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Main topics are the development and the build-up of an 18ton hybrid truck with a parallel hybrid drivetrain. With this truck it is possible to drive up to 3 kilometers in the pure electric driving mode. Presenter Andreas Eglseer, Engineering Center Steyr GmbH & Co. KG

There are now a wide variety of Hybrid and Electric Vehicles in or near production. They reduce or displace petroleum consumption with of various combinations of conventional IC engine, mechanical transmission, liquid fuel storage, electrical energy storage, electrical and electro-mechanical energy conversion, and vehicle-to-grid energy interface. These Electrified types of vehicles include Mild Hybrid, Full Hybrid, Plug-In Hybrid, Extended Range Electric, and Battery Electric. Some types differ in their actual usability for the real mixes of driving trips, and further that differ in their effectiveness to reduce or displace fuel in actual real world driving use. Vehicle size is also a factor in total vehicle utility in transporting people. If we may segment drivers by their driving needs, in each segment, we see a particular type of electrified vehicle that is better suited than others at minimizing fuel cost and petroleum consumption for the purposes of transporting people.

This study presents the comparison of vehicle handling performance results obtained using physical test tire data and a tire model developed by means of Finite Element Method. Real tires have been measured in laboratory to obtain the tire force and moment curves in terms of lateral force and align torque as function of tire slip angle and vertical force. The same tire construction has been modeled with Finite Element Method and explicit formulation to generate the force and moment response curves. Pacejka Magic Formula tire response models were then created to represent these curves from both physical and virtual tires. In the sequence, these tire response models were integrated into a virtual multibody vehicle model developed to assess handling maneuvers.

This paper describes the development of an analytical method to assess and optimize halfshaft joint angles to avoid excessive 3rd halfshaft order vibrations during wide-open-throttle (WOT) and light drive-away events. The objective was to develop a test-correlated analytical model to assess and optimize driveline working angles during the virtual design phase of a vehicle program when packaging tradeoffs are decided. A twelve degree-of-freedom (12DOF) system model was constructed that comprehends halfshaft dynamic angle change, axle torque, powertrain (P/T) mount rate progression and axial forces generated by tripot type constant velocity (CV) joints. Note: “tripot” and “tripod” are alternate nomenclatures for the same type of joint. Simple lumped parameter models have historically been used for P/T mount optimization; however, this paper describes a method for using a lumped parameter model to also optimize driveline working angles.

The SAE Fuel Cell Vehicle (FCV) Safety Working Group has been addressing FCV safety for over 9 years. The initial document, SAE J2578, was published in 2002. SAE J2578 has been valuable as a Recommended Practice for FCV development with regard to the identification of hazards and the definition of countermeasures to mitigate these hazards such that FCVs can be operated in the same manner as conventional gasoline internal combustion engine (ICE)-powered vehicles. SAE J2578 is currently being revised so that it will continue to be relevant as FCV development moves forward. For example, test methods were refined to verify the acceptability of hydrogen discharges when parking in residential garages and commercial structures and after crash tests prescribed by government regulation, and electrical requirements were updated to reflect the complexities of modern electrical circuits which interconnect both AC and DC circuits to improve efficiency and reduce cost.

The SAE FCV Safety Working Group has been addressing fuel cell vehicle (FCV) safety for over 8 years. The initial document, SAE J2578, was published in 2002. SAE J2578 has been valuable to FCV development with regard to the identification of hazards and the definition of countermeasures to mitigate these hazards such that FCVs can be operated in the same manner as conventional gasoline internal combustion engine (ICE)-powered vehicles. J2578 is currently being updated to clarify and update requirements so that it will continue to be relevant and useful in the future. An update to SAE J1766 for post-crash electrical safety was also published to reflect unique aspects of FCVs and to harmonize electrical requirements with international standards. In addition to revising SAE J2578 and J1766, the Working Group is also developing a new Technical Information Report (TIR) for vehicular hydrogen systems (SAE J2579).

Recent advancements in simulation software and computational hardware make it realizable to simulate a full vehicle system comprised of multiple sub-models developed in different modeling languages. The so-called, co-simulation allows one to develop a control strategy and evaluate various aspects of a vehicle system, such as fuel efficiency and vehicle drivability, in a cost-effective manner. In order to study the feasibility of the synchronized parallel processing in co-simulation this paper presents two co-simulation frameworks for a complete vehicle system with multiple heterogeneous subsystem models. In the first approach, subsystem models are co-simulated in a serial configuration, and the same sub-models are co-simulated in a parallel configuration in the second approach.

Powerful vehicles that are adequately designed to corner at high speeds can generate very high lateral forces at tire-road interface. These forces are counter balanced by chassis, suspension and brake components allowing the vehicle to confidently maneuver around a corner. Although these components may not damage under such high cornering loads, elastic deflections can significantly alter a vehicles performance. One such phenomenon is increased brake pedal travel, to engage brakes, after severe cornering maneuvers. Authors of this paper have worked together to solve exactly this problem on a very powerful luxury segment car.

To cope with the increasing number of advanced features (e.g., smart-phone integration and side-blind zone alert.) being deployed in vehicles, automotive manufacturers are designing flexible hardware architectures which can accommodate increasing feature content with as fewer as possible hardware changes so as to keep future costs down. In this paper, we propose a formal and quantitative definition of flexibility, a related methodology and a tool flow aimed at maximizing the flexibility of an automotive hardware architecture with respect to the features that are of greater importance to the designer. We define flexibility as the ability of an architecture to accommodate future changes in features with no changes in hardware (no addition/replacement of processors, buses, or memories). We utilize an optimization framework based on mixed integer linear programming (MILP) which computes the flexibility of the architecture while guaranteeing performance and safety requirements.

The friction material is arguably at the heart of any brake system, with its properties taking one of the most important roles in defining its performance characteristics. High performance applications, such as race track capable brake systems in high powered vehicles, exert considerable stress on the friction materials, in the form of very high heat flux loads, high clamp and brake torque loads, and high operating temperatures. It is important, for high performance applications, to select capable friction materials, and furthermore, it is important to understand fully what operating conditions the friction material will face in the considered application.

Standard CFD methods require a mesh that fits the boundaries of the computational domain. For a complex geometry the generation of such a grid is time-consuming and often requires modifications to the model geometry. This paper evaluates the Immersed Boundary (IB) approach which does not require a boundary-conforming mesh and thus would speed up the process of the grid generation. In the IB approach the CAD surfaces (in Stereo Lithography -STL- format) are used directly and this eliminates the surface meshing phase and also mitigates the process of the CAD cleanup. A volume mesh, consisting of regular, locally refined, hexahedrals is generated in the computational domain, including inside the body. The cells are then classified as fluid, solid and interface cells using a simple ray-tracing scheme. Interface cells, correspond to regions that are partially fluid and are intersected by the boundary surfaces.

The SAE FCV Safety Working Group has been addressing fuel cell vehicle (FCV) safety for over 7 years. The initial document, SAE J2578, was published in 2002. SAE J2578 has been valuable to the FCV development with regard to the identification of hazards and the definition of countermeasures to mitigate these hazards such that FCVs can be operated in the same manner as conventional gasoline IC-powered vehicles. The document is currently being updated to clarify and update requirements so that the document will continue to be relevant and useful in the future. In addition to developing draft revisions to SAE J2578, the working group has updated SAE J1766 and is developing a new recommended practice on vehicular hydrogen systems (SAE J2579). The documents are written from the standpoint of systems-level, performance-based requirements. A risk-based approach was used to identify potential electrical and fuel system hazards and provide criteria for acceptance.

An interactive tool for predicting the performance of vehicle designs in the pedestrian leg impact test has been developed. This tool allows users to modify the design of a vehicle front structure through the use of a graphical interface, and then evaluates the performance of the design with a response surface. This performance is displayed in the graphical interface, providing the user with nearly instantaneous feedback to his design changes. An example is shown that demonstrates how the tool can be used to help guide the user towards vehicle designs that are likely to improve performance. As part of the development of this tool, a simplified, parametric finite element model of the front structure of the vehicle was created. This vehicle model included eleven parameters that could be adjusted to change the structural dimensions and structural behavior of the model.

The GM Aerodynamics Laboratory (GMAL) was modified in 2001 to reduce the background noise level and provide a semi-anechoic test section for wind noise testing. The walls and ceiling of the test section were lined with acoustic foam and foam-filled turning vanes were installed in the corners. Portions of the wind tunnel circuit were also treated with fiberglass material covered by perforated sheet metal panels. High skin drag due to roughness of the foam surfaces, along with high blockage due to the large turning vanes, increased the wind tunnel circuit losses so that the maximum wind speed in the test section was reduced. The present study calculates the averaged total pressure losses at three locations to evaluate the reductions in skin drag and blockage from proposed modifications to the circuit, which were intended to increase the test section wind speed without compromising noise levels.

A correlation of aerodynamic wind tunnels was initiated between Chrysler, Ford and General Motors under the umbrella of the United States Council for Automotive Research (USCAR). The wind tunnels used in this correlation were the open jet tunnel at Chrysler's Aero Acoustic Wind Tunnel (AAWT), the open jet tunnel at the Jacobs Drivability Test Facility (DTF) that Ford uses, and the closed jet tunnel at General Motors Aerodynamics Laboratory (GMAL). Initially, existing non-competitive aerodynamic data was compared to determine the feasibility of facility correlation. Once feasibility was established, a series of standardized tests with six vehicles were conducted at the three wind tunnels. The size and body styles of the six vehicles were selected to cover the spectrum of production vehicles produced by the three companies. All vehicles were tested at EPA loading conditions. Despite the significant differences between the three facilities, the correlation results were very good.

Vehicle dynamics simulation with Hardware In the Loop (HIL) has been demonstrated to reduce development and validation time for dynamic control systems. For dynamic control systems such as Anti-lock Braking System (ABS) and Electronic Stability Control (ESC), an accurate vehicle dynamics performance simulation system requires the Electronic Brake Control Module (EBCM) coupled with the vehicles brake system hardware. This kind of HIL simulation-specific software tool can further increase efficiency by means of automation and optimization of the development and validation process. This paper presents a method for HIL vehicle dynamics simulator optimization through Brake Response Time (BRT) correlation. The paper discusses the differences between the physical vehicle and the HIL vehicle dynamics simulator. The differences between the physical and virtual systems are used as factors in the development of a Design Of Experiment (DOE) quantifying HIL simulator performance.

Hybrid vehicles may respond to fuel variables in unique ways; they could even require a unique driveability test. The Coordinating Research Council (CRC) conducted a program to determine the effect of ethanol content on driveability performance under cool ambient conditions. In addition to the 27 vehicles in the main fleet, four hybrid electric vehicles (HEVs) were tested using the same fuels and driveability procedure. These HEVs responded to fuel in a manner similar to conventional vehicles; however, the HEVs showed unique driving characteristics not well captured in the existing test.

A life cycle inventory (LCI) study evaluates the environmental performance of the ULSAB-AVC (UltraLight Steel Auto Body - Advanced Vehicle Concepts) vehicle product system. The LCI quantifies the inputs and outputs of each life cycle stage of the ULSAB-AVC PNGV-gas engine vehicle (998 kg) over the 193,000 km service lifetime of the vehicle. The use phase of the ULSAB-AVC PNGV-diesel engine variant (1031 kg) is also quantified. The data categories measured for each life cycle phase include resource and energy consumption, air and water pollutant emissions, and solid waste production. The ULSAB-AVC LCI study is based on the methods, model and data from the 1999 study by the United States Automotive Materials Partnership (USAMP), a consortium within the United States Council for Automotive Research. This model was modified to represent the ULSAB-AVC PNGV-gas engine vehicle for each life cycle phase as well as the use phase of the PNGV-diesel engine variant.

A new Diesel engine, called the Duramax 6600 (Fig.1), has been designed by Isuzu Motors (Isuzu) for an upcoming full-size General Motors (GM) pickup truck. It incorporates the latest Diesel technology in order to improve on the inherent strengths of a Diesel engine, such as fuel economy, torque and reliability, while also producing higher output, smoother driveability, and lower noise. The Duramax 6600 is an entirely new 90° V8 direct injection (DI) intercooled engine with a water-cooled turbocharger. Its fuel injection system employs a fully electronically controlled common rail system that has high-pressure injection capabilities. Isuzu had the design responsibility of the base engine, while GM Truck Group was responsible for designing the installation and packaging within the vehicle. Engine validation relied on Isuzu's proven validation process, in addition to GM Powertrain's expertise in engine validation.

This study investigates a methodology in which the general public's subjective interpretation of vehicle handling and performance can be predicted. Several vehicle handling measurements were acquired, and associated metrics calculated, in a controlled setting. Human evaluators were then asked to drive and evaluate each vehicle in a winter driving school setting. Using the acquired data, multiple linear regression and artificial neural network (ANN) techniques were used to create and refine mathematical models of human subjective responses. It is shown that artificial neural networks, which have been trained with the sets of objective and subjective data, are both more accurate and more robust than multiple linear regression models created from the same data.