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

Advancing state estimation for lithium-ion batteries with hysteresis: systematic extended Kalman filter tuning