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The recent development of diesel engine fuel injection systems has been dominated by how to manage the degrees of freedom that common rail multi-pulse systems now offer. A number of production engines already use four injection events while in research, work based on up to eight injection events has been reported. It is the degrees of freedom that lead to a novel experimental requirements. There is a potentially complex experimental program needed to simply understand how injection parameters influence the combustion process in steady state. Combustion behavior is not a continuum and as both injection and EGR rates are adjusted, distinct combustion modes emerge. Conventional calibration processes are severely challenged in the face of large number of degrees of freedom and as a consequence new development approaches are needed.

One of the most critical challenges currently facing the diesel engine industry is how to improve fuel economy under emission regulations. Improvement in fuel economy can be achieved by precisely controlling Air/Fuel ratio and by monitoring fuel consumption in real time. Accurate and repeatable measurements of fuel rate play a critical role in successfully controlling air/fuel ratio and in monitoring fuel consumption. Volumetric and gravimetric measurements are well-known methods for measuring fuel consumption of internal combustion engines. However, these methods are not suitable for obtaining fuel flow rate data used in real-time control/measurement. In this paper, neural networks are used to solve the problem concerning discontinuous data of fuel flow rate measured by using an AVL 733 s fuel meter. The continuous parts of discontinuous fuel flow rate are used to train and validate a neural network, which can then be used to predict the discontinuous parts of the fuel flow rate.

Measurement accuracy and repeatability for fuel rate is the key to successfully improve fuel economy of diesel engines as fuel economy could only be achieve by precisely controlling air/fuel ratio and monitor real-time fuel consumption. The volumetric and gravimetric measurement principles are well-known methods to measure the fuel consumption of internal combustion engines. However, the fuel flow rate measured by these methods is not suitable for either real-time control or real-time measurement purposes. The problem concerning discontinuous data of fuel flow rate measured by using an AVL 733s fuel meter was solved for the steady state scenario by using neural networks. It is easier to choose inputs of the neural networks for the steady state scenario because the inputs could be chosen as the particular inputs which excited the system in the application.

This work describes the application of Non-Linear Autoregressive Models with Exogenous Inputs (NLARX) in order to predict the NOx emissions of heavy-duty diesel engines. Two experiments are presented: 1.) a Non-Road-Transient-Cycle (NRTC) 2.) a composition of different engine operation modes and different engine calibrations. Data sets are pre-processed by normalization and re-arranged into training and validation sets. The chosen model is taken from the MATLAB Neural Network Toolbox using the algorithms provided. It is teacher forced trained and then validated. Training results show recognizable performance. However, the validation shows the potential of the chosen method.

In modern production diesel engine control systems, fuel path control is still largely conducted through a system of tables that set mode, timing and injection quantity and with common rail systems, rail pressure. In the hands of an experienced team, such systems have proved so far able to meet emissions standards, but they lack the analytical underpinning that lead to systematic solutions. In high degree of freedom systems typified by modern fuel injection, there is substantial scope to deploy optimising closed loop strategies during calibration and potentially in the delivered product. In an optimising controller, a digital algorithm will explicitly trade-off conflicting objectives and follow trajectories during transients that continue to meet a defined set of criteria. Such an optimising controller must be based on a model of the system behaviour which is used in real time to investigate the consequences of proposed control actions.

More and more stringent emission regulations require advanced control technologies for combustion engines. This goes along with increased monitoring requirements of engine behaviour. In case of emissions behaviour and fuel consumption the actual combustion efficiency is of highest interest. A key parameter of combustion conditions is the in-cylinder pressure during engine cycle. The measurement and detection is difficult and cost intensive. Hence, modelling of in-cylinder conditions is a promising approach for finding optimum control behaviour. However, on-line controller design requires real-time scenarios which are difficult to model and current modelling approaches are either time consuming or inaccurate. This paper presents a new approach of in-cylinder condition prediction. Rather than reconstructing in-cylinder pressure signals from vibration transferred signals through cylinder heads or rods this approach predicts the conditions.