Search Results

This paper presents the final phase of a study to develop the modeling methodology for an advanced steering assembly with a safety-enhanced steering wheel and an adaptive energy absorbing steering column. For passenger cars built before the 1960s, the steering column was designed to control vehicle direction with a simple rigid rod. In severe frontal crashes, this type of design would often be displaced rearward toward the driver due to front-end crush of the vehicle. Consequently, collapsible, detachable, and other energy absorbing steering columns emerged to address this type of kinematics. These safety-enhanced steering columns allow frontal impact energy to be absorbed by collapsing or breaking the steering columns, thus reducing the potential for rearward column movement in severe crashes. Recently, more advanced steering column designs have been developed that can adapt to different crash conditions including crash severity, occupant mass/size, seat position, and seatbelt usage.

Motivated by a combination of increasing consumer demand for fuel efficient vehicles, more stringent greenhouse gas, and anticipated future Corporate Average Fuel Economy (CAFE) standards, automotive manufacturers are working to innovate in all areas of vehicle design to improve fuel efficiency. In addition to improving aerodynamics, enhancing internal combustion engines and transmission technologies, and developing alternative fuel vehicles, reducing vehicle weight by using lighter materials and/or higher strength materials has been identified as one of the strategies in future vehicle development. Weight reduction in vehicle components, subsystems and systems not only reduces the energy needed to overcome inertia forces but also triggers additional mass reduction elsewhere and enables mass reduction in full vehicle levels.

Engine dynamometer testing was performed comparing fuels having different octane ratings and ethanol content in a Ford 3.5L direct injection turbocharged (EcoBoost) engine at three compression ratios (CRs). The fuels included midlevel ethanol “splash blend” and “octane-matched blend” fuels, E10-98RON (U.S. premium), and E85-108RON. For the splash blends, denatured ethanol was added to E10-91RON, which resulted in E20-96RON and E30-101 RON. For the octane-matched blends, gasoline blendstocks were formulated to maintain constant RON and MON for E10, E20, and E30. The match blend E20-91RON and E30-91RON showed no knock benefit compared to the baseline E10-91RON fuel. However, the splash blend E20-96RON and E10-98RON enabled 11.9:1 CR with similar knock performance to E10-91RON at 10:1 CR. The splash blend E30-101RON enabled 13:1 CR with better knock performance than E10-91RON at 10:1 CR. As expected, E85-108RON exhibited dramatically better knock performance than E30-101RON.

The integration of a HIL system involves many software and hardware elements. During the integration process, various errors may occur. In this paper, the Design For Six Sigma (DFSS) methodology is applied to develop an integration process for a generic type of HIL system. The development follows the DCOV (Design, Characterize, Optimize and Verify) four-phase approach. The result is an efficient process proven to meet the customer's needs, reduce integration errors and deliver a high quality HIL system within less time.

Since the environment of vehicle operation is dynamic in nature, dynamic methods should be used in vehicle durability analysis. Due to the constraints in current computer resources, simulation of vehicle durability tests and structural fatigue life assessment need special approaches and efficient CAE tools. The purpose of this paper is to present an efficient methodology and a feasible vehicle dynamic durability analysis process. Two examples of structural durability analysis using transient dynamics are given. The examples show that vehicle stress analysis and fatigue life prediction using dynamic method is now feasible by employing the presented method and process.

Seat Effective Amplitude Transmissibility (SEAT) values can be obtained from direct measurements at seat track and top or estimated from transmissibility data and seat track input. Vertical transmissibility was measured for sixteen seats and six subjects on the Ford Vehicle Vibration Simulator, and these 96 functions used to estimate the seat top response for rough road input. SEAT values were calculated, and good correlation to values computed from direct seat top measurements obtained (R2 of 0.86). Averaging transmissibilities and direct seat measurements over the 6 subjects to obtain correlations for the 16 seats improved R2 to 0.94, validating this approach.

Drive shaft modelling effects frontal crash finite element simulation. A 35 mph rigid barrier impact of a body on frame SUV with an one piece drive shaft and a unibody SUV with a two piece drive shaft have been studied and simulated using finite element analyses. In the model, the drive shaft can take significant load in frontal impact crash. Assumptions regarding the drive shaft model can change the predicted engine motion in the simulation. This change influences the rocker @ B-pillar deceleration. Two modelling methods have been investigated in this study considering both joint mechanisms and material failure in dynamic impact. Model parameters for joint behavior and failure should be determined from vehicle design information and component testing. A body on frame SUV FEA model has been used to validate the drive shaft modeling technique by comparing the simulation results with crash test data.

The Vehicle Exhaust Emissions Simulator was developed to evaluate the performance of vehicle emissions sampling and analytical systems. The simulator produces a representative tailpipe volume flow rate containing up to five emission constituents, injected via mass flow controllers (MFCs). Eliminating the variability of test results associated with the vehicle, driver, and dynamometer makes the simulator an ideal quality control tool for use in commissioning new test cells, checking data correlation between test cells, and evaluating overall system performance. Earlier vehicle emissions simulators being used in the industry were primarily for checking Constant Volume Samplers (CVSs) and Bag Benches but they did not have the ability to properly simulate tailpipe volume.

Most people still remember the introduction of the IBM PC in 1981 and the first Microsoft Windows operating system in 1985. These were the pioneering technologies that started a revolution in automotive test equipment in the service bay. What was once a purely mechanical garage environment where information was published annually in large paper manuals has evolved into a highly technical computing environment. Today vehicle networks link onboard vehicle control systems with diagnostic systems and updated service information is published daily over the Internet. A lot has changed over the last twenty years, and manufacturers of diagnostic test equipment are learning to deal with the constantly evolving computing platforms and host operating systems. This paper traces the history of automotive diagnostic equipment at Ford Motor Company and shares some of the hard lessons learned from the early systems.

To better reflect real world driving conditions, the EPA 5-Cycle Fuel Economy method encompasses high vehicle speeds, aggressive vehicle accelerations, climate control system use and cold temperature conditions in addition to the previously used standard City and Highway drive cycles in the estimation of vehicle fuel economy. A standard Powersplit Hybrid Electric Vehicle (HEV) system simulation environment has long been established and widely used within Ford to project fuel economy for the standard EPA City and Highway cycles. Direct modeling and simulation of the complete 5-Cycle fuel economy test set for HEV's presents significant new challenges especially with respect to modeling vehicle thermal management system and interactions with HEV features and system controls. It also requires a structured, systematic approach to validate the key elements of the system models and complete vehicle system simulations.

The viscous criterion (V*C) has been proposed by biomechanics researchers as a generic biomechanical index for potential soft tissue injury. It is defined by the product of the velocity of deformation and the instantaneous compression of torso and abdomen. This criterion requires calculation and differentiation of measured torso/abdomen compression data. Various computational algorithms for calculating viscous criterion are reviewed and evaluated in this paper. These include methods developed by Wayne State University (WSU), NHTSA (DOT) and Ford. An evaluation has been conducted considering the accuracy of these algorithms with both theoretical and experimental data from dummy rib compressions obtained during a crash test. Based on these results, it is found that: V*C results depend on the scheme used in the computation process, the sampling rate and filtering of original raw data. The NHTSA method yields the lowest V*C value.

QFD, FMEA, Process Improvement, Taguchi, Simultaneous Engineering, PDP, Project Management, DVP, DOE, …and the list goes on. Today's automotive product design engineers face a myriad of “tools” (methodologies, techniques, procedures) that are expected to be mastered and used in the course of performing their job. The list continually grows with new tools being added to the existing ones. And each new tool has an associated acronym to add to the confusion. New and inexperienced engineers are often confused by these tools being tossed at them …school did not cover all this ! The experienced engineer is often skeptical. After all, “if I have been a successful engineer for 20 years, why do I need to start doing these things now?” Nevertheless, most of these tools are truly needed by engineers today in order to be competitive in the increasingly complex and sophisticated world of automotive product design.

The LDH (Limiting Dome Height Test) is widely used at Ford Motor Co. stamping plants laboratories to monitor the formability of incoming sheet materials. Although the LDH test is very easy to implement and interpret, variability of the results and poor reproducibility between laboratories limit its acceptance. In this investigation, some of the causes of variability and differences between plant laboratories are discussed. Much of the experimental work was done at plant laboratories and the results are directly applicable to quality control (QC) machines. It was found that the binder force and the binder shape have a big influence on the results, and they should be carefully controlled. The binder cleaning procedure is also relevant to the test variability. Punch temperature has a much greater influence on QC machines than on research machines and a method for controlling the punch temperature in QC machines is presented.

The Vehicle Handling Model (VHM) is representative of a new type of vehicle dynamics programs which can be easily used on a personal computer by vehicle development engineers. It consists of a simulation kernel which solves the vehicle equations of motion and a hypertext GUI which controls the model data input, execution, and post processing. The vehicle model has 5 DOF, including the vehicle lateral, vertical, yaw, pitch, and roll motions. The simulation also includes suspension compliance, a simple non-linear tire model, a wind gust model and a human driver model to provide realistic vehicle and steering inputs. The simulation program was generated by AUTOSIM which uses a high level description of the system to generate Fortran source code. The GUI allows an engineer to setup the model, run the analysis, and display the results with just a few clicks of the mouse.

This paper describes the design and development of the Ford Driving Simulator. The simulator is a fixed-base device which provides real-time, interactive feedback to the driver through a combination of visual, auditory and tactile cues. The system is comprised of a modular buck, 150° field-of-view visual scene, a steering torque controller, high fidelity dynamics models, and an interactive experimenters station. Data acquisition systems have been developed to capture a broad spectrum of driver performance metrics.

A study was conducted to rank 34 ergonomic characteristics of automobiles by their degree of importance to older and younger drivers. The older drivers were students in AARP driver education classes; the younger drivers were students in university classes. Three geographic locations were sampled. The primary variable analyzed was age, but geographic location, gender, driving experience, and use of glasses were also investigated. Of the characteristics that were found to be of more than average importance to older drivers, seven were appreciably more important to older drivers than to younger drivers with the “control of glare light” being the most dominant.

Through Frame and Suspension Tuning (FAST) you can identify suspension and frame Set-ups in the lab with out risk to the car or driver. For on track verification the number of Set-ups can be reduced from an unlimited number to 2 or 3 and then optimized on the track, rather than developed on the track. This method can be used with all forms of racing. It has been applied to Indy, GT, Winston Cup, and Trans Am cars. Through the use of a road simulator we are able to evaluate and improve the frame and suspension dynamics in a laboratory. This paper will focus on the first step in the tuning process, frame tuning. If the frame is not tuned for the input energy conditions it can become an uncontrolled suspension component. The first step is to identify the frame dynamic characteristics. Operational deform shapes are measured to identify local and global motion. The frames are modified to optimize the response for the type of race track.

Dimensional variability must be managed. Assembly plant installation processes which require operator adjustment result in random, unpredictable variability. On the other hand, processes which use locators and quality controlled components eliminate operator adjustments. This results in managed variability with repeatable and dimensionally consistent end products. The 1994½ Windstar mini-van incorporated locators with a detailed component quality control plan and then went one step further. Dimensional variability was managed and verified during the design process to ensure that every vehicle produced would meet customer quality standards. To verify that the Windstar would achieve the customer quality standards, by design, a full vehicle dimensional variability model was developed. The body structure, interior trim, major instrument panel components, suspension, steering and various subsystems were modeled.

Vehicle interior noise due to powerplant radiation is a product of two factors, the noise level radiated by the powerplant and the effectiveness of the vehicle body and sound package in reducing this noise. Effective noise reduction strategies require that the appropriate target levels be set for these factors. This paper reviews studies performed at Ford to establish such targets and the theoretical basis of these studies. To set sound package targets engine noise simulators have been used to study the sound package noise reduction capabilities of various vehicles. These studies include the effect of noise source location on attenuation, the effect of powerplant location and dimensions, and the effect of interior microphone location. Engine noise targets are being established by testing powerplants in the hemi-anechoic dynamometer test cells of the new AEC Building. Studies include the effect of engine rpm, load, and microphone location.

Future European air quality standards for light duty diesel vehicles will include stringent NOx emission regulations. In order to meet these regulations, a lean NOx catalyst system may be necessary. Since the catalytic removal of NOx is very difficult with the large concentration of oxygen present in diesel exhaust, a reductant is usually added to the exhaust to increase the NOx conversion. This paper describes a lean NOx catalyst system for a Transit light-duty truck which uses a reductant solution of urea in water. In this work, a microprocessor was used to vary the amount of the reductant injected depending on the operating conditions of a 2,5 L naturally aspirated HSDI engine. The NOx conversions were 60% and 80% on the current European driving cycle and the U.S. FTP cycles, respectively. Data on the emissions of HC, CO, NOx, particulate mass and composition, individual HC species, aldehydes, PAH and most HC species were evaluated.