  Knowledge of remaining battery charge is fundamental to electric vehicle
deployment. Accurate measurements of state-of-charge (SOC) cannot be directly
obtained, and estimation methods must be used instead. This requires both a
good model of a battery and a well-designed state estimator. Here, hysteretic
reduced-order battery models and adaptive extended Kalman filter estimators are
shown to be highly effective, accurate SOC estimators. A battery model
parameterisation framework is proposed, which enhances standardised methods to
capture hysteresis effects. The hysteretic model is parameterised for three
independent NMC811 lithium-ion cells and is shown to reduce voltage RMS error
by 50% across 18-hour automotive drive-cycles. Parameterised models are used
alongside an extended Kalman filter, which demonstrates the value of adaptive
filter parameterisation schemes. When used alongside an extended Kalman filter,
adaptive covariance matrices yield highly accurate SOC estimates, reducing SOC
estimation error by 85%, compared to the industry standard battery model.
