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Biodiesel has been widely accepted as an alternative for fossil-derived diesel fuel for use in compression ignition (CI) engines. Poor oxidative stability and cold flow properties restrict the use of biodiesel beyond current B20 blend levels (20% biodiesel in 80% ULSD) for vehicle applications. Maintaining the properties of B20 as specified by ASTM D7476-08 is important because, once out of spec, B20 may cause injector coke formation, fuel filter plugging, increased exhaust emissions, and overall loss of engine performance. While the properties of fresh B20 may be within the specifications, under engine operating and longer storage conditions B20 could deteriorate. In a diesel engine, the fuel that goes to the injector and does not enter the cylinder is recycled back to the fuel tank. The re-circulated fuel returns to the fuel tank at an elevate temperature, which can cause thermal oxidation.

Climate change due to greenhouse gas emissions has led to new vehicle emissions standards which in turn have led to a call for vehicle technologies to meet these standards. Modeling of vehicle fuel consumption and emissions emerged as an effective tool to help in developing and assessing such technologies, to help in predicting aggregate vehicle fuel consumption and emissions, and to complement traffic simulation models. The paper identifies the current state of the art on vehicle fuel consumption and emissions modeling and its utilization to test the environmental impact of the Intelligent Transportation Systems (ITS)’ measures and to evaluate transportation network improvements. The study presents the relevant models to ITS in the key classifications of models in this research area. It demonstrates that the trends of vehicle fuel consumption and emissions provided by current models generally do satisfactorily replicate field data trends.

Automated driving functionalities delivered through Advanced Driver Assistance System (ADAS) have been adopted more and more frequently in consumer vehicles. The development and implementation of such functionalities pose new challenges in safety and functional testing and the associated validations, due primarily to their high demands on facility and infrastructure. This paper presents a rather unique Vehicle-in-the-Loop (VIL) test setup and methodology compared those previously reported, by combining the advantages of the hardware-in-the-loop (HIL) and traditional chassis dynamometer test cell in place of on-road testing, with a multi-agent real-time simulator for the rest of test environment.

Up to now, there is no standard methodology that addresses how driver distraction is affected by perceptual demand and working memory demand - aside from visual allocation. In 2009, the Peripheral Detection Task (PDT) became a NHTSA recommended measure for driver distraction [1]. Then the PDT task was renamed as the Detection Response Task (DRT) because the International Standards Organization (ISO) has identified this task as a potential method for assessing selective attention in detection of visual, auditory, tactile and haptic events while driving. The DRT is also under consideration for adoption as an ISO standard surrogate test for driver performance for new telematics designs. The Wayne State University (WSU) driver imaging group [2, 3] improved the PDT and created the Enhanced Peripheral Detection Task I (EPDT-I) [4]. The EPDT-I is composed of a simple visual event detection task and a video of a real-world driving scene.

This study undertakes an investigation of the effect of battery charge balance in hybrid electric vehicles (HEVs) on EPA fuel economy label values. EPA's updated method was fully implemented in 2011 and uses equations which weight the contributions of fuel consumption results from multiple dynamometer tests to synthesize city and highway estimates that reflect average U.S. driving patterns. For the US06 and UDDS cycles, the test results used in the computation come from individual phases within the overall certification driving cycles. This methodology causes additional complexities for hybrid vehicles, because although they are required to be charge-balanced over the course of a full drive cycle, they may have net charge or discharge within the individual phases. As a result, the fuel consumption value used in the label value calculation can be skewed.

An iterative learning control (ILC) algorithm has been developed for a test cell electro-hydraulic, fully flexible valve actuation system to track valve lift profile under steady-state and transient operation. A dynamic model of the plant was obtained from experimental data to design and verify the ILC algorithm. The ILC is implemented in a prototype controller. The learned control input for two different lift profiles can be used for engine transient tests. Simulation and bench test are conducted to verify the effectiveness and robustness of this approach. The simple structure of the ILC in implementation and low cost in computation are other crucial factors to recommend the ILC. It does not totally depend on the system model during the design procedure. Therefore, it has relatively higher robustness to perturbation and modeling errors than other control methods for repetitive tasks.

The availability of active safety systems, such as Lane Departure Warning (LDW), has recently been added as a rating factor in the U.S. New Car Assessment Program (NCAP). The objective of this study is to determine the relevance of the NCAP LDW confirmation test to real-world road departure crashes. This study is based on data collected as part of supplemental crash reconstructions performed on 890 road departure collisions from the National Automotive Sampling System, Crashworthiness Data System (NASS/CDS). Scene diagrams and photographs were examined to determine the lane departure and lane marking characteristics not available in the original data. The results suggest that the LDW confirmation test captures many of the conditions observed in real-world road departures. For example, 40% of all single vehicle collisions in the dataset involved a drift-out-of-lane type of departures represented by the test.

A unique concept for a multi-body test rig enabling the simulation of longitudinal, steering and vertical dynamics was developed at the Institute for Mechatronic Systems (IMS) at TU Darmstadt. A prototype of this IMS test rig is currently being built. In conjunction with the IMS test rig, the Vehicle Terrain Performance Laboratory (VTPL) at Virginia Tech further developed a full car, seven degree of freedom (7 DOF) simulation model capable of accurately reproducing measured displacement, pitch, and roll of the vehicle body due to terrain excitation. The results of the 7 DOF car model were used as the reference input to the multi-body IMS test rig model. The goal of the IMS/VTPL joint effort was to determine whether or not a controller for the IMS test rig vertical actuator could accurately reproduce wheel displacements due to different measured terrain constraints.

This study evaluates the performance of an indirect injection (IDI) diesel engine fueled with cotton seed biodiesel while assessing the engine's multi-fuel capability. Millions of tons of cotton seeds are available in the south of the US every year and approximately 10% of oil contained in the seeds can be extracted and transesterified. An investigation of combustion, emissions, and efficiency was performed using mass ratios of 20-50% cotton seed biodiesel (CS20 and CS50) in ultra-low sulfur diesel #2 (ULSD#2). Each investigation was run at 2400 rpm with loads of 4.2 - 6.3 IMEP and compared to the reference fuel ULDS#2. The ignition delay ranged in a narrow interval of 0.8-0.97ms across the blends and the heat release rate showed comparable values and trends for all fuel blends. The maximum volume averaged cylinder temperature increased by approximately 100K with each increase in 1 bar IMEP load but the maximum remained constants across the blends.

With advances in virtual prototyping, accurate digital modeling of driving posture is regarded as a fundamental step in the design of ergonomic driver-seat-cabin systems. Extensive work on driving postures has been carried out focusing on the measurement and prediction of driving postures and the determination of comfortable joint angle ranges. However, studies on postural sensitivity are scarce. The current study investigated whether a driver-selected posture actually represents the most preferred one, by comparing the former with ratings of postures selected at 20 predefined places around the original hip joint center (HJC). An experiment was undertaken in a lab setting, using two distinctive driving package geometries: one for a sedan and the other for an SUV. The 20 postural ratings were compared with that of the initial user-selected position.

In the past few years, the price of petroleum based fuels, especially vehicle fuels such as gasoline and diesel, have been increasing at a significant rate. Consequently, there is much more consumer interest related to reducing fuel consumption of conventional and hybrid electric vehicles (HEVs). The “pulse and glide” (PnG) driving strategy is first applied to a conventional vehicle to quantify the fuel consumption benefits when compared to steady state speed (cruising) conditions over the same time and distance. Then an HEV is modeled and tested to investigate if a hybrid system can further reduce fuel consumption with the proposed strategy. Note that the HEV used in this study has the advantage that the engine can be automatically shut off below a certain speed (∼40 mph, 64 kph) at low loads, however a driver must shut off the engine manually in a conventional vehicle to apply this driving strategy.

This work pertains to laboratory testing of truck cab suspensions for the purpose of improving in-cab ride quality. It describes the testing procedure of a complete truck cab suspension while still being mounted on the vehicle. It allows for testing with minimal amount of resources, limited to two mobile actuators and minimal modifications to the stock vehicle. The actuators can be attached to any axle through a set of modified brake drums and excite the drive axle in a vertical plane. The excitation signal sent to the actuators can be in phase for a heave type motion or out of phase for a roll motion. The chassis shock absorbers are replaced with rigid links to prevent the actuator input from becoming filtered by the primary suspension. This allows the input to reach the cab suspension more directly and the cab to be excited across a broader range of frequencies.

This paper investigates the use of an adaptive proportional-integral-derivative (APID) controller to reduce a combustion engine crankshaft speed pulsation. Both computer simulations and engine test rig experiments are used to validate the proposed control scheme. The starter/alternator (S/A) is used as the actuator for engine speed control. The S/A is an induction machine. It produces a supplemental torque source to cancel out the fast engine torque variation. This machine is placed on the engine crankshaft. The impact of the slowly varying changes in engine operating conditions is accounted for by adjusting the APID controller parameters on-line. The APID control scheme tunes the PID controller parameters by using the theory of adaptive interaction. The tuning algorithm determines a set of PID parameters by minimizing an error function. The error function is a weighted combination of the plant states and the required control effort.

Skyhook control has been used extensively for semiactive dampers for a variety of applications, most widely for passenger vehicle suspensions. This paper provides an experimental evaluation of how well skyhook control works for improving roll stability of a passenger vehicle. After discussing the formulation for various semiactive control methods that have been suggested in the past for vehicle suspensions, the paper includes the implementation of a semiactive system with magneto-rheological (MR) dampers on a sport utility vehicle. The vehicle is used for a series of road tests that includes lane change maneuvers, with different types of suspensions. The suspensions that are tested include the stock suspension, the uncontrolled MR dampers, skyhook control, and a new semiactive control method called “SIA skyhook.” The SIA Skyhook augments the conventional skyhook control with steering input, in order to account for the suspension requirements during a lateral maneuver.

Experiments were conducted to investigate the characteristics of a common rail fuel injection system using a flow rate test rig and a single cylinder research diesel engine. Experiments covered speeds and loads typical to engine conditions under Hybrid Electric Vehicle operation. Different injection modes were investigated including main injection, main-post injection and pilot-main injection. The analysis indicated that the common rail fuel pressure affects all the injection parameters including the start of fuel delivery, its duration and amount under all modes of injection. Also, the pressure waves produced in the system have an impact on the operation of the nozzle-needle and fuel delivery particularly in the main-post injection mode.

Simultaneous measurements of the radial and the tangential components of velocity are obtained in a high-speed, direct-injection diesel engine typical of automotive applications. Results are presented for engine operation with fuel injection, but without combustion, for three different swirl ratios and four injection pressures. With the mean and fluctuating velocities, the r-θ plane shear stress and the mean flow gradients are obtained. Longitudinal and transverse length scales are also estimated via Taylor's hypothesis. The flow is shown to be sufficiently homogeneous and stationary to obtain meaningful length scale estimates. Concurrently, the flow and injection processes are simulated with KIVA-3V employing a RNG k-ε turbulence model. The measured turbulent kinetic energy k, r-θ plane mean strain rates ( 〈Srθ〉, 〈Srr〉, and 〈Sθθ〉 ), deviatoric turbulent stresses , and the r-θ plane turbulence production terms are compared directly to the simulated results.

Realistic vehicle fuel economy studies require real-world vehicle driving behavior data along with various factors affecting the fuel consumption. Such studies require data with various vehicles usages for prolonged periods of time. A project dedicated to collecting such data is an enormous and costly undertaking. Alternatively, we propose to utilize two publicly available vehicle travel survey data sets. One is Puget Sound Travel Survey collected using GPS devices in 484 vehicles between 2004 and 2006. Over 750,000 trips were recorded with a 10-second time resolution. The data were obtained to study travel behavior changes in response to time-and-location-variable road tolling. The other is Atlanta Regional Commission Travel Survey conducted for a comprehensive study of the demographic and travel behavior characteristics of residents within the study area.

This study investigated driver glances while engaging in infotainment tasks in a stationary vehicle while surrogate driving: watching a driving video recorded from a driver’s viewpoint and projected on a large screen, performing a lane-tracking task, and performing the Tactile Detection Response Task (TDRT) to measure attentional effects of secondary tasks on event detection and response. Twenty-four participants were seated in a 2014 Toyota Corolla production vehicle with the navigation system option. They performed the lane-tracking task using the vehicle’s steering wheel, fitted with a laser pointer to indicate wheel movement on the driving video. Participants simultaneously performed the TDRT and a variety of infotainment tasks, including Manual and Mixed-Mode versions of Destination Entry and Cancel, Contact Dialing, Radio Tuning, Radio Preset selection, and other Manual tasks. Participants also completed the 0-and 1-Back pure auditory-vocal tasks.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2008 - 2011 EcoCAR: The NeXt Challenge Advanced Vehicle Technology Competition series organized by Argonne National Laboratory (ANL) and sponsored by General Motors (GM) and the U.S. Department of Energy (DoE). Following GM's vehicle development process, HEVT established goals that meet or exceed the competition requirements for EcoCAR in the design of a plug-in, range-extended hybrid electric vehicle. The challenge involves designing a crossover SUV powertrain to reduce fuel consumption, petroleum energy use and well-to-wheels (WTW) greenhouse gas (GHG) emissions. In order to interface with and control the vehicle, the team added a National Instruments (NI) CompactRIO (cRIO) to act as a hybrid vehicle supervisory controller (HVSC).

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2009 - 2011 EcoCAR: The NeXt Challenge Advanced Vehicle Technology Competition series organized by Argonne National Lab (ANL), and sponsored by General Motors Corporation (GM), and the U.S. Department of Energy (DOE). Following GM's Vehicle Development Process (VDP), HEVT established team goals that meet or exceed the competition requirements for EcoCAR in the design of a plug-in extended-range hybrid electric vehicle. The competition requires participating teams to improve and redesign a stock Vue XE donated by GM. The result of this design process is an Extended-Range Electric Vehicle (E-REV) that uses grid electric energy and E85 fuel for propulsion. The vehicle design is predicted to achieve an SAE J1711 utility factor corrected fuel consumption of 2.9 L(ge)/100 km (82 mpgge) with an estimated all electric range of 69 km (43 miles) [1].