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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.

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.

In the present work computational fluid dynamics (CFD) simulations of the flow field around a generic tractor-trailer truck are presented and compared with corresponding experimental measurements. A generic truck model was considered which is a detailed 1/8th scale replica of a Class-8 tractor-trailer truck. It contained a number of details such as bumpers, underbody, tractor chassis, wheels, and axles. CFD simulations were conducted with wind incident on the vehicle at 0 and 6 degree yaw. Two different meshing strategies (tet-dominant and hex-dominant) and three different turbulence models (Realizable k-ε, RNG k-ε, and DES) are considered. In the first meshing strategy an unstructured tetrahedral mesh was created over a large region surrounding the vehicle and in its wake. In the second strategy the mesh was predominantly hexahedral except for a few narrow regions around the front end and the underbody which were meshed with tetrahedral cells owing to complex topology.

This paper discusses the guiding philosophy, industry standards, and process and organizational elements utilized by International to shift to a systems engineering approach, within a ‘pull-through’ supply structure. The importance of requirements management in systems engineering, and associated methods are also discussed. The paper also highlights tools and techniques that support systems engineering. Additionally, the benefits of systems engineering are contrasted with the consequences of component-centric product engineering.

This paper discusses a practical use of the Systems Engineering Process as it is implemented in a Truck OEM. The process presented is focused on the Electrical and Electronics area, but can be applied to other systems on the vehicle and to the vehicle level requirements. Systems Engineering rationale is summarized based upon historical impacts and the application of Systems Engineering to address those impacts. Prior System Development Processes are reviewed in light of modern Systems Engineering approaches, leading to the synthesis of the Systems Engineering Documentation Set for the Vehicle and the Vehicle's Electrical and Electronic Systems. The analysis for this approach looks at the application of Systems Engineering Principles throughout the lifecycle of the vehicle, going beyond the boundaries of traditional requirements gathering and analysis.

On-road emission measurements of 23 VN-trucks on a randomly chosen driving cycle, consisting of 10 miles two-lane and 8 miles four-lane road, showed tailpipe NOx emissions on fleet average of 0.96 g/bhp-hr, or 1.06 g/bhp-hr when including the time the exhaust gas temperature was below 200°C. Complementary measurements in a SET-cycle (13 point OICA -cycle) on a chassis dynamometer showed a tailpipe emission of 0.008 g PM per bhp-hr. Moreover, cost analysis show that the diesel fuel consumption remains unchanged whether the truck running on ULSD is equipped with a Combined Exhaust gas AfterTreatment System (CEATS) installed or not.

International has developed a new generation 6.0L V8 DI diesel engine for the Ford F-Series full size pick-up trucks. This new engine features a number of state-of-the-art technologies designed to meet the US 2004 heavy-duty engine emission legislation and other requirements from the customers. A set of combustion development strategies was created. They were, the use of cooled Exhaust Gas Recirculation (EGR) to inhibit NOx formation, a centrally located nozzle and an optimized combustion bowl to improve fuel distribution and reduce soot formation, the use of increased injection pressure to enhance air/fuel mixing and increase soot oxidation rate, and a Variable Geometry Turbocharger (VGT) to provide sufficient air/fuel ratio over a broad speed range. The combustion development took full advantage of the “virtual lab” tools.

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.

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.