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Developing a new technology requires decision-makers to understand the technology's implications on an organization's objectives, which depend on user needs targeted by the technology. If these needs are common between two organizations, collaboration could result in more efficient technology development. For hybrid truck design, both commercial manufacturers and the military have similar performance needs. As the new technology penetrates the truck market, the commercial enterprise must quantify how the hybrid's superior fuel efficiency will impact consumer purchasing and, thus, future enterprise profits. The Army is also interested in hybrid technology as it continues its transformation to a more fuel-efficient force. Despite having different objectives, maximizing profit and battlefield performance, respectively, the commercial enterprise and Army can take advantage of their mutual needs.

The fidelity of the hybrid electric vehicle simulation is increased with the integration of a computationally-efficient finite-element based electric machine model, in order to address optimization of component design for system level goals. In-wheel electric motors are considered because of the off-road military application which differs significantly from commercial HEV applications. Optimization framework is setup by coupling the vehicle simulation to the constrained optimization solver. Utilizing the increased design flexibility afforded by the model, the solver is able to reshape the electric machine's efficiency map to better match the vehicle operation points. As the result, the favorable design of the e-machine is selected to improve vehicle fuel economy and reduce cost, while satisfying performance constraints.

The components in a hybrid electric vehicle (HEV) powertrain include the battery pack, an internal combustion engine, and the electric machines such as motors and possibly a generator. These components generate a considerable amount of heat during driving cycles. A robust thermal management system with advanced controller, designed for temperature tracking, is required for vehicle safety and energy efficiency. In this study, a hybridized mid-size truck for military application is investigated. The paper examines the integration of advanced control algorithms to the cooling system featuring an electric-mechanical compressor, coolant pump and radiator fans. Mathematical models are developed to numerically describe the thermal behavior of these powertrain elements. A series of controllers are designed to effectively manage the battery pack, electric motors, and the internal combustion engine temperatures.

The paper describes a new hybrid vehicle design option having very low energy storage capability, and in particular, a parallel hybrid with hydraulic storage and reapplication of braking energy. The operating efficiency of the propulsion system at light loads is substantially improved by splitting the engine into two segments, and finding ways of shutting down one or both engine segments whenever possible. The hybrid vehicle utilizes primarily current technologies. A diesel powered parallel hybrid as described demonstrates a reduction in fuel consumption of 53.9% on a volume basis when compared with an equivalent baseline vehicle.

Making manned and remotely-controlled wheeled and tracked vehicles easier to drive, especially off-road, is of great interest to the U.S. Army. If vehicles are easier to drive (especially closed hatch) or if they are driven autonomously, then drivers could perform additional tasks (e.g., operating weapons or communication systems), leading to reduced crew sizes. Further, poorly driven vehicles are more likely to get stuck, roll over, or encounter mines or improvised explosive devices, whereby the vehicle can no longer perform its mission and crew member safety is jeopardized. HMI technology and systems to support human drivers (e.g., autonomous driving systems, in-vehicle monitors or head-mounted displays, various control devices (including game controllers), navigation and route-planning systems) need to be evaluated, which traditionally occurs in mission-specific (and incomparable) evaluations.

Multiple models to estimate motion sickness (MS) have been proposed in the literature; however, few capture the influence of visual cues, limiting the models’ ability to predict MS that closely matches experimental MS data. This is especially significant in the presence of conflicts between visual and vestibular sensory signals. This paper provides an analysis of the gaps within existing MS estimation models and addresses these gaps by proposing the visual-vestibular motion sickness (VVMS) model. In this paper, the structure of the VVMS model, associated model parameters, and mathematical and physiological justification for selecting these parameters are presented. The VVMS model integrates vestibular sensory dynamics, visual motion perception, and visual-vestibular cue conflict to determine the conflict between the sensed and true vertical orientation of the passenger.

The purpose of this paper is to demonstrate the impact of low- floor bus seating configuration, passenger load factor (PLF) and passenger characteristics on individual boarding and disembarking (B-D) times -a key component of vehicle dwell time and overall transit system performance. A laboratory study was conducted using a static full-scale mock-up of a low-floor bus. Users of wheeled mobility devices (n=48) and walking aids (n=22), and visually impaired (n=17) and able-bodied (n=17) users evaluated three bus layout configurations at two PLF levels yielding information on B-D performance. Statistical regression models of B-D times helped quantify relative contributions of layout, PLF, and user characteristics viz., impairment type, power grip strength, and speed of ambulation or wheelchair propulsion. Wheeled mobility device users, and individuals with lower grip strength and slower speed were impacted greater by vehicle design resulting in increased dwell time.

The addition of synthetic traffic to a driving simulation greatly enhances the realism of the virtual world. Giving this traffic human-like behavior is likewise desirable, and has been the focus of some research over the past few years. This paper presents a modular architecture for including autonomous traffic in a driving simulation, and describes the first steps taken toward the application of this architecture to the DaimlerChrysler Auburn Hills Simulator. By separating the planning part of the agent from the low-level control and vehicle dynamics systems, the described architecture permits the inclusion of powerful, previously developed components in a straightforward way; in the present application, agents use Soar to reason about their actions. This paper gives an overview of the structures of the agents, and of the entire system, describes the components and their implementations, and discusses the current state of the project and plans for the future.

Stringent fuel economy and emissions regulations have driven development of new mixture preparation technologies and increased spark-ignition engine complexity. Additional degrees of freedom, brought about by devices such as cam phasers and charge motion control valves, enable greater range and flexibility in engine control. This permits significant gains in fuel efficiency and emission control, but creates challenges related to proper engine control and calibration techniques. Accurate experimental characterization of high degree of freedom engines is essential for addressing the controls challenge. In particular, this paper focuses on the evaluation of three experimental residual gas fraction measurement techniques for use in a spark ignition engine equipped with dual-independent variable camshaft phasing (VVT).

Analytical methods are used to evaluate the braking and steering performance of tractor-semitrailer, truckfull trailer, double, and triple combinations that have mechanical properties corresponding to current design and usage practices in the United States.

An experimental heat release rate analysis was conducted on a six cylinder, 12.7 liter Detroit Diesel Series 60 turbocharged engine operating under steady state conditions. The overall chemical, or gross, rate of heat release and the net apparent rate of heat release were determined from experimental measurements. The gross, time averaged, heat release rate was determined by two separate concepts/methods using exhaust gas concentration measurements from the Nicolet Rega 7000 Real Time Exhaust Gas Analyzer and the measured exhaust gas flow rate. The net apparent rate of heat release was determined from the in-cylinder pressure measurements for each of the six cylinders, averaged over 80 cycles. These pressure measurements were obtained using a VXI based Tektronix data acquisition system and LabVIEW software. A computer algorithm then computed the net apparent rate of heat release from the averaged in-cylinder pressure measurements.

Prior work in the literature have shown that pre-chamber spark plug technologies can provide remarkable improvements in engine performance. In this work, three passively fueled pre-chamber spark plugs with different pre-chamber geometries were investigated using in-cylinder high-speed imaging of spectral emission in the visible wavelength region in a single-cylinder direct-injection spark-ignition gasoline engine. The effects of the pre-chamber spark plugs on flame development were analyzed by comparing the flame progress between the pre-chamber spark plugs and with the results from a conventional spark plug. The engine was operated at fixed conditions (relevant to federal test procedures) with a constant speed of 1500 revolutions per minute with a coolant temperature of 90 oC and stoichiometric fuel-to-air ratio. The in-cylinder images were captured with a color high-speed camera through an optical insert in the piston crown.

Connected autonomy brings with it the means of significantly increasing vehicle Energy Economy (EE) through optimal Eco-Driving control. Much research has been conducted in the area of autonomous Eco-Driving control via various methods. Generally, proposed algorithms fall into the broad categories of rules-based controls, optimal controls, and meta-heuristics. Proposed algorithms also vary in cost function type with the 2-norm of acceleration being common. In a previous study the authors classified and implemented commonly represented methods from the literature using real-world data. Results from the study showed a tradeoff between EE improvement and run-time and that the best overall performers were meta-heuristics. Results also showed that cost functions sensitive to the 1-norm of acceleration led to better performance than those which directly minimize the 2-norm.

After-injection is the introduction of additional fuel to the combustion chamber after the end of the main injection. It is a persistent diesel fuel injection problem which usually results in reduced engine power and economy and increased emissions. After-injection is caused by uncontrolled pressure transients at the injector after the opening of the pump spill port. These pressure transients are related to the wave propagation phenomena in the high-pressure pipeline connecting the pump and injector. Use of experimental trial-and-error methods in attempts to control this phenomenon has met with limited success. The analytical control method described in another paper is used to determine design means by which after-injection may be controlled. Further investigation and evaluation of two design changes which release the injection system excess elastic energy in a controlled manner are considered herein. One design change is the addition of a control valve in the pump delivery chamber.

Exhaust gas rebreathing is considered to be a practical enabler that could be used in HCCI production engines. Recent experimental work at the University of Michigan demonstrates that the combustion characteristics of an HCCI engine using large amounts of hot residual gas by rebreathing are very sensitive to engine thermal conditions. This computational study addresses HCCI engine operation with rebreathing, with emphasis on the effects of engine thermal conditions during transient periods. A 1-D cycle simulation with thermal networks is carried out under load and speed transitions. A knock integral auto-ignition model, a modified Woschni heat transfer model for HCCI engines and empirical correlations to define burn rate and combustion efficiency are incorporated into the engine cycle simulation model. The simulation results show very different engine behavior during the thermal transient periods compared with steady state.

We have developed a methodology for analysis of Premixed Charge Compression Ignition (PCCI) engines that applies to conditions in which there is some stratification in the air-fuel distribution inside the cylinder at the time of combustion. The analysis methodology consists of two stages: first, a fluid mechanics code is used to determine temperature and equivalence ratio distributions as a function of crank angle, assuming motored conditions. The distribution information is then used for grouping the mass in the cylinder into a two-dimensional (temperature-equivalence ratio) array of zones. The zone information is then handed on to a detailed chemical kinetics model that calculates combustion, emissions and engine efficiency information. The methodology applies to situations where chemistry and fluid mechanics are weakly linked.

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.

Life cycle design (LCD) is a framework for designing product systems which are both economically and ecologically sustainable. Key elements of this framework are systems analysis, multicriteria analysis and multistakeholder participation. This paper illustrates the application of life cycle design for a comparative evaluation of a prototype sand-cast 2.0 l aluminum manifold for the 1995 Ford Contour and a 2.0 l equivalent of the 1.9 l multi-tube brazed aluminum manifold for the 1995 Ford Escort. A life cycle inventory analysis was performed for the intake manifolds by evaluating the energy and wastes in the raw material acquisition, material processing, manufacturing, use and retirement stages. The environmental data for the two manifolds were integrated with life cycle cost and performance data to enhance product design and decision making.

Level 2 (L2) partial driving automation systems are rapidly emerging in the marketplace. L2 systems provide sustained automatic longitudinal and lateral vehicle motion control, reducing the need for drivers to continuously brake, accelerate and steer. Drivers, however, remain critically responsible for safely detecting and responding to objects and events. This paper summarizes variations of L2 systems (hands-on and/or hands-free) and considers human drivers’ roles when using L2 systems and for designing Human-Machine Interfaces (HMIs), including Driver Monitoring Systems (DMSs). In addition, approaches for examining potential unintended consequences of L2 usage and evaluating L2 HMIs, including field safety effect examination, are reviewed. The aim of this paper is to guide L2 system HMI development and L2 system evaluations, especially in the field, to support safe L2 deployment, promote L2 system improvements, and ensure well-informed L2 policy decision-making.

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).