Modeling and Validation of the Radial Force Capability of Bearingless Hysteresis Drives

Circosta, Salvatore; Galluzzi, Renato; Bonfitto, Angelo; Castellanos, Luis M.; Amati, Nicola; Tonoli, Andrea

doi:10.3390/act7040069

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…As the current overshoot starts saturating, the force requested to the current is higher. It is worth mentioning that, as stated in [45], the SHMM-based magnetic suspension features a reduced force capability when compared to common solutions with soft iron.…”

Section: Response To the External Disturbance At Standstillmentioning

confidence: 98%

Analysis of a Shaftless Semi-Hard Magnetic Material Flywheel on Radial Hysteresis Self-Bearing Drives

et al. 2018

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Flywheel Energy Storage Systems are interesting solutions for energy storage, featuring advantageous characteristics when compared to other technologies. This has motivated research effort focusing mainly on cost aspects, system reliability and energy density improvement. In this context, a novel shaftless outer-rotor layout is proposed. It features a semi-hard magnetic FeCrCo 48/5 rotor coupled with two bearingless hysteresis drives. The novelty lies in the use of the semi-hard magnetic material, lending the proposed layout advantageous features thanks to its elevated mechanical strength and magnetic properties that enable the use of bearingless hysteresis drives. The paper presents a study of the proposed layout and an assessment of its energetic features. It also focuses on the modeling of the radial magnetic suspension, where the electromagnets providing the levitating forces are modeled through a one-dimensional approach. The Jiles-Atherton model is used to describe the magnetic hysteresis of the rotor material. The proposed flywheel features a mass of 61.2 kg, a storage capability of 600 Wh at the maximum speed of 18,000 rpm and achieves an energy density of 9.8 Wh/kg. The performance of the magnetic suspension is demonstrated to be satisfactory and the influence of the hysteresis of the rotor material is highlighted.Actuators 2018, 7, 87 2 of 22 lower component count and more compact layout than common solutions with separate electric motor and bearing system.FESSs provide various advantages compared to other energy storage technologies, including high power rating, long lifetime, fast recharging, short response time and high power density. These devices require little periodic maintenance, have a very low environmental impact, their charge/discharge cycles do not degrade storage capacity, and their state of charge is easily obtained by measuring the rotational speed [17]. The use of self-bearing drives in flywheel applications lends the system more compactness, higher reliability, lower component count and mass saving, thus strengthening the FESS advantages [18][19][20][21]. On the other hand, one of the main drawbacks of FESSs is the low energy density when compared to other storage technologies [1]. This aspect has motivated a research effort devoted to investigating higher energy density layouts. One solution is represented by flywheels made of composite materials, which achieve higher energy density compared to high-strength steel flywheels thanks to their light-weight, along with the very large mechanical strength permitting very high rotational speeds. A rim made of composite material can be much lighter than a steel flywheel when storing a comparable amount of energy. In this configuration, the rotor is usually in the shape of a hollow cylinder made of composite material such as carbon fiber reinforced plastic.Since the energy density depends directly on the mass of the whole FESS, it can be improved by designing as much mass of the system to contribute to storing energy. The index allowing quantif...

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Section: Response To the External Disturbance At Standstillmentioning

confidence: 98%

Analysis of a Shaftless Semi-Hard Magnetic Material Flywheel on Radial Hysteresis Self-Bearing Drives

et al. 2018

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“…Starting to increase the external field again, the closed loop is obtained, as shown in Figure 1. As pointed out in [4], hysteresis motors can be a viable solution also for high-speed applications due to their simple and robust rotor structure, as well as their rotor-dynamics features [5].…”

Section: Working Principlementioning

confidence: 99%

Fast Torque Computation of Hysteresis Motors and Clutches Using Magneto-static Finite Element Simulation

et al. 2019

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Hysteresis motor and clutches have several advantages, such as constant torque from zero to synchronous speed, low torque ripple, and low fabrication cost. Low efficiency and power factor are the main features that have limited the application of this type of electrical machine to few applications. Being a niche argument, little literature has addressed the problem of analytical and finite element (FE) modelling of hysteresis electrical machines. This paper first describes the most important contributions of the literature on the analytical and FE modelling of hysteresis motors and clutches and then proposes a method for the fast computation of its performance. The proposed procedure consists of two steps: first, a magneto-static FE simulation is performed considering the normal magnetization curve of the hysteresis material; then, the average torque is computed by a post-processing analysis. The proposed method is used to analyze a hysteresis clutch and the obtained results are compared with those achieved using a commercial finite element software that implements a vector hysteresis model.

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“…They are widely used as traction motors for transport vehicles and in-wheel motors for electric vehicles [13]. Due to low-weight and compact size, IPMS motors and generators have been gaining increasing attention for more electric aircraft applications where compact lightweight high-torque machines are needed [14,15].…”

Section: Introductionmentioning

confidence: 99%

Towards End-to-End Deep Learning Performance Analysis of Electric Motors

2021

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Convolutional Neural Networks (CNNs) and Deep Learning (DL) revolutionized numerous research fields including robotics, natural language processing, self-driving cars, healthcare, and others. However, DL is still relatively under-researched in physics and engineering. Recent works on DL-assisted analysis showed enormous potential of CNN applications in electrical engineering. This paper explores the possibility of developing an end-to-end DL analysis method to match or even surpass conventional analysis techniques such as finite element analysis (FEA) based on the ability of CNNs to predict the performance characteristics of electric machines. The required depth in CNN architecture is studied by comparing a simplistic CNN with three ResNet architectures. Studied CNNs show over 90% accuracy for an analysis conducted under a minute, whereas a FEA of comparable accuracy required 200 h. It is also shown that training CNNs to predict multidimensional outputs can improve CNN performance. Multidimensional output prediction with data-driven methods is further discussed in context of multiphysics analysis showing potential for developing analysis methods that might surpass FEA capabilities.

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Modeling and Validation of the Radial Force Capability of Bearingless Hysteresis Drives

Cited by 5 publications

References 27 publications

Analysis of a Shaftless Semi-Hard Magnetic Material Flywheel on Radial Hysteresis Self-Bearing Drives

Analysis of a Shaftless Semi-Hard Magnetic Material Flywheel on Radial Hysteresis Self-Bearing Drives

Fast Torque Computation of Hysteresis Motors and Clutches Using Magneto-static Finite Element Simulation

Towards End-to-End Deep Learning Performance Analysis of Electric Motors

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