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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.

Modern engine management systems increasingly rely on on-line identification schemes. These are used either for self-tuning regulators or the rapid parametrization of controllers. In this paper the on-line parameter identification of the wall-wetting dynamics is studied in detail. The identification is performed by exciting the fuel path dynamics of the engine at a constant operating point. The amount of fuel injected serves as input and the air-to-fuel ratio, which is measured with a linear oxygen sensor, as output. In order to gain precise information about the amount of fuel in the cylinder, a new measurement concept is used. For one, the placement of the lambda sensor close to the exhaust valve minimizes the effects of gas mixing on the measurements. Additionally, by an appropriate collection of the data, the sensor dynamics are bypassed. This is also illustrated by a measurement with a very fast NOx sensor.

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.

This paper describes the design, development and validation of a muffler for reducing exhaust noise from a commercial tele-handler. It also describes the procedure for modeling and optimizing the exhaust muffler along with experimental measurement for correlating the sound transmission loss (STL). The design and tuning of the tele-handler muffler was based on several factors including overall performance, cost, weight, available space, and ease of manufacturing. The analysis for predicting the STL was conducted using the commercial software LMS Virtual Lab (LMS-VL), while the experimental validation was carried out in the laboratory using the two load setup. First, in order to gain confidence in the applicability of LMS-VL, the STL of some simple expansion mufflers with and without extended inlet/outlet and perforations was considered. The STL of these mufflers were predicted using the traditional plane wave transfer matrix approach.

The NOx reduction performance under lean conditions over γ-alumina was evaluated using a micro-reactor system and a non-thermal plasma-equipped bench test system. Various alumina samples were obtained from alumina manufacturers to assess commercial alumina materials. In addition, γ-alumina samples were synthesized at Caterpillar with a sol-gel technique in order to control alumina properties. The deNOx performances of the alumina samples were compared. The alumina samples were characterized with analytical techniques such as inductively coupled plasma (ICP) emission spectroscopy, temperature programmed desorption (TPD) and surface area measurements (BET) to understand physical and chemical properties. The information derived from these techniques was correlated with the NOx reduction performance to identify key parameters of γ-alumina for optimizing materials for lean-NOx and plasma assisted catalysis.

An accurate estimation of cycle-by-cycle in-cylinder mass and the composition of the cylinder charge is required for spark-ignition engine transient control strategies to obtain required torque, Air-Fuel-Ratio (AFR) and meet engine pollution regulations. Mass Air Flow (MAF) and Manifold Absolute Pressure (MAP) sensors have been utilized in different control strategies to achieve these targets; however, these sensors have response delay in transients. As an alternative to air flow metering, in-cylinder pressure sensors can be utilized to directly measure cylinder pressure, based on which, the amount of air charge can be estimated without the requirement to model the dynamics of the manifold.

A two-zone NOx model intended for 1-D engine simulations was developed and used to model NOx emissions from a 2.5 L single-cylinder engine. The intent of the present work is to understand key aspects of a simple NOx model that are needed for predictive accuracy, including NOx formation and destruction phenomena in a DI Diesel combustion system. The presented two-zone model is fundamentally based on the heat release rate and thermodynamic incylinder data, and uses the Extended Zeldovich mechanism to model NO. Results show that the model responded very well to changes in speed, load, injection timing, and EGR level. It matched measured tail pipe NOx levels within 20%, using a single tuning setup. When the model was applied to varied injection rate shapes, it showed correct sensitivity to speed, load, injection timing, and EGR level, but the absolute level was well outside the target accuracy. The same limitation was seen when applying the Plee NOx model.

Technologies that provide potential for significant improvements in engine efficiency include, engine downsizing/downspeeding (enabled by advanced boosting systems such as an electrically driven compressor), waste heat recovery through turbocompounding or organic Rankine cycle and 48 V mild hybridization. FEV’s Integrated Turbocompounding/Waste Heat Recovery (WHR), Electrification and Supercharging (FEV-ITES) is a novel approach for integration of these technologies in a single unit. This approach provides a reduced cost, reduced space claim and an increase in engine efficiency, when compared to the independent integration of each of these technologies. This approach is enabled through the application of a planetary gear system. Specifically, a secondary compressor is connected to the ring gear, a turbocompounding turbine or organic Rankine cycle (ORC) expander is connected to the sun gear, and an electric motor/generator is connected to the carrier gear.

A new press/die system for restraining force control has been developed in order to facilitate an increased level of process control in sheet metal forming. The press features a built-in system for controlling drawbead penetration in real time. The die has local force transducers built into the draw radius of the lower tooling. These sensors are designed to give process information useful for the drawbead control. This paper focuses on developing models of the drawbead actuators and the die shoulder sensors. The actuator model is useful for developing optimal control methods. The sensor characterization is necessary in order to develop a relationship between the raw sensor outputs and a definitive process characteristic such as drawbead restraining force (DBRF). Closed loop control of local specific punch force is demonstrated using the die shoulder sensor and a PID controller developed off-line with the actuator model.

In modern engine control applications, there is a distinct trend towards model-based control schemes. There are various reasons for this trend: Physical models allow deeper insights compared to heuristic functions, controllers can be designed faster and more accurately, and the possibility of obtaining an automated application scheme for the final engine to be controlled is a significant advantage. Another reason is that if physical effects can be separated, higher order models can be applied for different subsystems. This is in contrast to heuristic functions where the determination of the various maps poses large problems and is thus only feasible for low order models. One of the most important parts of an engine management system is the air-to-fuel control. The catalytic converter requires the mean air-to-fuel ratio to be very accurate in order to reach its optimal conversion rate. Disturbances from the active carbon filter and other additional devices have to be compensated.

A fast, versatile nitric oxide (NO) measuring device applying the principles of chemiluminescence has been developed. This device consists of a small sensor head attachable to the engine exhaust system and a mobile cart containing all the necessary auxiliary aggregates and the signal processing unit. Optimization techniques based on a physico-chemical model and strict miniaturization were applied in the development of this NO measuring device. Subsequent tests utilizing a, solenoid valve and bottled calibration gas con- firmed the predicted dynamic behavior of the instrument. This device now shows an extremely short sampling delay and a higher bandwidth than common NO measuring devices can offer.

A three dimensional mathematical model of a Caterpillar mining truck has been developed to simulate transient structural deformation and suspension response of a large mining truck traversing a known rough terrain course. The model incorporates compliant (finite element) representations of the truck frame, dump body, and rear axle housing into a dynamic mechanical system simulation model. Model results - frame acceleration, axle housing elastic deformation, and suspension response (strut pressures and displacements) are correlated with measured data from an instrumented truck traversing the steel speed bump portion of the rough terrain course. Results demonstrate that complex truck behavior can be simulated by combining finite element and mechanical system simulations.

The two characteristics of wide-range air/fuel ratio sensors when located in front of and behind a three-way catalytic converter are investigated. Input as well as output gas concentration measurements and sensor readouts are presented. Behind a new converter almost no oxygen can be measured for rich air/fuel ratios. The wide-range sensor's signal is sensitive to changes in the gas composition when keeping the air/fuel ratio constant at a rich value. Since the gas compositions up- and down-stream of the converter differ, the sensor signals are not identical for the same rich air/fuel ratio before and after the converter. The various diffusion coefficients of the exhaust gas species flowing through the porous coating of the sensor combinded with the different up- and downstream gas compositions are responsible for the different sensor characteristics.

This research project investigates the feasibility of using a commercial finite element code to capture the dynamic response of a typical rubber-belted tractor for agricultural applications. The investigation focused on one of Caterpillar Inc.'s Ag Challenger Series tractors. A feasibility study concluded that Abaqus/Explicit [1], a finite element code utilizing the explicit scheme, had the desirable features needed to develop such a large-scale tractor model. These features include an efficient time integration scheme, three-dimensional generalized multiple contacts, and nonlinear material characterization. The fully-assembled tractor model was successful in simulating the forward motion. A preliminary validation indicated that the tractor model was able to predict a trend which was observed in field tests accurately.

This paper describes a multi-sensor data set, suitable for testing algorithms to detect and track pedestrians and cyclists, with an autonomous vehicle’s sensor suite. The data set can be used to evaluate the benefit of fused sensing algorithms, and provides ground truth trajectories of pedestrians, cyclists, and other vehicles for objective evaluation of track accuracy. One of the principal bottlenecks for sensing and perception algorithm development is the ability to evaluate tracking algorithms against ground truth data. By ground truth we mean independent knowledge of the position, size, speed, heading, and class of objects of interest in complex operational environments. Our goal was to execute a data collection campaign at an urban test track in which trajectories of moving objects of interest are measured with auxiliary instrumentation, in conjunction with several autonomous vehicles (AV) with a full sensor suite of radar, lidar, and cameras.

To address the need of increasing fuel economy requirements, automotive Original Equipment Manufacturers (OEMs) are increasing the number of turbocharged engines in their powertrain line-ups. The turbine-driven technology uses a forced induction device, which increases engine performance by increasing the density of the air charge being drawn into the cylinder. Denser air allows more fuel to be introduced into the combustion chamber, thus increasing engine performance. During the inlet air compression process, the air is heated to temperatures that can result in pre-ignition resulting and reduced engine functionality. The introduction of the charge air cooler (CAC) is therefore, necessary to extract heat created during the compression process. The present research describes the physics and develops the optimized simulation method that defines the process and gives insight into the development of CACs.

The Electronic Control Unit (ECU) of an Electric Power Steering (EPS) system is a core device to decide how much assistance an electric motor applies on a steering wheel. The EPS ECU plays an important role in EPS systems. The effectiveness of an ECU needs to be thoroughly tested before mass production. Hardware-in-the-loop simulation provides an efficient way for the development and testing of embedded controllers. This paper focuses on the development of a HiL system for testing EPS controllers. The hardware of the HiL system employs a dSPACE HiL simulator. The EPS plant model is an integrated model consisting of a Vehicle Dynamics model of the dSPACE Automotive Simulation Model (ASM) and the Nexteer Steering model. The paper presents the design of an EPS HiL system, the simulation of sensors and actuators, the functions of the ASM Vehicle Dynamics model, and the integration method of the ASM Vehicle Dynamics model with a Steering model.

To reduce the number and severity of accidents, automakers have invested in active safety systems to detect and track neighboring vehicles to prevent accidents. These systems often employ RADAR and LIDAR, which are not degraded by low lighting conditions. In this research effort, reflections from deer were measured using two sensors often employed in automotive active safety systems. Based on a total estimate of one million deer-vehicle collisions per year in the United States, the estimated cost is calculated to be $8,388,000,000 [1]. The majority of crashes occurs at dawn and dusk in the Fall and Spring [2]. The data includes tens of thousands of RADAR and LIDAR measurements of white-tail deer. The RADAR operates from 76.2 to 76.8 GHz. The LIDAR is a time-of-flight device operating at 905 nm. The measurements capture the deer in many aspects: standing alone, feeding, walking, running, does with fawns, deer grooming each other and gathered in large groups.

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.

The proliferation of small silicon micro-chips has led to a large assortment of low-cost transducers for data acquisition. Production vehicles on average exploit more than 60 on board sensors, and that number is projected to increase beyond 200 per vehicle by 2020. Such a large increase in sensors is leading the fourth industrial revolution of connectivity and autonomy. One major downfall to installing many sensors is compromises in their accuracy and processing power due to cost limitations for high volume production. The same common errors in data acquisition such as sampling, quantization, and multiplexing on the CAN bus must be accounted for when utilizing an entire array of vehicle sensors. A huge advantage of onboard sensors is the ability to calculate vehicle parameters during a daily drive cycle to update ECU calibration factors in real time. One such parameter is driveline damping, which changes with gear state and drive mode. A damping value is desired for every gear state.