Geared devices are commonly used to match the operating speed and torque of the power source with the second-mover. Such geared devices are usually mechanical gears. As counterparts of the conventional mechanical gears, magnetic gears are becoming promising devices, mainly due to the merits of physical isolation between moving parts, no gear lubrication, no mechanical fatigue, inherent overload protection and reduced maintenance, etc. Recently, several new topologies have been proposed to achieve better performances. In this paper, a general review of magnetic gears is presented, including a discussion of the most common and upcoming topologies and a description of the working principle. A comparison of different topologies is carried out, in terms of gear ratio and torque density. The main computational techniques generally adopted for coaxial magnetic gears are listed and described. Some key aspects concerning the design, the existed challenges and potential applications are also discussed. This paper aims to provide a comprehensive overview of the magnetic gear for readers working in this field.

The main aim of this paper is the imposition of constraints for the multiobjective optimizations of magnetic gears. In order to reduce the computational effort, a semianalytic design procedure is adopted, providing a fast simulation tool that can be embedded within an optimization loop. Magnetic, thermal, and mechanical constraints are discussed and embedded into a stochastic algorithm, in order to identify immediately infeasible geometries. The algorithm that has been developed provides a useful initial design tool that can be adopted to locate the optimal solutions in the search space. This paper also presents the multistage magnetic gear topology as the counterpart of the multistage mechanical gearboxes.

Mechanical differentials are essential drivetrain components of automobiles and other wheeled vehicles, allowing the outer drive wheel to rotate faster than the inner drive wheel during turns. This paper presents a comprehensive description of a novel and recently patented alternative based on magnetic gears, which achieves the same functionality while providing distinctive advantages such as reduced maintenance, absence of lubrication, high efficiency. The paper describes the operation principle of such magnetic gear differential and two alternative constructive options, provides a dynamic model which allows the study of the device in driving conditions, presents a description of a prototype and validates finite element simulations with experimental results.

This paper proposes a procedure for computing magnetic losses in coaxial magnetic gears. These magnetic structures are made of permanent magnets and ferromagnetic poles in relative motion transferring torque between two shafts in a contactless way. The loss computation in magnetic materials is crucial to define the system performance. The flux distribution inside the iron parts is computed by means of the finite element method and a model of iron losses taking into account the rotational nature of the flux loci is applied. The procedure highlights where the major loss sources are present and gives the opportunity to evaluate some corrective measures to reduce their effects. Particular attention is devoted to the 2D modeling in presence of permanent magnets segmentation.

The traditional approaches in material research and hardware design are insufficient to address the evolving Operation and Maintenance (O&M) demands in contemporary power electronics. Overengineering and data acquisition practices lead to unsustainable costs and reduced profit margins. Digital Twins (DTs), defined as real-time simulation models of physical systems, emerge as promising solutions to meet stringent O&M requirements. In power electronics, DTs offer significant potential in thermal management, crucial for control performance, safety, and system lifespan. This paper aims to analyze the development of computationally efficient and high-fidelity DTs tailored for power electronics applications, emphasizing their predictive reliability of critical temperatures. The proposed physics-based approach is enhanced by integrating data-driven Artificial Intelligence (AI)-based techniques to achieve this goal. The predictive reliability of the DTs produced through this workflow is then experimentally validated for a power electronic converter designed for induction heating applications. Experimental results show that the integration of data-driven AI-based techniques allows for maintaining very high predictive accuracy even when multiple semiconductor component suppliers are considered for the same product, which is often the case for industrial products. Additionally, by implementing and executing the DT in a low-power microprocessor, the real-time execution is demonstrated, affirming its practical applicability.

The integration of electric motors into various industrial and automotive applications emphasizes the critical necessity for reliable performance and operational efficiency. The advent of advanced digital technologies offers opportunities for predictive maintenance strategies. Digital Twins (DTs), mathematical models simulating a system’s physical behavior in real-time, present a transformative approach to enhance real-time monitoring of critical quantities, which is imperative to improve operational efficiency and minimize downtime. In this paper, we explore the feasibility and efficacy of deploying real-time physics-based DTs for condition monitoring in electric motor applications. Particularly, we focus on employing on-the-edge DTs, implemented on low-power onboard microprocessors, ensuring continuous communication with the physical asset for reliable real-time monitoring. The study applies DT technology to a high-voltage high-density Electric Vehicle (EV) motor, assessing its predictive capabilities in a real-world scenario. Results showcase the potential of DTs in revolutionizing condition monitoring, thereby meeting the evolving operational and maintenance requirements of contemporary electric motor systems.