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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.

To accurately evaluate the energy consumption benefits provided by connected and automated vehicles (CAV), it is necessary to establish a reasonable baseline virtual driver, against which the improvements are quantified before field testing. Virtual driver models have been developed that mimic the real-world driver, predicting a longitudinal vehicle speed profile based on the route information and the presence of a lead vehicle. The Intelligent Driver Model (IDM) is a well-known virtual driver model which is also used in the microscopic traffic simulator, SUMO. The Enhanced Driver Model (EDM) has emerged as a notable improvement of the IDM. The EDM has been shown to accurately forecast the driver response of a passenger vehicle to urban and highway driving conditions, including the special case of approaching a signalized intersection with varying signal phases and timing. However, most of the efforts in the literature to calibrate driver models have focused on passenger vehicles.

The handling, stability, and rollover resistance of vehicles is presently being studied by both the automotive industry and the National Highway and Traffic Safety Administration (NHTSA). However, to study the handling and rollover behavior of each vehicle on the road is not feasible. The ability to categorize and compare the rollover and handling behavior of various vehicles is a subject of considerable research interest. This paper examines the possibility of characterizing vehicle classes through the use of a three degree-of-freedom linear model. Initially, segregation is studied by evaluating the eigenvalue location in the complex domain for vehicle sideslip velocity, yaw rate, and roll angle. Then the influence of numerator dynamics on vehicle behavior is studied and vehicle class segregation is attempted through evaluation of the amplitude ratio of the frequency responses for sideslip velocity, yaw rate, and roll angle.

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.

An existing tire model was investigated for additional normal load-dependent characteristics to improve the large truck simulations developed by the National Highway Traffic Safety Administration (NHTSA) for the National Advanced Driving Simulator (NADS). Of the existing tire model coefficients, plysteer, lateral friction decay, aligning torque stiffness and normalized longitudinal stiffness were investigated. The findings of the investigation led to improvements in the tire model. The improved model was then applied to TruckSim to compare with the TruckSim table lookup tire model and test data. Additionally, speed-dependent properties for the NADS tire model were investigated (using data from a light truck tire).

This paper discusses an improved design of a vehicle-based mobile off-road terrain profile measurement system. The proposed system includes an apparatus of sensors and on-board data acquisition hardware, equipped on a platform vehicle used to measure and record the relevant data while the vehicle travels through the off-road or terrain surface to be surveyed. A unique post-processing algorithm is then used to derive the elevation profile based on the collected data. The derived elevation profile data could be used to characterize the roughness of an off-road testing course or perform a general geographical survey or mapping. The major technical issue addressed in this system is to eliminate the effect of platform vehicle vibration on sensor measurement which if left unaddressed will result in large measurement error due to high amplitude pitch and roll movements of the platform vehicle.

The goal of this research was to find vehicle characteristics which may contribute to steering maneuver induced rollover accidents. This work involved studying vehicle handling dynamics using the Vehicle Dynamics Analysis, Non-Linear (VDANL) computer simulation. The simulation was used to predict vehicle responses while performing 28 different steering induced maneuvers for each of 51 vehicles. Various measures of vehicle response (metrics), such as response times, percent overshoots, etc., were computed for each vehicle from simulation predictions. These vehicle directional response metrics were analyzed in an attempt to identify vehicle characteristics that might be good predictor/explanatory variables for vehicle rollover propensity. The metrics were correlated, using the Statistical Analysis System (SAS) software and logistic regression, with single vehicle accident data from the state of Michigan for the years 1986 through 1988.

For simulation and analysis of vehicles there is a need to have a means of generating drive cycles which have properties similar to real world driving. A method is presented which uses measured vehicle speed from a number of vehicles to generate a Markov chain model. This Markov chain model is capable of generating drive cycles which match the statistics of the original data set. This Markov model is then used in an iterative fashion to generate drive cycles which match constraints imposed by the user. These constraints could include factors such number of stops, total distance, average speed, or maximum speed. In this paper, systematic analysis was done for a PHEV fleet which consists of 9 PHEVs that were instrumented using data loggers for a period of approximately two years. Statistical analysis using principal component analysis and a clustering approach was carried out for the real world velocity profiles.

During the past few years, the National Highway Traffic Safety Administration's (NHTSA) Vehicle Research and Test Center has generated a plethora of reliable vehicle test data during their efforts to study vehicle rollover propensity. This paper provides further analyses of a small selection of some of the data. The analyses provided here derive in part from the previous work, trying to answer some of the questions spawned by earlier analyses. The purpose of this paper is to introduce several new concepts to the study of vehicle roll stability and provide case studies using the results available from the NHTSA testing. Results from several severe maneuvers are studied in detail to gain understanding of vehicle response in these cases.

The widely used Extended Kalman Filter (EKF) is applied to a planar model of an articulated vehicle to predict jackknifing events. The states of hitch angle and hitch angle rate are estimated using a vehicle model and the available or “measured” states of lateral acceleration and yaw rate from the prime mover. Tuning, performance, and compromises for the EKF in this application are discussed. This application of the EKF is effective in predicting the onset of instability for an articulated vehicle under low-μ and low-load conditions. These conditions have been shown to be most likely to render heavy articulated vehicles vulnerable to jackknife instability. Options for model refinements are also presented.

This paper presents the details of the model for the physical steering system used on the National Advanced Driving Simulator. The system is basically a hardware-in-the-loop (steering feedback motor and controls) steering system coupled with the core vehicle dynamics of the simulator. The system's torque control uses cascaded position and velocity feedback and is controlled to provide steering feedback with variable stiffness and dynamic properties. The reference model, which calculates the desired value of the torque, is made of power steering torque, damping function torque, torque from tires, locking limit torque, and driver input torque. The model also provides a unique steering dead-band function that is important for on-center feel. A Simulink model of the hardware/software is presented and analysis of the simulator steering system is provided.

This paper discusses the derivation and validation of planar models of articulated vehicles that were developed to analyze jackknife stability on low-μ surfaces. The equations of motion are rigorously derived using Lagrange's method, then linearized for use in state-space models. The models are verified using TruckSim™, a popular nonlinear solid body vehicle dynamics modeling package. The TruckSim™ models were previously verified using extensive on-vehicle experimental data [1, 2]. A three-axle articulated model is expanded to contain five axles to avoid lumping the parameters for the drive and semitrailer tandems. Compromises inherent in using the linearized models are discussed and evaluated. Finally, a nonlinear tire cornering force model is coupled with the 5-axle model, and its ability to simulate a jackknife event is demonstrated. The model is shown to be valid over a wide range of inputs, up to and including loss of control, on low-and-medium-μ surfaces.

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.

Building and testing of physical prototypes for optimization purposes consume significant amount of time, manpower and financial resources. Mathematical formulation and solution of vehicle multibody dynamics equations are also not feasible because of the massive size of the problem. This paper proposes a methodology for vehicle design optimization that does not involve physical prototyping or exhaustive mathematics. The proposed method is fast, cost effective and saves considerable manpower. The methodology uses an industry acknowledged multibody dynamics simulation software (ADAMS) and a flexible architecture to explore large design spaces.

A methodology has been developed to generate military vehicle driving cycles for use in vehicle simulation models. This methodology is based upon the mission profile for a vehicle, which is typically given within a vehicle's specifications and lists the types of terrains that the vehicle is likely to encounter. A simplistic vehicle powertrain and road load model and the Bekker vehicle-soil interaction model are used to estimate the vehicle performance over each type of terrain. Two types of driving cycles are generated within a Graphical User Interface developed within MATLAB using the results of the vehicle models: Linear modes driving cycles, and Real-world driving cycles.

The Buckeye Bullet set domestic as well as international speed records for electric vehicles in 2004. The next generation of land speed vehicle from Ohio State called the Buckeye Bullet 2 (henceforth the BB2) will again challenge and hopefully achieve several new speed records. The Buckeye Bullet suspension worked relatively well but was found to not be quite optimal for the vehicle. The purpose of the work outlined here was to develop a new front and rear suspension for the BB2 that would be an improvement over the suspension of the original Bullet. Previous to the start of this work part of the suspension had already been designed in the form of an upright/control arm setup. This paper works on taking the suspension to completion from this point of design. Work done includes developing the final design, determining suspension parameters, building an ADAMS model, and testing the ADAMS model.

This paper describes the development and experimental validation of a Plug-in Hybrid Electric Vehicle (PHEV) dynamic simulator that enables development, testing, and calibration of a traction control strategy. EcoCAR 2 is a three-year competition between fifteen North American universities, sponsored by the Department of Energy and General Motors that challenges students to redesign a Chevrolet Malibu to have increased fuel economy and decreased emissions while maintaining safety, performance, and consumer acceptability. The dynamic model is developed specifically for the Ohio State University EcoCAR 2 Team vehicle with a series-parallel PHEV architecture. This architecture features, in the front of the vehicle, an ICE separated from an automated manual transmission with a clutch as well as an electric machine coupled via a belt directly to the input of the transmission. The rear powertrain features another electric machine coupled to a fixed ratio gearbox connected to the wheels.

Heavy trucks are involved in many accidents every year and Electronic Stability Control (ESC) is viewed as a means to help mitigate this problem. ESC systems are designed to reduce the incidence of single vehicle loss of control, which might lead to rollover or jackknife. As the working details and control strategies of commercially available ESC systems are proprietary, a generic model of an ESC system that mimics the basic logical functionality of commercial systems was developed. This paper deals with the study of the working of a commercial ESC system equipped on an actual tractor trailer vehicle. The particular ESC system found on the test vehicle contained both roll stability control (RSC) and yaw stability control (YSC) features. This work focused on the development of a reliable RSC software model, and the integration of it into a full vehicle simulation (TruckSim) of a heavy truck.

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 covers research conducted at the National Highway Traffic Safety Administration's Vehicle Research and Test Center (VRTC) examining the performance of semitrailer anti-lock braking systems (ABS). For this study, a vehicle dynamics model was constructed for the combination of a 4×2 tractor and a 48-foot trailer, using TruckSim. ABS models for the tractor and trailer, as well as brake dynamics and surface friction models, were created in Simulink so that the effect of varying ABS controller parameters and configurations on semitrailer braking performance could be studied under extreme braking maneuvers. The longitudinal and lateral performances of this tractor-trailer model were examined for a variety of different trailer ABS controller models, including the 2s1m, 4s2m, and 4s4m configurations. Also, alternative controllers of the same configuration were studied by varying the parameters of the 2s1m controller.