Second-order generalized integrator (SOGI) based phase-locked loops (PLLs) are widely used for grid synchronization in single-phase grid-connected power converters. Previously, the estimated frequency of the PLL stage is fed back to the frontend SOGI block to make SOGI-PLLs frequency-adaptive, which increases the implementation complexity, and makes the tuning sensitive, thus reducing stability margins. Alternatively, a frequencyfixed SOGI based PLL (briefly called FFSOGI-PLL) is proposed to ensure stability and simple implementation in this letter. It is commonly known that the in-phase and quadrature-phase signals generated by the frequency-fixed SOGI are of different amplitudes in the presence of frequency drifts, which causes second harmonic ripples in the estimated parameters of the PLL loop. To deal with this issue, a simple yet effective method is developed in FFSOGIPLL. The standard SOGI-PLL is firstly introduced, followed by the working principle and small-signal model of FFSOGI-PLL. The FFSOGI-PLL is then compared with the SOGI-PLL in terms of stability and transient performance. Finally, experimental results are presented to demonstrate the effectiveness of FFSOGI-PLL.

In this paper, a new open-loop architecture with good dynamic performance and strong harmonic rejection capability is proposed for single-phase grid synchronization under distorted conditions. Different from previous single-phase grid synchronization algorithms based on the phase-locked loop technique, the proposed method is to estimate the frequency and phase angle of the grid voltage in an open-loop manner so that fast dynamic response and enhanced system stability can be achieved. First, an open-loop frequency estimation algorithm is introduced under ideal grid condition. Then, it is extended to distorted grid voltages through the combination of the developed frequency estimation unit and a prefiltering stage consisting of a second-order low-pass filter and a cascaded delayed signal cancellation (DSC) module. In addition, a transient process smoothing unit is designed to achieve smooth frequency transients in cases where the grid voltage experiences fast and large changes. The working principle of the new frequency estimation algorithm and the developed single-phase grid synchronization approach is given in detail, together with some simulation and experiment results for verifying their performance.

In the fault diagnosis process of a photovoltaic (PV) array, it is difficult to discriminate single faults and compound faults with similar signatures. Furthermore, the data collected in the actual field experiment also contains strong noise, which leads to the decline of diagnostic accuracy. In order to solve these problems, a new eigenvector composed of the normalized PV voltage, the normalized PV current and the fill factor is constructed and proposed to characterize the common faults, such as open circuit, short circuit and compound faults in the PV array. The combination of these three feature characteristics can reduce the interference of external meteorological conditions in the fault identification. In order to obtain the new eigenvectors, a multi-sensory system for fault diagnosis in a PV array, combined with a data-mining solution for the classification of the operational state of the PV array, is needed. The selected sensors are temperature sensors, irradiance sensors, voltage sensors and current sensors. Taking account of the complexity of the fault data in the PV array, the Kernel Fuzzy C-means clustering method is adopted to identify these fault types. Gaussian Kernel Fuzzy C-means clustering method (GKFCM) shows good clustering performance for classifying the complex datasets, thus the classification accuracy can be effectively improved in the recognition process. This algorithm is divided into the training and testing phases. In the training phase, the feature vectors of 8 different fault types are clustered to obtain the training core points. According to the minimum Euclidean Distances between the training core points and new fault data, the new fault datasets can be identified into the corresponding classes in the fault classification stage. This strategy can not only diagnose single faults, but also identify compound fault conditions. Finally, the simulation and field experiment demonstrated that the algorithm can effectively diagnose the 8 common faults in photovoltaic arrays.

The physics-based fractional-order model (FOM) for lithium-ion batteries has shown good application prospects due to its mechanisms and simplicity. To adapt the model to higher-level applications, this paper proposes an improved FOM considering electrolyte-phase diffusion (FOMe) and then proposes a complete method for parameter identification based on three characteristic SOC intervals: the positive solid phase, negative solid phase, and electrolyte phase. The method mainly determines the above three characteristic intervals and identifies four thermodynamic parameters and five dynamic parameters. Furthermore, the paper describes a framework, which first verifies the model and parameter identification method separately based on pseudo two-dimensional model simulations, and secondly verifies FOMe and its parameters as a whole based on the experiments. The results, which are based on simulations and actual $\mathrm{L}{\mathrm{i}}_{0.8}\mathrm{C}{\mathrm{o}}_{0.1}\mathrm{M}{\mathrm{n}}_{0.1}{\mathrm{O}}_2$ lithium-ion batteries under multiple typical operating profiles and comparisons with other parameter identification methods, show that the proposed model and parameter identification method is highly accurate and efficient.

This paper discusses skid steering applied to four wheel drive electric vehicles. In such vehicles, steering is achieved by differentially varying the speeds of the lines of wheels on different sides of the vehicle in order to induce yaw. Skid steer wheeled vehicles require elaborate tire model, so author choose the unite semi-empirical tire model. From this model, longitudinal and lateral tire force can be calculated by slip ratio directly. The vehicle model has 3-DOF, longitudinal, lateral and yaw direction, irrespective of suspension. Induction motor is chosen as the driven motor, and the control method is rotor flux field oriented vector control. To satisfy the requirement of the turn radius, the longitudinal slip must be controlled, so a method of slip limitation feedback is used in the simulation. When the vehicle is turning on a slippery surface, because of the drop at the coefficient of road adhesion, the drive wheels may slip. The traction control system reduces the engine torque and brings the slipping wheels into the desirable skid range. Some simulation results about the steering accuracy and maneuverability are given in the paper.