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The paper proposes a design optimisation of an Interior Permanent Magnet synchronous motors with maximum output power density and suitable for wide constant-power region operation. In this paper, analytical magnetic and electrical models of the machine are developed to calculate parameters and variables of the machine needed for a design optimization such as flux, resistance and inductances. And then, the thermal aspect is modelled using a thermal lumped-parameter network which allows to estimate the machine temperatures at key points such as the windings and the magnet. These models are included in the optimization loop and so are evaluated at each iteration. The optimization method uses a differential evolution algorithm (DEA). Finally, output performances of the designed motor are verified by finite element analysis (FEA).

This paper presents a methodology to design the powertrain of an electrical vehicle (EV) in an optimal way. The electric vehicle optimal design is carried out using multiobjective genetic optimization algorithm. The developed methodology is based on the coupling of a genetic algorithm with powertrain component models. It allows determining the drive train components specifications for imposed vehicle performances, taking into account the dynamic model of the vehicle and all the components interactions. In this way, the components can be sized taking into account the whole system behavior in an optimal global design. The developed methodology is performed on the European driving cycle (NEDC) to estimate energy consumption gains but also powertrain mass reduction in comparison with a classical step-by-step methodology. This optimal procedure is notably important to increase electric vehicle range or reduce battery size and thus electric vehicle cost.