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The fuel energy consumption of subsonic air transportation is examined. The focus is on identification and quantification of fundamental engineering design tradeoffs which drive the design of subsonic tube and wing transport aircraft. The sensitivities of energy efficiency to recent and forecast technology developments are also examined.

Research Octane Number (RON) and Motor Octane Number (MON) are used to describe gasoline combustion which describe antiknock performance under different conditions. Recent literature suggests that MON is less important than RON in modern cars and a relaxation in the MON specification could improve vehicle performance. At the same time, for the same octane number change, increasing RON appears to provide more benefit to engine power and acceleration than reducing MON. Some workers have advocated the use of an octane index (OI) which incorporates both parameters instead of either RON or MON to give an indication of gasoline knock resistance. Previous Concawe work investigated the effect of RON and MON on the power and acceleration performance of two Euro 4 gasoline passenger cars during an especially-designed acceleration test cycle.

Higher compression ratio and turbocharging, with engine downsizing can enable significant gains in fuel economy but require engine operating conditions that cause engine knock under high load. Engine knock can be avoided by supplying higher-octane fuel under such high load conditions. This study builds on previous MIT papers investigating Octane-On-Demand (OOD) to enable a higher efficiency, higher-boost higher compression-ratio engine. The high-octane fuel for OOD can be obtained through On-Board-Separation (OBS) of alcohol blended gasoline. Fuel from the primary fuel tank filled with commercially available gasoline that contains 10% by volume ethanol (E10) is separated by an organic membrane pervaporation process that produces a 30 to 90% ethanol fuel blend for use when high octane is needed. In addition to previous work, this paper combines modeling of the OBS system with passenger car and medium-duty truck fuel consumption and octane requirements for various driving cycles.

Engine downsizing is a key approach employed by many vehicle manufacturers to help meet fleet average CO2 emissions targets. With gasoline engines in particular reducing engine swept volume while increasing specific output via technologies such as turbocharging, direct injection (DI) and variable valve timing can significantly reduce frictional and pumping losses in engine operating areas commonly encountered in legislative drive cycles. These engines have increased susceptibility to abnormal combustion phenomena such as knock due to the high brake mean effective pressures which they generate. This ultimately limits fuel efficiency benefits by demanding use of a lower geometric compression ratio and sub-optimal late combustion phasing at the higher specific loads experienced by these engines.

Lubricant viscosity along the engine cylinder liner varies by an order of magnitude due to local temperature variation and vaporization effects. Tremendous potential exists for fuel economy improvement by optimizing local viscosity variations for specific operating conditions. Methods for analytical estimation of friction and wear in the power-cylinder system are reviewed and used to quantify opportunities for improving mechanical efficiency and fuel economy through lubricant formulation tailored specifically to liner temperature distributions. Temperature dependent variations in kinematic viscosity, density, shear thinning, and lubricant composition are investigated. Models incorporating the modified Reynolds equation were used to estimate friction and wear under the top ring and piston skirt of a typical 11.0 liter diesel engine.

One of the limits on the maximum fuel efficiency benefit to be gained from turbocharged, downsized gasoline engines is the occurrence of pre-ignitions at low engine speed. These pre-ignitions may lead to high pressures and extreme knock (megaknock or superknock) which can cause severe engine damage. Though the mechanism leading to megaknock is not completely resolved, pre-ignitions are thought to arise from local autoignition of areas in the cylinder which are rich in low ignition delay “contaminants” such as engine oil and/or heavy ends of gasoline. These contaminants are introduced to the combustion chamber at various points in the engine cycle (e.g. entering from the top land crevice during blow-down or washed from the cylinder walls during DI wall impingement).

The NOx emission and knock characteristics of a PFI engine operating on ethanol/gasoline mixtures were assessed at 1500 and 2000 rpm with λ =1 under Wide-Open-Throttle condition. There was no significant charge cooling due to fuel evaporation. The decrease in NOx emission and exhaust temperature could be explained by the change in adiabatic flame temperature of the mixture. The fuel knock resistance improved significantly with the gasohol so that ignition could be timed at a value much closer or at MBT timing. Changing from 0% to 100% ethanol in the fuel, this combustion phasing improvement led to a 20% increase in NIMEP and 8 percentage points in fuel conversion efficiency at 1500 rpm. At 2000 rpm, where knocking was less severe, the improvement was about half (10% increase in NIMEP and 4 percentage points in fuel conversion efficiency).

Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.