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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).

We propose a model of in-cylinder air mass and residual gas fraction of a turbocharged SI engine with Variable Valve Timing (VVT) actuators. VVT devices are used to produce internal exhaust gas recirculation at part load, providing beneficial effects in terms of fuel consumption and pollutant emissions. At full load, VVT actuators permit to push back knock limit by scavenging fresh air to the exhaust pipes. Modeling in-cylinder composition is an essential task for control purpose. Actually, VVT actuators affect in-cylinder fresh air charge. This has an impact on engine torque output (leading to driveability problems), and on Fuel/Air Ratio (leading to pollution peaks). In this paper, we present a model of in-cylinder air mass and residual gas fraction using only commercial-line sensors (engine speed, intake manifold pressure and VVT actuators positions). It is designed for real-time control purpose. The model does not necessitate a lot of calibration time.

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.

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.

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.

In the whole engine development process, 0D/1D simulation has become a powerful tool, from conception to final calibration. Within the context of control strategy design, a turbocharged spark ignition (SI) engine with variable camshaft timing has been modelled on the AMESim platform. This paper presents the different models and the methodology used to design, calibrate and validate the simulator. The validated engine model is then used for engine control purposes related to downsizing concept. Indeed, the presented control strategy acts on the in-cylinder trapped mass, the in-cylinder burnt gas fraction and the air scavenging from the intake to the exhaust. Consequently, it permits to reduce not only the fuel consumption and pollutant emissions but also to improve the transient response of the turbocharger

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.

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.