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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.

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.

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.

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

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.