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Technical Paper

Evaluation of Different ADAS Features in Vehicle Displays

2019-04-02
2019-01-1006
The current study presents the results of an experiment on driver performance including reaction time, eye-attention movement, mental workload, and subjective preference when different features of Advanced Driver Assistance Systems (ADAS) warnings (Forward Collision Warning) are displayed, including different locations (HDD (Head-Down Display) vs HUD (Head-Up Display)), modality of warning (text vs. pictographic), and a new concept that provides a dynamic bird’s eye view for warnings. Sixteen drivers drove a high-fidelity driving simulator integrated with display prototypes of the features. Independent variables were displayed as modality, location, and dynamics of the warnings with driver performance as the dependent variable including driver reaction time to the warning, EORT (Eyes-Off-Road-Time) during braking after receiving the warning, workload and subject preference.
Technical Paper

Evaluation of Low Mileage GPF Filtration and Regeneration as Influenced by Soot Morphology, Reactivity, and GPF Loading

2019-04-02
2019-01-0975
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.
Technical Paper

Vehicle Velocity Prediction and Energy Management Strategy Part 1: Deterministic and Stochastic Vehicle Velocity Prediction Using Machine Learning

2019-04-02
2019-01-1051
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.
Technical Paper

Research on the Driving Stability Control System of the Dual-Motor Drive Electric Vehicle

2019-04-02
2019-01-0436
In order to improve the steering stability of the dual-motor drive electric vehicle, Taking the yaw rate and the sideslip angle as the control variables, Using the improved two degree of freedom linear dynamic model and seven degree of freedom nonlinear vehicle dynamics model, The hierarchical structure is used to establish the dual-motor drive electric vehicle steering stability control strategy which consist of the upper direct yaw moment decision-making layer based on the sliding mode controller and the lower additional yaw moment distribution layer based on the optimization theory. The Matlab/Simulink-Carsim joint simulation platform was built. The control strategy proposed in this paper was simulated and verified under the snake test condition and double-line shift test condition.
Technical Paper

Survey of Automotive Privacy Regulations and Privacy-Related Attacks

2019-04-02
2019-01-0479
Privacy has been a rising concern. The European Union has established a privacy standard called General Data Protection Regulation (GDPR) in May 2018. Furthermore, the Facebook-Cambridge Analytica data incident made headlines in March 2018. Data collection from vehicles by OEM platforms is increasingly popular and may offer OEMs new business models but it comes with the risk of privacy leakages. Vehicular sensor data shared with third-parties can lead to misuse of the requested data for other purposes than stated/intended. There exists a relevant regulation document introduced by the Alliance of Automobile Manufacturers (“Auto Alliance”), which classifies the vehicular sensors used for data collection as covered and non-sensitive parameters.
Technical Paper

Analyzing and Preventing Data Privacy Leakage in Connected Vehicle Services

2019-04-02
2019-01-0478
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.
Technical Paper

Vehicle Velocity Prediction and Energy Management Strategy Part 2: Integration of Machine Learning Vehicle Velocity Prediction with Optimal Energy Management to Improve Fuel Economy

2019-04-02
2019-01-1212
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.
Technical Paper

Study on the Controlled Field Test Scenarios of Automated Vehicles

2018-08-07
2018-01-1633
Function and performance test of automated vehicles in the closed field is a necessary way to verify their safety, intelligence and comfort. The design and number of test scenarios will influence if the automated vehicles can be tested and evaluated effectively and fast. Based on the interrelationship among the vehicle, driver’s (or control system) driving strategy and road, we use the permutation and combination method to compare the relative position and movement relations between an automated vehicle (vehicle under test) and the surrounding vehicles to generate a total possible test scenarios group.
Technical Paper

Study of Effects of Thermal Insulation Techniques on a Catalytic Converter for Reducing Cold Start Emissions

2018-04-03
2018-01-1431
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.
Technical Paper

An Integrated Deformed Surfaces Comparison Based Validation Framework for Simplified Vehicular CAE Models

2018-04-03
2018-01-1380
Significant progress in modeling techniques has greatly enhanced the application of computer simulations in vehicle safety. However, the fine-meshed impact models are usually complex and take lots of computational resources and time to conduct design optimization. Hence, to develop effective methods to simplify the impact models without losing necessary accuracy is of significant meaning in vehicle crashworthiness analysis. Surface deformation is frequently regarded as a critical factor to be measured for validating the accuracy of CAE models. This paper proposes an integrated validation framework to evaluate the inconsistencies between the deformed surfaces of the original model and simplified model. The geometric features and curvature information of the deformed surfaces are firstly obtained from crash simulation. Then, the magnitude and shape discrepancy information are integrated into the validation framework as the surface comparison objects.
Technical Paper

Infrared Borescopic Evaluation of High-Energy and Long-Duration Ignition Systems for Lean/Dilute Combustion in Heavy-Duty Natural-Gas Engines

2018-04-03
2018-01-1149
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.
Technical Paper

Voronoi Partitions for Assessing Fuel Consumption of Advanced Technology Engines: An Approximation of Full Vehicle Simulation on a Drive Cycle

2018-04-03
2018-01-0317
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.
Technical Paper

Cooling Parasitic Considerations for Optimal Sizing and Power Split Strategy for Military Robot Powered by Hydrogen Fuel Cells

2018-04-03
2018-01-0798
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.
Technical Paper

A Data Mining and Optimization Process with Shape and Size Design Variables Consideration for Vehicle Application

2018-04-03
2018-01-0584
This paper presents a design process with data mining technique and advanced optimization strategy. The proposed design method provides insights in three aspects. First, data mining technique is employed for analysis to identify key factors of design variables. Second, relationship between multiple types of size and shape design variables and performance responses can be analyzed. Last but not least, design preference can be initialized based on data analysis to provide priori guidance for the starting design points of optimization algorithm. An exhaust system design problem which largely contributes to the improvement of vehicular Noise, Vibration and Harshness (NVH) performance is employed for the illustration of the process. Two types of design parameters, structural variable (gauge of component) and layout variable (hanger location), are considered in the studied case.
Technical Paper

Personalized Driver Workload Estimation in Real-World Driving

2018-04-03
2018-01-0511
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.
Technical Paper

Infrared Borescopic Analysis of Ignition and Combustion Variability in a Heavy-Duty Natural-Gas Engine

2018-04-03
2018-01-0632
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.
Technical Paper

Driver Identification Using Multivariate In-vehicle Time Series Data

2018-04-03
2018-01-1198
All drivers come with a driving signature during a driving. By aggregating adequate driving data of a driver via multiple driving sessions, which is already embedded with driving behaviors of a driver, driver identification task could be treated as a supervised machine learning classification problem. In this paper, we use a random forest classifier to implement the classification task. Therefore, we collected many time series signals from 60 driving sessions (4 sessions per driver and 15 drivers totally) via the Controller Area Network. To reduce the redundancy of information, we proposed a method for signal pre-selection. Besides, we proposed a strategy for parameters tuning, which includes signal refinement, interval feature extraction and selection, and the segmentation of a signal. We also explored the performance of different types of arrangement of features and samples.
Journal Article

Assessing a Hybrid Supercharged Engine for Diluted Combustion Using a Dynamic Drive Cycle Simulation

2018-04-03
2018-01-0969
This study uses full drive cycle simulation to compare the fuel consumption of a vehicle with a turbocharged (TC) engine to the same vehicle with an alternative boosting technology, namely, a hybrid supercharger, in which a planetary gear mechanism governs the power split to the supercharger between the crankshaft and a 48 V 5 kW electric motor. Conventional mechanically driven superchargers or electric superchargers have been proposed to improve the dynamic response of boosted engines, but their projected fuel efficiency benefit depends heavily on the engine transient response and driver/cycle aggressiveness. The fuel consumption benefits depend on the closed-loop engine responsiveness, the control tuning, and the torque reserve needed for each technology. To perform drive cycle analyses, a control strategy is designed that minimizes the boost reserve and employs high rates of combustion dilution via exhaust gas recirculation (EGR).
Technical Paper

Effects of Engine Speed on Spray Behaviors of the Engine Combustion Network “Spray G” Gasoline Injector

2018-04-03
2018-01-0305
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.
Technical Paper

High-Speed Imaging Studies of Gasoline Fuel Sprays at Fuel Injection Pressures from 300 to 1500 bar

2018-04-03
2018-01-0294
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.
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