For Electric Vehicles (EVs), Weight and losses reduction are important factors not only in reducing the energy consumption and cost but also in increasing autonomy. This paper describes the application of an evolutionary algorithm for multiobjective optimization in the traction chain (TC) of pure EV. In this study, the optimisation algorithm is based on the Strength Pareto Evolutionary Algorithm (SPEA-II) and the fitness function is defined so as to minimize the electric vehicle cost (EVC), the electric vehicle weight (EVW) and the losses in the electric vehicle (EVL). Also, in this study, different requirements are considered as constraints like the efficiency of the permanent magnets engine, the number of conductor in the slots, the winding temperature…The simulation results show the effectiveness of the approach and reduction in EVC, EVW and EVL while ensuring that the electric vehicle performance is not sacrificed.
As good candidates driving motors of electric vehicle, permanent magnet motors (PMM) exhibit high efficiency and high power density. On the other hand, they are susceptible to suffer irreversible demagnetization and insulation failure of coils under severe thermal condition. In this context, to develop more efficient PMM, it is a necessity to carry out more thermal analysis in parallel with the traditional electromagnetic design. In this Paper, The thermal behavior over the cross-section of PMM is calculated analytically and after that, it is simulated by using finite element method.Firstly, a thermal analytical model is produced. Second, this model has been validated by simulation into the design program called FEMM. Finally, the obtained results confirm that the developed thermal model is capable of a sufficient degree of accuracy in the thermal design of PMM. Also, it has been found that the thermal design can be rewarded by major improvements in the overall performance.
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