Search Results

A key element of General Motors' Advanced Propulsion Technology Strategy is the electrification of the automobile. The objectives of this strategy are reduced fuel consumption, reduced emissions and increased energy security/diversification. The introduction of hybrid vehicles was one of the first steps as a result of this strategy. To determine future opportunities and direction, an extensive study was completed to better understand the ability of Plug-in Hybrid Electric Vehicles (PHEV) and Extended-Range Electric Vehicles (E-REV) to address societal challenges. The study evaluated real world representative driving datasets to understand actual vehicle usage. Vehicle simulations were conducted to evaluate the merits of PHEV and E-REV configurations. As derivatives of conventional full hybrids, PHEVs have the potential to deliver a significant reduction in petroleum usage.

Due to competitive pressures and the need to rapidly develop new products for the automotive marketplace, the automotive industry has to rapidly develop and validate automotive subsystems and components. While many CAE tools are employed to decrease the time needed for a number of brake engineering tasks such as stress analysis, brake system sizing, thermo-fluid analysis, and structural dynamics, brake lining wear and the associated concept of “lining life” are still predominantly developed and validated through resource intensive public road vehicle testing. The goal of this paper is to introduce and detail an energy-based, lumped-parameter CAE approach to predict brake lining life in passenger cars and light trucks.

In an endeavor to improve upon historically subjective and hardware-based steering tuning development, a team was formed to find an optimal and objective solution using Design For Six Sigma (DFSS). The goal was to determine the best valve assembly design within a hydraulic power-steering assist system to yield improved steering effort and feel robustness for all vehicle models in a future truck program. The methodology utilized was not only multifaceted with several Design of Experiments (DOEs), but also took advantage of a CAE-based approach leveraging modeling capabilities in ADAMS for simulating full-vehicle, On-Center Handling behavior. The team investigated thirteen control factors to determine which minimized a realistic, compounded noise strategy while also considering the ideal steering effort function (SEF) desired by the customer. In the end, it was found that response-dependent variability dominated the physics of our valve assembly design concept.

As a virtual manufacturing press line, line die forming simulation provides a full range math-based engineering tool for stamping die developments of automotive structure and closure panels. Much beyond draw-die-only formability analysis that has been widely used in stamping simulation community during the last decade, the line die formability analysis allows incorporating more manufacturing requirements and resolving more potential failures before die construction and press tryout. Representing the most difficult level in formability analysis, conducting line die formability analysis of automotive body side outers exemplifies the greatest technological challenge to stamping CAE community. This paper discusses some critical issues in line die analysis of the body side outers, describes technical challenges in applications, and finally demonstrates the impact of line die forming simulation on the die development.

By adapting vehicle control systems to the skill level of the driver, the overall vehicle active safety provided to the driver can be further enhanced for the existing active vehicle controls, such as ABS, Traction Control, Vehicle Stability Enhancement Systems. As a follow-up to the feasibility study in [1], this paper provides some recent results on data-driven driving skill characterization. In particular, the paper presents an enhancement of discriminant features, the comparison of three different learning algorithms for recognizer design, and the performance enhancement with decision fusion. The paper concludes with the discussions of the experimental results and some of the future work.

Model-based design is a collection of practices in which a system model is at the center of the development process, from requirements definition and system design to implementation and testing. This approach provides a number of benefits such as reducing development time and cost, improving product quality, and generating a more reliable final product through the use of computer models for system verification and testing. Model-based design is particularly useful in automotive control applications where ease of calibration and reliability are critical parameters. A novel application of the model-based design approach is demonstrated by The Ohio State University (OSU) student team as part of the Challenge X advanced vehicle development competition. In 2008, the team participated in the final year of the competition with a highly refined hybrid-electric vehicle (HEV) that uses a through-the-road parallel architecture.

This paper demonstrates the benefit of using simulation and robust optimization for the problem of balancing vehicle noise, vibration, and ride performance over road impacts. The psychophysics associated with perception of vehicle performance on an impact is complex because the occupants encounter both tactile and audible stimuli. Tactile impact vibration has multiple dimensions, such as impact hardness and memory shake. Audible impact sound also affects occupant perception of the vehicle quality. This paper uses multiple approaches to produce the similar, robust, optimized tuning strategies for impact performance. A Design for Six Sigma (DFSS) project was established to help identify a balanced, optimized solution. The CAE simulations were combined with software tools such as iSIGHT and internally developed Kriging software to identify response surfaces and find optimal tuning.

In this article, the authors illustrate the benefits of axiomatic design (AD) for robust optimization and how to integrate axiomatic design into a total robust design process. Similar to traditional robust design, the purpose of axiomatic design is to improve the probability of a design in meeting its functional targets at early concept generation stage. However, axiomatic design is not a standalone method or tool and it needs to be integrated with other tools to be effective in a total robust development process. A total robust development process includes: system design, parameter design, tolerance design, and tolerance specifications [1]. The authors developed a step-by-step procedure for axiomatic design practices in industrial applications for consistent and efficient deliverables. The authors also integrated axiomatic design with the CAD/CAE/statistical/visualization tools and methods to enhance the efficiency of a total robust development process.

A common occurrence in computer aided design is the need to make changes to an existing CAD model to compensate for shape changes which occur during a manufacturing process. For instance, finite element analysis of die forming or die tryout results may indicate that a stamped panel springs back after the press line operation so that the final shape is different from nominal shape. Springback may be corrected by redesigning the die face so that the stamped panel springs back to the nominal shape. When done manually, this redesign process is often time consuming and expensive. This article presents a computer program, FESHAPE, that reshapes the CAD or finite element mesh models automatically. The method is based on the technique of volume morphing pioneered by Sederberg and Parry [Sederberg 1986] and refined in [Sarraga 2004]. Volume morphing reshapes regions of surfaces or meshes by reshaping volumes containing those regions.

This paper describes the methodology used to design and perform a CFD analysis of a Chevrolet Impala SS Funny Car body. This body was designed for the purpose of making it available for teams to race it in the National Hot Rod Association (NHRA) drag racing series beginning with the 2007 race season. Several challenges were presented in this project: (1) This was the first time a General Motors drag racing body for use in professional classes (Funny Car or otherwise) was ever designed in CAD. (2) The body was originally designed as a 2007 Chevrolet Monte Carlo. After the tooling was completed, changes in Chevrolet’s product lineup required that the body be changed to a 2007 Impala SS. (3) Budget constraints precluded CFD analysis until after the bodies were already being manufactured. There were several teams that raced the new body during the 2007 race season. One of these teams won the Funny Car Driver’s Championship.

This paper outlines a newly developed method for predicting the coupled response of structures from their uncoupled forced responses without having to know the forces acting on such structures. It involves computing the forced response of originally uncoupled structures with several mass loadings at a potential coupling point. The response data obtained from such computations is then used to predict the coupled response. The theory for discrete linear systems is outlined in the paper and a numerical example is given to demonstrate the validity, advantages and limitations of the method. The method is primarily devised to obtain coupled response of linear dynamic systems from independent and uncoupled analytical simulations. Its application significantly decreases computation time by reducing the simulation model size and is excellent for “what if” scenarios where a large number of simulations would otherwise be necessary.

A set of functions for characterizing the mechanical properties of a steering “short gear” is described. They cover the kinematic, stiffness, assist, and friction performance of a power assisted (or manual) steering gear from the input shaft to the inner ends of the tie rods. Their use in describing the performance of a generalized steering gear is described. They have particular application to describing the steering feel performance of a vehicle. They can be used to specify the steering subsystem performance for desired steering feel for a given vehicle. They can also be used for experimental characterization of steering subsystems, can be used in vehicle dynamics simulations, and can be synthesized from a set of vehicle level performance targets. Along with their description, their use in simulation and methods to synthesize their values are described.

This paper discusses a standard procedure to reduce brake squeal using CAE and robust synthesis & analysis techniques. There are several techniques available to evaluate the stability of a system. Complex eigenvalue analysis is used for predicting and reducing squeal propensity. The complex eigenvalue method was implemented using SOL110 in version 2001 of MSC/NSTRAN for this study. We applied the signal to noise ratio using an orthogonal matrix to evaluate the main parameter effects and minimize the sensitivity.

This paper presents a project that was done to solve an integration problem between a brake system and a cruise control system on a GM vehicle program, each of which was supplied by a different supplier. This paper presents how the problem was resolved using a CAE tool which was a combination of formulated MS/Excel spreadsheet, Overdrive (GM internal code), and iSIGHT of Engineous Software Inc, which is a process integrator and process automator. A sensitivity study of system reliability was conducted using iSIGHT. The most sensitive factor was found through the sensitivity study. Thereafter, a Robust design was obtained. The recommended Robust Design was implemented in the vehicle program, which led to a substantial cost saving. The CAE software tool (the combination) developed through the problem solving process will be used to ensure quality of brake and cruise system performance for future vehicle programs.

In GM R&D Powertrain/Engine Control Group, rapid prototyping controller (RPC) systems with Matlab/Simulink are used extensively to design, simulate and implement advanced engine control algorithms and models. However, those RPC systems use powerful microprocessors with large amounts of RAM contrary to engine control modules (ECM) in production vehicles. Therefore, a thorough analysis on the comparatively much more complicated algorithms and models cannot be performed during the research stage, since there are not enough tools to enable the smooth transition from Matlab/Simulink to the production type processor. The Real-Time Interface (RTI) Blockset for a production like microprocessor would close the transition gap between rapid prototyping controller systems and production type microprocessors by leveraging the power and popularity of Matlab/Simulink in control engineering world and automatic code generation tools.

In the automotive industry, vehicle durability analysis is based on test schedule encompassing multiple road surfaces (events) including rough roads, potholes, etc. Traditionally, in the Computer Aided Engineering (CAE) world, road load data for various road surfaces are measured/predicted and fatigue life is predicted for each individual road surface. Fatigue life for the complete test schedule is then calculated with Miner’s rule by summing fatigue damage for each road surface with an appropriate number of repetitions. A major pitfall of this approach is that it does not consider the effect of the largest rainflow range across the entire test schedule. The method described in this paper was developed to perform fatigue analysis of structures subjected to diverse road surfaces and also consider the case in which the maximum overall peak and minimum overall valley do not occur over the same road surface.

In developing the 2007 Model Year Saturn VUE Green Line hybrid vehicle, a vehicle model for prediction of fuel economy and performance was developed. This model was developed in Matlab / Simulink / Stateflow by augmenting an existing conventional vehicle model to include hybrid components and controls. The generic structure and the functionalities of the model are presented. This simulation model was used for rapid concept selection and requirements balancing early in the vehicle development process. Engine usage and energy distributions are shown based on simulation results. Fuel economy breakdown was also discussed.

Combustion feedback using cylinder pressure sensors, ion current sensors or alternative sensing techniques is actively under investigation by the automotive industry to meet future legislative emissions requirements. One of the drawbacks of many rapid prototyping engine management systems is their available analog interfaces, often limited to 10-12 bits with limited bandwidth, sampling rate and very simple anti-aliasing filters. Processing cylinder pressure or other combustion feedback sensors requires higher precision, wider bandwidths and more processing power than is typically available. For these reasons, Ricardo in collaboration with GM Research has developed a custom, high precision analog input subsystem for the rCube rapid prototyping control system that is specifically targeted at development of combustion feedback control systems.

4-Post durability test simulations using a nonlinear FEA model have been executed by engineers responsible for structural durability performance and validation. An integrated Body and Chassis, full FEA model has been used. All components of the test load input were screened and only the most damaging events were incorporated in the simulation. These events included the Potholes, Belgian Block Tracks, Chatter Bump Stops, Twist Ditches, and Driveway Ramps. The CAE technology Virtual Proving Ground (eta/VPG®*) was used to model the full system and the 4-Post test fixtures. The nonlinear dynamic FE solver LS-DYNA** was used in this analysis. The fatigue damage of each selected event was calculated separately and then added together according to the test schedule. Due to the lack of stress/strain information from hardware test, only the analyzed fatigue damage results of the baseline model were scaled to correlate with physical test data.

With Statistical Energy Analysis (SEA) proven to be a powerful tool for airborne noise analysis, the capability of predicting the exterior sound field around a vehicle at high frequencies (the load case in the SEA analysis) is of particular interest to OEMs and suppliers. This paper employs the High Frequency Boundary Element Method (HFBEM) to simulate the scattered exterior sound field distribution due to a monopole source. It is shown that the proposed method is able to efficiently predict the spatial and frequency averaged sound pressure levels reasonably well up to 10 kHz, even at points in the near field of the vehicle body.