Search Results

Adaptation of predictive combustion models for their use in in-cycle closed-loop combustion control of a multi-cylinder engine is studied in this article. Closed-loop combustion control can adjust the operation of the engine closer to the optimal point despite production tolerances, component variations, normal disturbances, ageing or fuel type. In the fastest loop, in-cycle closed-loop combustion control was proved to reduce normal variations around the operational point to increase the efficiency. However, these algorithms require highly accurate predictive models, whilst having low complexity for their implementation. Three models were used to exemplify the proposed adaptation methods: the pilot injection’s ignition delay, the pilot burned mass, and the main injection’s ignition delay. Different approaches for the adaptation of the models are studied to obtain the demanded accuracy under the implementation constraints.

This study investigates particulate matter (PM) and regulated emissions from renewable rapeseed oil methyl ester (RME) biodiesel in pure and blended forms and contrasts that to conventional diesel fuel. Environmental and health concerns are the major motivation for combustion engines research, especially finding sustainable alternatives to fossil fuels and reducing diesel PM emissions. Fatty acid methyl esters (FAME), including RME, are renewable fuels commonly used from low level blends with diesel to full substitution. They strongly reduce the net carbon dioxide emissions. It is largely unknown how the emissions and characteristics of PM get altered by the combined effect of adding biodiesel to diesel and implementing modern engine concepts that reduce nitrogen oxides (NOx) emissions by exhaust gas recirculation (EGR).