This article introduces an efficient modeling approach based on the Wiener structure to reinforce the capacity of classical equivalent circuit models (ECMs) in capturing the nonlinearities of lithium-ion (Li-ion) batteries. The proposed block-oriented modeling architecture is composed of a simple linear ECM followed by a static output nonlinearity block, which helps achieving a superior nonlinear mapping property while maintaining the real-time efficiency. The observability of the established battery model is analytically proven. This article also introduces an efficient parameter estimator based on extended-kernel iterative recursive least squares algorithm for real-time estimation of the parameters of the proposed Wiener model. The proposed approach is applied for state-of-charge (SoC) estimation of 3.4-Ah 3.6-V nickel-manganese-cobalt-based Li-ion cells using the extended Kalman filter (EKF). The results show about 1.5% improvement in SoC estimation accuracy compared with the EKF algorithm based on the second-order ECM. A series of real-time tests are also carried out to demonstrate the computational efficiency of the proposed method.

Complementary features of batteries and supercapacitors can be effectively used in a Hybrid Energy Storage System (HESS). The utilization of the HESS in Electric Vehicles (EVs) offers many advantages such as efficient regenerative braking, battery safety, and improved vehicle acceleration. In this paper, a new Regenerative Braking System (RBS) is proposed for EVs with HESS and driven by Brushless DC (BLDC) motor. During regenerative braking, the BLDC acts as a generator. Hence, using appropriate switching algorithm, the DC-link voltage is boosted and the energy is transferred to the supercapacitor or the battery through the inverter. The harvested energy can be utilized to improve the vehicle acceleration and/or keep the battery pack from deep discharging during driving uphill. In order to provide a reliable and smooth brake, braking force distribution is realized through an Artificial Neural Network (ANN). Simultaneously, the braking current is adjusted by a PI controller for constant torque braking. In order to evaluate the performance of the proposed RBS, different simulations and experiments are carried out. The results confirm high capability of the proposed RBS.

Online accurate estimation of supercapacitor State-of-Health (SoH) and State-of-Energy (SoE) is essential to achieve efficient energy management and real-time condition monitoring in Electric Vehicle (EV) applications. In this paper, for the first time, Unscented Kalman Filter (UKF) is used for online parameter and state estimation of the supercapacitor. In the proposed method, a nonlinear state-space model of the supercapacitor is developed, which takes the capacitance variation and selfdischarge effects into account. The observability of the considered model is analytically confirmed using a graphical approach (GA). The SoH and SoE are then estimated based on the supercapacitor online identified model with the designed UKF. The proposed method provides better estimation accuracy over KF and Extended KF (EKF) algorithms since the linearization errors during the filtering process are avoided. The effectiveness of the proposed approach is demonstrated through several experiments on a laboratory testbed. An overall estimation error below 0.5% is achieved with the proposed method. In addition, Hardware-in-the-Loop (HIL) experiments are conducted and real-time feasibility of the proposed method is guaranteed.

Dynamic state estimation plays a critical role in achieving real-time wide-area monitoring of power systems. On the other hand, false data injection (FDI) attacks are substantial threats, which can undesirably ruin the state estimation results. To tackle the foregoing problem, an effective Secure Hybrid Dynamic State Estimation (SHDSE) approach which involves a dynamic model of the attack vector is proposed in this paper. In the proposed method, an initial estimation of the system states is first obtained using a designed Unknown Input Observer (UIO). Subsequently, based on the system and UIO models and the initial estimations of the states, a dynamic model for the attack vector is extracted. Ultimately, the attack model is augmented with the main system model for co-estimation of the attack and the system states using a Kalman Filter (KF). The onset of the FDI attack is rapidly detected by the accurate estimation of the attack vector. The effectiveness of the proposed approach is demonstrated under different FDI attack scenarios by a thorough theoretical analysis as well as simulations on IEEE 14-bus and 57-bus test systems. In order to show that the proposed method can keep up with typical scan rates of commercial Phasor Measurement Units (PMUs), a series of Software-in-the-Loop (SIL) experiments are also conducted and the real-time feasibility of the proposed approach is guaranteed.

Differential protection is the most common type of protection in power transformers. However, inrush current due to transformer energization may appear as fault current to the protective relay, causing a false trip of the transformer. Therefore, discrimination of the transformer inrush current from internal faults is necessary for improving the security of the protection scheme. In this paper, a new method for discrimination of the transformer inrush current from internal fault current is proposed. First, nonlinear state-space model of a real single-phase transformer is derived, which incorporates the nonlinear phenomena of hysteresis and magnetic saturation. Based on the derived model, an Extended Kalman Filter (EKF) is used for estimation of the primary winding current. A residual signal is defined as the difference between the measured and estimated currents. When a healthy transformer is energized, the EKF perfectly estimates the primary winding current and hence, the residual signal is almost zero. However, when the transformer is faulty, the EKF cannot effectively estimate the current due to the existing large model mismatches. Consequently, a large residual signal is created. The superiorities of the proposed algorithm are demonstrated using different experimental scenarios.