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A 1200-V, 600-A silicon carbide (SiC) JFET half-bridge module has been developed for drop-in replacement of a 600-V, 600-A IGBT intelligent power module (IPM). Advances in the development of SiC field effect transistors have resulted in reliable high yield devices that can be paralleled and packaged to produce high-voltage and high-current power modules not only competitive with existing IGBT technology but the modules have expanded capabilities. A SiC vertical junction field effect transistor VJFET has been produced with the properties of lower conduction loss, zero tail current, higher thermal conductivity, and higher power density when compared to a similarly rated silicon IGBT or any practical SiC MOSFETs previously reported. Three prototype SiC JFET half-bridge modules with gate drivers have been successfully integrated into a three-phase 30-kW (continuous), 100-kW (intermittent) AC synchronous motor drive designed to control a traction motor in an electric vehicle.

Designing an efficient cooling system with low power consumption is of high interest in the automotive engineering community. Heat generated due to the propulsion system and the on-board electronics in ground vehicles must be dissipated to avoid exceeding component temperature limits. In addition, proper thermal management will offer improved system durability and efficiency while providing a flexible, modular, and reduced weight structure. Traditional cooling systems are effective but they typically require high energy consumption which provides motivation for a paradigm shift. This study will examine the integration of passive heat rejection pathways in ground vehicle cooling systems using a “thermal bus”. Potential solutions include heat pipes and composite fibers with high thermal properties and light weight properties to move heat from the source to ambient surroundings.

An approach is being pursued for a series hybrid electric vehicle (SHEV). The twin goals of maximizing Fuel Economy (FE) and improving consumer acceptance has led to a SHEV powertrain using energy storage as a means for filtering drive cycle power demands on the engine, rather than an energy source for supplying all-electric mode. The concept is intended to minimize, if not eliminate, the battery in the SHEV without resorting to full range proportional control of the engine and generator. An initial optimization study reported for a mid-size SHEV showed a 4.5 kWh Li-ion battery pack was still required. In a new research, a sports car class SHEV was studied, which inspires this manuscript. The challenge with this vehicle is to reduce the ESS size even more because the available space allocation is only one fourth of the battery size in the mid-size. In this manuscript, a controller is developed that allows a hybridized SHEV to be realized with a light ESS.

An experimental investigation was conducted on a small-bore, high-speed diesel engine to study the effect of different operating parameters on combustion and engine-out emissions in the conventional and low temperature regimes. For the conventional diesel combustion, the spray behavior is analyzed and a differentiation is made between the conditions in the small-bore and the larger bore quiescent chamber engines. The effects of the injection pressure, exhaust gas recirculation (EGR), injection timing and swirl ratio (SR) on combustion and engine-out emission are investigated. The trade-off between NOx and smoke, measured in Bosch smoke unit, (BSU), is investigated with a special attention to the low temperature combustion regime, (LTC). The results showed that the LTC regime could be reached at fairly high EGR rates under all the injection pressures investigated in this work. The margin for the variation in EGR was limited just before the misfiring EGR.

A dynamic modeling framework was established to predict status (position, displacement, velocity, acceleration, and shape) of a towed vehicle system with different driver inputs. This framework consists of three components: (1) a state space model to decide position and velocity for the vehicle system based on Newton’s second law; (2) an angular acceleration transferring model, which leads to a hypothesis that the each towed unit follows the same path as the towing vehicle; and (3) a polygon model to draw instantaneous polygons to envelop the entire system at any time point. Input parameters of this model include initial conditions of the system, real-time locations of a reference point (e.g. front center of the towing vehicle) that can be determined from a beacon and radar system, and instantaneous accelerations of this system, which come from driver maneuvers (accelerating, braking, steering, etc.) can be read from a data acquisition system installed on the towing vehicle.

An experimental study was carried out to visualize the spray and combustion inside an AVL single-cylinder research diesel engine converted for optical access. The injection system was a hydraulically-amplified electronically-controlled unit injector capable of high injection pressure up to 180 MPa and injection rate shaping. The injection characteristics were carefully characterized with injection rate meter and with spray visualization in high-pressure chamber. The intake air was supplied by a compressor and heated with a 40kW electrical heater to simulate turbocharged intake condition. In addition to injection and cylinder pressure measurements, the experiment used 16-mm high-speed movie photography to directly visualize the global structures of the sprays and ignition process. The results showed that optically accessible engines provide very useful information for studying the diesel combustion conditions, which also provided a very critical test for diesel combustion models.

In this paper, the capability of three methods of modelling detonation of high explosives (HE) buried in soil viz., (1) coupled discrete element & particle gas methods (DEM-PGM) (2) Structured - Arbitrary Lagrangian-Eulerian (S-ALE), and (3) Arbitrary Lagrangian-Eulerian (ALE), are investigated. The ALE method of modeling the effects of buried charges in soil is well known and widely used in blast simulations today [1]. Due to high computational costs, inconsistent robustness and long run times, alternate modeling methods such as Smoothed Particle Hydrodynamics (SPH) [2, 9] and DEM are gaining more traction. In all these methods, accuracy of the analysis relies not only on the fidelity of the soil and high explosive models but also on the robustness of fluid-structure interaction. These high-fidelity models are also useful in generating fast running models (FRM) useful for rapid generation of blast simulation results of acceptable accuracy.

Currently, when the Army studies ride quality and comfort, 6 Watts of absorbed power is commonly cited as a target. However, absorbed power is dependent upon vehicle speed and road roughness and does not fully describe the direct interaction between the occupant and the seat cushion. Recently, there has been a great deal of growth in Body Pressure Distribution (BPD) measurement technologies. This growth in technology allows for new perspectives in quantifying ride comfort. Up to this point, the Army has not capitalized on these new technologies and still heavily on absorbed power to quantify ride comfort. This paper explores how the Army has benefited from pressure mapping technologies to complement absorbed power in quantifying ride comfort of military vehicles.

A Cyber-Physical System (CPS) facilitates the embedding of computational intelligence, communication, control, and new mechanisms for sensing and actuation into physical systems, such as the transportation infrastructure. They are a combination of computation and physical processes each affecting the outcome of the other. The wireless networked CPS sensors are envisaged to provide rapid response capabilities such as real time analysis, distributed coordination, classify events, condition based maintenance etc, to make critical decisions in networked transportation systems. To achieve the above it is required to fuse data from multiple heterogeneous sensors data. However, such ability is currently impeded by a lack of expressive and standardized syntactic and semantic models for the sensors data for proper exchange of information with the cyber and physical applications.

A simulation approach is defined that integrates a military mission assessment tool (One Semi-Automated Forces) with a commercial automotive control/energy consumption development tool (Autonomie). The objective is to enable vehicle energy utilization and fuel consumption impact assessments relative to US Army mission effectiveness and commercial drive cycles. The approach to this integration will be described, along with its potential to meet its objectives.

The Powertrain Analysis and Computational Environment (PACE) is a powertrain simulation tool that provides an advanced behavioral modeling capability for the powertrain subsystems of conventional or hybrid-electric vehicles. Due to its origins in Argonne National Lab’s Autonomie, PACE benefits from the reputation of Autonomie as a validated modeling tool capable of simulating the advanced hardware and control features of modern vehicle powertrains. However, unlike Autonomie that is developed and executed in Mathwork’s MATLAB/Simulink environment, PACE is developed in C++ and is targeted for High-Performance Computing (HPC) platforms. Indeed, PACE is used as one of several actors within a comprehensive ground vehicle co-simulation system (CRES-GV MERCURY): during a single MERCURY run, thousands of concurrent PACE instances interact with other high-performance, distributed MERCURY components.

The Powertrain Analysis and Computational Environment (PACE) is a forward-looking powertrain simulation tool that is ready for a High-Performance Computing (HPC) environment. The code, written in C++, is one actor in a comprehensive ground vehicle co-simulation architecture being developed by the CREATE-GV program. PACE provides an advanced behavioral modeling capability for the powertrain subsystem of a conventional or hybrid-electric vehicle that exploits the idea of reusable vehicle modeling that underpins the Autonomie modeling environment developed by the Argonne National Laboratory. PACE permits the user to define a powertrain in Autonomie, which requires a single desktop license for MATLAB/Simulink, and port it to a cluster computer where PACE runs with an open-source BSD-3 license so that it can be distributed to as many nodes as needed.

This workbook, a companion to the book Road Vehicle Dynamics, will enable students and professionals from a variety of disciplines to engage in problem-solving exercises based on the material covered in each chapter of that book. Emphasizing application more than theory, the workbook presents systematic rules of analysis that students can follow in a step-by-step manner to understand the efficiencies or shortcomings of various techniques. Readers will gain a greater understanding of the factors influencing ride, handling, braking, acceleration, and vehicle safety.

The SiC Junction Field Effect Transistor (JFET) technology has continued to mature, allowing for a wider range of product offerings that are expected to play an important role in the future aerospace and hybrid vehicle system designs. This paper will give an overview of vertical trench SiC JFET technology detailing the high-temperature dc characteristics of the discrete devices also show power module switching behavior up to 100A. Additional characterization of the all-SiC power modules used as solid-state circuit breakers will be given.

Research interests in autonomous driving have increased significantly in recent years. Several methods are being suggested for performance optimization of autonomous vehicles. However, weather conditions such as rain, snow, and fog may hinder the performance of autonomous algorithms. It is therefore of great importance to study how the performance/efficiency of the underlying scene understanding algorithms vary with such adverse scenarios. Semantic segmentation is one of the most widely used scene-understanding techniques applied to autonomous driving. In this work, we study the performance degradation of several semantic segmentation algorithms caused by rain for off-road driving scenes. Given the limited availability of datasets for real-world off-road driving scenarios that include rain, we utilize two types of synthetic datasets.