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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.

A method for detecting drivers' intentions is essential to facilitate operating mode transitions between driver and driver assistance systems. We propose a driver behavior recognition method using Hidden Markov Models (HMMs) to characterize and detect driving maneuvers and place it in the framework of a cognitive model of human behavior. HMM-based steering behavior models for emergency and normal lane changes as well as for lane keeping were developed using a moving base driving simulator. Analysis of these models after training and recognition tests showed that driver behavior modeling and recognition of different types of lane changes is possible using HMMs.

The challenge of developing a robust, real-time driver gaze classification system is that it has to handle difficult edge cases that arise in real-world driving conditions: extreme lighting variations, eyeglass reflections, sunglasses and other occlusions. We propose a single-camera end-toend framework for classifying driver gaze into a discrete set of regions. This framework includes data collection, semi-automated annotation, offline classifier training, and an online real-time image processing pipeline that classifies the gaze region of the driver. We evaluate an implementation of each component on various subsets of a large onroad dataset. The key insight of our work is that robust driver gaze classification in real-world conditions is best approached by leveraging the power of supervised learning to generalize over the edge cases present in large annotated on-road datasets.

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.

This paper presents the results of a study of how people interacted with a production voice-command based interface while driving on public roadways. Tasks included phone contact calling, full address destination entry, and point-of-interest (POI) selection. Baseline driving and driving while engaging in multiple-levels of an auditory-vocal cognitive reference task and manual radio tuning were used as comparison points. Measures included self-reported workload, task performance, physiological arousal, glance behavior, and vehicle control for an analysis sample of 48 participants (gender balanced across ages 21-68). Task analysis and glance measures confirm earlier findings that voice-command interfaces do not always allow the driver to keep their hands on the wheel and eyes on the road, as some assume.

Currently, states that are out of compliance with the National Ambient Air Quality Standards must, according to the Clean Air Act Amendments of 1990 (CAAA), develop and implement control strategies that demonstrate specific degrees of reduction in emissions-with the degree of reduction depending upon the severity of the problem. One tool that has been developed to aid regulators in both deciding an appropriate course of action and to demonstrate the desired reductions in mobile emissions is EPA's Mobile 5a emission estimation model. In our study, Mobile 5a has been used to examine the effects of regulatory strategies, as applied to the Northeast United States, on vehicle emissions under worst-case ozone-forming conditions.

Rare earths are a group of elements whose availability has been of concern due to monopolistic supply conditions and environmentally unsustainable mining practices. To evaluate the risks of rare earths availability to automakers, a first step is to determine raw material content and value in vehicles. This task is challenging because rare earth elements are used in small quantities, in a large number of components, and by suppliers far upstream in the supply chain. For this work, data on rare earth content reported by vehicle parts suppliers was assessed to estimate the rare earth usage of a typical conventional gasoline engine midsize sedan and a full hybrid sedan. Parts were selected from a large set of reported parts to build a hypothetical typical mid-size sedan. Estimates of rare earth content for vehicles with alternative powertrain and battery technologies were made based on the available parts' data.

This paper describes a post-race analysis of team KOMMIT EVT’s electric motorcycle data collected during the 2016 Pikes Peak International Hill Climb (PPIHC). The motorcycle consumed approximately 4 kWh of battery energy with an average and maximum speed of 107 km/h and 149 km/h, respectively. It was the second fastest electric motorcycle with a finishing time of 11:10.480. Data was logged of the motorcycle’s speed, acceleration, motor speed, power, currents, voltages, temperatures, throttle position, GPS position, rider’s heart rate and the ambient environment (air temperature, pressure and humidity). The data was used to understand the following factors that may have prevented a faster time: physical fitness of the rider, thermal limits of the motor and controller, available battery energy and the sprocket ratio between the motor and rear wheel.

This paper assesses the potential improvement of automotive powertrain technologies 25 years into the future. The powertrain types assessed include naturally-aspirated gasoline engines, turbocharged gasoline engines, diesel engines, gasoline-electric hybrids, and various advanced transmissions. Advancements in aerodynamics, vehicle weight reduction and tire rolling friction are also taken into account. The objective of the comparison is the potential of anticipated improvements in these powertrain technologies for reducing petroleum consumption and greenhouse gas emissions at the same level of performance as current vehicles in the U.S.A. The fuel consumption and performance of future vehicles was estimated using a combination of scaling laws and detailed vehicle simulations. The results indicate that there is significant potential for reduction of fuel consumption for all the powertrains examined.

Enhanced information management techniques made available through emerging Information Technology platforms hold a promise of providing significant improvements in both the effectiveness and efficiency of developing complex products. Determining actual management implementations that deliver on this promise has often proven elusive. Work in conjunction with the Lean Aircraft Initiative at MIT has revealed a straight forward use of Information Technology that portends significant cost reductions. By integrating previously separate types of data involved in the process of product development, engineers and designers can make decisions that will significantly reduce ultimate costs. Since the results presented are not specific to particular technologies or manufacturing processes, the conclusions are broadly applicable.

Battery packs for Hybrids, Plug-in Hybrids, and Electric Vehicles are assembled from a system of modules (sheets) with a tight sheet metal casing around them. Each module consists of an array of individual cells which vary in the composition of electrodes and separator from one manufacturer to another. In this paper a general procedure is outlined on the development of a constitutive and computational model of a cylindrical cell. Particular emphasis is placed on correct prediction of initiation and propagation of a tearing fracture of the steel can. The computational model correctly predicts rupture of the steel can which could release aggressive chemicals, fumes, or spread the ignited fire to the neighboring cells. The initiation site of skin fracture depends on many factors such as the ductility of the casing material, constitutive behavior of the system of electrodes, and type of loading.

Concept of a new decoupled cylinder liner design for internal combustion (IC) engines is presented from the framework of axiomatic design to improve friction and wear characteristics. In the current design, the piston rings fail to satisfy their functional requirements at the two dead centers of the piston stroke where lubrication is poor. It is proposed that by using undulated cylindrical surfaces selectively along the cylinder liner, much of the existing friction and wear problems of IC engines may be solved. The main idea behind undulated surface is to trap wear particles at the piston-cylinder interface in order to minimize plowing, and thus maintain low friction even in areas where lubrication fails to be hydrodynamic. In dry sliding tests using a modified engine motored at low speeds, undulated cylinders operated for significantly longer time than smooth cylinders without catastrophic increase in friction.

A method for determining the flame contour based on the flame arrival time at the fiber optic (FO) spark plug and at the head gasket ionization probe (IP) locations has been developed. The experimental data were generated in a single-cylinder Ricardo Hydra spark-ignition engine. The head gasket IP, constructed from a double-sided copper-clad circuit board, detects the flame arrival time at eight equally spaced locations at the top of the cylinder liner. Three other IP's were also installed in the cylinder head to provide additional intermediate data on flame location and arrival time. The FO spark plug consists of a standard spark plug with eight symmetrically spaced optical fibers located in the ground casing of the plug. The cylinder pressure was recorded simultaneously with the eleven IP signals and the eight optical signals using a high-speed PC-based data acquisition system.

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.

The paper describes the design and construction of a two cylinder apparatus to measure heat transfer under conditions of oscillating pressure and oscillating flow such as found in Stirling-cycle machines. The apparatus consists of two large single stage air compressors joined by a rigid drive shaft between the two crank shafts. The compressors are 27.94 cm (11-in) diameter by 22.86 cm (9-in) stroke. The apparatus is powered by a 25 HP variable speed DC motor. Belts and a jack shaft provide wide speed ranges. The test section, which is connected between the compressor cylinders, is a 44.45 mm (1.75-in) diameter tube and about 254 cm (100-in) long. The test section is configured for measuring wall heat flux, and gas pressure as a function of time. An LDV system is being installed for measurement of gas velocity as a function of time and position. A fast response micro thermocouple measures gas temperature as a function of time and position.

The nearly 77 million baby boomers, born between 1946 and 1964, can say that they are the automobile generation. Now turning 60 one every seven seconds, what are the new safety challenges and opportunities posed by the next generation of older adults? This paper presents a modified Haddon matrix to identify key product development, design and liability issues confronting the automobile industry and related stakeholders. The industry is now at a critical juncture to address the development of key technological innovations as well as the changing policy and liability environments being reshaped by an aging population.

Advanced driver assistance systems (ADAS) are an increasingly common feature of modern vehicles. The influence of such systems on driver behavior, particularly in regards to the effects of intermittent warning systems, is sparsely studied to date. This paper examines dynamic changes in physiological and operational behavior during lane departure warnings (LDW) in two commercial automotive systems utilizing on-road data. Alerts from the systems, one using auditory and the other haptic LDWs, were monitored during highway driving conditions. LDW events were monitored during periods of single-task driving and dual-task driving. Dual-task periods consisted of the driver interacting with the vehicle’s factory infotainment system or a smartphone to perform secondary visual-manual (e.g., radio tuning, contact dialing, etc.) or auditory-vocal (e.g. destination address entry, contact dialing, etc.) tasks.

Internal combustion (IC) engines still power most of the vehicles on road and will likely to remain so in the near future, especially for heavy duty applications in which electrification is typically more challenging. Therefore, continued improvements on IC engines in terms of efficiency and longevity are necessary for a more sustainable transportation sector. Two important design objectives for heavy duty engines with wet liners are to reduce friction loss and to lower the risks of cavitation damages, both of which can be greatly influenced by the piston-liner clearance and the design of the piston skirt. However, engine design optimization is difficult due to the nonlinear interactions between the key design variables and the design objectives, as well as the multi-physics and multi-scale nature of the mechanisms that are relevant to the design objectives.

With the recent advancement in sensing and controller technologies architecture design of an autonomous driving system becomes an important issue. Researchers have been developing different sensors and data processing technologies to solve the issues associated with fast processing, diverse weather, reliability, long distance recognition performance, etc. Necessary considerations of diverse traffic situations and safety factors of autonomous driving have also increased the complexity of embedded software as well as architecture of autonomous driving. In these circumstances, there are almost countless numbers of possible architecture designs. However, these design considerations have significant impacts on cost, controllability, and system reliability. Thus, it is crucial for the designers to make a challenging and critical design decision under several uncertainties during the conceptual design phase.

The goal of this interview analysis was to explore and document the perceptions of two unique lane centering systems (S90’s Pilot Assist and CT6’s Super Cruise). Both systems offer a similar type of functionality (adaptive cruise control and lane centering), but have significantly different design philosophies and HMI (Human-Machine Interface) implementations. Twenty-four drivers drove one of the two vehicle models for a month as part of a field operational test (FOT) study. Upon vehicle return, drivers took part in a 60-minute semi-structured interview covering their perceptions of the vehicle’s various advanced driver-assistance systems (ADAS). Transcripts of the interviews were coded by two researchers, who tagged each statement with relevant system and perception code labels. For analysis, the perception codes were grouped into larger thematic bins of safety, comfort, driver attention, and system performance.