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Reducing greenhouse gas emissions to limit global warming is becoming one of the key issues of the 21st century. As a growing contributor to this phenomenon, the aeronautic transport sector has recently taken drastic measures to limit its impact on CO2 and pollutants, like the aviation industry entry in the European carbon market or the ACARE objectives. However the defined targets require major improvements in existing propulsion systems, especially on the gas generator itself. Regarding small power engines for business aviation, rotorcrafts or APU, the turboshaft is today a dominant technology, despite quite high specific fuel consumption. In this context, solutions based on Diesel Internal Combustion Engines (ICE), well known for their low specific fuel consumption, could be a relevant alternative way to meet the requirements of future legislations for low and medium power applications (under 1000kW).

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.

The powertrain simulation tools are nowadays an efficient support to optimize cost and duration of the whole engine technological developments. They can deliver optimized simulator versions for various targets such as system understanding, design investigation, non-measurable value access or virtual bench use for control and calibration. Under the condition of an accurate modelling and simulation know-how to take into account the simulator using constraints, the simulation can become an undisputable support for powertrain design as the test bed already is. The goal of this paper is to present the large range of the powertrain simulation capabilities for the specific application of a downsized turbocharged GDI engine with twin VVT embedded in a concept car. The modelling framework is first presented and different items are laid-out. A first part is dedicated to the engine air path and in particular to the modelling of gas exchange phenomena such as back-flow.

Due to more and more complex powertrain architectures and the necessity to optimize them on the whole driving conditions, simulation tools are becoming indisputable for car manufacturers and suppliers. Indeed, simulation is at the basis of any algorithm aimed at finding the best compromise between fuel consumption, emissions, drivability, and performance during the conception phase. For hybrid vehicles, the energy management strategy is a key driver to ensure the best fuel consumption and thus has to be optimized carefully as well. In this regard, the coupling of an offline hybrid strategy optimizer (called HOT) based on Pontryagin’s minimum principle (PMP) and an online equivalent-consumption-minimization strategy (ECMS) generator is presented. Additionally, methods to estimate the efficiency maps and other overall characteristics of the main powertrain components (thermal engine, electric motor(s), and battery) from a few design parameters are shown.

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.