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Battery health determination by subspace parameter estimation and sliding mode control for an all-electric Personal Rapid Transit vehicle — the ULTra

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

Abstract

The paper describes a real-time adaptive battery modelling methodology for use in an all electric personal rapid transit (PRT) vehicle. Through use of a sliding-mode observer and online subspace parameter estimation, the voltages associated with monitoring the state of charge (SoC) of the battery system are shown to be accurately estimated, even with erroneous initial conditions in both the model and parameters. In this way, problems such as self- discharge during storage of the cells and SoC drift (as usually incurred by coulomb-counting methods due to overcharging or ambient temperature fluctuations) are overcome. Moreover, through online monitoring of the degradation of the estimated parameters, battery ageing (State of Health) can be monitored and, in the case of safety- critical systems, cell failure may be predicted in time to avoid inconvenience to passenger networks. Due to the adaptive nature of the proposed methodology, this system can be implemented over a wide range of operating environments, applications and battery topologies, by adjustment of the underlying state-space model

Topics: H600 Electronic and Electrical Engineering
Publisher: Institution of Electronic and Electrical Engineers
Year: 2008
DOI identifier: 10.1109/PESC.2008.4592650
OAI identifier: oai:eprints.lincoln.ac.uk:2409

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Citations

  1. (1993). Aging effects in valveregulated lead-acid batteries”, doi
  2. (2004). Aging mechanisms and service life of lead-acid batteries”, doi
  3. (1998). An adaptive battery monitoring system for an electric vehicle," doi
  4. (2000). Battery state of health estimation through coup de fouet," doi
  5. Elecktrochemische Kinetik”, doi
  6. (2006). Fundamentals of battery dynamics”, doi
  7. (2007). New estimation filtering for battery management systems of lead-acid cells in hybrid electric vehicles”,
  8. (2005). Nonlinear observers for predicting state-of-charge and state-ofhealth of lead-acid batteries for hybrid-electric vehicles," doi
  9. (1999). System identification – Theory for the user”, 2nd ed, doi
  10. (1998). Takahiro Yanagihara, “State of charge estimation of sealed lead-acid batteries used for electric vehicles”, doi

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