Hybrid algorithm for multi-objective optimization of PMSM using massively distributed finite element analysis

Krotsch, Jens; Piepenbreier, Bernhard

doi:10.1109/optim.2010.5510429

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…The calculation of the total loss was given by the next equations. The core loss (p c ) is calculated from the amount of the hysteresis loss and the eddy current loss [5,6].…”

Section: The Model Of the Pms Motormentioning

confidence: 99%

Design Considerations by the Optimisation of PMS Motors

Kollar

2015

Acta Tech Jaur

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The determination of the targets, the input, implicit and design parameter is very important part in the design optimization of the permanent magnetic synchronous (PMS) motors for electric vehicles. The connections were defined between them, investigations were made which assisted to the appropriate optimization. In this paper the researches for the design optimization are presented which define the targets correctly and describes the results of the calculations and simulations of the slot fill factor.

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“…The calculation of the total loss was given by the next equations. The core loss (p c ) is calculated from the amount of the hysteresis loss and the eddy current loss [5,6].…”

Section: The Model Of the Pms Motormentioning

confidence: 99%

Design Considerations by the Optimisation of PMS Motors

Kollar

2015

Acta Tech Jaur

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“…To determine the remoteness Krotsch [17] calculates the local density in and . In this work the number of particles enclosed by a hyper-cube around every non-dominated particle with the size and the average distance of the enclosed particles are calculated -the same is done for the objective space which provides the measures and .…”

Section: Pre-evaluation and Spawningmentioning

confidence: 99%

Kriging-Assisted Multi-Objective Particle Swarm Optimization of permanent magnet synchronous machine for hybrid and electric cars

Bittner

Hahn

2013

2013 International Electric Machines &Amp; Drives Conference

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In this paper a novel combination of a multiobjective particle swarm optimization algorithm and a Kriging metamodel is presented, which is used for the design optimization of a permanent magnet synchronous machine (PMSM) for electric and hybrid cars. As the evaluation of mostly conflicting objective functions requires computational expensive numerical field simulations, a fast converging optimization algorithm which depends on only a few exact evaluations is needed. Instead of running exact calculations on every design the objective values are estimated by means of a Kriging model in advance and only the most promising design candidates are being computed in a more accurate way. Furthermore, good solutions are searched for directly on the metamodel and particles are spawned at the corresponding positions in the parameter space. The performance of the proposed algorithm is investigated with the help of an analytical test case and used for design optimization of a 10 poles PMSM with 11 parameters and 3 objectives.

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“…Artificial intelligence techniques have been used in the design optimization of PMSMs [4,5]. Because of the nonlinear/complex structures of PMSMs, stochastic methods are most suitable for design optimization studies and consequently the number of studies of artificial intelligence techniques in engineering optimization has also increased depending on the new algorithms.…”

Section: Introductionmentioning

confidence: 99%

An intelligent design optimization of a permanent magnet synchronous motor by artificial bee colony algorithm

Mutluer¹,

Bilgin²

2016

Turk J Elec Eng & Comp Sci

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The artificial bee colony algorithm is one of the latest stochastic methods based on swarm intelligence. The algorithm simulates the foraging behavior of honeybees. The structure of the algorithm is quite simple and its coding is very easy. This paper proposes a design optimization based on geometrical variables to obtain a highly efficient surfacemounted permanent magnet synchronous motor with concentrated winding by use of the artificial bee colony algorithm.Input parameters for the algorithm are the geometrical variables of the motor. This approach is more advantageous than finite element analysis requiring a long period of time. Results of the artificial bee colony algorithm are compared with results of a genetic algorithm and checked with a commercial design program. The results emphasize the effectiveness of the algorithm on the design optimization of the permanent magnet synchronous motor.

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Hybrid algorithm for multi-objective optimization of PMSM using massively distributed finite element analysis

Cited by 9 publications

References 18 publications

Design Considerations by the Optimisation of PMS Motors

Design Considerations by the Optimisation of PMS Motors

Kriging-Assisted Multi-Objective Particle Swarm Optimization of permanent magnet synchronous machine for hybrid and electric cars

An intelligent design optimization of a permanent magnet synchronous motor by artificial bee colony algorithm

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