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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 an analytical approach to model an interior permanent magnet motor for a hybrid electric vehicle. Therefore, an analytical model for the calculation of parameters of an interior permanent magnet motor is presented. Furthermore, these parameter values are compared with good agreement to those from finite-element analysis and experimental data. An analytical model to simulate the behaviour of the motor and its control are developed and validated by comparison with experimental data. The thermal analysis of the motor prototype is also done. At the end, the presented model is embedded in the hybrid vehicle simulator and improvements are proposed, such as an analytical approach based on the finite element results to include the core saturation effect.

Reducing greenhouse gas emissions to alleviate global warming will certainly be one of the major challenges of the 21st century. Transportation plays a very important part in this, which is why the European Commission and the European manufacturers have found an agreement to limit the average emissions of vehicles to 130 gCO₂/km in 2012 and 95 gCO₂/km in 2020. Cutting vehicles' consumption of hydrocarbons is becoming a critical issue to reach these ambitious targets. Electric vehicles, characterized by zero direct CO₂ emissions, seem to be a relevant way to achieve these CO₂ emissions. Despite their capabilities to emit no local pollution and to operate silently, electric vehicles have also one important drawback: the limited autonomy offered to the customer. As for conventional vehicles, energy consumption for electric vehicles is very dependant of driving conditions, such as driving cycles and ambient temperature operating conditions for instance.

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.