Abstract: A novel state-of-charge (SOC) estimator is presented based on a combination of the interactive multi-model algorithm and the extended Kalman filter. The estimator is used in SOC estimation for nonlinear systems of lithium-ion batteries. First, the dynamic characteristics of the lithium-ion battery are described by two Thevenin circuit models, which have different parameters. Then, interactive multi-model extended Kalman filter and conventional extended Kalman filter are applied in numerical simulations to estimate the SOC in cases of hybrid pulse power characterization and urban dynamometer driving schedule, and then in a hardware experiment in case of constant current discharge. An analysis of results shows the effectiveness of interactive multi-model extended Kalman filter and its advantage over conventional methods with respect to estimation errors. The added computational cost of the new estimator is reasonable.