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Novel battery model of an all-electric personal rapid transit vehicle to determine state-of-health through subspace parameter estimation and a Kalman Estimator

By C Gould, Chris Bingham, D A Stone and P Bentley

Abstract

Abstract--The paper describes a real-time adaptive\ud battery model for use in an all-electric Personal Rapid\ud Transit vehicle. Whilst traditionally, circuit-based models\ud for lead-acid batteries centre on the well-known Randles’\ud model, here the Randles’ model is mapped to an equivalent\ud circuit, demonstrating improved modelling capabilities and\ud more accurate estimates of circuit parameters when used in\ud Subspace parameter estimation techniques. Combined with\ud Kalman Estimator algorithms, these techniques are\ud demonstrated to correctly identify and converge on voltages\ud associated with the battery State-of-Charge, overcoming\ud problems such as SoC drift (incurred by coulomb-counting\ud methods due to over-charging or ambient temperature\ud fluctuations).\ud Online monitoring of the degradation of these estimated\ud parameters allows battery ageing (State-of-Health) to be\ud assessed and, in safety-critical systems, cell failure may be\ud predicted in time to avoid inconvenience to passenger\ud networks.\ud Due to the adaptive nature of the proposed methodology,\ud this system can be implemented over a wide range of\ud operating environments, applications and battery\ud topologies

Topics: H600 Electronic and Electrical Engineering
Publisher: Institution of Electronic and Electrical Engineers
Year: 2008
DOI identifier: 10.1109/SPEEDHAM.2008.4581332
OAI identifier: oai:eprints.lincoln.ac.uk:2408

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Citations

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