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

By B S Bhangu, P Bentley, D A Stone and Chris Bingham

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 methodolog

Topics: H600 Electronic and Electrical Engineering
Publisher: Institute of Electronic and Electrical Engineering
Year: 2005
DOI identifier: 10.1109/EPE.2005.219601
OAI identifier: oai:eprints.lincoln.ac.uk:2552

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Citations

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