vehicles using non-linear observers

Bhangu, B S and Bentley, P and Stone, D A and Bingham, Chris (2005) State-of-charge and state-of-health prediction of lead-acid batteries for hybrid electric vehicles using non-linear observers. In: Power Electronics and Applications, 2005 European Conference on, September 2005, Dresden, Germany.

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State-of-charge and state-of-health prediction of lead-acid batteries for hybrid electric vehicles using non-linear observers
The paper describes the application of state-estimation techniques for the real-time prediction of state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Approaches based on the extended Kalman filter (EKF) are presented to provide correction for offset, drift and state divergence - an unfortunate feature of more traditional coulomb-counting techniques. Experimental results are employed to demonstrate the relative attributes of the proposed methodology
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Full text URL: http://dx.doi.org/10.1109/EPE.2005.219601

Abstract

The paper describes the application of state-estimation techniques for the real-time prediction of state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Approaches based on the extended Kalman filter (EKF) are presented to provide correction for offset, drift and state divergence - an unfortunate feature of more traditional coulomb-counting techniques. Experimental results are employed to demonstrate the relative attributes of the proposed methodology

Item Type:Conference or Workshop Item (Presentation)
Additional Information:The paper describes the application of state-estimation techniques for the real-time prediction of state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Approaches based on the extended Kalman filter (EKF) are presented to provide correction for offset, drift and state divergence - an unfortunate feature of more traditional coulomb-counting techniques. Experimental results are employed to demonstrate the relative attributes of the proposed methodology
Keywords:State of Charge prediction, State of Health predction, electric vehicle, non-linear observers, Battery management systems (BMS), Energy storage, Estination technique, Hybrid electric vehicle (HEV), Modelling
Subjects:H Engineering > H600 Electronic and Electrical Engineering
Divisions:College of Science > School of Engineering
ID Code:2552
Deposited By:INVALID USER
Deposited On:23 May 2010 20:07
Last Modified:28 Aug 2014 09:24

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