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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.