A comparative study of state-of-charge estimation algorithms for lithium-ion batteries in wireless charging electric vehicles

With the rapid development of electric vehicles, lithium-ion battery has been widely used in energy storage systems. Accurate estimation of state of charge (SOC) is crucial for charging or discharging the batteries safely and reliably, as well as for indicating the remaining driving range for drivers. In this paper, four popular model-based SOC estimation algorithms, namely extended Kalman filter, unscented Kalman filter (UKF), sliding mode observer and nonlinear observer (NLO) are compared in terms of prediction accuracy, tracking ability to initial SOC error, and computation complexity. Since most resonant topologies for wireless charging systems are featured as constant current, a charging cycle combined with a constant current stage and several pulsed current stages is developed to evaluate the performance of these algorithms. Experimental results indicate that the UKF method performs better than the other three methods in terms of prediction accuracy, while the NLO performs best in terms of tracking ability to initial SOC error and it has the similar accuracy with the UKF method.