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Modern diesel engines are equipped with an increasing number of actuators set to improve human comfort and fuel consumptions while respecting the restricted emissions regulations. In spite of the great progress made in the electronic and data-processing domains, the physical-based emissions models remain time consuming and too complicated to be used in a dynamic calibrating process. Therefore, until these days, the calibration of the engine's cartographies is done manually by experimental experts on dynamic test bed, but the results are not often the best compromise in the consumption-emissions formula due to the increasing number of actuators and to the nonlinear and complex relations between the different variables involved in the combustion process. Recently, neural networks are successfully used to model dynamic multiple inputs - multiple outputs processes by learning from examples and without any additional or detailed information about the process itself.

Modern diesel engines are typically equipped with common rail injection system, variable geometry turbocharger and exhaust gas recirculation system in order to meet the emissions standards. While the electronic fuel control has been extensively developed and used in the common rail injection systems, the “in-air cylinders filling” control remains poorly exploited. In this paper, we suggest a dynamic engine optimization process that predicts, under transient conditions, the optimal values of the intake pressure and the compressor mass flow rate to be applied to the engine based on pollution criteria such as the opacity. The optimization procedure and a physical mean value model describing the functioning of a variable geometry turbocharged diesel engine and its smoke's opacity are shown in details. The simulations results of the engine's model are in excellent agreement with the experimental data collected on test bench.

In the context of LEV and ULEV, and to improve performances and passenger comfort, the closed loop optimisation of the engine torque is one of the major element of the engine control strategy. To do that, the knowledge of instantaneous engine torque is needed. In this paper, we are looking for economical and reliable methods to estimate the average indicated torque (of each cylinder) during its combustion stroke based on crankangle measurements. These methods use an engine model which must be both simple for ‘on line’ implementation and accurate enough for a good estimation. We will be presenting and discussing the assumptions and the requirement to meet for this determination.

Actual stringent regulations on emission level imply highly efficient control strategies, which can be based on the instantaneous engine torque or the in-cylinder pressure. To reach this objective, while avoiding costly direct measurements, the estimation of one of these variables is required. In this paper, two methods are presented based on the correlation between the crankshaft velocity and the indicated torque or pressure. In the “mechanistic method”, the model based on the dynamics of the reciprocating engine and on a correlation with the combustion process provides a relationship between the fluctuating component of the instantaneous crankshaft acceleration and the average indicated torque of the firing cylinders. Thus, an indicated torque signature of each cylinder can be estimated from the observation of the crankshaft acceleration.