Search Results

The U.S. Environmental Protection Agency’s (EPA’s) Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) tool was created to estimate greenhouse gas (GHG) emissions from light-duty vehicles. ALPHA is a physics-based, forward-looking, full vehicle computer simulation capable of analyzing various vehicle types with different powertrain technologies, showing realistic vehicle behavior, and auditing of all energy flows in the model. In preparation for the midterm evaluation (MTE) of the 2017-2025 light-duty GHG emissions rule, ALPHA has been refined and revalidated using newly acquired data from model year 2013-2016 engines and vehicles. The robustness of EPA’s vehicle and engine testing for the MTE coupled with further validation of the ALPHA model has highlighted some areas where additional data can be used to add fidelity to the engine model within ALPHA.

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).

A GT-SUITE vehicle-aftertreatment model has been developed to examine the cold-start emissions reduction capabilities of a Vacuum Insulated Catalytic Converter (VICC). This converter features a thermal management system to maintain the catalyst monolith above its light-off temperature between trips so that most of a vehicle’s cold-start exhaust emissions are avoided. The VICC thermal management system uses vacuum insulation around the monoliths. To further boost its heat retention capacity, a metal phase-change material (PCM) is packaged between the monoliths and vacuum insulation. To prevent overheating of the converter during periods of long, heavy engine use, a few grams of metal hydride charged with hydrogen are attached to the hot side of the vacuum insulation. The GT-SUITE model successfully incorporated the transient heat transfer effects of the PCM using the effective heat capacity method.

The Energy Boundary Element Analysis (EBEA) has been utilized in the past for computing the exterior acoustic field at high frequencies (above ∼400Hz) around vehicle structures and numerical results have been compared successfully to test data [1, 2 and 3]. The Energy Finite Element Analysis (EFEA) has been developed for computing the structural vibration of complex structures at high frequencies and validations have been presented in previous publications [4, 5]. In this paper the EBEA is utilized for computing the acoustic field around a vehicle structure due to external acoustic noise sources. The computed exterior acoustic field comprises the excitation for the EFEA analysis. Appropriate loading functions have been developed for representing the exterior acoustic loading in the EFEA simulations, and a formulation has been developed for considering the acoustic treatment applied on the interior side of structural panels.

In this research, off-road vehicle simulation is performed with tire-soil interaction model. The predictive semi-analytical model, which is originally developed for tire-snow interaction model by Lee [4], is applied as a tire-soil interaction model and is implemented to MSC/ADAMS, commercial multi-body dynamic software. It is applied to simulate the handling maneuver of military vehicle HMMWV. Two cases are simulated with Michigan sandy loam soil property. Each case has two maneuvers, straight-line brake and step steer (J-turn). First, tire-soil interaction model and conventional on-road tire model are simulated on the flat road of the same frictional coefficient. The proposed tire-soil interaction model provided larger force under the same slip. Second, the same maneuvers are performed with real off-road frictional coefficient. The proposed tire-soil model can be validated and the behavior of the off-road vehicle can be identified through two simulation cases.

The cooling system of Series Hybrid Electric Vehicles (SHEVs) is more complicated than that of conventional vehicles due to additional components and various cooling requirements of different components. In this study, a numerical model of the cooling system for a SHEV is developed to investigate the thermal responses and power consumptions of the cooling system. The model is created for a virtual heavy duty tracked SHEV. The powertrain system of the vehicle is also modeled with Vehicle-Engine SIMulation (VESIM) previously developed by the Automotive Research Center at the University of Michigan. VESIM is used for the simulation of powertrain system behaviors under three severe driving conditions and during a realistic driving cycle. The output data from VESIM are fed into the cooling system simulation to provide the operating conditions of powertrain components.

Assessments of reach capability using human figure models are commonly performed by exercising each joint of a kinematic chain, terminating in the hand, through the associated ranges of motion. The result is a reach envelope determined entirely by the segment lengths, joint degrees of freedom, and joint ranges of motion. In this paper, the validity of this approach is assessed by comparing the reach envelopes obtained by this method to those obtained in a laboratory study of men and women. Figures were created in the Jack human modeling software to represent the kinematic linkages of participants in the laboratory study. Maximum reach was predicted using the software's kinematic reach-envelope generation methods and by interactive manipulation. Predictions were compared to maximum reach envelopes obtained experimentally. The findings indicate that several changes to the normal procedures for obtaining maximum reach envelopes for seated tasks are needed.

Improving the performance characteristics of a typical disc brake encompasses a number of design strategies as well as limitations imposed by cost objectives. Utilizing pad loading uniformity in a design is one strategy that offers an improvement in desired performance characteristics, including a reduction in tapered lining wear as well as a possible reduced propensity for noise generation. To approach this design strategy, a procedure has been developed to tailor the brake pad lining profile to maximize pad loading uniformity in a multibody dynamics model of a typical disc brake. In determining an optimal lining configuration, a suitable compromise for gaining beneficial performance improvements in a cost effective manner is reached. The implementation of this design strategy involves the parametric definition of the lining profile by introducing a series of variables that are linked to the profile markers.

In this paper, a simulation-based dynamic rollover evaluation procedure is described. This work is based on the worst-case methodology developed at the University of Michigan, and is the result of a collaborated research project between the University of Michigan and TRW Inc. The target vehicle studied in this paper is a large production volume SUV. This vehicle is equipped with a production-intent TRW Vehicle Stability Control (VSC) system. The main goals of this paper are to (i) study the rollover propensity of this SUV, as influenced by vehicle and environment parameters such as vehicle speed, road condition, etc.; and (ii) investigate whether, and by how much, does the VSC system influence the rollover propensity of this SUV. The modeling, evaluation procedure, and preliminary evaluation results are reported.

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 automotive industry has been witnessing a major shift towards downsized boosted direct injection engines due to diminishing petroleum reserves and increasingly stringent emission targets. Boosted engines operate at a high mean effective pressure (MEP), resulting in higher in-cylinder pressures and temperatures, effectively leading to increased possibility of abnormal combustion events like knock and pre-ignition. Therefore, the compression ratio and boost pressure in modern engines are restricted, which in-turn limits the engine efficiency and power. To mitigate conditions where the engine is prone to knocking, the engine control system uses spark retard and/or mixture enrichment, which decrease indicated work and increase specific fuel consumption. Several researchers have advocated water injection as an approach to replace or supplement existing knock mitigation techniques.

Mcity at the University of Michigan in Ann Arbor provides a realistic off-roadway environment in which to test vehicles and drivers in complex traffic situations. It is intended for testing of various levels of vehicle automation, from advanced driver assistance systems (ADAS) to fully self-driving vehicles. In a recent human factors study of interfaces for teen drivers, we performed parallel experiments in a driving simulator and Mcity. We implemented driving scenarios of moderate complexity (e.g., passing a vehicle parked on the right side of the road just before a pedestrian crosswalk, with the parked vehicle partially blocking the view of the crosswalk) in both the simulator and at Mcity.

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.

A research effort was initiated to establish an empirical data base and to develop predictive models of normal human in-vehicle seated reaching motions while driving. A driving simulator was built, in which a variety of targets were positioned at typical locations a driver would possibly reach. Reaching motions towards these targets were performed by demographically representative subjects and measured by a state-of-the-art motion analysis system. This paper describes the experiment conducted to collect the movement data, and the new techniques that are being developed to process, analyze, and model the data. Some initial findings regarding the role of torso assistive motion, the effect of speed used in completing a motion on multi-segment dynamic postures, and illustrative results from kinematic modeling are presented.

Real-time simulation of tracked vehicle dynamics demands very efficient modeling of the vehicle track. Multi-body dynamics models which model the response of each track pitch are complete, but require on the order of 100 degrees of freedom to capture lateral track dynamics and an additional 200 degrees of freedom to capture longitudinal (stretching) track dynamics. The sheer size of such models renders them difficult to use for rapid estimates of track response. This paper summarizes an efficient alternative for modeling vehicle tracks, as illustrated herein by a model for longitudinal track dynamics. The present model is a hybrid discrete/continuous model in which the track is modeled as a continuous uniform elastic rod which is kinematically coupled to discrete models for the sprocket, wheels, and rollers. Solution efficiency derives from transforming the dynamic track model to one employing modal coordinates.

High-fidelity overall vehicle simulations require efficient computational routines for the various vehicle subsystems. Typically, these simulations blend theoretical dynamic system models with empirical results to produce computer models which execute efficiently. Provided that the internal combustion engine is a dominant source of vehicle vibration, knowledge of its dynamic characteristics throughout its operating envelope is essential to effectively predict vehicle response. The present experimental study was undertaken to determine the rigid body modal content of engine block vibration of a modern, heavy-duty Diesel engine. Experiments were conducted on an in-line six-cylinder Diesel engine (nominally rated at 470 BHP) which is used in both commercial Class-VIII trucks, and on/off-road military applications. The engine was mounted on multi-axis force transducers in a dynamometer test cell in the standard three-point configuration.

Mobility in the automotive and transportation sectors has been experiencing a period of unprecedented evolution. A growing need for efficient, clean and safe mobility has increased momentum toward sustainable technologies in these sectors. Toward this end, battery electric vehicles have drawn keen interest and their market share is expected to grow significantly in the coming years, especially in light-duty applications such as passenger cars. Although the battery electric vehicles feature high performance and zero tailpipe emission characteristics, economic and technical issues such as battery cost, driving range, recharging time and infrastructure remain main hurdles that need to be fully addressed. In particular, the low power density of the battery limits its broad adoption in heavy-duty applications such as class 8 semi-trailer trucks due to the required size and weight of the battery and electric motor.

A novel application in the field of Life Cycle Assessment is presented that investigates optimal vehicle retirement timing and design life. This study integrates Life Cycle Energy Analysis (LCEA) with Dynamic Replacement Modeling and quantifies the energy tradeoffs between operating an older vehicle versus replacing it with a new more energy efficient model. The decision to keep or replace a vehicle to minimizes life cycle energy consumption is influenced by several factors including vehicle production energy, current vehicle's fuel economy and its deterioration with age, the improvement in fuel economy technology of new model vehicles and annual vehicle miles traveled (VMT). Model simulations explore vehicle replacement under incremental improvements in vehicle technology and leapfrog technology improvements such as with the PNGV (Partnership for a New Generation of Vehicles).

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.

Combatting the modification of automotive control systems is a current and future challenge for OEMs and suppliers. ‘Chip-tuning’ is a manifestation of manipulation of a vehicle's original setup and calibration. With the increase in automotive functions implemented in software and corresponding business models, chip tuning will become a major concern. Recognizing and reporting of tuned control units in a vehicle is required for technical as well as legal reasons. This work approaches the problem by capturing the behavior of relevant control units within a machine learning system called a recognition module. The recognition module continuously monitors vehicle's sensor data. It comprises a set of classifiers that have been trained on the intended behavior of a control unit before the vehicle is delivered. When the vehicle is on the road, the recognition module uses the classifier together with current data to ascertain that the behavior of the vehicle is as intended.