Characterisation and Model Based Optimization of a Complete Diesel Engine/SCR System

Abstract

Paper II Modelling diesel engine combustion and NO x formation for model based control and simulation of engine and exhaust aftertreatment system C.

The State of the Art in Selective Catalytic Reduction Control

“…Research in this area of Integrated Emission Management strategies is still in the development phase and only limited publications on the interaction between engine and aftertreatment control systems are found in the literature [45].…”

“…Another example of a combined heavy duty diesel engine and SCR system is presented by [45]. The proposed optimization method is based on a Sequential Quadratic Programming (SQP) algorithm and is claimed to have been successful in finding the instantaneous optimal balance between NOx reduction across the combined engine-SCR system and engine fuel economy.…”

Optimisation-oriented modelling of the NOx emissions of a Diesel engine

“…Identifying the parameters of a feedback controller can also be cast as an optimisation problem [4,5]. Finally, the results from numerical optimal control provide implications for well-suited control structures [6][7][8][9] or serve as training data for function approximators such as artificial neural networks [10,11]. The purpose of utilising model-based optimisation within all these tasks is to reduce the fuel consumption while simultaneously honoring the limits imposed on the pollutant emissions.…”

“…In [17], the authors claim that these intermediate quantities are chosen such that they can be calculated from air-path quantities by simple models. This choice eliminates the need for a complete combustion model to predict the NO x emissions, which is the general practice [8,[19][20][21][22][23]. In addition, the overall execution speed, including the submodels interfacing the NO x -formation model with the air-path quantities, is indicated to almost reach the level typical for empirical models.…”

From static to dynamic optimisation of Diesel-engine control

“…Adaptive methods try to automatically improve the engine calibration during normal operation [9]. Dynamic optimisation, originally performed directly on the test bench [10], provides implications for suitable control structures [11], [12], [13], [14]. Alternatively, universal function approximators such as neural networks can be trained by the optimised patterns [15], [16].…”