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Many powertrain structural sub-systems are often tested under steady state conditions on a dynamometer or in a full vehicle. This process (while necessary) is costly and time intensive, especially when evaluating the effect of component properties on transient phenomena, such as driveline clunk. This paper proposes a laboratory experiment that provides the following: 1) a bench experiment that demonstrates transient behavior of a non-linear clutch damper under non-rotating conditions, 2) a process to efficiently evaluate multiple non-linear clutch dampers, and 3) generates benchmark time domain data for validation of non-linear driveline simulation codes. The design of this experiment is based on a previous experimental work on clunk. A commercially available non-linear clutch damper is selected and the experiment is sized accordingly. The stiffness and hysteresis properties of the clutch damper are assumed from the measured quasi-static torque curve provided by the manufacturer.

An ignition delay correlation was developed for a toluene reference fuel (TRF) blend that is representative of automotive gasoline fuels exhibiting two-stage ignition. Ignition delay times for the autoignition of a TRF 91 blend with an antiknock index of 91 were predicted through extensive chemical kinetic modeling in CHEMKIN for a constant volume reactor. The development of the correlation involved determining nonlinear least squares curve fits for these ignition delay predictions corresponding to different inlet pressures and temperatures, a number of fuel-air equivalence ratios, and a range of exhaust gas recirculation (EGR) rates. In addition to NO control, EGR is increasingly being utilized for managing combustion phasing in spark ignition (SI) engines to mitigate knock. Therefore, along with other operating parameters, the effects of EGR on autoignition have been incorporated in the correlation to address the need for predicting ignition delay in SI engines operating with EGR.

In recent years, there has been a sustained effort by the automotive OEMs and suppliers to improve the vehicle driveline efficiency. This has been in response to customer demands for greater vehicle fuel economy and increasingly stringent government regulations. The automotive rear axle is one of the major sources of power loss in the driveline, and hence represents an area where power loss improvements can have a significant impact on overall vehicle fuel economy. Both the friction induced mechanical losses and the spin losses vary significantly with the operating temperature of the lubricant. Also, the preloads in the bearings can vary due to temperature fluctuations. The temperatures of the lubricant, the gear tooth contacting surfaces, and the bearing contact surfaces are critical to the overall axle performance in terms of power losses, fatigue life, and wear.

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.

The tractor trailer models discussed in this paper were for a real-time hardware-in-the-loop (HIL) simulation to test heavy truck electronic stability control (ESC) systems [1]. The accuracy of the simulation results relies on the fidelity and accuracy of the vehicle parameters used. However in this case where hardware components are part of the simulation, their accuracy also affects the proper working of the simulation and ESC unit. Hence both the software and hardware components have to be validated. The validation process discussed in this paper is divided into two sections. The first section deals with the validation of the TruckSim vehicle model, where experimental data is compared with simulation results from TruckSim. Once the vehicle models are validated, they are incorporated in the HIL simulation and the second section discusses the validation of the whole HIL system with ESC.

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.

Engine control units (ECUs) on heavy trucks have been capable of storing “last stop” or “hard stop” data for some years. These data provide useful information to accident reconstruction personnel. In past studies, these data have been analyzed and compared to higher-resolution on-vehicle data for several heavy trucks and several makes of passenger cars. Previous published studies have been quite helpful in understanding the limitations and/or anomalies associated with these data. This study was designed and executed to add to the technical understanding of heavy truck event data recorders (EDR), specifically data associated with a modern Cummins power plant ECU. Emergency “full-treadle” stops were performed at many combinations of load-speed-surface coefficient conditions. In addition, brake-in-curve tests were performed on wet Jennite for various conditions of disablement of the braking system.

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 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).

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.

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 describes a method to evaluate vehicle stability and controllability when the vehicle operates in the nonlinear range of lateral dynamics. The method is applied to open-loop steering maneuvers as well as closed-loop path-following maneuvers. Although path-following maneuvers are more representative of real world driving intent, they are usually considered inappropriate for objective assessment because of repeatability and accuracy issues. The automated test driver (ATD) can perform path-following maneuvers accurately and with good repeatability. This paper discusses the usefulness of application of the automated test drivers and path-following maneuvers. The dynamic mode of instability is not directly obtained from measurable outputs such as yawrate and lateral acceleration as in open-loop maneuvers. A few metrics are defined to quantify deviation from desired or ideal behavior in terms of observed “unexpected” lateral force and moment.

Plug in hybrid electric vehicles (PHEVs) have gained interest over last decade due to their increased fuel economy and ability to displace some petroleum fuel with electricity from power grid. Given the complexity of this vehicle powertrain, the energy management plays a key role in providing higher fuel economy. The energy management algorithm on PHEVs performs the same task as a hybrid vehicle energy management but it has more freedom in utilizing the battery energy due to the larger battery capacity and ability to be recharged from the power grid. The state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining overall fuel consumption.

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.

Crankshaft angular position measurements are fundamental to all modern automotive engines. These measurements are required to control fuel injection timing as well as ignition timing. However, many other functions can be performed from such measurements through the use of advanced signal processing. These additional functions are essentially diagnostic in nature although there is potential for substitution of primary fuel and ignition control functions. This paper illustrates the application of crankshaft angular position measurement to the estimation of individual cylinder indicated and/or brake torque in IC engines from measurement of crankshaft position/velocity.

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.

Soil response to differences in tire size and inflation pressure was measured for a JD 9600 combine with 18.4R38 dual tires, 30.5L32 single tires, 68x50.00-32 single tires at 103 and 166 kPa inflation pressure and a John Deere half-track system on two different soils (Kokomo and Crosby) near Urbana, Ohio. A loaded 42.3 m3 grain cart was included on the Kokomo soil for comparative purposes. The Ohio State Soil Physical Properties Measurement System was used to sample and measure the bulk density, air-filled porosity, air permeability and cone penetration resistance between 10 and 50 cm depths. The results for Kokomo soil show the grain cart had the greatest effect with an average decrease in total porosity of 12.90 percent, compared to 7.95%, 6.05%, 4.56%, 3.06%, and 2.04% for singles, tracks, duals, wide overinflated, and wide rated pressure tires, respectively, on the combine.