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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 paper 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 (FOC) 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.

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 scientific literature is paying particular attention to Ultra-Fast Charging Stations (UFCSs) that can make charging times of Electric Vehicles (EVs) comparable with the refuelling time of internal combustion engine vehicles. In this context, scheduling charging algorithms to manage the available resources deserve significant attention. This paper proposes an online scheduling algorithm for UFCSs equipped with Battery Energy Storage Systems. The charging profile is obtained by considering the power and energetic constraints related to both infrastructure and EVs. The constraints are assessed according to the efficiency of the infrastructure, for which a power losses estimation approach was proposed and then detailed for the UFCS realized at our department. Even, the dependence of the maximum EV charging rate on the State of Charge (SoC) is considered. These aspects are usually neglected. Thus, starting from the measurements of the maximum charging rate for two commercial EVs, numerical simulations were performed using the proposed algorithm according to two different scheduling policies, i.e., the not-preemptive ‘First Come Best Served’ and the preemptive ‘Round Robin SoC’. The numerical results highlight the difference between the two policies regarding the allocation of resources among vehicles and how the SoC of the BESS affects the overall charging profiles.

A new field-oriented control strategy for induction motor is proposed in the paper. It is called Maximum Torque Per Watt (MTPW) and allows obtaining the minimum value of the sum of the stator and rotor losses due to joule effect, and of the iron losses, for a given value of the reference torque and of the motor speed. Iron losses have been modeled according to Steinmetz equation, separating hysteresis and eddy currents and taking into account the dependence both on the frequency and on the peak value of the flux density. Numerical and experimental results are presented to confirm the validity of the proposed approach, which allows achieving significant improvements in the efficiency of induction motor drive.

The passive balancing (PB) circuits are still the most common solution for the voltage equalization of series-connected battery cells due to the higher reliability, lower cost and control simplicity compared to the active balancing (AB) ones. PB circuits are usually designed considering a desired equalization time and constraints on size and power dissipation of the equalization resistors. However, the performance of the PB circuits strongly depend on the battery cells technology as well as the voltage imbalance condition between the most charged cell and the least charged one of a series-connected battery pack. Therefore, additional parameters need to be considered for optimizing the design of the PB circuits. This paper presents a systematic approach for the design of PB circuits for lithium-ion battery packs that takes into account for real cells operating conditions. An extensive numerical analysis is reported to demonstrate the validity of the proposed methodology.

This paper deals with a new adaptive balancing control algorithm for passive and distributed Battery Management System (BMS) for automotive applications. A calibrated first-order model for the battery cells and thermal model of the BMS slaves unit have been considered in the development of the proposed control. It performs the balancing process of the cells by managing the temperature of the BMS slave unit. Taking into account the thermal constraints of the system, the proposed control dynamically selects the number of the cells to be balanced for each battery module and the optimal temperature of the BMS slave units in order to minimize the equalization time. A deep numerical analysis is presented to validate the performance of the proposed approach.

This paper deals with the design and the implementation of a passive modular battery management system (BMS) for high power battery packs, designed for motorsport applications. The modular structure of the BMS is composed of a power board for the passive balancing of the cells and a control board based on Texas Instruments®BQ76PL455-Q1 device. The proposed system is designed to meet the restricted specifications of a motor racing application like E-Kart. In detail, an optimized BMS algorithm is proposed, which allows achieving high performance in terms of safety and equalization time.

This paper proposes a high-efficiency modulation technique for a cascaded H-bridge (CHB) multi-level converter with an integrated battery energy storage system (BESS) for electric vehicles (EVs) ultra-fast charging station. The proposed modulation technique allows improving the conversion efficiency taking advantage of the several redundancies of the power conversion system. In detail, the choice of the output vector is based on the space vector control, whereas its redundancy is selected with the aim to minimize the actual power losses of the converter.

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.

This paper presents a recursive nonideality compensation method for Voltage Source Inverter (VSI) controlled by Predictive Current Control (PCC) in the field of electrical drives. An accurate mathematical model of the effects of VSI nonlinearities, such as dead time and control delay, is here reported including the magnitude loss and the ripple distortion. For every sampling interval, the exact voltage error and ripple time-shift is calculated using only reference and real phase currents. As soon as a steady-state operation is detected, the voltage error of the entire fundamental period is stored in an array and then used to accurately compensate the VSI output voltage in the next fundamental period. The error array is renewed after every transient and a look-up-table is step-by-step built including the motor operating conditions and related voltage error. Experimental and numerical results validate the effectiveness of the proposed algorithm in reducing the VSI voltage and current distortions and their influence on the motor torque and efficiency.

This paper deals with the optimal sizing and power flow control for grid-connected multi-source power converter system for EV charging infrastructure with integrated energy storage system. The sizing of the power conversion system is based on cost-benefit analysis which takes into account several factors to define the e-mobility scenario and different topologies of power conversion units. The proposed solution allows minimizing the impact of EV charging processes on the electric grid as well as their cost by means of suitable use of a renewable source and an energy storage system. © 2015 IEEE.