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The engine concept partially premixed combustion (PPC) has proved higher gross indicated efficiency compared to conventional diesel combustion engines. The relatively simple implementation of the concept is an advantage, however, high gas exchange losses has made its use challenging in multi-cylinder heavy duty engines. With high rates of exhaust gas recirculation (EGR) to dilute the charge and hence limit the combustion rate, the resulting exhaust temperatures are low. The selected boost system must therefore be efficient which could lead to large, complex and costly solutions. In the presented work experiments and modelling were combined to evaluate different turbocharger configurations for the PPC concept. Experiments were performed on a multi-cylinder engine. The engine was modified to incorporate long route EGR and a single-stage turbocharger, however, with compressed air from the building being optionally supplied to the compressor.

Partially Premixed Combustion (PPC) has shown to be a promising advanced combustion mode for future engines in terms of efficiency and emission levels. The combustion timing should be suitably phased to realize high efficiency. However, a simple constant model based predictive controller is not sufficient for controlling the combustion during transient operation. This article proposed one learning based model predictive control (LBMPC) approach to achieve controllability and feasibility. A learning model was developed to capture combustion variation. Since PPC engines could have unacceptably high pressure-rise rates at different operation points, triple injection is applied as a solvent, with the use of two pilot fuel injections. The LBMPC controller utilizes the main injection timing to manage the combustion timing. The cylinder pressure is used as the combustion feedback. The method is validated in a multi-cylinder heavy-duty PPC engine for transient control.