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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.