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This technical paper collection discusses the modeling, analysis, and validation of commercial vehicle chassis, suspension, and tire modeling and simulation. Topics include commercial vehicle dynamics; chassis control devices such as ABS, traction control, yaw/roll stability control, and their interaction with suspension controls; modeling and simulation of ride comfort, as well as passive and active suspension control methodologies. Authors are encouraged to discuss the validation of their modeling and simulation.

Abstract Reliability analysis of a large-scale system under random dynamic loads can be a very time-consuming task since it requires repeated studies of the system. In many engineering problems, for example, wave loads on an offshore platform, the excitation loads are defined using a power spectral density (PSD) function. For a given PSD function, one needs to generate many time histories to make sure the excitation load is modeled accurately. Global and local approximation methods are available to predict the system response efficiently. Each way has their advantages and shortcomings. The combined approximations (CA) method is an efficient method, which combines the advantages of local and global approximations. This work demonstrates two methodologies that utilize CA to reduce the cost of crude or separable Monte Carlo simulation (MCS) of linear dynamic systems when the excitation loads are defined using PSD functions.

Road vibrations cause fatigue failures in vehicle components and systems. Therefore, reliable and accurate damage and life assessment is crucial to the durability and reliability performances of vehicles, especially at early design stages. However, durability and reliability assessment is difficult not only because of the unknown underlying damage mechanisms, such as crack initiation and crack growth, but also due to the large uncertainties introduced by many factors during operation. How to effectively and accurately assess the damage status and quantitatively measure the uncertainties in a damage evolution process is an important but still unsolved task in engineering probabilistic analysis. In this paper, a new procedure is developed to assess the durability and reliability performance, and characterize the uncertainties of damage evolution of components under constant amplitude loadings.

This paper focuses on the manufacturer's conflict in the conceptual design of commercial vehicles between highly customized special vehicles and the greatest possible degree of standardization. Modularity and standardization are crucial success factors for realizing high variance at the best cost efficiency in development and production as well for achieving the highest quality standards at reduced efforts for technical validation. The presented virtual design approach for commercial vehicle concepts allows for purposeful design and integration of new concepts and technologies on the component level in an existing product portfolio - not neglecting manufacture's portfolio requirements concerning standardization and modularity. The integrated tool chain helps to bring trade-offs to a head that exist in balancing between dedicated vehicles with best customer-relevant characteristics and standardized vehicles with the highest degree of commonality.

Numerous test data have been generated in many testing institutions over the years and the historical information from previous similar designs and operating conditions can shed light on the current and future designs since they would share some common features when the changes are not drastic. To effectively utilize the historical information for current and future designs, two steps are necessary: (1) finding an approach to consistently correlate the test data; (2) utilizing Bayesian statistics, which can provide a rigorous mathematical tool for extracting useful information from the historical data. In this paper, a procedure for test sample size reduction is proposed based on historical fatigue S-N test data and Bayesian statistics. First, the statistical information is extracted from a large amount of fatigue test data collected over the years.

In an effort to characterize the dynamics typical of school bus operation, National Renewable Energy Laboratory (NREL) researchers set out to gather in-use duty cycle data from school bus fleets operating across the country. Employing a combination of Isaac Instruments GPS/CAN data loggers in conjunction with existing onboard telemetric systems resulted in the capture of operating information for more than 200 individual vehicles in three geographically unique domestic locations. In total, over 1,500 individual operational route shifts from Washington, New York, and Colorado were collected. Upon completing the collection of in-use field data using either NREL-installed data acquisition devices or existing onboard telemetry systems, large-scale duty-cycle statistical analyses were performed to examine underlying vehicle dynamics trends within the data and to explore vehicle operation variations between fleet locations.

This paper reviews the correlation concepts and tools available, with the emphasis on their historical origins, mathematical properties and applications. Two of the most commonly used statistical correlation indicators, i.e., modal assurance criterion (MAC) for structural deformation pattern identification/correlation and the coefficient of determination (R2) for data correlation are investigated. The mathematical structure of R2 is critically examined, and the physical meanings and their implications are discussed. Based on the insights gained from these analyses, a data scatter measure and a dependency measure are proposed. The applications of the measures for both linear and nonlinear data are also discussed. Finally, several worked examples in vehicle dynamics analysis and statistical data analyses are provided to demonstrate the effectiveness of these concepts.

Due to recent infrastructural development and emerging competitive automotive markets, there is seen a huge shift in customer’s demand and vehicle drivability pattern in commercial vehicle industry. Now apart from ensuring better vehicle durability and best in class tyre life and fuel mileage, a vehicle manufacturer also has to focus on other key attributes like driver’s safety and ride comfort. Thus, for ensuring enhanced drivability, key parameters for ensuring better vehicle handling includes optimization of bump steer and brake steer. Both bump steer and brake steer are vehicle’s undesirable phenomenon where a driver is forced to constantly make steering wheel correction in order to safely maneuver the vehicle in the desired path.

Increasing demands for high fuel efficiency requires imperative light weighting of automotive structure, but this adversely affects the NVH performance since transfer path of noise sources like engine, road, wind to vehicle interior through the panels weakens. Also increasing customer centric approach drives the urge to provide world-class comfort which is cost-effective too. Sound Packaging helps us to optimize this transfer paths through the panel, and can effectively complement efforts of testing team, by reducing the redundant iterations required to conclude to most effective trims to be put on. statistical energy analysis based simulations is employed for carrying out these iterations at the virtual validation gateway itself.

In today’s automobile industry refined NVH performance is a key feature and of high importance governing occupant comfort and overall quality impression of vehicle. In this paper interior noise and vibration measurement is done on one of the light truck and few dominant low frequency noise booms were observed in operation range. Modal analysis was done for the cabin at virtual as well as experimental level and few modes were found close to these noise booms. Vibrations were measured across the cabin mounts and it was found that the isolation of front mounts is not effective at lower frequencies. Taking this as an input, the mount design was modified to shift the natural frequency and hence improve the isolation behavior at the lowest dominant frequency. This was followed by static and dynamic measurement of the mounts at test rig level to characterize the dynamic performance and stiffness conclusion.

Body-over-Frame is the primary type of construction used in SUVs, pick-ups, and other commercial vehicles in India. In this type of construction, the body, engine, suspensions etc. are mounted on the ladder frame. Since the frame acts as the skeleton of the vehicle, optimal design of frame at the concept stage of the vehicle program is critical for meeting all structural performance targets. Frontloading of these targets aids in architecture development and reduces future design modifications. The natural frequency response from the frame directly affects the NVH performance of the vehicle. This paper focuses on frontloading the natural frequency targets by performing concept-level simulations on the ladder frame even before creation of 3D concept data. A parametric beam model is created based on the reference vehicles. The beam model has been validated with correlation of more than 85% compared with CAE and physical testing outputs of existing vehicles.

Despite numerous research efforts, there is no reliable and widely accepted tool for the prediction of erosion prone material surfaces due to collapse of cavitation bubbles. In the present paper an Erosion Aggressiveness Index (EAI) is proposed, based on the pressure loads which develop on the material surface and the material yield stress. EAI depends on parameters of the liquid quality and includes the fourth power of the maximum bubble radius and the bubble size number density distribution. Both the newly proposed EAI and the Cavitation Aggressiveness Index (CAI), which has been previously proposed by the authors based on the total derivative of pressure at locations of bubble collapse (DP/Dt>0, Dα/Dt<0), are computed for a cavitating flow orifice, for which experimental and numerical results on material erosion have been published. The predicted surface area prone to cavitation damage, as shown by the CAI and EAI indexes, is correlated with the experiments.

We have recently obtained experimental data and used them to develop computational models to quantify occupant impact responses and injury risks for military vehicles during frontal crashes. The number of experimental tests and model runs are however, relatively small due to their high cost. While this is true across the auto industry, it is particularly critical for the Army and other government agencies operating under tight budget constraints. In this study we investigate through statistical simulations how the injury risk varies if a large number of experimental tests were conducted. We show that the injury risk distribution is skewed to the right implying that, although most physical tests result in a small injury risk, there are occasional physical tests for which the injury risk is extremely large. We compute the probabilities of such events and use them to identify optimum design conditions to minimize such probabilities.

Smart technologies enabling connection among vehicles and between vehicles and infrastructure as well as vehicle automation to assist human operators are receiving significant attention as a means for improving road transportation systems by reducing fuel consumption – and related emissions – while also providing additional benefits through improving overall traffic safety and efficiency. For truck applications, which are currently responsible for nearly three-quarters of the total U.S. freight energy use and greenhouse gas (GHG) emissions, platooning has been identified as an early feature for connected and automated vehicles (CAVs) that could provide significant fuel savings and improved traffic safety and efficiency without radical design or technology changes compared to existing vehicles. A statistical analysis was performed based on a large collection of real-world U.S. truck usage data to estimate the fraction of total miles that are technically suitable for platooning.

The most recent 2010 emissions standards for heavy-duty engines have established a tailpipe limit of oxides of nitrogen (NOX) emissions of 0.20 g/bhp-hr. However, it is projected that even when the entire on-road fleet of heavy-duty vehicles operating in California is compliant with 2010 emission standards, the National Ambient Air Quality Standards (NAAQS) requirement for ambient particulate matter and Ozone will not be achieved without further reduction in NOX emissions. The California Air Resources Board (CARB) funded a research program to explore the feasibility of achieving 0.02 g/bhp-hr NOX emissions.

The research on the characteristics of vehicle movement is the premise to guarantee the smooth operation of electric vehicles, and it’s also the basis for developing the vehicle ability in depth. Therefore, it’s essential to study on the vehicle movement characteristics. And steering on ramp is a typical working condition for tracked vehicle. Firstly, the kinematics and dynamics of tracked vehicle during the steering process on ramp are analyzed in detail aiming at the problem that it’s complex and difficult to describe the process of steering, and the dynamics model of tracked vehicle is established in the condition of the offset of instantaneous steering center and the sliding of the track and other factors. Second, the relationships between driving force, steering radius and slop are obtained by simulation, and the variation rules of these parameters are analyzed. Finally, the model of steering on ramp is verified using electric tracked vehicle.

A cargo box is a key structure in a pickup truck which is used to hold various items. Therefore, a cargo box must be durable and robust under different ballast conditions when subjected to road load inputs. This paper discusses a Design for Six Sigma (DFSS) approach to improve the durability of cargo box panel in its early development phase. Traditional methods and best practices resulted in multiple iterations without an obvious solution. Hence, DFSS tools were proposed to find a robust and optimum solution. Key control factors/design parameters were identified, and L18 Orthogonal Array was chosen to optimize design using CAE tools. The optimum design selected was the one with the minimum stress level and the least stress variation. This design was confirmed to have significant improvement and robustness compared to the initial design. DFSS identified load paths which helped teams finally come up with integrated shear plate to resolve the durability concern.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

This SAE Recommended Practice establishes uniform test procedures and performance requirements for engine-off heating, ventilation, and air conditioning (HVAC) systems in order to achieve driver thermal comfort in both winter and summer rest periods. This specification will apply to heavy trucks with and without sleeper compartments, including but is not limited to Class 6, 7, and 8 powered vehicles.

This SAE Recommended Practice establishes uniform test procedures and performance requirements for engine-off heating, ventilation, and air conditioning (HVAC) systems in order to achieve driver thermal comfort in both winter and summer rest periods. This specification will apply to heavy trucks with and without sleeper compartments, including but is not limited to Class 6, 7, and 8 powered vehicles.