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In the current Ford Pro-Trailer Backup Assist (TBA) system, trailer hitch angle is determined utilizing the reverse camera of the vehicle. In addition to being sensitive to environmental factors such as lighting conditions and occlusion, the vision-based approach is difficult to be applied to gooseneck or fifth wheel trailers. In this paper, a yaw rate based hitch angle observer is proposed as an alternative sensing solution for TBA. Based on the kinematic model of the vehicle-trailer, an instantaneous hitch angle is first derived by utilizing vehicle yaw rate, trailer yaw rate, vehicle velocity and vehicle/trailer parameters provided by the TBA system. Due to signal errors and parameter uncertainties, this instantaneous hitch angle may be noisy, especially at lower vehicle speed.

A computational model of a mid-size sport utility vehicle was developed using MADYMO. The model includes a detailed description of the suspension system and tire characteristics that incorporated the Delft-Tyre magic formula description. The model was correlated by simulating a vehicle suspension kinematics and compliance test. The correlated model was then used to simulate a J-turn vehicle dynamics test maneuver, a roll and non-roll ditch test, corkscrew ramp and a lateral trip test, the results of which are presented in this paper. The results indicate that MADYMO is able to reasonably predict the vehicle and occupant responses in these types of applications and is potentially suited as a tool to help setup a suite of vehicle configurations and test conditions for rollover sensor testing. A suspension system sensitivity study is presented for the laterally tripped non-roll event.

Onboard, embedded cellular modems are enabling a range of new connectivity features in vehicles and rich, real-time data set transmissions from a vehicle’s internal network up to a cloud database are of particular interest. However, there is far too much information in a vehicle’s electrical state for every vehicle to upload all of its data in real-time. We are thus concerned with which data is uploaded and how that data is processed, structured, stored, and reported. Existing onboard data processing algorithms (e.g. for DTC detection) are hardcoded into critical vehicle firmware, limited in scope and cannot be reconfigured on the fly. Since many use cases for vehicle data analytics are still unknown, we require a system which is capable of efficiently processing and reporting vehicle deep data in real-time, such that data reporting can be switched on/off during normal vehicle operation, and that processing/reporting can be reconfigured remotely.

The search for improvements in occupant protection under vehicle impact is hampered by a real lack of reliable biomechanical data. To help fill this void, General Motors has initiated joint research with independent researchers such as the School of Medicine, U. C. L. A. – in this case to study localized head and facial trauma — and has developed such unique laboratory tools as “Tramasaf,” a human-simulating headform, and “MetNet,” a pressure-sensitive metal foam. Research applied directly to product design also has culminated in developments such as the Side-Guard Beam for side impact protection.

Sheet metal forming of automobile body panel consists of two processes performed in series: binder forming and punch forming. Due to differences in deformation characteristics of the two forming processes, their analysis methods are different. The binder wrap surface shape and formed part shape are calculated using different mathematical models and different finite element codes, e.g., WRAPFORM and PANELFORM, respectively. The output of the binder forming analysis may not be directly applicable to the subsequent punch forming analysis. Interpolation, or approximation, of the calculated binder wrap surface geometry is needed. This surface representation requirement is carried out using computer aided geometric design tools. This paper discusses the use of such a tool, SURFPLAN, to link WRAPFORM and PANELFORM calculations.

Electric vehicles are highly sensitive to variations in environmental factors (like temperature, drive style, grade, etc.). The distribution of real-world range of electric vehicles due to these environmental factors is an important consideration in target setting. This distribution can be obtained by running several simulations of an electric vehicle for a number of high-frequency velocity, grade, and temperature real-world trip profiles. However, in order to speed up simulation time, a unique set of drive profiles that represent the entire real-world data set needs to be developed. In this study, we consider 40,000 unique velocity and grade profiles from various real-world applications in EU. We generate metadata that describes these profiles using trip descriptor variables. Due to the large number of descriptor variables when considering second order effects, we normalize each descriptor and use principal component analysis to reduce the dimensions of our dataset to six components.

The ability to extract and evaluate the time history of structural deformations or crush during vehicle crashes represents a significant challenge to automotive safety researchers. Current methods are limited by the use of electro-mechanical devices such as string pots and/or linear variable displacement transducers (LVDT). Typically, one end of the transducer must be mounted to a point on the structure that will remain un-deformed during the event; the other end is then attached to the point on the structure where the deformation is to be measured. This approach measures the change in distance between these two points and is unable to resolve any movement into its respective X, Y, or Z directions. Also, the accuracy of electro-mechanical transducers is limited by their dynamic response to crash conditions. The photogrammetry technique has been used successfully in a wide variety of applications including aerial surveying, civil engineering and documentation of traffic accidents.

This paper reports a study of the dynamic characteristics of body mounts in body on frame vehicles and their effects on structural and occupant CAE results. The body mount stiffness and damping are computed from spring-damper models and component test results. The model parameters are converted to those used in the full vehicle structural model to simulate the vehicle crash performance. An effective body mount in a CAE crash model requires a set of coordinated damping and stiffness to transfer the frame pulse to the body. The ability of the pulse transfer, defined as transient transmissibility[1]1, is crucial in the early part of the crash pulse prediction using a structural model such as Radioss[2]. Traditionally, CAE users input into the model the force-deflection data of the body mount obtained from the component and/or full vehicle tests. In this practice, the body mount in the CAE model is essentially represented by a spring with the prescribed force-deflection data.

This paper presents a Short Time Fourier Transform based algorithm to identify unknown parameters in fuel dynamics system during engine cold start and warm-up. A first order system is used to model the fuel dynamics in a port fuel injection engine. The feed forward transient fuel compensation controller is designed based on the identified model. Experiments are designed and implemented to verify the proposed algorithm. Different experiment settings are compared.

Time to torque (TTT) is a quantity used to measure the transient torque response of turbocharged engines. It is referred as the time duration from an idle-to-full step torque command to the time when 95% of maximum torque is achieved. In this work, we seek to control multiple engine actuators in a collaborative way such that the TTT is minimized. We pose the TTT minimization problem as an optimization problem by parameterizing each engine actuator’s transient trajectory as Fourier series, followed by minimizing proper cost function with the optimization of those Fourier coefficients. We first investigate the problem in CAE environment by constructing an optimization framework that integrates high-fidelity GT (Gamma Technology) POWER engine model and engine actuators’ Simulink model into ModeFrontier computation platform. We conduct simulation optimization study on two different turbocharged engines under this framework with evolutionary computation algorithms.

Wavelets are a powerful mathematical tool used to multi-resolution time-frequency decomposition of signals, in order to analyze them in different scales and obtain different aspects of the information. Despite being a relatively new tool, wavelets have being applied in several areas of human knowledge, especially in signal processing, with emphasis in encoding and compression of image, video and audio. Based on a previous successful applications (FRAZIER, 1999) together a commitment to quality results, this paper evaluates the use of the Discrete Wavelet Transform (DWT) as an compression algorithm to reduce the amount of data collected in road load signals (load history) which are used by the durability engineering teams in the automotive industry.

A detailed description of the oxidative stability of GM's DEXRON®-VI Factory Fill Automatic Transmission Fluid (ATF) is provided, which can be integrated into a working algorithm to estimate the end of useful oxidative life of the fluid. As described previously, an algorithm to determine the end of useful life of an automatic transmission fluid exists and is composed of two simultaneous counters, one monitoring bulk oxidation and the other monitoring friction degradation [1]. When either the bulk oxidation model or the friction model reach the specified limit, a signal can be triggered to alert the driver that an ATF change is required. The data presented in this report can be used to develop the bulk oxidation model. The bulk oxidation model is built from a large series of bench oxidation tests. These data can also be used independent of a vehicle to show the relative oxidation resistance of this fluid, at various temperatures, compared to other common lubricants.

Variations in human skull thickness affecting human head dynamic impact responses were studied by finite element modeling techniques, experimental measurements, and histology examinations. The aims of the study were to better understand the influences of skull thickness variations on human head dynamic impact responses and the injury mechanisms of human head during direct impact. The thicknesses of the frontal bone of seven human cadaver skulls were measured using ultrasonic technology. These measurements were compared with previous experimental data. Histology of the skull was recorded and examined. The measured data were analyzed and then served as a reference to vary the skull thickness of a previously published three-dimensional finite element human head model to create four models with different skull thickness. The skull thicknesses modeled are 4.6 mm, 5.98 mm, 7.68 mm, and 9.61 mm.

The dilemma of using a shoulder belt force limiter with a 3-point belt system is selecting a limit load that will balance the reduced risk of significant thoracic injury due to the shoulder belt loading of the chest against the increased risk of significant head injury due to the greater upper torso motion allowed by the shoulder belt load limiter. However, with the use of air bags, this dilemma is more manageable since it only occurs for non-deploy accidents where the risk of significant head injury is low even for the unbelted occupant. A study was done using a validated occupant dynamics model of the Hybrid III dummy to investigate the effects that a prescribed set of shoulder belt force limits had on head and thoracic responses for 48 and 56 km/h barrier simulations with driver air bag deployment and for threshold crash severity simulations with no air bag deployment.

There has been a general consensus in the scientific literature that a rim gouging, not scraping, into a roadway surface generates very high forces which can cause a vehicle to overturn in some situations. However, a paper published in 2004 attempts to minimize the forces created during wheel rim gouging and the effect on vehicle rollover. This paper relied largely on heavily filtered lateral acceleration data and discounted additional test runs by the authors and NHTSA that did not support the supposed conclusions. This paper will discuss the effect of rim gouging using accepted scientific methods, including full vehicle testing where vehicle accelerations were measured during actual rim gouging events and static testing of side forces exerted by wheels mounted on a moving test fixture. The data analyzed in this paper clearly shows that forces created by rim gouges on pavement can be thousands of Newtons and can contribute to vehicle rollover.

This paper describes (1) the findings from the implementation of a component test methodology for body, engine and transmission mounts [1, 2 and 3], and (2) the associated CAE model development and mount design robustness enhancement. A series of component tests on light truck body, engine and transmission mounts have been conducted to not only obtain their characteristics as inputs for crashworthiness analysis, but also drive mount design direction for frontal impacts.

The front rail plays an important role in the performance of body-on-frame (BOF) vehicles in frontal crashes. New developments in materials and forming technology have led to the exploration of different configurations to improve crash performance. This paper presents the initial stages of an ongoing study to investigate the effects of the cross section of steel columns on crash performance in automotive applications. Because accurate prediction of the performance of these rails can help reduce the amount of physical crash testing necessary, the focus of this paper is on appropriate testing and modeling procedures for different rail configurations. In the first part of this paper, the Finite Element Analysis (FEA) methodology is presented with respect to correlation with real world tests. The effects of various parameters are described, along with the optimum configuration for model correlation.

The front end module technology is a system developed to make the interface with vehicle body in accordance with costumer requirements. This modular system also has characteristics to reinforce the structure (chassis, main rails, shotguns), respecting its robustness (tolerances of the body) in accordance with NVH performance. The decision of having a FEM design made by steel and plastic was taken due to NVH specification, impact and safety requirements. Other items were either considered such as: fixation on the body of the vehicle, constraints between bumper beam and engine cooling module. Simulation tools including: durability test (static and dynamic) and modal F.E.A analysis, CAE system, crash test performance, aerodynamics required to insure results desired results.

The optimization method and CAE analysis have been widely used in structure design for crash safety. Combining the CAE analysis and optimization approach, vehicle structure design for crash can be implemented more efficiently. One of the recent safety desirables in structure design is to reduce vehicle pitch and drop. At frontal impact tests with unbelted occupants, the interaction between occupant's head and interior header/sun visor, which is caused by excessive vehicle pitch and drop, is not desired in vehicle crash development. In order to comply with the federal frontal crash requirements for unbelted occupant, it is necessary to manage the vehicle pitch and drop by improving structure design. In this paper, a systematic process of CAE analysis with optimization approach is applied for discovering the major structural components affecting vehicle pitch and drop.

In vehicle safety engineering, it is important to determine the severity of occupant injury during a crash. Computer simulations are widely used to study how occupants move in a crash, what they collide during the crash and thus how they are injured. The vehicle motion is typically defined for the occupant simulation by specifying a crash pulse. Many computer models used to analyze occupant kinematics do not calculate both vehicle motion and occupant motion at the same time. This paper presents a framework of response surface methodology for the crash pulse prediction and vehicle structure design optimization. The process is composed of running simulation at DOE sampling data points, generating surrogate models (response surface models), performing sensitivity analysis and structure design optimization for time history data (e.g., crash pulse).