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

Gould, C and Bingham, Chris and Stone, D A and Bentley, P (2008) Novel battery model of an all-electric personal rapid transit vehicle to determine state-of-health through subspace parameter estimation and a Kalman Estimator. In: Power Electronics, Electrical Drives, Automation and Motion, 2008. SPEEDAM 2008. International Symposium on , 11-13 June 2008, Ischia, Italy.

Documents
Novel battery model of an all-electric personal rapid transit vehicle to determine state-of-health through subspace parameter estimation and a Kalman Estimator
The paper describes a real-time adaptive battery model for use in an all-electric personal rapid transit vehicle. Whilst traditionally, circuit-based models for lead-acid batteries centre on the well-known Randles' model, here the Randles' model is mapped to an equivalent circuit, demonstrating improved modelling capabilities and more accurate estimates of circuit parameters when used in Subspace parameter estimation techniques. Combined with Kalman Estimator algorithms, these techniques are demonstrated to correctly identify and converge on voltages associated with the battery State-of-Charge, overcoming problems such as SoC drift (incurred by coulomb-counting methods due to over-charging or ambient temperature fluctuations). Online monitoring of the degradation of these estimated parameters allows battery ageing (State-of-Health) to be assessed and, in 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.
[img]
[Download]
[img]
Preview
PDF
gould_1.pdf - Whole Document

1MB

Official URL: http://dx.doi.org/10.1109/SPEEDHAM.2008.4581332

Abstract

Abstract--The paper describes a real-time adaptive
battery model for use in an all-electric Personal Rapid
Transit vehicle. Whilst traditionally, circuit-based models
for lead-acid batteries centre on the well-known Randles’
model, here the Randles’ model is mapped to an equivalent
circuit, demonstrating improved modelling capabilities and
more accurate estimates of circuit parameters when used in
Subspace parameter estimation techniques. Combined with
Kalman Estimator algorithms, these techniques are
demonstrated to correctly identify and converge on voltages
associated with the battery State-of-Charge, overcoming
problems such as SoC drift (incurred by coulomb-counting
methods due to over-charging or ambient temperature
fluctuations).
Online monitoring of the degradation of these estimated
parameters allows battery ageing (State-of-Health) to be
assessed and, in 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.

Item Type:Conference or Workshop Item (Presentation)
Additional Information:Abstract--The paper describes a real-time adaptive battery model for use in an all-electric Personal Rapid Transit vehicle. Whilst traditionally, circuit-based models for lead-acid batteries centre on the well-known Randles’ model, here the Randles’ model is mapped to an equivalent circuit, demonstrating improved modelling capabilities and more accurate estimates of circuit parameters when used in Subspace parameter estimation techniques. Combined with Kalman Estimator algorithms, these techniques are demonstrated to correctly identify and converge on voltages associated with the battery State-of-Charge, overcoming problems such as SoC drift (incurred by coulomb-counting methods due to over-charging or ambient temperature fluctuations). Online monitoring of the degradation of these estimated parameters allows battery ageing (State-of-Health) to be assessed and, in 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.
Keywords:Kalman Filter, system identification, parameter estimation, Battery charge estimation
Subjects:H Engineering > H600 Electronic and Electrical Engineering
Divisions:College of Science > School of Engineering
ID Code:2408
Deposited By:INVALID USER
Deposited On:01 May 2010 19:21
Last Modified:13 Mar 2013 08:37

Repository Staff Only: item control page