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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.

As European and Chinese tailpipe emission regulations for gasoline light-duty vehicles impose particulate number limits, automotive manufacturers have begun equipping some vehicles with a gasoline particulate filter (GPF). Increased understanding of how soot morphology, reactivity, and GPF loading affect GPF filtration and regeneration characteristics is necessary for advancing GPF performance. This study investigates the impacts of morphology, reactivity, and filter soot loading on GPF filtration and regeneration. Soot morphology and reactivity are varied through changes in fuel injection parameters, known to affect soot formation conditions. Changes in morphology and reactivity are confirmed through analysis using a transmission electron microscope (TEM) and a thermogravimetric analyzer (TGA) respectively.

The rapid development of connected and automated vehicle technologies together with cloud-based mobility services are revolutionizing the transportation industry. As a result, huge amounts of data are being generated, collected, and utilized, hence providing tremendous business opportunities. However, this big data poses serious challenges mainly in terms of data privacy. The risks of privacy leakage are amplified by the information sharing nature of emerging mobility services and the recent advances in data analytics. In this paper, we provide an overview of the connected vehicle landscape and point out potential privacy threats. We demonstrate two of the risks, namely additional individual information inference and user de-anonymization, through concrete attack designs. We also propose corresponding countermeasures to defend against such privacy attacks. We evaluate the feasibility of such attacks and our defense strategies using real world vehicular data.

This presentation is an assessment of the turbulence-stress scale-similarity in an IC engine, which is used for modeling subgrid dissipation in LES. Residual stresses and Leonard stresses were computed after applying progressively smaller spatial filters to measured and simulated velocity distributions. The velocity was measured in the TCC-II engine using planar and stereo PIV taken in three different planes and with three different spatial resolutions, thus yielding two and three velocity components, respectively. Comparisons are made between the stresses computed from the measured velocity and stress computed from the LES resolved-scale velocity from an LES simulation. The results present the degree of similarity between the residual stresses and the Leonard stresses at adjacent scales. The specified filters are systematically reduced in size to the resolution limits of the measurements and simulation.

Non-reacting spray behaviors of the Engine Combustion Network “Spray G” gasoline fuel injector were investigated at flash and non-flash boiling conditions in an optically accessible single cylinder engine and a constant volume spray chamber. High-speed Mie-scattering imaging was used to determine transient liquid-phase spray penetration distances and observe general spray behaviors. The standardized “G2” and “G3” test conditions recommended by the Engine Combustion Network were matched in this work and the fuel was pure iso-octane. Results from the constant volume chamber represented the zero (stationary piston) engine speed condition and single cylinder engine speeds ranged from 300 to 2,000 RPM. As expected, the present results indicated the general spray behaviors differed significantly between the spray chamber and engine. The differences must be thoughtfully considered when applying spray chamber results to guide spray model development for engine applications.

Nozzle length has been proven influencing fuel spray characteristics, and subsequently fuel-air mixing and combustion processes. However, almost all existing related studies are conducted when fuel is subcooled, of which fuel evaporation is extremely weak, especially at the near nozzle region. In addition, injector tip can be heated to very high temperature in SIDI engines, which would trigger flash boiling fuel spray. Therefore, in this study, effect of nozzle length on spray characteristics is investigated under superheated conditions. Three single-hole injectors with different nozzle length were studied. High speed backlit imaging technique was applied to acquire magnified near nozzle spray images based on an optical accessible constant volume chamber. Fuel pressure was maintained at 15 MPa, and n-hexane was chosen as test fuel.

Hybridization of vehicles is considered as the most promising technology for automakers and researchers, facing the challenge of optimizing both the fuel economy and emission of the road transport. Extensive studies have been performed on power-split hybrid electric vehicles (PS-HEVs). Despite of the fact that their excellent fuel economy performance in city driving conditions has been witnessed, a bottle neck for further improving the fuel economy of PS-HEVs has been encountered due to the inherent engine-generator-motor power circulation of the power-split system under medium-low speed cruising scenarios. Due to the special mechanical constraints of the power-split device (PSD), the conventional periodic cruising strategy like Pulse and Glide cannot be applied to PS-HEVs directly.

Military vehicles are typically armored, hence the open surface area for heat rejection is limited. Hence, the cooling parasitic load for a given heat rejection can be considerably higher and important to consider upfront in the system design. Since PEMFCs operate at low temp, the required cooling flow is larger to account for the smaller delta temperature to the air. This research aims to address the combined problem of optimal sizing of the lithium ion battery and PEM Fuel Cell stack along with development of the scalable power split strategy for small a PackBot robot. We will apply scalable physics-based models of the fuel cell stack and balance of plant that includes a realistic and scalable parasitic load from cooling integrated with existing scalable models of the lithium ion battery. This model allows the combined optimization that captures the dominant trends relevant to component sizing and system performance.

Optical imaging diagnostics of combustion are most often performed in the visible spectral band, in part because camera technology is most mature in this region, but operating in the infrared (IR) provides a number of benefits. These benefits include access to emission lines of relevant chemical species (e.g. water, carbon dioxide, and carbon monoxide) and obviation of image intensifiers (avoiding reduced spatial resolution and increased cost). High-speed IR in-cylinder imaging and image processing were used to investigate the relationships between infrared images, quantitative image-derived metrics (e.g. location of the flame centroid), and measurements made with in-cylinder pressure transducers (e.g. coefficient of variation of mean effective pressure). A 9.7-liter, inline-six, natural-gas-fueled engine was modified to enable exhaust-gas recirculation (EGR) and provide borescopic optical access to one cylinder for two high-speed infrared cameras.

A primary goal of large eddy simulation, LES, is to capture in-cylinder cycle-to-cycle variability, CCV. This is a first step to assess the efficacy of 35 consecutive computed motored cycles to capture the kinetic energy in the TCC-III engine. This includes both the intra-cycle production and dissipation as well as the kinetic energy CCV. The approach is to sample and compare the simulated three-dimensional velocity equivalently to the available two-component two-dimensional PIV velocity measurements. The volume-averaged scale-resolved kinetic energy from the LES is sampled in three slabs, which are volumes equal to the two axial and one azimuthal PIV fields-of-view and laser sheet thickness. Prior to the comparison, the effects of sampling a cutting plane versus a slab and slabs of different thicknesses are assessed. The effects of sampling only two components and three discrete planar regions is assessed.

Drivers often engage in secondary in-vehicle activity that is not related to vehicle control. This may be functional and/or to relieve monotony. Regardless, drivers believe they can safely do so when their perceived workload is low. In this paper, we describe a data acquisition system and machine learning based algorithms to determine perceived workload. Data collected were from on-road driving in light and heavy traffic, and individual physiological measures were recorded while the driver also performed in-vehicle tasks. Initial results show how the workload function can be personalized to an individual, and what implications this may have for vehicle design.

Natural gas (NG) is attractive for heavy-duty (HD) engines for reasons of cost stability, emissions, and fuel security. NG cannot be reliably compression-ignited, but conventional gasoline ignition systems are not optimized for NG and are challenged to ignite mixtures that are lean or diluted with exhaust-gas recirculation (EGR). NG ignition is particularly challenging in large-bore engines, where completing combustion in the available time is more difficult. Using two high-speed infrared (IR) cameras with borescopic access to one cylinder of an HD NG engine, the effect of ignition system on the early flame-kernel development and cycle-to-cycle variability (CCV) was investigated. Imaging in the IR yielded strong signals from water emission lines, which located the flame front and burned-gas regions and obviated image intensifiers. A 9.7-liter, six-cylinder engine was modified to enable exhaust-gas recirculation and to provide optical access.

Previous work done at the University of Michigan shows the capability of the vacuum-insulated catalytic converter (VICC) to retain heat during soak and the resulting benefits in reducing cold start emissions. This paper provides an improved version of the design which overcomes some of the shortcomings of the previous model and further improves the applicability and benefits of VICC. Also, newer materials have been evaluated and their effects on heat retention and emissions have studied using the 1-D after treatment model. Cold start emissions constitute around 60% to 80% of all the hydrocarbon and CO emissions in present day vehicles. The time taken to achieve the catalyst light-off temperature in a three-way catalytic converter significantly affects the emissions and fuel efficiency. The current work aims at developing a method to retain heat in catalytic converter, thus avoiding the need for light-off and reducing cold start emissions effectively.

High-pressure gasoline fuel injection is a means to improve combustion efficiency and lower engine-out emissions. The objective of this study was to quantify the effects of fuel injection pressure on transient gasoline fuel spray development for a wide range of injection pressures, including over 1000 bar, using a constant volume chamber and high-speed imaging. Reference grade gasoline was injected at fuel pressures of 300, 600, 900, 1200, and 1500 bar into the chamber, which was pressurized with nitrogen at 1, 5, 10, and 20 bar at room temperature (298 K). Bulk spray imaging data were used to quantify spray tip penetration distance, rate of spray tip penetration and spray cone angle. Near-nozzle data were used to evaluate the early spray development.

Morphological features of voids were characterized for T300/924 12-ply and 16-ply composite laminates at different porosity levels through the implementation of a digital microscopy (DM) image analysis technique. The composite laminates were fabricated through compression molding. Compression pressures of 0.1MPa, 0.3MPa, and 0.5MPa were selected to obtain composite plaques at different porosity levels. Tension-tension fatigue tests at load ratio R=0.1 for composite laminates at different void levels were conducted, and the dynamic stiffness degradation during the tests was monitored. Fatigue mechanisms were then discussed based on scanning electron microscope (SEM) images of the fatigue fracture surfaces. The test results showed that the presence of voids in the matrix has detrimental effects on the fatigue resistance of the material, depending on the applied load level.

Plenoptic particle tracking velocimetry (PTV) shows great potential for three-dimensional, three-component (3D3C) flow measurement with a simple single-camera setup. It is therefore especially promising for applications in systems with limited optical access, such as internal combustion engines. The 3D visualization of a plenoptic imaging system is achieved by inserting a micro-lens array directly anterior to the camera sensor. The depth is calculated from reconstruction of the resulting multi-angle view sub-images. With the present study, we demonstrate the application of a plenoptic system for 3D3C PTV measurement of engine-like air flow in a steady-state engine flow bench. This system consists of a plenoptic camera and a dual-cavity pulsed laser. The accuracy of the plenoptic PTV system was assessed using a dot target moved by a known displacement between two PTV frames.

ADAS features development involves multidisciplinary technical fields, as well as extensive variety of different sensors and actuators, therefore the early design process requires much more resources and time to collaborate and implement. This paper will demonstrate an alternative way of developing prototype ADAS concept features by using remote control car with low cost hobby type of controllers, such as Arduino Due and Raspberry Pi. Camera and a one-beam type Lidar are implemented together with Raspberry Pi. OpenCV free open source software is also used for developing lane detection and object recognition. In this paper, we demonstrate that low cost frame work can be used for the high level concept algorithm architecture, development, and potential operation, as well as high level base testing of various features and functionalities. The developed RC vehicle can be used as a prototype of the early design phase as well as a functional safety testing bench.

Reliable, accurate data on vehicle occupant characteristics could be used to personalize the occupant experience, potentially improving both satisfaction and safety. Recent improvements in 3D camera technology and increased use of cameras in vehicles offer the capability to effectively capture data on vehicle occupant characteristics, including size, shape, posture, and position. In previous work, the body dimensions of standing individuals were reliably estimated by fitting a statistical body shape model (SBSM) to data from a consumer-grade depth camera (Microsoft Kinect). In the current study, the methodology was extended to consider seated vehicle occupants. The SBSM used in this work was developed using laser scan data gathered from 147 children with stature ranging from 100 to 160 cm and BMI from 12 to 27 kg/m2 in various sitting postures.

Most of the present electric vehicle (EV) on-board chargers utilize a conventional design, i.e., a boost-type Power Factor Correction (PFC) controller followed by an isolated DC/DC converter. Such design usually yields a ~94% wall-to-battery efficiency and 2~3kW/L power density at most, which makes a high-power charger, e.g., 20kW module difficult to fit in the vehicle. As described in this paper, first, an E-mode GaN HEMT based 7.2kW single-phase charger was built. Connecting three such modules to the three-phase grid allows a three-phase >20kW charger to be built, which compared to the conventional three-phase charger, saves the bulky DC-bus capacitor by using the indirect matrix converter topology. To push the efficiency and power density to the limit, comprehensive optimization is processed to optimize the single-phase module through incorporating the GaN HEMT switching performance and securing its zero-voltage switching.