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Engine downsizing is potentially one of the most effective strategies being explored to improve fuel economy. A main problem of downsizing using a turbocharger is the small range of stable functioning of the turbocharger centrifugal compressor at high boost pressures, and hence the measurement of the performance maps of both compressor and turbine. Automotive manufacturers use mainly numerical simulations for internal combustion engines simulations, hence the need of an accurate extrapolation model to get a complete turbine performance map. These complete maps are then used for internal combustion engines calibration. Automotive manufacturers use commercial softwares to extrapolate the turbine narrow performance maps, both mass flow characteristics and the efficiency curve.

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.

This paper develops an exhaust manifold pressure estimation method for a Diesel engine equipped with a variable geometry turbine (VGT) turbocharger. Extrapolated VGT data-maps are used directly for the estimation of the exhaust pressure using a non-iterative Newton-Raphson based method suitable for real-time applications. This approach can give more accurate estimations than traditional methods because it takes into account the turbine speed effect on the turbine mass flow rate. All this without increasing the calculation load significantly. The proposed exhaust manifold estimation can be used to relieve the exhaust manifold pressure physical sensor during engine operating conditions where its reliability is low. The estimator is evaluated in transient with two different engine cycles using a engine model validated in a benchmark as a reference.

In the past few years, the increasing market penetration of downsized engines has reduced the pollutant emissions of internal combustion engines. The addition of a turbocharger to the air path has usually enabled the dynamic performances of the vehicles to be maintained. However, in the development process, deciding on the appropriate set of components is not straightforward and a lengthy fitting process is usually required to find the right turbocharger. Car manufacturers usually have access to a limited library of compressors and turbines which have actually been built and for which measurement campaigns have been carried out. This study is motivated by the need to extend the libraries available for simulation in order to provide a substantial increase in freedom in the matching process.

A modernization of tools and processes is vital to develop more innovative products that will integrate complex technologies and comply with ever stricter standards. The upcoming Euro 6 and Euro 7, the European emission standards that define acceptable exhaust emissions limits for new vehicles have increased the workload of engine design and control design engineers. They now have to integrate complex engine actuation and control systems, required to optimize engine performance, while keeping emissions at the lowest level possible. The growing number of systems driven by controls, as well as the multiple interactions between them, makes the engineering tasks more complex as engineers need to take a higher number of degrees of freedom into account. This has a direct impact on costs and times. In a quest of rationalization, the deployment of system simulation for plant modeling permits substantial gains in process efficiency all over the development process.