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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.

As congestion increases and commute times lengthen with the growing urbanization, many customers will look for effective mobility solutions. Two-wheeler are one of the solutions to deal with these issues, in particular if equipped with electrified powertrains for minimized local noise and air pollutant emissions. Scooters powertrain technology is predominantly based on Spark Ignition Engine (ICE) associated with a Continuously Variable Transmissions (CVT) and a Centrifugal Clutch. Nevertheless, even though CVT gives satisfaction in simplicity, fun to drive, cost effectiveness and vehicle dynamics, its efficiency is an undeniable drawback. Indeed, a conventional CVT is wasting more than 50% of ICE effective power in customer driving conditions. Consequently, those vehicles have high fuel consumption relative to their size, and are equipped with overpowered and heavy internal combustion engines, allowing a large area for further improvements.

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 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.

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.

Regulations in terms of pollutant emissions are becoming more and more constraining. The car manufacturers need to adopt a global optimisation approach of engine and exhaust after-treatment systems. An engine architecture definition coupled to an adapted control strategy seem to be an ideal way to address this issue. The problem is particularly complex, considering the trade off between the drivability which must be maintained, the reduction of the in-cylinder pollutant emissions, the reduction of the fuel consumption and the optimisation of the operating conditions to reach high conversion efficiencies via exhaust gas after-treatment systems. Sophisticated control strategies and models can only be developed with a complete understanding of the physical phenomena occurring in the combustion chamber, thanks to experimental measurements and engine system simulations.

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.

In order to meet the CO₂ challenge, today a wide variety of solutions are developed in the automotive industry such as advanced technologies (downsizing, VVA, VCR), new combustion modes (HCCI, stratified and lean combustion), hybridization, electrification or alternative fuels. Furthermore, couplings between these solutions can be envisaged, increasing considerably the number of degrees of freedom which have to be accounted for in the development of future powertrains. Consequently, for time and cost reasons, it is not obvious to evaluate and optimize the full potential of new concepts only by the mean of experimental investigation. In this context, system simulation appears as a powerful and relevant complement to engine tests for its flexibility and its high CPU efficiency. This paper focuses on the development of a methodology combining both simulation and experimental tools to quantify the interest of innovative solutions in the very first steps of their development.

This paper presents the development of torque-based engine control strategies for a downsized SI engine, from simulation design to final validation on a demonstration car. One main issue to reach performance, fuel consumption and pollutant emission demands is in-cylinder mass observation and control. A simulation-based approach is first presented to design accurate observers from a reference simulator. In this study, a multivariable and non-linear control has been developed and focused on in-cylinder mass trajectories. It has been tested on a real time Software-In-the-Loop platform before a complete validation and calibration on the test bed. Finally, the complete torque-based engine control has been successfully integrated on the vehicle.