State-of-Charge Estimation of Supercapacitors Using a Switching Extended Kalman Filter

State-of-charge (SoC) estimation plays a key role in ensuring the stable and efficient operation of supercapacitor energy storage systems. However, most of the existing methods suffer from the following two problems: (1) They are limited to SoC estimation of a single supercapacitor, ignoring the practical situation where cells are usually combined with balancing circuits. (2) The modeling of supercapacitors tends to be a simple linear time-invariant system, which is inaccurate. In this paper, a nonlinear switching system model is proposed with the combination of an equivalent circuit model (ECM) and a switched resistor passive balancing circuit, where the capacitance change is also taken into account. Then, a switching extended Kalman filter (EKF) is proposed to realize the SoC estimation, in which the filter parameters can be dynamically changed according to the system model. A hardware platform is established for experimental verifications. The results indicate that the proposed method can improve the SoC estimation accuracy of the supercapacitor during the activation of the balancing circuit, with mean absolute errors of no more than 0.8744% under different operating conditions.