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This paper describes and experimentally compares various strategies to control the gas flows of a high speed automotive diesel engine equipped with external exhaust gas recirculation (EGR) valve and variable geometry turbocharger (VGT). The strategies differ in their choice of controlled variables and design approaches. Some of the strategies rely on standard measurements such as intake manifold pressure and compressor mass air flow. Others measure or estimate less readily available quantities such as exhaust manifold pressure and EGR flow fraction. The focus of this paper is on the experimental details of controller implementation. The control schemes are evaluated on their emissions and fuel economy over the extra urban part of the new European drive cycle. The implementation effort and chronometric load are also compared.

The development of advanced model-based engine control strategies, such as economic model predictive control (eMPC) for diesel engine fuel economy and emission optimization, requires accurate and low-complexity models for controller design validation. This paper presents the NOx and smoke emissions modeling of a light duty diesel engine equipped with a variable geometry turbocharger (VGT) and a high pressure exhaust gas recirculation (EGR) system. Such emission models can be integrated with an existing air path model into a complete engine mean value model (MVM), which can predict engine behavior at different operating conditions for controller design and validation before physical engine tests. The NOx and smoke emission models adopt an artificial neural network (ANN) approach with Multi-Layer Perceptron (MLP) architectures. The networks are trained and validated using experimental data collected from engine bench tests.