This paper systematically covers the significant developments of the last decade, including surrogate modeling of electrical machines and direct and stochastic search algorithms for both single- and multi-objective design optimization problems. The specific challenges and the dedicated algorithms for electric machine design are discussed, followed by benchmark studies comparing response surface (RS) and differential evolution (DE) algorithms on a permanent-magnet-synchronous-motor design with five independent variables and a strong nonlinear multiobjective Pareto front and on a function with eleven independent variables. The results show that RS and DE are comparable when the optimization employs only a small number of candidate designs and DE performs better when more candidates are considered.

A Review of Recent Developments in Electrical Machine Design Optimization Methods With a Permanent-Magnet Synchronous Motor Benchmark Study

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Vector Control And Dynamics Of Ac Drives

This paper presents a metaheuristic algorithm inspired in evolutionary computation and swarm intelligence concepts and fundamentals of echolocation of micro bats. The aim is to optimize the mono and multiobjective optimization problems related to the brushless DC wheel motor problems, which has 5 design parameters and 6 constraints for the mono-objective problem and 2 objectives, 5 design parameters, and 5 constraints for multiobjective version. Furthermore, results are compared with other optimization approaches proposed in the recent literature, showing the feasibility of this newly introduced technique to high nonlinear problems in electromagnetics.

Bat-Inspired Optimization Approach for the Brushless DC Wheel Motor Problem

Several factors including fossil fuels scarcity, prices volatility, greenhouse gas emissions or current pollution levels in metropolitan areas are forcing the development of greener transportation systems based on more efficient electric and hybrid vehicles. Most of the current hybrid electric vehicles use electric motors containing powerful rare-earth permanent magnets. However, both private companies and estates are aware of possible future shortages, price uncertainty and geographical concentration of some critical rare-earth elements needed to manufacture such magnets. Therefore, there is a growing interest in developing electric motors for vehicular propulsion systems without rare-earth permanent magnets. In this paper this problematic is addressed and the state-of-the-art of the electric motor technologies for vehicular propulsion systems is reviewed, where the features required, design considerations and restrictions are addressed.

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Rare-earth-free propulsion motors for electric vehicles: A technology review

This paper presents a multiphysics modeling of a switched reluctance motor (SRM) to simulate the acoustic radiation of the electrical machine. The proposed method uses a 2-D finite-element model of the motor to simulate its magnetic properties and a multiphysics mechatronic model of the motor and controls to simulate operating conditions. Magnetic forces on the stator are calculated using finite-element analysis and are used as the excitation on a forced response analysis that contains a finite-element model of the motor stator structure. Finally, sound power levels are calculated using the boundary element method. Simulation results of the model are shown and compared with experimental measurements for a four-phase 8/6 SRM.

Multiphysics NVH Modeling: Simulation of a Switched Reluctance Motor for an Electric Vehicle

This paper derives the analytical characterization of the Maxwell radial vibrations due to pulsewidth modulation (PWM) supply in induction machines and, particularly, in traction motors supplied with an asynchronous switching frequency. The number of nodes and the velocity of these particular force waves are experimentally validated by visualizing some operational deflection shapes of the stator. It is shown that according to the switching frequency, these forces can be responsible for high magnetic-noise levels during starting and braking. A simple rule to avoid PWM noise is then proposed and applied to an industrial traction motor. Experimental results show that the choice of the switching frequency can have a 15-dB impact on the sound power level emitted by the motor during starting and that a lower switching frequency can sometimes lead to lower magnetic noise. In agreement with analytical predictions, the new proposed switching frequency that avoids resonances between PWM exciting forces and corresponding stator modes reduces the magnetic noise of 5 dB during starting.

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Characterization and Reduction of Audible Magnetic Noise Due to PWM Supply in Induction Machines

This paper describes the modeling of a permanent magnet synchronous machine (PMSM) by using lumped models (LMs). Designing electrical machines necessarily involves several fields of physics, such as electromagnetics, thermics, mechanics, and acoustics. Magnetic, electrical, electronic, and thermal parts are represented by LMs, whereas vibro-acoustic and mechanical parts are represented by analytical models. The aim of this study is to build a design model of a PMSM for traction applications. Each model is parameterized to optimize the machine. The method of taking into account saturation and movement is described. These fast, LMs make it possible to couple the software used with optimization tools. Simulation results are presented and compared with the finite-element method and the experiments performed.

/pdf/multiphysics-modeling-of-a-permanent-magnet-synchronous-1086xogzpr.pdf

Multiphysics Modeling of a Permanent Magnet Synchronous Machine by Using Lumped Models

Induction motor design requires making numerous tradeoffs, especially when including electromagnetic noise criterion besides usual criteria like efficiency and cost. Moreover, adding the noise objective significantly increases computational time as it must be evaluated at variable speed in order to take into account resonance effects. In that case, the application of multiobjective optimization algorithms can be hard for their computational cost as for the difficulty to interpret multidimensional results in both design variables and objectives spaces. This paper first describes a fast analytical model of a variable-speed induction machine which calculates both motor performances and sound power level of electromagnetic origin. This model is then coupled to Nondominating Sorting Genetic Algorithm (NSGA-II) in order to perform global constrained optimizations with respect to several objectives (e.g., noise level, efficiency and material cost). As induction machine design involves both continuous and discrete variables, a modified NSGA-II algorithm handling mixed variables is detailed. Finally, some optimization results are presented and analyzed by the aid of several visualization tools.

Multiobjective Optimization of Induction Machines Including Mixed Variables and Noise Minimization

Although many empirical rules have been established for correctly choosing the number of stator and rotor slots so as to limit the audible magnetic noise level radiated by induction machines, these rules never take into account the stator natural frequencies or the fact that the motor is run at variable speed. In this paper, we present a fast simulation tool for the variable-speed magnetic noise emitted by induction machines, based on fully analytical models. On the basis of these models, we derive and experimentally validate an analytical expression for magnetic vibrations due to slotting reluctance harmonics, confirming the prime importance of slot combination in magnetic noise radiation. We ran simulations on a 700-W squirrel-cage motor in order to quantify the noise emitted by all possible combinations of slot numbers in two- and three-pole pairs, including odd slot numbers. We thus obtained a database that efficiently replaces the old empirical rules for slot combination numbers and helps in designing quiet induction motors. Similar databases can be built for other power ranges.

Optimal Slot Numbers for Magnetic Noise Reduction in Variable-Speed Induction Motors

This paper presents a fully analytical model of the electromagnetic and vibro-acoustic behavior of variable-speed squirrel-cage induction machines. This model is integrated in a fast simulation tool that can be used to design motors with low magnetic noise levels on their whole speed range. Some simulation results at variable speed are favorably compared with experiments at both vibratory (spectrograms) and acoustic levels (sonagrams) on several motors, on a wide power range (from 700 W to 250 kW). These extensive tests show that the model correctly predicts the main exciting forces, motor natural frequencies, and resonances during adjustable-speed drive. The simulation tool, called Diva, has been successfully applied to design a new low-noise ALSTOM motor.

/pdf/prediction-of-audible-magnetic-noise-radiated-by-adjustable-1iwvfh2y95.pdf

Michel Hecquet

Papers

Characterization and Reduction of Audible Magnetic Noise Due to PWM Supply in Induction Machines

Multiphysics Modeling of a Permanent Magnet Synchronous Machine by Using Lumped Models

Multiobjective Optimization of Induction Machines Including Mixed Variables and Noise Minimization

Optimal Slot Numbers for Magnetic Noise Reduction in Variable-Speed Induction Motors

Prediction of Audible Magnetic Noise Radiated by Adjustable-Speed Drive Induction Machines