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In this study the fuel influence of several bio-fuel candidates on homogeneous engine combustion systems with direct injection is investigated. The results reveal Ethanol and 2-Butanol as the two most knock-resistant fuels. Hence these two fuels enable the highest efficiency improvements versus RON95 fuel ranging from 3.6% - 12.7% for Ethanol as a result of a compression ratio increase of 5 units. Tetrahydro-2-methylfuran has a worse knock resistance and a decreased thermal efficiency due to the required reduction in compression ratio by 1.5 units. The enleanment capability is similar among all fuels thus they pose no improvements for homogeneous lean burn combustion systems despite a significant reduction in NOX emissions for the alcohol fuels as a consequence of lower combustion temperatures.

Downsizing of highly-boosted spark-ignition (SI) engines is limited by pre-ignition, which may lead to extremely strong knocking and severe engine damage. Unfortunately, the concerning mechanisms are generally not yet fully understood, although several possible reasons have been suggested in previous research. The primary objective of the present paper is to investigate the influence of molecular bio-fuel structure on the locations of pre-ignition in a realistic, highly-charged SI engine at low speed by state-of-the-art optical measurements. The latter are conducted by using a high-sensitivity UV endoscope and an intensified high-speed camera. Two recently tested bio-fuels, namely tetrahydro-2-methylfuran (2-MTHF) and 2-methylfuran (2-MF), are investigated. Compared to conventional fuels, they have potential advantages in the well-to-tank balance. In addition, both neat ethanol and conventional gasoline are used as fuels.

Knock control is one of the most vital functions for safe and fuel-efficient operation of gasoline engines. However, all knock control strategies rely on accurate knock detection to operate the engine close to the optimal set point. Knock detection is usually calibrated on the engine test bench, requiring the engine to run with knocking combustion in a time-consuming multi-stage campaign. Model-based calibration significantly reduces calibration loops on the test bench. However, this method requires a large effort in building and validating the model, which is often limited by the lack of function documentation, available measurements or hardware representation. As the software models are often not available, function structures vary between manufacturers and sub model functions are often documented as black boxes. Hence, using the model-based approach is not always possible.