Ciro Attaianese

Resolvers are widely used in safety-critical and harsh environments due to their robustness against temperature, vibration, and electromagnetic interference. Conventional resolver-to-digital converters (RDCs) often rely on monolithic integrated circuits (ICs) integrating excitation, demodulation, and tracking loops, which add cost and board footprint while limiting flexibility. This paper presents a fully embedded RDC that eliminates dedicated RDC ICs by exploiting PWM and ADC resources of modern microcontrollers. The method combines: (i) a GaN half-bridge resonant excitation engineered to align resolver sine/cosine peaks with ADC sample-and-hold instants; (ii) differential 16-bit ADC acquisition with hardware PWM–ADC synchronization at sub-10-ns resolution; and (iii) a software phase-locked loop (PLL) with quality-driven autotuning that minimizes the ripple of a monitoring function to optimize sampling timing, and performs automatic gain/offset calibration. Experiments on a 1.1-kW PMSM test bench comparing the proposed method with a commercial RDC-IC channel show comparable or superior position/speed accuracy (ripple < 0.5%), finer velocity quantization, robust tracking during acceleration, and significant reductions in cost and PCB area. The results indicate that excitation engineering and metric-based autotuning are effective enablers for next-generation embedded resolver interfaces in electric drives.

This article presents a closed-form modeling framework that links DC offset faults in the phase-current sensors of a permanent magnet synchronous machine (PMSM) drive to a distinctive signature in the battery current, which is typically measured by the battery management systems (BMSs) in battery-electric vehicles. Starting from the $dq$ -axis model of the PMSM and the field-oriented control (FOC) structure, the proposed approach analytically derives the steady-state response of the controlled phase currents in the presence of arbitrary DC sensor offsets. By linearizing the DC-link power balance, it is demonstrated that the battery current exhibits a harmonic component at the angular frequency $p \omega _{r}$ , whose phasor amplitude and phase depend on: 1) the magnitude and angle of the offset space vector; 2) the machine and controller parameters; and 3) the DC-link dynamics (capacitor and battery Thevenin resistance). A compact expression is obtained for the battery current phasor as a function of the three per-phase DC offsets, which is used to enable detection, isolation, and quantitative estimation of the fault without additional sensors or intrusive tests. The proposed model clarifies operating-point dependencies and highlights intrinsic limitations at very low speeds. Experimental results and sensitivity analysis validate the analytical model and confirm the effectiveness of the proposed approach.

DC offset faults of phase current sensors in AC Permanent Magnet Synchronous Machines (PMSMs) are widely studied in the literature since they can lead to heavy torque oscillations and thermal instability of the drive. This paper presents the experimental implementation of an algorithm for the real time estimation and compensation of these faults in PMSM drives controlled by Field Oriented Control (FOC). The method uses equations derived from the analytically closed form solution of the steady-state d-q axes model of PMSM, taking into account the full FOC dynamics and DC offset faults of the phase current sensors. The algorithm allows for detecting, isolating, and estimating the fault conditions by identifying the phase current offsets, both when using three sensors and two current sensors. The real time compensation is based on the developed model, does not require additional hardware, and uses only a digital filter of the measured phase currents for compensating online the offset fault. Experimental results and sensitivity analysis to the motor parameters variations are presented to validate the proposed algorithm.

In the paper, an algorithm for field oriented controlled permanent magnet synchronous machine (PMSM) drives is carried out, which allows for isolating, detecting and estimating both the scaling gain and the offset faults, affecting separately or simultaneously one or more phase current sensors. In previous papers, the authors proposed an analytical model able to determine the currents in a PMSM drive affected by offset and scaling gain faults. Its analytical solution required the solution of an overdetermined system of equations, performed by means of a new methodology developed by the authors. In this paper that model is analytically inverted to carry out an algorithm for detection, isolation, and estimation (FDIE) of scaling gain and offset faults affecting current sensors of PMSM current signature. The output of the proposed FDIE algorithm is represented by the estimated values of scaling gains and offsets of the three current sensors of the drive. To the best of knowledge of the authors, a similar algorithm does not exist in the literature, and this justifies the innovative contribution of this paper. Eventually, an extensive experimental validation confirms the effectiveness of the proposed FDIE algorithm.

PPP (Partial Power Processing) involves designing a converter that splits the processed power between two converters: one with a fixed conversion ratio and high efficiency, and the other with a variable conversion ratio and lower efficiency. Although the potential of this class of converter can be highly effective, it is not suitable for all applications. Literature lacks consistent terminology and clear definitions of which converter topologies perform true PPP. Some papers analyze the PPP as a general concept, others comparing specific conversion topologies. This paper aims to test a flyback converter prototype in the two layouts, explaining efficiency variations through advanced modeling and design methods. As expected, the partial power converter outperforms the full power converter. Performance is evaluated in terms of semiconductor component stress, snubber circuit impact, and overall efficiency.

This paper presents the closed-form analytical determination of the current signature of a surface-mounted permanent magnet synchronous motor drive controlled by means of field oriented control strategy, when a fault occurs in one or more current sensors. Both DC offset and gain fault for all phase current sensors are considered. The proposed model includes the response of the control loop and related gains, eliminating the need for phase voltage sensors and complicated state estimation. However, the proposed approach requires the solution of an overdetermined system of equations. Then a new methodology of solution is proposed to save computing time, as an alternative to the classical one based on the Moore-Penrose pseudoinverse matrix. Experimental results and sensitivity analysis to parameter variations confirm the validity of the proposed model.

The detection and the compensation of current sensor faults in an AC Permanent Magnet Motor drive play an important role in avoiding mechanical and electrical damages. This paper deals with a new online compensation technique for current offset faults of stator current sensors in a Surface Permanent Magnet Synchronous Machine (SPMSM) electrical drives. The compensation algorithm is based on an accurate analytical model of the SPMSM electrical drive that takes into account the response of the Field Oriented Control (FOC) loop during the fault conditions. Staring from the proposed model, the method allows for effectively estimating and then compensating the offset faults present in the measured currents. The goodness of the proposed method is verified by means of numerical simulations.

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.

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.