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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.

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.

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 convenient way of modelling turbochargers is based on data maps. These models are easy to put into place, require low CPU charge and are control-oriented. Data relative to compressor and turbine are read from tables: pressure ratio and efficiency are determined as functions of mass flow rate and rotary speed on two distinct data maps. Nevertheless, this type of model has drawbacks: Usually, only higher turbocharger speed data are mapped (> 90000 rpm) although the low rpm zone is the most useful zone for normalized driving cycles simulations. Moreover, maps are poorly discretized, leading to the use of specific extra-interpolation methods (many are identified in [5]). These methods are purely mathematical, which gives inaccurate results in extrapolation zones. Relation between pressure ratio and efficiency is then broken (i.e., if one implements a pumping model for the compressor, the pressure ratio will be affected, but not the efficiency).

Data maps are easy to put in place and require very low calculation time. As a consequence they are often valued over fully physic-based models. This is particularly true when it is question of turbochargers. However, even if these maps are directly provided by the manufacturer, they usually do not cover the entire engine operating range and are poorly discretized. That's why before implementing them into any model they need to be interpolated and extrapolated. This paper introduces a new interpolation/extrapolation method based on the idea of integrating more physics into the widespread Jensen and Kristensen's method [6]. It essentially relies on the turbo machinery equation analysis performed by Martin during his PhD thesis [9, 10, 11] and the interpolation and extrapolation strategies that he proposed. In most cases the new strategies presented in this paper rely on improvements of the models he proposed.

Model-based tuning is a way followed by car manufacturers to reduce development costs. In this context, a new methodology has been developed in order to adapt a tur-bocharged diesel engine in the case of non-standard external conditions. Indeed, variable geometry turbine and fuel injection command laws are developed for standard conditions (20°C, altitude=0m). Turbocharger and fuel injection actuators pre-positioning maps should be adjusted regarding the inducted air mass density (influenced by the external temperature and pressure), in order to meet thermal, mechanical and pollutant emissions constraints. In order to reduce the use of climatic tests bench and extreme conditions tests in foreign countries, a model of a turbocharged diesel engine coupled to an optimization loop has been used to take into account the effect of non-standard external conditions on pre-positioning maps.

To improve the prediction of the combustion processes in spark ignition engines, a 0D flame/wall interaction submodel has been developed. A two-zones combustion model is implemented and the designed submodel for the flame/wall interaction is included. The flame/wall interaction phenomenon is conceived as a dimensionless function multiplying the burning rate equation. The submodel considers the cylinder shape and the flame surface that spreads inside the combustion chamber. The designed function represents the influence of the cylinder walls while the flame surface propagates across the cylinder. To determine the validity of the combustion model and the flame/wall interaction submodel, the system was tested using the available measurements on a 2 liter SI engine. The model was validated by comparing simulated cylinder pressure and energy release rate with measurements. A good agreement between the implemented model and the measurements was obtained.