Magnetostrictive actuators play an important role in the perception of usefulness of smart materials and devices. Their applications are potentially wider than that of piezoelectric actuators because of the higher energy density and intrinsic robustness. However, the non-negligible hysteresis and complexity of their characteristics make the design and control quite difficult and has limited their diffusion in industrial applications. Nevertheless, the scientific literature presents a wide offer of results in design and geometries, modeling and control that may be exploited for applications. This paper gives a reasoned review of the main results achieved in the literature about design, modeling and control of magnetostrictive actuators exploiting the direct effects of magnetostriction (Joule and Wiedemann). Some perspectives and challenges about magnetostrictive actuators development are also gathered.

Electric vehicles (EVs) penetration growth is essential to reduce transportation-related local pollutants. Most countries are witnessing a rapid development of the necessary charging infrastructure and a consequent increase in EV energy demand. In this context, power demand forecasting is an essential tool for planning and integrating EV charging as much as possible with the electric grid, renewable sources, storage systems, and their management systems. However, this forecasting is still challenging due to several reasons: the still not statistically significant number of circulating EVs, the different users’ behavior based on the car parking scenario, the strong heterogeneity of both charging infrastructure and EV population, and the uncertainty about the initial state of charge (SOC) distribution at the beginning of the charge. This paper aims to provide a forecasting method that considers all the main factors that may affect each charging event. The users’ behavior in different urban scenarios is predicted through their statistical pattern. A similar approach is used to forecast the EV’s initial SOC. A machine learning approach is adopted to develop a battery-charging behavioral model that takes into account the different EV model charging profiles. The final algorithm combines the different approaches providing a forecasting of the power absorbed by each single charging session and the total power absorbed by charging hubs. The algorithm is applied to different parking scenarios and the results highlight the strong difference in power demand among the different analyzed cases.

Magnetostrictive actuators show interesting performances related to high-precision actuation with high-energy density, which focused the interest of research in the past years. Nevertheless, the output deformation is related to the current and the applied stress, through a complex multi-input multi-output (MIMO) hysteresis process. As a consequence, a reliable and accurate control task cannot neglect the stress monitoring. Such task, if performed by an ad hoc stress sensor, e.g., a load cell, will face with the sensor's placement in the device, resulting in an increase in cost, complexity, and bulkiness of the system. This issue can be circumvent if the current and magnetic flux measurements, available in the control chain, are exploited. To this aim, a novel self-sensing approach to deliver real-time estimation of the stress is presented in this paper, involving some recent results of a thermodynamic compatible MIMO model of hysteresis for magnetostrictive materials.