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Increasing the degrees of freedom in the air path has become a popular way to reduce the fuel consumption and pollutant emissions of modern combustion engines. That is why technical definitions will usually contain components such as multi or single-stage turbocharger, throttle, exhaust gas recirculation loops, wastegate, variable valve timing or phasing, etc. One of the biggest challenges is to precisely quantify the gas flows through the engine. They include fresh and burnt gases, with trapping and scavenging phenomena. An accurate prediction of these values leads to an efficient control of the engine air fuel ratio and torque. Fuel consumption and pollutant emissions are then minimized. In this paper, we propose to use an artificial neural network- based model as a prediction tool for the engine volumetric efficiency. Results are presented for a downsized turbocharged spark-ignited engine, equipped with inlet and outlet variable valve timing.

The energy management of a hybrid vehicle defines the vehicle power flow that minimizes fuel consumption and exhaust emissions. In a combined hybrid the complex architecture requires a multi-input control from the energy management. A classic optimal control obtained with dynamic programming shows that thanks to the high efficiency hybrid electric variable transmission, energy losses come mainly from the internal combustion engine. This paper therefore proposes a sub-optimal control based on the maximization of the engine efficiency that avoids multi-input control. This strategy achieves two aims: enhanced performances in terms of fuel economy and a reduction of computational time.