An analytical approach is proposed in developing a lumped parameter thermal model for the stator coils of naturally-cooled Axial-Flux Tooth-Coil Permanent-Magnet Machines. The model comprises one tooth, the coil around it, the air channels between coils and the teeth supporting structure; each component is subdivided into cuboidal or arc sub-elements. An expression of the convective exchange coefficient is proposed to take into account the radial position of each tooth. Experimental results validate the proposed model, which can also be used in the design stage of a machine.

Some electromagnetic and thermal aspects concerning special permanent magnet machines, employed in damping seismic oscillations in structural systems, are examined. The power generated by the machine during a seismic event is regulated by a suited converter and delivered to an external resistor. Some modeling features and selected simulations are examined, together with test results obtained by a prototype. Some special tests, aimed to identify the thermal parameters of the machines, are presented and discussed.

This paper presents an analytical study of the air-gap magnetic field of a surface permanent magnet (SPM) linear, slot-less machine with a Halbach PM configuration, under the no-load condition. While other analytical formulations of the magnetic field generated by PMs are available, they exhibit some drawbacks, such as only providing a Fourier series, or being suitable to determine magnetic field average values, but not local magnetic field distributions. On the contrary, the proposed approach allows the determination of a unique, closed-form formulation for the slot-less machine air-gap field. This is obtained starting from the complex expression of the magnetic field of a conductor, inside the air gap, between two parallel smooth iron surfaces, obtained by means of the method of images. The magnetic field due to an infinitesimal conductor belonging to a current sheet is then integrated along a segment, providing the expression of the magnetic field due to the corresponding linear current density distribution, for current sheets perpendicular or parallel to the iron surfaces. Any Halbach PM segment disposition can, hence, be obtained via a suitable combination of field distributions generated by couples of current sheets with perpendicular and parallel orientation. Lastly, the no-load magnetic field expression with a Halbach array of PMs is retrieved. The proposed analytical model provides an accurate representation of the magnetic field distribution produced by any Halbach array, with an arbitrary number of segments and orientations. Additionally, the results obtained from the proposed analytical expressions are compared with FEM simulations realized by commercial software, and show an excellent agreement.

This paper deals with an adaptive multi-objective optimisation process for the design of electric motors by means of supervised learning techniques. The process is based on a multi-objective optimisation approach which involves many objective functions (mass, rotor inertia, average total losses), constraints (geometrical feasibility, current and voltage limit, torque-speed profile, thermal constraints) and a large number of design variables (rotor, stator and winding parameters) that describe the physical properties of the motor. The design of computer experiments is based on a Low Discrepancy Sequence (LDS). The electric motor performance indices are evaluated through multiphysics simulations (electromagnetic, thermal) carried out by Motor-CAD™ software. Artificial Intelligence (AI) is adopted to approximate the physical model behaviour. Artificial Neural Networks (ANN) are exploited, in this way a large set of design variables combinations can be investigated with a reasonable computational effort. Since the multi-objective optimisation of electric motor is particularly complex, a special algorithm had to be developed to find Pareto-Optimal solutions. A new Adaptive Pareto Algorithm allows to reduce the computational cost and to achieve an even distribution of the optimal solutions in the Pareto optimal front. The algorithm is particularly effective when it is integrated with a global approximation model, the infill operation allows to improve the metamodel approximation. The derived electric motors show the best compromise performances among millions of possible configurations.