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.