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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.

Recent advancements in simulation software and computational hardware make it realizable to simulate a full vehicle system comprised of multiple sub-models developed in different modeling languages. The so-called, co-simulation allows one to develop a control strategy and evaluate various aspects of a vehicle system, such as fuel efficiency and vehicle drivability, in a cost-effective manner. In order to study the feasibility of the synchronized parallel processing in co-simulation this paper presents two co-simulation frameworks for a complete vehicle system with multiple heterogeneous subsystem models. In the first approach, subsystem models are co-simulated in a serial configuration, and the same sub-models are co-simulated in a parallel configuration in the second approach.

Standard CFD methods require a mesh that fits the boundaries of the computational domain. For a complex geometry the generation of such a grid is time-consuming and often requires modifications to the model geometry. This paper evaluates the Immersed Boundary (IB) approach which does not require a boundary-conforming mesh and thus would speed up the process of the grid generation. In the IB approach the CAD surfaces (in Stereo Lithography -STL- format) are used directly and this eliminates the surface meshing phase and also mitigates the process of the CAD cleanup. A volume mesh, consisting of regular, locally refined, hexahedrals is generated in the computational domain, including inside the body. The cells are then classified as fluid, solid and interface cells using a simple ray-tracing scheme. Interface cells, correspond to regions that are partially fluid and are intersected by the boundary surfaces.

The objective of this project was to model and study the interior noise in an Off-Highway Truck cab using Statistical Energy Analysis (SEA). The analysis was performed using two different modeling techniques. In the first method, the structural members of the cab were modeled along with the panels and the interior cavity. In the second method, the structural members were not modeled and only the acoustic cavity and panels were modeled. Comparison was done between the model with structural members and without structural members to evaluate the necessity of modeling the structure. Correlation between model prediction of interior sound pressure and test data was performed for eight different load conditions. Power contribution analysis was performed to find dominant paths and 1/3rd octave band frequencies.

Fast-running metamodels (surrogates or response surfaces) that approximate multivariate input/output relationships of time-consuming CAE simulations facilitate effective design trade-offs and optimizations in the vehicle development process. While the cross-validated nonparametric metamodeling methods are capable of capturing the highly nonlinear input/output relationships, it is crucial to ensure the adequacy of the metamodel error estimates. Moreover, in order to circumvent the so-called curse-of-dimensionality in constructing any nonlinear multivariate metamodels from a realistic number of expensive simulations, it is necessary to reliably eliminate insignificant inputs and consequently reduce the metamodel prediction error by focusing on major contributors. This paper presents a robust data-adaptive nonparametric metamodeling procedure that combines a convergent variable screening process with a robust 2-level error assessment strategy to achieve better metamodel accuracy.

This paper presents the damping effectiveness of free-layer damping materials through standard Oberst bar testing, solid plate excitation (RTC3) testing, and prediction through numerical schemes. The main objective is to compare damping results from various industry test methods to performance in an automotive body structure. Existing literature on laboratory and vehicle testing of free-layer viscoelastic damping materials has received significant attention in recent history. This has created considerable confusion regarding the appropriateness of different test methods to measure material properties for damping materials/treatments used in vehicles. The ability to use the material properties calculated in these tests in vehicle CAE models has not been extensively examined. Existing literature regarding theory and testing for different industry standard damping measurement techniques is discussed.

A new Diesel engine, called the Duramax 6600 (Fig.1), has been designed by Isuzu Motors (Isuzu) for an upcoming full-size General Motors (GM) pickup truck. It incorporates the latest Diesel technology in order to improve on the inherent strengths of a Diesel engine, such as fuel economy, torque and reliability, while also producing higher output, smoother driveability, and lower noise. The Duramax 6600 is an entirely new 90° V8 direct injection (DI) intercooled engine with a water-cooled turbocharger. Its fuel injection system employs a fully electronically controlled common rail system that has high-pressure injection capabilities. Isuzu had the design responsibility of the base engine, while GM Truck Group was responsible for designing the installation and packaging within the vehicle. Engine validation relied on Isuzu's proven validation process, in addition to GM Powertrain's expertise in engine validation.

An integrated stochastic design framework that facilitates practical applications involving time-consuming CAE simulations is described. The probabilistic performance measure that addresses stochastic uncertainties in CAE modeling and simulations is used to support design decision-making. Two enabling metamodeling methods using cross-validated radial basis functions (CVRBF) and a corresponding uniform sampling method are introduced to approximate highly nonlinear CAE model input/output relationships. A vehicle restraint system example is used to demonstrate the effectiveness of the proposed framework and enabling techniques.

As one of many pack-level battery simulation approaches developed within the General Motors-led Computer-Aided Engineering of Automotive Batteries (CAEBAT) Phase 1 project, the system approach treats the entire battery pack as a dynamic system which includes multiple engineering disciplines for simulation. It is the most efficient approach of all the CAEBAT battery pack-level approaches in terms of computational time and resources. This paper reports the application of the system approach for a 24-cell liquid-cooled prototype battery pack. It also summarizes the verification of the approach by comparing the simulation results with the measurement data. The results using the system approach are found to have a very good agreement with the measurements.

This paper describes recently developed test methods and instrumentation to address the specific noise and vibration measurement challenges posed by large-diameter single-piece tubular aluminum propeller (prop) shafts with high modal density. The prop shaft application described in this paper is a light duty truck, although the methods described are applicable to any rotating shaft with similar dynamic properties. To provide a practical example of the newly developed methods and instrumentation, impact FRF data were acquired in-situ for two typical prop shafts of significantly different diameter, in both rotating and stationary conditions. The example data exhibit features that are uniquely characteristic of large diameter single-piece tubular shafts with high modal density, including the particular effect of shaft rotation on the measurements.

The largest application of mercury in automotive applications occurs in underhood and trunk lamp activation switches. A reduction of mercury in this application will have a significant impact on automotive mercury usage. Using environmentally conscious design and manufacturing principles, this paper will investigate functional alternatives for the activation of underhood (U/H) and trunk lamp applications. Five alternatives to perform the activation function will be analyzed in four areas over their life cycles: Environmental Economic Engineering Manufacturing Each alternative will be ranked on criteria in each of these four areas using documented LCA processes. Totals will be generated for each area, then weighted and added to arrive at an overall score. Four groups of weightings will be used based on the vehicle type: small cars, mid-size cars, large/luxury cars, and trucks.

As the computer efficiency and capability increase, so as the Computer Aided Engineering (CAE) technologies improve. Recently Robust Design or Reliability Based Design Optimization (RBDO) technologies have been utilized in all sorts of industries including automotive. The process generally involves identifying key input design variables and key performance output variables, determining a sampling plan for CAE simulations, building a response surface model (RSM), analyzing the results, and finding the optimized design that meets the reliability criteria. Yet little was addressed on the robustness of a CAE design model in the process. A systematic method of defining Robustness in a CAE design model was developed. How robust a CAE model is and how far away an optimized design is from the More Robust Region (MRR) are addressed in this paper.

This paper describes the validation of RAMSIS, a 3-D CAD human model for ergonomic vehicle evaluation. At GM NAO, the model’s capability to correctly predict position and posture in vehicle CAD environments was tested. H- and Eye point locations between RAMSIS manikins and their human counterparts were compared. At GM/SAAB the model’s postural discomfort predictability was evaluated. Changes in postural discomfort predictions of the RAMSIS manikins were compared to that of the human subjects when they evaluated two different driving buck conditions. We concluded that RAMSIS has good position, posture and postural discomfort prediction capabilities and is a useful CAD ergonomic evaluation and design tool for vehicle interiors.

This paper describes the validation of RAMSIS, a 3-D CAD human model for ergonomic vehicle evaluation at General Motors (GM). The model’s capability to correctly predict position and posture in vehicle CAD environments was tested. H- and Eye point locations between RAMSIS manikins and their human counterparts were compared. We concluded that RAMSIS has good position and posture prediction capabilities and is a useful CAD ergonomic evaluation and design tool for vehicle interiors.

The advancement in embedded systems and positional accuracy with base station GPS modules created opportunity to develop high performance autonomous ground vehicles. However, the development of vehicle model and making accurate state estimations play vital role in reducing the cross track error. The present research focus on developing Linear Quadratic Gaussian (LQG) with Kalman estimator for autonomous ground vehicle to track various routes, that are made with the series of waypoints. The model developed in the LQG controller is a kinematic bicycle model, which mimics 1/5th scale truck. Further, the cubic spline fit has been used to connect the waypoints and generate the continuous desired/target path. The testing and implementation has been done at APS labs, MTU on the mentioned vehicle to study the performance of controller. Python has been used for simulations, controller coding and interfacing the sensors with controller.

In recent truck applications, single-piece large-diameter propshafts, in lieu of two-piece propshafts, have become more prevalent to reduce cost and mass. These large-diameter props, however, amplify driveline radiated noise. The challenge presented is to optimize prop shaft modal tuning to achieve acceptable radiated noise levels. Historically, CAE methods and capabilities have not been able to accurately predict propshaft airborne noise making it impossible to cascade subsystem noise requirements needed to achieve desired vehicle level performance. As a result, late and costly changes can be needed to make a given vehicle commercially acceptable for N&V performance prior to launch. This paper will cover the development of a two-step CAE method to predict modal characteristics and airborne noise sensitivities of large-diameter single piece aluminum propshafts fitted with different liner treatments.

While significant progress has been made in recent years to develop hybrid and battery electric vehicles for passenger car and light-duty applications to meet future fuel economy targets, the application of hybrid powertrains to heavy-duty truck applications has been very limited. The relatively lower energy and power density of batteries in comparison to diesel fuel and the operating profiles of most heavy-duty trucks, combine to make the application of hybrid powertrain for these applications more challenging. The high torque and power requirements of heavy-duty trucks over a long operating range, the majority of which is at constant cruise point, along with a high payback period, complexity, cost, weight and range anxiety, make the hybrid and battery electric solution less attractive than a conventional powertrain.

Ride comfort is a critical factor to customer perception of vehicle quality as it is related to vehicle experience when driving. It adds value to the product and, consequently, to vehicle brand. It has become a demand not only for passenger unibody vehicles but also to larger segments including mid-size trucks. Ride quality is usually quantified as harshness which is a measure of how the vehicle transmits the road irregularities to the customer at the tactile points such as the steering wheel and seats. Improving harshness requires tuning of different parts including tires, chassis frame/subframe and suspension mounts and bushings. This paper describes the methodology to enhance the harshness performance for a mid-size truck using a full vehicle CAE model. The influence of stiffnesses of body mounts and control arms bushings to harshness response is investigated through sensitivity analysis and the optimal configuration is found.