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The combination of continuously-acting high level controllers and control allocation techniques allows various driving modes to be made available to the driver. The driving modes modify the fundamental vehicle performance characteristics including the understeer characteristic and also enable varying emphasis to be placed on aspects such as tire slip and energy efficiency. In this study, control and wheel torque allocation techniques are used to produce three driving modes. Using simulation of an empirically validated model that incorporates the dynamics of the electric powertrains, the vehicle performance, longitudinal slip and power utilization during straight-ahead driving and cornering maneuvers under the different driving modes are compared.

With deflected-disc dampers, digressive force-velocity shapes are achieved via the combined effects of disc stack stiffness and hub-offset. The degree of digressiveness can be adjusted to alter vehicle performance by changing the proportion of these parameters. Optimizing this relationship can yield substantial vehicle performance improvements, but the time consuming iterative process of developing a new disc stack for each hub-offset discourages experimentation. To enable more efficient digressiveness comparisons, a regression-based computational method has been developed which converts disc stack stiffness from one hub-offset to other offsets directly, without iteration. Once an initial disc stack for one offset has been tuned by traditional methods, stacks for other offsets can be calculated that maintain overall damper control.

Engineering has continuously strived to improve the vehicle development process to achieve high quality designs and quick to launch products. The design process has to have the tools and capabilities to help ensure both quick to the market product and a flawless launch. To achieve high fidelity and robust design, mistakes and other quality issues must be addressed early in the engineering process. One way to detect problems early is to use the math based modeling and simulation techniques of the analysis group. The correlation of the actual vehicle performance to the predictive model is crucial to obtain. Without high correlation, the change management process begins to get complicated and costs start to increase exponentially. It is critical to reduce and eliminate the risk in a design up front before tooling begins to kick off. The push to help achieve a high rate of correlation has been initiated by engineering management, seeing this as an asset to the business.

As an innovative electric vehicle with some new approaches to energy usage and vehicle performance balance, the Chevy Volt required a special relationship between the OEM and tire supplier community. This paper details this relationship and how advanced tools and technology were leveraged between OEM and supplier to achieve tire component and overall vehicle performance results.

In order to assess the possible ways of energy transfer from the various sources of excitation in a vehicle assembly to a given target location, frequency based substructuring technique and transfer path analysis are used. These methods help to locate the most important energy transfer paths for a specific problem, and to evaluate their individual effects on the target, thus providing valuable insight into the mechanisms responsible for the problem. The Source-Path-Receiver concept is used. The sources can be from the road surface, engine, transmission, transfer case, prop-shaft, differential, rotating components, chain drives, pumps, etc., and the receiver can be driver/passenger ears, steering column, seats, etc. This paper is devoted to identify the noise transfer paths and the force transmissibility among the interfaces of different components in the vehicle for the low to mid frequency range.

For many years, the computer aided math model has been the foundation for lowering cost and reducing time to market for many manufacturing industries. The automotive industry applies a variety of tools and methods to evaluate the expected vehicle performance to a forever expanding set of requirements. These mathematical predictions of performance are then repeated for both a set of design cycles and a multitude of vehicles in the product portfolio. This paper presents a CAE perspective of the unique problems of the large scale virtual vehicle engineering factory and a set of solutions. Different strategies to create the various complex math models required are explored. These strategies include using COTS FEA pre-processers, producing FEA models internal to the CAD tools, as well as custom built tools, macros and process automation tools.

Brake Assist System (BAS) requirements have been established by the Economic Commission for Europe (ECE) in R13H. Electronic Stability Control (ESC) systems typically have the value added function of Panic Brake Assist (PBA) which is defined as a Category C (sensitive to multiple criteria) Brake Assist System. PBA is designed to force the vehicle into Antilock Brake System (ABS) and to maintain ABS control when the driver spikes the brake pedal and then temporarily reduces brake pedal force before reasserting more brake pedal force. ECE test protocol requires the use of brake ramp applications to define the mean acceleration force (maF) curve which is used to define the brake pedal force where ABS activates (FABS). After completing the brake ramp application test maneuvers and completing the data processing to define the maF curve, FABS, upper, and FABS, lower, the test driver then proceeds to run the panic brake assist portion of the test.

The electrical architecture design of a rear back glass defrost grid system encompasses many critical criteria that must be integrated into the design. For example, the defrost clip location and interface to the glass must meet all vehicle performance requirements. If the defrost clip to the glass interface is not resistant to vibration and relative movement, detachment and loss of function can occur. This paper describes a back glass defrost clip with a solder joint that is robust to manufacturing variations and customer usage conditions. A designed experiment using Design for Six Sigma methodologies was performed to understand the effects of the attachment interface to the electrical wiring pigtail, and parameters that affect performance. The working constraints, testing set up, validation, and root cause investigation of the clip detachment phenomenon is covered in this paper.

The U.S. Renewable Fuel Standard 2 (RFS2) mandates the use of advanced renewable fuels such as cellulosic ethanol to be blended into gasoline in the near future. As such, determining the impact of these new fuel blends on vehicle performance is important. Therefore, General Motors conducted engine dynamometer evaluations on the impact of cellulosic ethanol blends on port fuel injected (PFI) intake valve deposits and gasoline direct injected (GDI) fuel injector plugging. Chemical analysis of the test fuels was also conducted and presented to support the interpretation of the engine results. The chemical analyses included an evaluation of the specified fuel parameters listed in ASTM International's D4806 denatured fuel ethanol specification as well as GC/MS hydrocarbon speciations to help identify any trace level contaminant species from the new ethanol production processes.

Rubber isolators and bushings are very important components for vehicle performance. However, one often finds it is difficult to get the dynamic properties to be readily used in CAE analysis, either from suppliers or from OEM's own test labs. In this paper, the author provides an analytical method to obtain the dynamic stiffness of an exhaust isolator, using ABAQUS and iSight, with tested or targeted isolator static stiffness information. The analysis contains two steps. The first step is to select the (rubber/EPDM) material properties for the FE isolator model by matching the static stiffness with either the targeted spring rate (linear or nonlinear) or the (tested) load / deflection curve. The second step is to perform dynamic analysis on the statically “validated” FE isolator model to obtain its dynamic properties.