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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.

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.

Investigators of vehicular incidents often seek to recover data stored within on-board computer systems. For commercial vehicles, the primary source for this information is the engine control module (ECM). The data stored in these modules, not unlike passenger vehicles, varies widely among manufacturers, as do the hardware and software required to recover such data. Further, the options, and associated risks, involved with attempting to recover this data has a similarly wide variance relative to the engine manufacturer, incident related circumstances, and the tools currently available to perform such downloads. There are two primary paths available to obtain this data: (1) via the vehicle data bus (e.g. SAE J1939 or J1708 ) or (2) direct-to-module (DTM) connection. When using the DTM method, power is applied to an ECM, and the module measures the various engine control and monitoring components for validity.

The EcoCAR Challenge team at The Ohio State University has designed an extended-range electric vehicle capable of 50 miles all-electric range via a 22 kWh lithium-ion battery pack, with range extension and limited parallel operation supplied by a 1.8 L dedicated E85 engine. This vehicle is designed to drastically reduce fuel consumption, while meeting Tier II Bin 5 emissions standards. This vehicle design is implemented in a GM crossover utility vehicle as part of the EcoCAR Challenge. This paper explains the implementation of the vehicle's control strategy in order to maintain high efficiency and improve drivability. The vehicle control strategy employs both distinct operating modes and an Equivalent Consumption Minimization Strategy (ECMS) to find the most efficient operating point. The ECMS strategy does an online search for the most efficient torque split in order to meet the driver's command.

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.

The soil response to traffic loads as affected by tire inflation pressure, track width and drawbar pull was measured. The change in soil physical properties caused by a John Deere 8870 tractor at two tire pressure settings and CATERPILLAR Challenger 65 and 75 tractors with 64 and 89 cm wide belt tracks, were measured at two load levels; no draft (tractor only) and tractor pulling a 12.5 m field cultivator. The Ohio State University Soil Physical Properties Measurement System was used to measure cone penetration resistance, air permeability, air-filled porosity, and bulk density. The results show the dual overinflated tires caused the greatest change, followed by the CATERPILLAR Challenger 65 track, then the CATERPILLAR Challenger 75 track, and finally dual correctly inflated tires caused the least effect on soil physical properties. These results were consistent at each depth. The effects of the two draft levels give the same ranking of the tractive units.

There is currently a large effort in industry to make natural gas a viable alternative fuel for internal combustion engines. While the use of natural gas offers several advantages such as reduced emissions and potentially higher efficiency, it also has some inherent difficulties. Among these is the challenge of producing a consistently homogeneous air/fuel mixture while retaining the advantages which accompany modern, multi-point, fuel injection systems. The purpose of the research described here is to investigate the in-cylinder mixture formation process in a port injected natural gas fueled engine. Planar laser-induced fluorescence has been used to produce qualitative air fuel ratio maps in the engine cylinder, in selected planes, throughout the intake and compression strokes. The process consists of impinging a sheet of ultraviolet laser light on various planes parallel to, and normal to, the cylinder axis.

This paper presents an evaluation of a vehicle dynamics model intended to be used for the National Advanced Driving Simulator (NADS). Dynamic validation for high performance simulation is not merely a comparison between experimental and simulation plots. It involves strong insight of vehicle's subsystems mechanics, limitations of the mathematical formulations, and experimental predictions. Lateral, longitudinal, and ride dynamics are evaluated using field test data, and analytical diagnostics. The evaluation includes linear and non-linear range of vehicle dynamics response.

The automotive NVH research infrastructure at Ohio State includes the Center for Automotive Research, the Acoustics and Dynamics Laboratory, and the Gear Dynamics and Gear Noise Research Laboratory. This paper describes the facilities of these laboratories. Two unique existing facilities, namely the transmission error measurement of gears and a laboratory for the experimental measurement of engine breathing systems, will be emphasized. Also covered are the enhancements that are envisioned through a recent grant from the Ohio Board of Regents.

One obstacle hindering the use of port fuel injection in natural gas engines is poor idle performance due to incomplete mixing of the cylinder charge prior to ignition. Fuel injection timing has a strong influence on the mixing process. The purpose of this work is to determine the impact of fuel injection timing on in-cylinder fuel distribution. Equivalence ratio maps have been acquired by Planar Laser Induced Fluorescence in an optical engine with a production cylinder head. Experimental results have been used to determine the injection timing which produces the most uniform fuel distribution for the given engine.

The National Highway Traffic Safety Administration's Vehicle Research and Test Center (VRTC) plans to evolve the state-of-the-art of steering system modeling for driving simulators with the ultimate goal being the development of a high fidelity steering feel model for the National Advanced Driving Simulator (NADS). The VRTC plans on developing reliable research tools that can be used to determine the necessary features for a steering model that will provide good objective and subjective steering feel. This paper reviews past and continuing work conducted at the VRTC and provides a plan for future work that will achieve this goal.

Evaluating the safety of Autonomous Vehicles (AV) is a challenging problem, especially in traffic conditions involving dynamic interactions. A thorough evaluation of the vehicle’s decisions at all possible critical scenarios is necessary for estimating and validating its safety. However, predicting the response of the vehicle to dynamic traffic conditions can be the first step in the complex problem of understanding vehicle’s behavior. This predicted response of the vehicle can be used in validating vehicle’s safety. In this paper, models based on Machine Learning were explored for predicting and classifying driver’s response. The Naturalistic Driving Study dataset (NDS), which is part of the Strategic Highway Research Program-2 (SHRP2) was used for training and validating these Machine Learning models.

A two-degree-of-freedom Roll Simulator has been developed to study the occupant kinematics of Recreational Off-Highway Vehicles (ROVs). To validate the roll simulator, test data was collected on a population of ROVs on the market today. J-turn maneuvers were performed to find the minimum energy limits required to tip up the vehicles. Two sets of tests were performed: for the first set, 10 vehicles were tested, where the motion was limited by safety outriggers to 10-15 degrees of roll; and for the second set, three of these vehicles were re-tested with outriggers removed and the vehicle motion allowed to reach 90 degrees of roll. These quarter-turn rollover tests were performed autonomously using an Automatic Steering Controller (ASC) and a Brake and Throttle Robot (BTR). Lateral and longitudinal accelerations as well as roll rate and roll angle were recorded for all tests.

This paper is a follow up to a study published in 2005 1 on the same topic and presents a study that was conducted to compare vehicle speed change and acceleration data from full-scale testing to results generated by computer simulation using the SImulation MOdel Non-linear (SIMON) vehicle dynamic simulation model version 3.1 within the Human Vehicle Environment (HVE) software version 5.2. SIMON will be referred to in this paper as the computer or simulation model, while HVE will be referred to as the computer software. In the previous study, a simple method to model the curb was developed and version 2.0 of the simulation model was validated, for delta-v, up to approximately 6.7 m/s (15 mph) and for vertical accelerations, up to speeds of approximately 4.5 m/s (10 mph).

There have been many studies regarding the stability of vehicles following a sudden air loss event in a tire. Previous works have included literature reviews, full-scale vehicle testing, and computer modeling analyses. Some works have validated physics-based computer vehicle simulation models for passenger vehicles and other works have validated models for heavy commercial vehicles. This work describes a study wherein a validated vehicle dynamics computer model has been applied to extrapolate results to higher event speeds that are consistent with travel speeds on contemporary North American highways. This work applies previously validated vehicle dynamics models to study the stability of a five-axle commercial tractor-semitrailer vehicle following a sudden air loss event for a steer axle tire. Further, the work endeavors to understand the analytical tire model for tires that experience a sudden air loss.

This paper documents the vehicle response of a tractor-semitrailer following a sudden air loss (Blowout) in a steer axle tire while traveling at highway speeds. The study seeks to compare full-scale test data to predicted response from detailed heavy truck computer vehicle dynamics simulation models. Full-scale testing of a tractor-semitrailer experiencing a sudden failure of a steer axle tire was conducted. Vehicle handling parameters were recorded by on-board computers leading up to and immediately following the sudden air loss. Inertial parameters (roll, yaw, pitch, and accelerations) were measured and recorded for the tractor and semitrailer, along with lateral and longitudinal speeds. Steering wheel angle was also recorded. These data are presented and also compared to the results of computer simulation models. The first simulation model, SImulation MOdel Non-linear (SIMON), is a vehicle dynamic simulation model within the Human Vehicle Environment (HVE) software environment.