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As Electric and Hybrid Electric Vehicles (EVs and HEVs) become more prevalent, there is a need to change the power source from gasoline on the vehicle to electricity from the grid in order to mitigate requirements for onboard energy storage (battery weight) as well as to reduce dependency on oil by increasing dependency on the grid (our coal, gas, and renewable energy instead of their oil). Traditional systems for trains and buses rely on physical contact to transfer electrical energy to vehicles in motion. Until recently, conventional magnetically coupled systems required a gap of less than a centimeter. This is not practical for vehicles of the future.

The current ability of the Virtual Aerodynamic/ Aeroacoustic Wind Tunnel to predict interior vehicle sound pressure levels is demonstrated using an automobile model which has variable windshield angles. This prediction method uses time-averaged flow solutions from a lattice gas CFD code coupled with wave number-frequency spectra for the various flow regimes to calculate the side window vibration from which the sound pressure level spectrum at the driver's ear is determined. These predictions are compared to experimental wind tunnel data. The results demonstrate the ability of this methodology to correctly predict wind noise spectral trends as well as the overall loudness at the driver's ear. A more sophisticated simulation method employing the same lattice gas code is investigated for prediction of the time-accurate flow field necessary to compute the actual side glass pressure spectra.

The question of whether increasing the fuel economy of light-duty natural gas fueled vehicles can improve their economic competitiveness in the U.S. market, and help the US Department of Energy meet stated goals for such vehicles is explored. Key trade-offs concerning costs, exhaust emissions and other issues are presented for a number of possible advanced engine designs. Projections of fuel economy improvements for a wide range of lean-burn engine technologies have been developed. It appears that compression ignition technologies can give the best potential fuel economy, but are less competitive for light-duty vehicles due to high engine cost. Lean-burn spark ignition technologies are more applicable to light-duty vehicles due to lower overall cost. Meeting Ultra-Low Emission Vehicle standards with efficient lean-burn natural gas engines is a key challenge.

A METHOD is described in this paper by which the rates of gear wear under different conditions can be determined by the use of the radioactive tracer technique. With this method one can measure the minutest amount of wear at loads and speeds much below critical destructive conditions. This method makes possible the continuous determination of rates of gear wear at all loads and speeds in actual full-scale units. In this investigation, the radioactive tracer technique has been used to determine the rates of gear wear when using a straight mineral oil and when using an extreme-pressure gear lubricant.

A laboratory wear test is used to evaluate the wear protection properties of new and used engine oils formulated for FFV service. Laboratory-blended mixtures of these oils with methanol and water have also been tested. The test consists of a steel ball rotating against three polished cast iron discs. Oil samples are obtained at periodic intervals from a fleet of 3.0L Taurus vehicles operating under controlled go-stop conditions. To account for the effects of fuel dilution, some oils are tested before and after a stripping procedure to eliminate gasoline, methanol and other volatile components. In addition to TAN and TBN measurements, a capillary electrophoresis technique is used to evaluate the formate content in the oils. The results suggest that wear properties of used FFV lubricants change significantly with their degree of usage.

This paper presents a simple method of using Voronoi partitions for estimating vehicle fuel economy from a limited set of engine operating conditions. While one of the overarching goals of engine research is to continually improve vehicle fuel economy, evaluating the impact of a change in engine operating efficiency on the resulting fuel economy is a non-trivial task and typically requires drive cycle simulations with experimental data or engine model predictions and a full suite of engine controllers over a wide range of engine speeds and loads. To avoid the cost of collecting such extensive data, proprietary methods exist to estimate fuel economy from a limited set of engine operating conditions. This study demonstrates the use of Voronoi partitions to cluster and quantize the fuel consumed along a complex trajectory in speed and load to generate fuel consumption estimates based on limited simulation or experimental results.

The variation of viscosity with temperature and shear rate plays an important role in the analysis of lubrication of automotive systems. In this paper, a method for predicting the viscosity of non-Newtonian fluids, such as multigrade engine oils, over a wide range of temperatures and shear rates is outlined. This expression determines viscosity parameters for shear thinning fluids in terms of easily measured viscosity values at some reference state. A comparison of predictions with experimental data suggests that viscosity for multigrade engine oils can be predicted to within experimental uncertainty. The proposed method can be used in assessing lubricant viscosity at shear rates greater than 106 s-1, which are beyond the capability of current laboratory instruments. A comparative study with multigrade oils shows that performance at very high shear rates cannot be accurately gauged from high temperature, high shear (HTHS) viscosity measurements.

A test cell was developed for evaluating a 6-speed automatic transmission. The target vehicle had an internal combustion 5.4L gasoline V8 engine. An electric dynamometer was used to closely simulate the engine characteristics. This included generating mean torque from the ECU engine map, with a transient capability of 10,000 rpm/second. Engine inertia was simulated with a transient capability of 20,000 rpm/second, and torque pulsation was simulated individually for each piston, with a transient capability of 50,000 rpm/second. Quantitative results are presented for the correlation between the engine driven and the dynamometer driven transmission performance over more than 60 test cycles. Concerns about using the virtual engine in validation testing are discussed, and related to the high frequency transient performance required from the electric dynamometer. Qualitative differences between the fueled engine and electric driven testing are presented.

A piezoelectrically driven vibrating cantilever blade is damped by a number of mechanisms including viscous damping in a still fluid and aerodynamic damping in a flow. By measuring the damping of devices operating at resonance in the 1 to 5 kHz region, one can measure such properties as mass flow, absolute pressure or the product of molecualar mass and viscosity. In the case of the mass flow measurement, the device offers a mechanical alternative to hotwire and hot film devices for the automotive application.

An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction.

There is a pressing need to develop accurate and robust approaches for predicting vehicle speed to enhance fuel economy/energy efficiency, drivability and safety of automotive vehicles. This paper details outcomes of research into various methods for the prediction of vehicle velocity. The focus is on short-term predictions over 1 to 10 second prediction horizon. Such short-term predictions can be integrated into a hybrid electric vehicle energy management strategy and have the potential to improve HEV energy efficiency. Several deterministic and stochastic models are considered in this paper for prediction of future vehicle velocity. Deterministic models include an Auto-Regressive Moving Average (ARMA) model, a Nonlinear Auto-Regressive with eXternal input (NARX) shallow neural network and a Long Short-Term Memory (LSTM) deep neural network. Stochastic models include a Markov Chain (MC) model and a Conditional Linear Gaussian (CLG) model.

The 2013 Ford Fusion will be launched with an optional automatic engine start-stop feature. To realize engine start-stop on a vehicle equipped with a conventional powertrain, there are two major challenges in the vehicle system controls. First, the propulsive torque delivery from a stopped engine has to be fast. The vehicle launch delay has to be minimized such that the corporate vehicle attributes can be met. Second, the fuel economy improvement offered by this technology has to justify the cost associated with it. In pursuing fuel economy, the driver's comfort and convenience should be minimally impacted. To tackle these challenges, a vehicle system control strategy has been developed to accurately interpret the driver's intent, monitor the vehicle subsystem's power demands, schedule engine automatic stop and re-start, and coordinate the fast and smooth torque delivery to the wheels.

Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor.

A major driving force for change in light-duty vehicle design and technology is the National Highway Traffic Safety Administration (NHTSA) and the U.S. Environmental Protection Agency (EPA) joint final rules concerning Corporate Average Fuel Economy (CAFE) and greenhouse gas (GHG) emissions for model years 2017 (MY17) through 2025 (MY25) passenger cars and light trucks. The chief goal of this current study is to compare the already rapid pace of fuel economy improvement and technological change over the previous decade to the required rate of change to meet regulations over the next decade. EPA and NHTSA comparisons of the model year 2005 (MY05) US light-duty vehicle fleet to the model year 2015 (MY15) fleet shows improved fuel economy (FE) of approximately 26% using the same FE estimating method mandated for CAFE regulations. Future predictions by EPA and NHTSA concerning ensemble fleet fuel economy are examined as an indicator of required vehicle rate-of-change.

Intake and exhaust valve control has been combined with engine calibration control by an on-board computer to achieve a Variable Displacement Engine with improved BSFC during part throttle operation. The advent of the on-board computer, with its ability to provide integrated algorithms for the fast accurate flexible control of the entire powertrain, has allowed practical application of the valve disabler mechanism. The engine calibration basis and the displacement selection criteria are discussed, as are the fuel economy, emissions and behavior of a research vehicle on selected drive cycles ( Metro, Highway and Steady State ). Additionally, the impact upon vehicle driveability and other related subsystems ( e.g., transmission ) is addressed.

As vehicle tailpipe emission levels decrease with improvements in emission control technology and reformulation of gasolines, exhaust hydrocarbon levels begin to approach the levels in ambient air. Hydrocarbon speciation at these low levels requires high sensitivity capillary gas chromatography methods. In this study, a mixture of “synthetic” exhaust was prepared at two concentration levels (approximately 5 ppm C and 10 ppm C), and was analyzed by the widely-used Auto/Oil Air Quality Improvement Research Program (AQIRP) Phase II (gas chromatography) speciation method with a sensitivity of 0.005 ppm C for individual species. The mixture at each concentration level, along with a sample of ambient air, was analyzed a total of 20 times on 10 separate days over a 2½ week period. Concentrations of total hydrocarbons (HCs) and individual species (using the AQIRP library) were measured; averages and standard deviations were calculated.

Growing environmental concerns and stringent vehicle emissions regulations has created an urge in the automotive industry to move towards electrified propulsion systems. Reducing and eliminating the emission from public transportation vehicles plays a major role in contributing towards lowering the emission level. Battery electric buses are regarded as a type of promising green mass transportation as they provide the advantage of less greenhouse gas emissions per passenger. However, the electric bus faces a problem of limited range and is not able to drive throughout the day without being recharged. This research studies a public bus transit system example which servicing the city of Ann Arbor in Michigan and investigates the impact of different electrification levels on the final CO2 reduction. Utilizing models of a conventional diesel, hybrid electric, and battery electric bus, the CO2 emission for each type of transportation bus is estimated.

A simple set of equations has been developed to characterize automotive fuel behavior in fuel tanks, fuel vapor systems and fuel rails, particularly under hot weather conditions. The system of equations links the vapor pressure P, the temperature T, and the mass fraction evaporated Z. Parameters are determined empirically from laboratory vapor pressure and distillation tests. With appropriate values for heat capacity, heat of vaporization, and vapor composition, the equations can be used to estimate upper flammability limits, fuel weathering under hot fuel handling conditions, pressure rise in tanks, and evaporative vapor generation. The equations were developed as part of a larger fuel vapor system model.

The worldwide automotive industry is currently preparing for a market introduction of hydrogen-fueled powertrains. These powertrains in fuel cell electric vehicles (FCEVs) offer many advantages: high efficiency, zero tailpipe emissions, reduced greenhouse gas footprint, and use of domestic and renewable energy sources. To realize these benefits, hydrogen vehicles must be competitive with conventional vehicles with regards to fueling time and vehicle range. A key to maximizing the vehicle's driving range is to ensure that the fueling process achieves a complete fill to the rated Compressed Hydrogen Storage System (CHSS) capacity. An optimal process will safely transfer the maximum amount of hydrogen to the vehicle in the shortest amount of time, while staying within the prescribed pressure, temperature, and density limits. The SAE J2601 light duty vehicle fueling standard has been developed to meet these performance objectives under all practical conditions.

As part of a general EGR system model, an adiabatic thermodynamic vacuum EGR valve actuator model was developed and validated. The long term goal of the work is improved system operation by correctly specifying and allocating EGR system component requirements.