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The (Vehicle Inertia Parameter Evaluation Rig) VIPER II is a full vehicle mass and inertia parameter measurement machine. The VIPER II expands upon the capabilities of its predecessor and is capable of measuring vehicles with a mass of up to 45,360 kg (100,000 lb), an increase in capacity of 18,100 kg (40,000 lb). The VIPER II also exceeds its predecessor in both the length and width of vehicles it can measure. The VIPER II's maximum vehicle width is 381 cm (150 in) an increase of 76 cm (30 in) and maximum distance from the vehicle CG to the outer most axle is 648 cm (255 in) an additional 152 cm (60 in) The VIPER II is capable of performing measurements including vehicle CG height, pitch, roll, and yaw moments of inertia and the roll/yaw cross product of inertia. While being able to measure both heavier and larger vehicles, the VIPER II is designed to maintain a maximum error of 3% for all inertia measurements and 1% for CG height.

This paper presents a feedback linearization control technique as applied to a Roll Simulator. The purpose of the Roll Simulator is to reproduce in-field rollovers of ROVs and study occupant kinematics in a laboratory setting. For a system with known parameters, non-linear dynamics and trajectories, the feedback linearization algorithm cancels out the non-linearities such that the closed-loop dynamics behave in a linear fashion. The control inputs are computed values that are needed to attain certain desired motions. The computed values are a form of inverse dynamics or feed-forward calculation. With increasing system eigenvalue, the controller exhibits greater response time. This, however, puts a greater demand on the translational actuator. The controller also demonstrates that it is able to compensate for and reject a disturbance in force level.

This paper describes the mechanical design of a Suspension Parameter Identification Device and Evaluation Rig (SPIDER) for wheeled military vehicles. This is a facility used to measure quasi-static suspension and steering system properties as well as tire vertical static stiffness. The machine operates by holding the vehicle body nominally fixed while hydraulic cylinders move an “axle frame” in bounce or roll under each axle being tested. The axle frame holds wheel pads (representing the ground plane) for each wheel. Specific design considerations are presented on the wheel pads and the measurement system used to measure wheel center motion. The constraints on the axle frames are in the form of a simple mechanism that allows roll and bounce motion while constraining all other motions. An overview of the design is presented along with typical results.

This research paper builds onto the wealth of technical information that has been published in the past by engineers such as Flick, Radlinski, and Heusser. For this paper, the pushrod force versus chamber pressure data published by Heusser are supplemented with data taken from brake chamber types not reported on by Heusser in 1991. The utility of Heusser's braking force relationships is explored and discussed. Finally, a straightforward and robust method for calculating truck braking performance, based on the brake stroke measurements and published heavy truck braking test results, is introduced and compared to full-scale vehicle test data.

This research was performed using a designed experiment to evaluate the loss of dry surface braking performance and stability that could be associated with the disablement of specific brake positions on a tractor-semitrailer. The experiment was intended to supplement and update previous research by Heusser, Radlinski, Flick, and others. It also sought to establish reasonable limits for engineering estimates on stopping performance degradation attributable to partial or complete brake failure of individual S-cam air brakes on a class 8 truck. Stopping tests were conducted from 30 mph and 60 mph, with the combination loaded to GCW (80,000 lb.), half-payload, and with the flatbed semitrailer unladen. Both tractor and semitrailer were equipped with antilock brakes. Along with stopping distance, brake pressures, longitudinal acceleration, road wheel speed, and steering wheel position and effort were also recorded.

Vehicle dynamics models employed in heavy truck simulation often treat the semitrailer as a torsionally rigid member, assuming zero deflection along its longitudinal axis as a moment is applied to its frame. Experimental testing, however, reveals that semitrailers do twist, sometimes enough to precipitate rollover when a rigid trailer may have remained upright. Improving the model by incorporating realistic trailer roll stiffness values can improve assessment of heavy truck dynamics, as well as an increased understanding of the effectiveness of stability control systems in limit handling maneuvers. Torsional stiffness measurements were conducted by the National Highway Traffic Safety Administration (NHTSA) for eight semitrailers of different types, including different length box vans, traditional and spread axle flat beds, and a tanker.

This paper presents a methodology for validating vehicle stability and control computer simulations. Validation is defined as showing that, within some specified operating range of the vehicle, a simulation's predictions of a vehicle's responses agree with the actual measured vehicle's responses to within some specified level of accuracy. The method uses repeated experimental runs at each test condition to generate sufficient data for statistical analyses. The acquisition and reduction of experimental data, and the processing path for simulation data, are described. The usefulness of time domain validation for steady state and slowly varying transients is discussed. The importance of frequency domain validation for thoroughly validating a simulation is shown. Both qualitative and quantitative methods for the comparison of the simulation predictions with the actual test measurements are developed.

This paper describes an experimental program of tire research used to quantify the concept of the “relaxation length” of the fully rolling, steered tire. Two methods of developing lateral force are compared for the general car with the result that a first-order differential equation is obtained from which the change is lateral force with time, or distance, as the steer angle variation is computed.

This paper describes the development of a more accurate shock absorber model in order to obtain better vehicle simulation results. Previous shock models used a single spline to represent shock force versus shock velocity curves. These models produced errors in vehicle simulations because the damper characteristics are better represented by the application of a hysteresis loop in the model. Thus, a new damper model that includes a hysteresis loop is developed using Matlab Simulink. The damper characteristics for the new model were extracted from measurements made on a shock dynamometer. The new model better represents experimental shock data. The new shock model is incorporated into two different lumped-parameter vehicle models: one is a three degree-of-freedom vehicle handling model and the other is a seven degree-of-freedom vehicle ride model. The new damper model is compared with the previous model for different shock mileages (different degrees of wear).

This research focuses on the development and performance of analytical models to simulate a tractor-semitrailer in straight-ahead braking. The simulations were modified and tuned to simulate full-treadle braking with all brakes functioning correctly, as well as the behavior of the tractor-semitrailer rig under full braking with selected brakes disabled. The models were constructed in TruckSim and based on a tractor-semitrailer used in dry braking performance testing. The full-scale vehicle braking research was designed to define limits for engineering estimates on stopping distance when Class 8 air-braked vehicles experience partial degradation of the foundation brake system. In the full scale testing, stops were conducted from 30 mph and 60 mph, with the combination loaded to 80,000 lbs (gross combined weight or GCW), half payload, and with the tractor-semitrailer unladen (lightly loaded vehicle weight, or LLVW).

In 2007, a software model of a Roll Stability Control (RSC) system was developed based on test data for a Volvo tractor at NHTSA's Vehicle Research and Test Center (VRTC). This model was designed to simulate the RSC performance of a commercially available Electronic Stability Control (ESC) system. The RSC model was developed in Simulink and integrated with the available braking model (TruckSim) for the truck. The Simulink models were run in parallel with the vehicle dynamics model of a truck in TruckSim. The complete vehicle model including the RSC system model is used to simulate the behavior of the actual truck and determine the capability of the RSC system in preventing rollovers under different conditions. Several simulations were performed to study the behavior of the model developed and to compare its performance with that of an actual test vehicle equipped with RSC.

Validation was performed on an existing heavy truck vehicle dynamics computer model with roll stability control (RSC). The first stage in this validation was to compare the response of the simulated tractor to that of the experimental tractor. By looking at the steady-state gains of the tractor, adjustments were made to the model to more closely match the experimental results. These adjustments included suspension and steering compliances, as well as auxiliary roll moment modifications. Once the validation of the truck tractor was completed for the current configuration, the existing 53-foot box trailer model was added to the vehicle model. The next stage in experimental validation for the current tractor-trailer model was to incorporate suspension compliances and modify the auxiliary roll stiffness to more closely model the experimental response of the vehicle. The final validation stage was to implement some minor modifications to the existing RSC model.

This paper deals with the ongoing efforts at The Vehicle Research and Test Center (VRTC) in East Liberty, Ohio in promoting the safe operation of heavy trucks. The associated research evaluated two vehicle dynamics simulations for their accuracy in predicting tractor-trailer handling metrics. The goals of the research were threefold: 1. Establish a generic “benchmark” parametric data set for the three-axle truck/two-axle trailer vehicle 2. Demonstrate the accuracy of experimental data that was collected for the tractor-trailer vehicle of this study 3. Demonstrate the accuracy of two vehicle simulations by comparing their predicted responses to experimentally observed vehicle responses and metrics.

During Phase VI of the National Highway Traffic Safety Administration's (NHTSA) Light Vehicle Rollover Research Program, three of the twenty-six light vehicles tested exhibited significant response asymmetries with respect to left versus right steer maneuvers. This paper investigates possible vehicle asymmetric characteristics and unintended inputs that may cause vehicle asymmetric response. An analysis of the field test data, results from suspension and steering parameter measurements, and a summary of a computer simulation study are also given.

This study involves two areas of research. The first is the finalization of the Pedestrian Crash Data Study (PCDS) in order to provide detailed information regarding the vehicle/pedestrian accident environment and how it has changed from the interim PCDS information. The pedestrian kinematics, injury contact sources, and injuries were analyzed relative to vehicle geometry. The second area presented is full-scale attempts at reconstruction of two selected PCDS cases using the Polar II pedestrian dummy to determine if the pre-crash motion of the pedestrian and vehicle could somehow be linked to the injuries and vehicle damage documented in the case.

Computer simulations are popular for modeling vehicle system dynamics. However, further refinement of the vehicle dynamic model is required for extensive use in the automotive industry. In this paper, the model refining procedure is illustrated by developing reliable kinematic models verified with laboratory test results; instrument test data; and a mathematical optimization method. More specifically, simple kinematic models are developed for reduced computation times using ADAMS. They are tuned by the gradient-based optimization technique using the results from a laboratory testing facility, which includes the compliance effect in order to use the kinematic models in dynamic simulations. Also the Magic Formula tire model is developed using the optimization method and tire property data for the STI (Systems Technology, Incorporated) tire model.

This paper presents the results of an in-depth study of the measurement of occupant kinematic response on the S-E-A Roll Simulator. This roll simulator was built to provide an accurate and repeatable test procedure for the evaluation of occupant protection and restraint systems during roll events within a variety of occupant compartments. In the present work this roll simulator was utilized for minimum-energy, or threshold type, rollover events of recreational off-highway vehicles (ROVs). Input profiles for these tests were obtained through a separate study involving autonomous full vehicle tests [1]. During simulated roll events anthropomorphic test device (ATD) responses were measured using on-board high speed video, an optical three-dimensional motion capture system (OCMS) and an array of string potentiometers.

This paper will illustrate the development of the modeling of rollover sequences. During the past few years, a lot of research has been focused on the rollover propensity of vehicles. As to what happens after the vehicle rolls over, attention is only paid to occupant kinematics and occupant injury. Some simple questions such as how many rolls in the rollover are not answered unless a rollover test is run. The rollover sequences including roll number, roll speed and roll distance are very important to the accident reconstructionists as well as design engineers. Since the cost for running a rollover test is so high today, it is very economic and time-efficient to obtain the preliminary results from a mathematical model. Roll number and roll distance versus time are to be obtained through the mathematical model which is based on several rollover tests, vehicle inertia parameters, and the Coulomb friction, a non-linear term in the equation.

For some vehicle dynamics applications, an estimate of a vehicle's center of gravity (cg) height and mass moments of inertia can suffice. For other applications, such as vehicle models and simulations used for vehicle development, these values should be as accurate as possible. This paper presents several topics related to inertial parameter estimation and measurement. The first is a simple but reliable method of estimating vehicle mass moment of inertia values from data such as the center of gravity height, roof height, track width, and other easily measurable values of any light road vehicle. The second is an error analysis showing the effect, during a simple static cg height test, of vehicle motion (relative to the support system) on the vehicle's calculated cg height. A method of accounting for this motion is presented. Similarly, the effects of vehicle motion are analyzed for subsequent mass moment of inertia tests.

This paper presents the development of a tire model for use in the simulation of vehicle dynamics. The model was developed to predict tire lateral and longitudinal force responses to dynamic inputs. In this new tire model, the contact patch of a tire is lumped into a number of elements to study the dynamic behavior of the displacement of the tire contact patch in the lateral and longitudinal directions. For each displacement, a differential equation governing the dynamic behavior of the displacement to the dynamic inputs is derived. Based on the differential equations for the lateral and longitudinal displacements, difference equations are derived for the purpose of simulating tire output responses. Since system parameters, such as mass, damping and stiffness, in the difference equations are unknown, estimation of system parameters is performed using the differential equations and experimental data measured for this research.