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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 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.

Aerodynamic performance testing of heavy duty fans involve complicated test setups with specialized equipment and measurement systems as summarized in ANSI 210-07 standard. This paper describes virtual testing and simulation methods to obtain the fan aerodynamic performance data using commercial Computational Fluid Dynamics (CFD) software. Two different virtual test environments were used during the analysis. The first one is a virtual test chamber which is constructed based on the actual fan system installation. The second one is a virtual flow tube which approximates a fan flow test set-up as outlined in ANSI 210-07. The virtual fan is created from (i) the laser scan of the actual fan and (ii) the design specifications of the fan. The virtual test conditions simulate the actual test arrangement by imposing free boundary at flow inlet/outlet and proper fan rotation. The aerodynamic flow rate is controlled by a variable orifice located at the virtual test chamber outlet.

A key issue in complex systems design is measuring the ‘goodness’ of a design, i.e. finding a criterion through which a particular design is determined to be the ‘best’. Traditional choices in aerospace systems design, such as performance, cost, revenue, reliability, and safety, individually fail to fully capture the life cycle characteristics of the system. Furthermore, current multi-criteria optimization approaches, addressing this problem, rely on deterministic, thus, complete and known information about the system and the environment it is exposed to. In many cases, this information is not be available at the conceptual or preliminary design phases. Hence, critical decisions made in these phases have to draw from only incomplete or uncertain knowledge. One modeling option is to treat this incomplete information probabilistically, accounting for the fact that certain values may be prominent, while the actual value during operation is unknown.

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.

Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability, and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide low error velocity prediction. We developed an LSTM deep neural network that uses different groups of datasets collected in Fort Collins, Colorado.

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.

In support of the Federal Motor Carrier Safety Administration’s (FMCSA’s) ongoing interest in connected and automated commercial vehicles, this report summarizes analyses conducted to quantify variability in stopping distance tests conducted on commercial truck tractors. The data used were retrieved from tests performed under the controlled conditions specified for FMVSS-121 air brake system compliance testing. The report explores factors affecting the variability of the service brake stopping distance as defined by 49 CFR 571.121, S5.3.1 Stopping Distance—trucks and buses stopping distance. Variables examined in this analysis include brake type, weight, wheelbase, and tractor antilock braking system (ABS). This analysis uses existing test data collected between 2010 and 2019. Several of the examined parameters affected both tractor stopping distance and stopping distance variability.

To date, model validation metric is prominently designed for non-dynamic model responses. Though metrics for dynamic responses are also available, they are specifically designed for the vehicle impact application and uncertainties are not considered in the metric. This paper proposes the validation metric for general dynamic system responses under uncertainty. The metric makes use of the popular U-pooling approach and extends it for dynamic responses. Furthermore, shape deviation metric was proposed to be included in the validation metric with the capability of considering multiple dynamic test data. One vehicle impact model is presented to demonstrate the proposed validation metric.

While machine learning in autonomous vehicles development has increased significantly in the past few years, the use of reinforcement learning (RL) methods has only recently been applied. Convolutional Neural Networks (CNNs) became common for their powerful object detection and identification and even provided end-to-end control of an autonomous vehicle. However, one of the requirements of a CNN is a large amount of labeled data to inform and train the neural network. While data is becoming more accessible, these networks are still sensitive to the format and collection environment which makes the use of others’ data more difficult. In contrast, RL develops solutions in a simulation environment through trial and error without labeled data. Our research expands upon previous research in RL and Proximal Policy Optimization (PPO) and the application of these algorithms to 1/18th scale cars by expanding the application of this control strategy to a full-sized passenger vehicle.

Exhaust gas recirculation (EGR) cooler fouling has emerged as an important issue in diesel engine development. Uncertainty about the level of impact that fuel chemistry may have upon this issue has resulted in a need to investigate the cooler fouling process with emerging non-traditional fuel sources to gage their impact on the process. This study reports experiments using both ultra-low sulfur diesel (ULSD) and 20% biodiesel (B20) at elevated exhaust hydrocarbon conditions to investigate the EGR cooler fouling process. The results show that there is little difference between the degradation in cooler effectiveness for ULSD and B20 at identical conditions. At lower coolant temperatures, B20 exhibits elevated organic fractions in the deposits compared with ULSD, but this does not appear to lead to incremental performance degradation under the conditions studied.

Gear meshing noise is a common noise issue in manual transmission, its noise generation mechanism has been studied extensively [1, 2]. But most of time we have situations where multiple gear sets are connected in series and the noise and vibration behavior for a multi-stage gear can be quite different due to vibration inter-actions or interferences among multiple gear sets. In this paper, a two-stage gear driveline model was built using MSC ADAMS. Vibration order contents of a two-stage gear driveline were analyzed by both CAE simulation and theoretical calculations. In addition to gear meshing vibration orders of each gear set, the orders resulted from modulations between individual gear meshing and their harmonics were evident in the results. These special order contents were verified by experimental results, and also evidenced on transmission end of line tester results at transmission supplier GJT in Ganzhou, China.

Developing a complete understanding of the structure and behavior of the near-wall region (NWR) in reciprocating, internal combustion (IC) engines and of its interaction with the core flow is needed to support the implementation of advanced combustion and engine operation strategies, as well as predictive computational models. The NWR in IC engines is fundamentally different from the canonical steady-state turbulent boundary layers (BL), whose structure, similarity and dynamics have been thoroughly documented in the technical literature. Motivated by this need, this paper presents results from the analysis of two-component velocity data measured with particle image velocimetry near the head of a single-cylinder, optical engine. The interaction between the NWR and the core flow was quantified via statistical moments and two-point velocity correlations, determined at multiple distances from the wall and piston positions.

For better understanding of soot formation and oxidation processes in a biodiesel spray flame, the morphology, microstructure and sizes of soot particles directly sampled in a spray flame fuelled with soy-methyl ester were investigated using transmission electron microscopy (TEM). The soot samples were taken at different axial locations in the spray flame, 40, 50 and 70 mm from injector nozzle, which correspond to soot formation, peak, and oxidation zones, respectively. The biodiesel spray flame was generated in a constant-volume combustion chamber under a diesel-like high pressure and temperature condition (6.7 MPa, 1000K). Density, diameter of primary particles and radius of gyration of soot aggregates reached a peak at 50 mm from the injector nozzle and was lower or smaller in the formation or oxidation zones of the spray.

For a better understanding of soot formation and oxidation processes in conventional diesel and biodiesel spray flames, the morphology, microstructure and sizes of soot particles directly sampled in spray flames fuelled with US#2 diesel and soy-methyl ester were investigated using transmission electron microscopy (TEM). The soot samples were taken at 50mm from the injector nozzle, which corresponds to the peak soot location in the spray flames. The spray flames were generated in a constant-volume combustion chamber under a diesel-like high pressure and high temperature condition (6.7MPa, 1000K). Direct sampling permits a more direct assessment of soot as it is formed and oxidized in the flame, as opposed to exhaust PM measurements. Density of sampled soot particles, diameter of primary particles, size (gyration radius) and compactness (fractal dimension) of soot aggregates were analyzed and compared. No analysis of the soot micro-structure was made.

Trail-Braking (TB) is a common cornering technique used in rally racing to negotiate tight corners at (moderately) high speeds. In a previous paper by the authors it has been shown that TB can be generated as the solution to the minimum-time cornering problem, subject to fixed final positioning of the vehicle after the corner. A TB maneuver can then be computed by solving a non-linear programming (NLP). In this work we formulate an optimization problem by relaxing the final positioning of the vehicle with respect to the width of the road in order to study the optimality of late-apex trajectories typically followed by rally drivers. We test the results on a variety of corners. The optimal control inputs are approximated by simple piecewise linear input profiles defined by a small number of parameters. It is shown that the proposed input parameterization can generate close to optimal TB along the various corner geometries.

A direct solution to Global Warming would be to reflect a part of sunlight back into Space. A system tradeoff study is being developed with three of the concepts that are being evaluated as long-endurance high-altitude reflectors. The first concept is a high aspect ratio solar powered flying wing towing reflector sheets. This concept is named “Flying Carpet”. Second is a centrifugally stretched high altitude solar reflector (CSHASR). The CSHASR has 4 rotors made of reflector sheets with a hub stretching to 60 percent of the radius, held together by an ultralight quad-rotor structure. Each rotor is powered by a solar-electric motor. A variation on this concept, forced by nighttime descent rate concerns, is powered by tip-mounted solar panels and propellers with some battery storage augmenting rotational inertia as well as energy storage. The third concept is an Aerostatically Balanced Reflector (ABR) sheet, held up by hydrogen balloons.

The environmental impact of aircraft, specifically in the areas of noise and NOx emissions, has been a growing community concern. Coupled with the increasing cost and diminishing supply of traditional fossil fuels, these concerns have fueled substantial interest in the research and development of alternative power sources for aircraft. In 2003, NASA and the Department of Defense awarded a five year research cooperative agreement to a team of researchers from three different universities to address the design and analysis of revolutionary aeropropulsion technologies.

The blade crossing event of a coaxial counter-rotating rotor is a potential source of noise and impulsive blade loads. Blade crossings occur many times during each rotor revolution. In previous research by the authors, this phenomenon was analyzed by simulating two airfoils passing each other at specified speeds and vertical separation distances, using the compressible Navier-Stokes solver OVERFLOW. The simulations explored mutual aerodynamic interactions associated with thickness, circulation, and compressibility effects. Results revealed the complex nature of the aerodynamic impulses generated by upper/lower airfoil interactions. In this paper, the coaxial rotor system is simulated using two trains of airfoils, vertically offset, and traveling in opposite directions. The simulation represents multiple blade crossings in a rotor revolution by specifying horizontal distances between each airfoil in the train based on the circumferential distance between blade tips.