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.

Rollover detection and prevention are among the most critical aspects affecting the stability and safety assessment of heavy vehicles, especially for off-road driving applications. This topic has been studied in the past and analyzed in depth in terms of vehicle modelling and control algorithms design able to prevent the rollover risk. However, it still represents a serious problem for automotive carmakers due to the huge counts among the main causes for traffic accidents. The risk also becomes more challenging to predict for off-road heavy vehicles, for which the incipient rollover might be triggered by external factors, i.e., road irregularities, bank angles as well as by aggressive input from the driver. The recent advances in road profile measurement and estimation systems make road-preview-based algorithms a viable solution for the rollover detection. This paper describes a model-based formulation to analytically evaluate the load transfer dynamics and its variation due to the presence of road perturbations, i.e., road bank angle and irregularities. An algorithm to detect and predict the rollover risk for heavy vehicles is also presented, even in presence of irregular road profiles, with the calculation of the ISO-LTR Predictive Time through the Phase-Plane analysis. Furthermore, the artificial intelligence techniques, based on the recurrent neural network approach, is also presented as a preliminary solution for a realistic implementation of the methodology. The paper finally assess the efficacy of the proposed rollover predictive algorithm by providing numerical results from the simulation of the most severe maneuvers in realistic off-road driving scenarios, also demonstrating its promising predictive capabilities.

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.