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The field of automotive engineering has devoted much research to reduce fuel consumption to attain sustainable energy usage. Friction reductions in powertrain components can improve engine fuel economy. Quantitative accounting of friction is complex because it is affected by many physical aspects such as oil viscosity, temperature, surface roughness and component rotation speed. The purpose of this paper is two-fold: first, to develop a useful tool for evaluating the friction in engine and accessories based on test data; second, to exercise the tool to evaluate the fuel economy gain in a drive cycle for several friction reduction technologies.

To improve fuel efficiency and facilitate handling of the vehicle in a dense city environment, it should be as small as possible given its intended application. This downsizing trend impacts the size of the engine bay, where the air filter box has to be packed in a reduced space, still without increased pressure drop, reduced load capacity nor lower filtering efficiency. Due to its flexibility and reduced cost, CFD simulations play an important role in the optimization process of the filter design. Even though the air-flow through the filter box changes as the dust load increases, the current modeling framework seldom account for such time dependence. Volvo Car Corporation presents an industrial affordable model to solve the time-dependent dust load on filter elements and calculate the corresponding flow behavior over the life time of the air filter box.

This paper quantifies the potential of electric propulsion systems to reduce petroleum use and greenhouse gas (GHG) emissions in the 2030 U.S. light-duty vehicle fleet. The propulsion systems under consideration include gasoline hybrid-electric vehicles (HEVs), plug-in hybrid vehicles (PHEVs), fuel-cell hybrid vehicles (FCVs), and battery-electric vehicles (BEVs). The performance and cost of key enabling technologies were extrapolated over a 25-30 year time horizon. These results were integrated with software simulations to model vehicle performance and tank-to-wheel energy consumption. Well-to-wheel energy and GHG emissions of future vehicle technologies were estimated by integrating the vehicle technology evaluation with assessments of different fuel pathways. The results show that, if vehicle size and performance remain constant at present-day levels, these electric propulsion systems can reduce or eliminate the transport sector's reliance on petroleum.

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.

The Vapor Phase Catalytic Ammonia Removal (VPCAR) system is being developed to recycle water for future NASA Exploration Missions [1,2,3,4,5]. Reduced gravity testing of the VPCAR System has been initiated to identify any potential problems with microgravity operation. Two microgravity testing campaigns have been conducted on NASA's C-9B Reduced Gravity Aircraft. These tests focused on the fluid dynamics of the unit's Wiped-Film Rotating Disk (WFRD) evaporator. The experiments used a simplified system to study the process of forming a thin film on a rotating disk. The configuration simulates the application of feed in the VPCAR's WFRD evaporator. The first round of aircraft testing, which was completed in early 2006, indicated that a problem with microgravity operation of the WFRD existed. It was shown that in reduced gravity the VPCAR wiper did not produce a uniform thin film [6]. The film was thicker near the axis of rotation where centrifugal forces are small.

The transportation sector in the United States is a major contributor to global energy consumption and carbon dioxide emission. To assess the future potentials of different technologies in addressing these two issues, we used a family of simulation programs to predict fuel consumption for passenger cars in 2020. The selected technology combinations that have good market potential and could be in mass production include: advanced gasoline and diesel internal combustion engine vehicles with automatically-shifting clutched transmissions, gasoline, diesel, and compressed natural gas hybrid electric vehicles with continuously variable transmissions, direct hydrogen, gasoline and methanol reformer fuel cell hybrid electric vehicles with direct ratio drive, and battery electric vehicle with direct ratio drive.

Commercial aviation relies almost entirely on subsonic fixed wing aircraft to constantly move people and goods from one place to another across the globe. While air travel is an effective means of transportation providing an unmatched combination of speed and range, future subsonic aircraft must improve substantially to meet efficiency and environmental targets. The NASA Fundamental Aeronautics Subsonic Fixed Wing (SFW) Project addresses the comprehensive challenge of enabling revolutionary energy-efficiency improvements in subsonic transport aircraft combined with dramatic reductions in harmful emissions and perceived noise to facilitate sustained growth of the air transportation system. Advanced technologies, and the development of unconventional aircraft systems, offer the potential to achieve these improvements.

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.

A Multi-Objective Optimization (MOO) problem concerning the thermal control problem of Multifunctional Structures (MFSs) is here addressed. In particular the use of Multi-Objective algorithms from an optimization tool and Self-Organizing Maps (SOM) is proposed for the identification of the optimal topological distribution of the heating components for a multifunctional test panel, the Advanced Bread Board (ABB). MFSs are components that conduct many functions within a single piece of hardware, shading the clearly defined boundaries that identify traditional subsystems. Generally speaking, MFSs have already proved to be a disrupting technology, especially in aeronautics and space application fields. The case study exploited in this paper refers to a demonstrator breadboard called ABB. ABB belongs to a particular subset of an extensive family of MFS, that is, of thermo-structural panels with distributed electronics and a health monitoring network.

Decreasing fuel consumption and emissions in automobiles has been an active research topic in recent years. Vehicles with alternative powertrain systems, especially hybrid-electric vehicles (HEVs), have shown significant reduction in fuel consumption and emissions, and therefore have attracted many researchers to this field. The focus is usually on the development of optimal power management control methods. For parallel HEVs, the primary control variable is the torque split between the internal combustion engine and the electric motor. More advanced approaches also simultaneously search for the optimal gear number and engine on/off state, which can further reduce the fuel consumption but also complicate the problem. In the literature on HEVs, the emphasis is typically only on fuel efficiency and sometimes the emissions. The drivability of the vehicle is usually not considered during the optimization process.

Global warming and climate change have led to a greater interest in the implementation of biofuels in internal combustion engines. In spark ignited engines, biofuels have been shown to improve efficiency and knock resistance while decreasing emissions of unburned hydrocarbons, carbon monoxide and particles. This study investigates the effect of biofuels on SI engine combustion through a graphical compilation of previously reported results. Experimental data from 88 articles were used to evaluate the trends of the addition of different biofuels in gasoline. Graphs illustrating engine performance, combustion phasing and emissions are presented in conjunction with data on the physiochemical properties of each biofuel component to understand the observed trends. Internal combustion engines have the ability to handle a wide variety of fuels resulting in a broad range of biofuel candidates.

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.

Thermoacoustic engine has been proven to be a promising technology for automotive exhaust waste heat recovery to save fossil fuel and reduce emission thanks to its ability to convert heat into acoustic energy which, hence, can be harvested in useful electrical energy. In this paper, based on the practical thermodynamic parameters of the automotive exhaust gas, including mass flow rate and temperature, two traveling-wave thermoacoustic engines are designed and optimized for the typical heavy-duty and light-duty vehicles, respectively, to extract and reutilize their exhaust waste heat. Firstly, nonlinear thermoacoustic models for each component of a thermoacoustic engine are established in the frequency domain, by which any potential steady operating point of the engine is available.

Thermal Barrier Coatings (TBCs) may be used on the inner surfaces of exhaust manifolds in heavy-duty diesel engines to improve the fuel efficiency and prolong the life of the component. The coatings need to have a long thermal cyclic life and also be able to reduce the temperature in the substrate material. A lower temperature of the substrate material reduces the oxidation rate and has a positive influence on the thermo-mechanical fatigue life. A test rig for evaluating these properties for several different coatings simultaneously in the correct environment was developed and tested for two different TBCs and one oxidation-resistant coating. Exhausts were redirected from a diesel engine and led through a series of coated pipes. These pipes were thermally cycled by alternating the temperature of the exhausts. Initial damage in the form of cracks within the top coats of the TBCs was found after cycling 150 times between 50°C and 530°C.

With increasing demands to reduce fuel consumption and CO2 emissions, it is necessary to recover waste heat from modern Heavy Duty (HD) truck engines. Organic Rankine Cycle (ORC) has been acknowledged as one of the most effective systems for Waste Heat Recovery (WHR) due to its simplicity, reliability and improved overall efficiency. The expander and working fluid used in ORC WHR greatly impact the overall performance of an integrated engine and WHR system. This paper presents the effects of volumetric expanders on the ORC WHR system of a long haulage HD truck engine at a steady-state engine operating point chosen from a real-time road data. Performance of a long haulage HD truck engine is analyzed, based on the choice of three volumetric expanders for its WHR system, using their actual performance values. The expanders are: an oil-free open-drive scroll, a hermetic scroll and an axial piston expander with working fluids R123, R245fa and ethanol, respectively.

An analysis method for characterizing fuel behavior during spark-ignition engine starting has been developed and applied to several sets of start-up data. The data sets were acquired from modern production vehicles during room temperature engine start-up. Two different engines, two control schemes, and two engine temperatures (cold and hot) were investigated. A cycle-by-cycle mass balance for the fuel was used to compare the amount of fuel injected with the amount burned or exhausted as unburned hydrocarbons. The difference was measured as “fuel unaccounted for”. The calculation for the amount of fuel burned used an energy release analysis of the cylinder pressure data. The results include an overview of starting behavior and a fuel accounting for each data set Overall, starting occurred quickly with combustion quality, manifold pressure, and engine speed beginning to stabilize by the seventh cycle, on average.

As vehicles become electrified and more intelligent in terms of sensing, actuation and processing; a number of interesting possibilities arise in controlling vehicle dynamics and driving behavior. Over-actuation with in- wheel motors, all wheel steering and active camber is one such possibility, which facilitate the control strategies that push boundaries in energy consumption and safety. Optimal control can be used to investigate the best combinations of control inputs to an over-actuated system. This paper shows how an optimal control problem can be formulated and solved for an over-actuated vehicle case, and highlights the translation of this optimal solution to a real-world scenario, enabling intelligent means to improve vehicle efficiency. This paper gives an insight into Dynamic Programming (DP) as an offline optimal control method that guarantees the global optimum.

The use of turbochargers with downsized internal combustion engines improves road vehicles’ energy efficiency but introduces additional sound sources of strong acoustic annoyance on the turbocharger’s compressor side. In the present study, direct noise computations (DNC) are carried out on a passenger vehicle turbocharger compressor. The work focuses on assessing the influence of grid parameters on the acoustic predictions, to further advance the maturity of the acoustic modelling of such machines with complex three-dimensional features. The effect of the boundary layer mesh structure, and of the spatial resolution of the mesh, on the simulated acoustic signatures is investigated on detached eddy simulations (DES). Refinements in the core mesh are applied in areas of major acoustic production, to generate cells with sizes proportional to the local Taylor microscale values.

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.