The performance of Voltage Source Inverter (VSI) is affected by power semiconductor nonlinearities, dead time, and control delay. The compensation of these nonlinear effects is necessary to meet requirements in terms of current quality and high efficiency, especially for grid-connected power conversion systems. In this paper, the effects of nonlinearities are widely analyzed and a new compensation technique for a steady-state current-controlled VSI is proposed. It is based on a step-by-step voltage compensation on the basis of the current error evaluated within each control interval. As a result, a complete compensation is achieved within a whole period of the fundamental. To cope with the zero-current clamping effect, the proposed compensation technique is implemented in a recursive way. For slow-dynamic applications, such as photovoltaic systems, this method allows achieving high performance with very low computational requirements.

Failure of a phase current sensor in permanent magnet synchronous machine (PMSM) drive can highly impact the performance of the drive. Especially, offset faults can cause torque oscillations, which can lead to an unacceptable deterioration of the drive's performance and mechanical damages. The diagnosis of this fault condition is of fundamental importance in many applications and it is often solved by means of highly computational state estimation techniques. This article presents a new model-based approach to systematically and accurately detect and estimate phase current sensor offsets in PMSM drives. The steady-state analytical solution of the PMSM drive model, including the control loop of the field oriented control and the sensor offset disturbance, has been found, allowing for estimating the effect of the fault on the electric drive performance. Then, a simple and effective diagnostic algorithm has been developed to detect, isolate, and accurately estimate the location and the magnitude of the fault eliminating any need for state estimation or observers. Numerical and experimental results have been reported to validate the proposed model and diagnostic algorithm even in case of motor parameters variation.

A Multi-Source Electric Vehicle Charging Station (MS-EVCS) is a local entity that combines the grid energy with Distributed Energy Resources (DERs) with the aim of reducing the grid impact due to electric vehicles (EVs) charging events. The integration of stationary and in-vehicle Energy Storage Systems (ESSs) in MS-EVCSs has gained increasing interest thanks to the possibility of storing energy at off-peak hours to be made available at peak-hours. However, the ESS technology and the vehicle-to-grid (V2G) concept show several issues due to cost, battery life cycle, reliability, and management. The design of the MS-EVCS energy management system is of primary importance to guarantee the optimal usage of the available resources and to enhance the system benefits. This study presents a novel energy management strategy for Real-Time (RT) control of MS-EVCS considering DERs, stationary ESS, and V2G. The proposed energy management control allows defining the MS-EVCS control policy solving several cascaded-problems with the aim of achieving the minimum operating cost when the battery degradation and the stochastic nature of the sources are considered. The key feature of the proposed methodology is the lower computational effort with respect to traditional optimal control methodologies while achieving the same optimal solution.

The work presented in this article is motivated by a project related to the electrification of commercial aircraft. Energy storage systems (ESSs) for hybrid-electric aircraft applications require the ability to provide accurate diagnoses to insure system availability and reliability. In aerospace applications, battery packs may consist of thousands of interconnected cells and the associated electrical/electronic hardware, which brings a series of challenges for designing the battery management system (BMS). This article uses the tools of structural analysis to determine the placement of sensors that are needed by the BMS to enable monitoring and fault diagnosis at the individual cell level. First, the degree of analytical redundancy (AR) in the battery system that can be used for diagnostic strategies is determined. Then, structural models of different battery pack architectures are used to study how different measurements (current, voltage, and temperature) may improve the ability to monitor and diagnose a battery system. Possible sensor placement strategies that would enable the diagnosis of individual sensor faults, individual cell faults, and connection faults for different battery pack topologies are analyzed as well. A software-in-the-loop (SIL) framework is utilized to validate the proposed approach.

The fault of current sensors in AC electric drives can cause inaccurate tracking of the control reference and torque oscillations, that can lead to damage of mechanical components. Therefore, the development of accurate detection techniques of these faults plays a crucial role for the proper management of the electric drive and for the fault isolation and estimation. In the paper, a model-based method for the detection of phase current sensor gain faults in a Permanent Magnet Synchronous Motor (PMSM) drive with Field Oriented Control (FOC) is proposed. At first, a mathematical model is presented, which allows an accurate determination of the analytical closed-form expression of the steady-state stator currents, taking into account the effects both of the current control regulators and of any current sensors gain faults. Starting from this model, a low-computation algorithm has been carried out, which allows not only to detect and isolate the current sensors affected by the gain fault, but also to estimate the gain values starting from the measured phase currents and motor speed. The model and the diagnostic algorithm performance is verified by means of numerical and experimental results.

The adoption of the smart and sustainable charging infrastructures for electric vehicles (EVs) represents a key-point to accelerate the electro-mobility uptake. This paper presents an optimal power flow management for a smart charging micro-grid that minimizes the cost and reduces the impact on the grid of EVs requests. This goal is achieved by means of an optimal integration and control of renewable energy, stationary energy storage system, and V2G into the charging infrastructure. In particular, the optimal power flow management solves a stochastic multilayer optimization problem based on the definition of different priority levels representing the coordination of the ESS with the other sources. The output of the algorithm is the day-ahead prediction of the energy generation and, thus, the optimization of energy flow during the entire day. Possible variation of energy sources during the operation are also taken into account by means of a real time control adaptation.