In this paper, a new loss minimization control algorithm for inverter-fed permanent-magnet synchronous motors (PMSMs), which allows for the reduction of the power losses of the electric drive without penalty on its dynamic performance, is analyzed, experimentally realized, and validated. In particular, after a brief recounting of two loss minimization control strategies, namely, the "search control" and the "loss-model control," both a new modified dynamic model of the PMSM (which takes into account the iron losses) and an innovative "loss-model" control strategy are presented. Experimental tests on a specific PMSM drive employing the proposed loss minimization algorithm have been performed, aiming to validate the actual implementation. The main results of these tests confirm that the dynamic performance of the drive is maintained, and in small motors enhancement up to 3.5% of the efficiency can be reached in comparison with the PMSM drive equipped with a more traditional control strategy.

We present a numerical study of stochastic resonance in magnetic systems described by Preisach hysteresis model. It is shown that stochastic resonance occurs in these systems. Specifically, the signal-to-noise ratio (SNR) and the signal amplification (SA) present a maximum as a function of noise intensity. We also found that the hysteresis loops, dynamically described by the system, are strongly modified near the maxima of SNR and of SA.

We investigate the stochastic resonance phenomenon in a physical system based on a tunnel diode. The experimental control parameters are set to allow the control of the frequency and amplitude of the deterministic modulating signal over an interval of values spanning several orders of magnitude. We observe both a regime described by the linear-response theory and the nonlinear deviation from it. In the nonlinear regime we detect saturation of the power spectral density of the output signal detected at the frequency of the modulating signal and a dip in the noise level of the same spectral density. When these effects are observed we detect a phase and frequency synchronization between the stochastic output and the deterministic input.

Battery models have gained great importance in recent years, thanks to the increasingly massive penetration of electric vehicles in the transport market. Accurate battery models are needed to evaluate battery performances and design an efficient battery management system. Different modeling approaches are available in literature, each one with its own advantages and disadvantages. In general, more complex models give accurate results, at the cost of higher computational efforts and time-consuming and costly laboratory testing for parametrization. For these reasons, for early stage evaluation and design of battery management systems, models with simple parameter identification procedures are the most appropriate and feasible solutions. In this article, three different battery modeling approaches are considered, and their parameters’ identification are described. Two of the chosen models require no laboratory tests for parametrization, and most of the information are derived from the manufacturer’s datasheet, while the last battery model requires some laboratory assessments. The models are then validated at steady state, comparing the simulation results with the datasheet discharge curves, and in transient operation, comparing the simulation results with experimental results. The three modeling and parametrization approaches are systematically applied to the LG 18650HG2 lithium-ion cell, and results are presented, compared and discussed.

In this paper noise enhanced stability in magnetic systems is studied by both an Ising-type model and a Preisach–Arrhenius model as well as a dynamic Preisach model. It is shown that in one nonequilibrium Ising system noise enhanced stability occurs and that dynamic Preisach model has the capability to predict the occurrence of noise enhanced stability in magnetic systems. On the contrary, in a Preisach–Arrhenius model of a single quadrant magnetic material, noise enhanced stability is not detected.