-The study of thermal behaviour is useful to identify causes of failure in electrical machines. This work details a two dimensional model for computing the magnetic and thermal finite element solution of induction motor using a weak coupling algorithm. In order to improve the accuracy of the adopted numerical formulation, the decrease of the electric conductivity with temperature is taking into account.

The paper deals with the application of the artificial bee colony (ABC) method for hysteresis parameters identification. For the first time, the ABC method will be applied on hysteresis model optimization. For this purpose, two hysteresis models are tested: the first is based on a physical magnetic material behavior, which is Jiles-Atherton and the second is simpler, Fröhlich hysteresis model built on mathematical considerations. This method's robustness will be assessed, by comparing the experimental signals to model results. I. INTRODUCTION Electrical engines and electromagnetic machines are designed according to the customer requests and safety norms. Furthermore, the advanced technologies have to target the optimized operating conditions. For this purpose, it is necessary to build robust numerical modeling based on the magnetic material behavior. In literature, several models have been proposed and studied in large material application. It can be splitted into families: analytical ones: such as Rayleigh [1], Potter [2] or Fröhlich [3][4][5], which offer comfortable implementation in finite element code, but still limited to low or high magnetization behavior description. The second family is based on physical considerations such as the Preisach model [6] and the Jiles-Atherton model [7]. These are considered as the most robust and reliable models can be applied on soft and hard magnetic materials by parameter identification process. This task remains complex due to the inter dependency of each parameter to the other. The first investigation realized in this topic is based on iterative procedure [8]. It leads to good approximation of parameter's values but often presents convergence problems and can engender numerical instability. Other authors have proposed deterministic optimization methods [9][10], where the results were successful but request a great time calculation. Since two decades, stochastic optimization methods like generic algorithms and neural network [11][12][13] have been investigated. Other works combined between generic algorithms and simulated annealing [14]. It results accurate solution in a very short time. Recently, swarm intelligence method is used in electromagnetic applications. The most famous one is the particle swarm optimization (PSO) [15], inspired by the collective behavior of birds and fishes. Many experts have investigated this method

Purpose -Designers of electrical machines need a clear understanding of the mechanism of noise generation, in order to be able to reduce the noises which are produced under the influence of forces due to the magnetic field. The purpose of this paper is to develop a new approach to give a best estimation of these forces. Design/methodology/approach -A model is developed to calculate the distribution of local forces using the virtual work principle in finite element context including ferromagnetic hysteresis. The forces are calculated using a formulation based on the energy derivation. The nonlinear behaviour of ferromagnetic material is considered by combining a Jiles-Atherton model and finite element method through the fixed-point iterative technique. Findings -The effects of accurate behaviour of magnetic material are not always taken into account when calculating the local forces in electromagnetic devices. The introduction of hysteresis phenomenon in the analysed device gives a good prediction of magnetic induction. The expression used to compute the force includes an integral which is estimated numerically and not a constant term. Originality/value -The developed approach is more accurate than the classical methods using constant magnetic permeability or a first magnetization curve.