EV/HEV Li-ion battery modelling and State-of-Function determination

The paper describes the application of a realtime, adaptive battery modelling methodology to Li-ion batteries. This methodology allows accurate estimation of the State-of-Function (SoF) of batteries for an Electric or Hybrid-Electric vehicle. Through use of a Kalman Estimator and online battery model parameter estimation, the voltages associated with monitoring the State of Charge (SoC) of the battery system are shown to be accurately estimated, even given erroneous initial conditions in both the model and parameters. In this way, problems such as self-discharge during non-use of the cells and SoC drift (as usually incurred by coulomb-counting methods due to over-charging or ambient temperature fluctuations) are overcome. A further benefit of the adaptive nature of the parameter estimation allows battery ageing (State of Health - SoH) to be monitored and, in the case of safety-critical systems, cell failure may be predicted in time to avoid inconvenience to passenger networks. Moreover, the ability to accurately predict the SoF and changes in battery parameters allows charging scenarios to be optimized to extend lifetime and facilitate future “Vehicle-to-Grid” (V2G) implementation.