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In order to detect degradation of engine oil lubricant, bench testing along with a number of diesel-powered Ford trucks were instruments and tested. The purpose of the bench testing was primarily to determine performance aspects such as repeatability, hysteresis effects and so on. Vehicle testing was conducted by designing and installing a separate oil reservoir along with a circulation system which was mounted in the vicinity of the oil pan. An innovative oil sensor was directly installed on the reservoir which can measure five (5) independent oil parameters (viscosity, density, permittivity, conductance, temperature). In addition, the concept is capable of detecting the oil level continuously during normal engine operation. The sensing system consists of an ultrasonic transducer for the oil level detection as well as a Tuning Fork mechanical resonator for the oil condition measurement.

This work reports on measurement and analysis of the galvanic interaction between steel self-piercing rivets (SPRs) having several different surface conditions and magnesium alloy substrates under consideration for use in automotive structural assemblies. Rivet surface conditions included uncoated steel, conventional Zn-Sn barrel plating and variations of commercial aluminizing processes, including supplemental layers and sealants. Coating characteristics were assessed using open circuit potential (OCP) measurement, potentiodynamic polarization scanning (PDS), and electrochemical impedance spectroscopy (EIS). The degree of galvanic coupling was determined using zero-resistance ammeter (ZRA) and the scanning vibrating electrode technique (SVET), which also permitted characterization of galvanic current flows in situ.

Vehicle to Vehicle Communication use case performance heavily relies on market penetration rate. The more vehicles support a use case, the better the customer experience. Enabling these use cases with acceptable quality on vehicles without built-in navigation systems, elaborate map matching and highly accurate sensors is challenging. This paper introduces a simulation framework to assess system performance in dependency of vehicle positioning accuracy for matching approach path traces in Decentralized Environmental Notification Messages (DENMs) in absence of navigation systems supporting map matching. DENMs are used for distributing information about hazards on the road network. A vehicle without navigation system and street map can only match its position trajectory with the trajectory carried in the DENM.

With the trending electrification of vehicle accessory drives brings new control concepts useful in many cases to optimize energy management within the powertrain system. Considering that direct engine drives do not have as much flexibility as independent electric drives, it is apparent that several advantages are to be expected from electric drives. New developed high efficient electric drives can be implemented when considering many vehicle sub-systems. Combinations of continuous varying and discrete flow control devices offer thermal management opportunities across several vehicle attributes including fuel economy, drivability, performance, and cabin comfort. Often new technologies are integrated with legacy systems to deliver maximum value. Leveraging both electrical and mechanical actuators in some cases presents control challenges in optimizing energy management while delivering robust system operation.

The usage of the universal exhaust gas oxygen (UEGO) sensor to control the air-fuel ratio (AFR) in gasoline engines allowed to significantly improve the efficiency of the combustion process and reduce tailpipe emissions. The diagnostics of this sensor is very important to ensure proper operation and indicate the need for service when the sensor fails to accurately determine the AFR upstream of the catalyst. California air resources board (CARB) has imposed several legislations around the operation of the UEGO sensor and particularly when specific faults would cause tailpipe emissions to exceed certain limits. In this paper, the possible sensor faults are reviewed, and a non-intrusive diagnostics monitor is proposed to detect, identify and estimate the magnitude of the fault present. This paper extends the approach in [4] where technical details are emphasized and algorithm improvements are discussed.

This paper focuses on finding and analyzing the relevant parameters affecting traffic flow when autonomous vehicles are introduced for ride hailing applications and autonomous shuttles are introduced for circulator applications in geo-fenced urban areas. For this purpose, different scenarios have been created in traffic simulation software that model the different levels of autonomy, traffic density, routes, and other traffic elements. Similarly, software that specializes in vehicle dynamics, physical limitations, and vehicle control has been used to closely simulate realistic autonomous vehicle behavior under such scenarios. Different simulation tools for realistic autonomous vehicle simulation and traffic simulation have been merged together in this paper, creating a realistic simulator with Hardware-in-the-Loop (HiL), Traffic-in-the-Loop (TiL), and Software in-the-Loop (SiL) simulation capabilities.

Autonomous Vehicles (AVs) operate based on image information and 3D maps generated by sensors like cameras, LIDARs and RADARs. This information is processed by the on-board processing units to provide the right actuation signals to drive the vehicle. For safe operation, these sensors should provide continuous high quality data to the processing units without interruption in all driving conditions like dust, rain, snow and any other adverse driving conditions. Any contamination on the sensor surface/lens due to rain droplets, snow, and other debris would result in adverse impact to the quality of data provided for sensor fusion and this could result in error states for autonomous driving. In particular, snow is a common contamination condition during driving that might block a sensor surface or camera lens. Predicting and preventing snow accumulation over the sensor surface of an AV is important to overcome this challenge.

The transportation sector is facing three revolutions: shared mobility, electrification, and autonomous driving. To inform decision making and guide smart transportation system development at the city-level, it is critical to model and evaluate how travelers will behave in these systems. Two key components in such models are (1) individual travel demands with high spatial and temporal resolutions, and (2) travelers’ sociodemographic information and trip purposes. These components impact one’s acceptance of autonomous vehicles, adoption of electric vehicles, and participation in shared mobility. Existing methods of travel demand generation either lack travelers’ demographics and trip purposes, or only generate trips at a zonal level. Higher resolution demand and sociodemographic data can enable analysis of trips’ shareability for car sharing and ride pooling and evaluation of electric vehicles’ charging needs.

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.

Autonomous ground vehicles can use a variety of techniques to navigate the environment and deduce their motion and location from sensory inputs. Visual Odometry can provide a means for an autonomous vehicle to gain orientation and position information from camera images recording frames as the vehicle moves. This is especially useful when global positioning system (GPS) information is unavailable, or wheel encoder measurements are unreliable. Feature-based visual odometry algorithms extract corner points from image frames, thus detecting patterns of feature point movement over time. From this information, it is possible to estimate the camera, i.e., the vehicle’s motion. Visual odometry has its own set of challenges, such as detecting an insufficient number of points, poor camera setup, and fast passing objects interrupting the scene. This paper investigates the effects of various disturbances on visual odometry.

Progress in the design and application of the magneto-elastic torque sensor to automotive drivetrain systems has taken the technology from the concept level to the point where it is considered production feasible. The latest generation of the sensors shows promising results regarding both the capabilities and applications to powertrain controls. Sensor designs, electronics and packaging layout are maturing. Well-defined component specifications and requirements are becoming available. The sensor utilities for real-time shift analysis and friction element control are established through vehicle-level investigation to demonstrate the production feasibility of the technology for transmission torque sensing.

An advanced powertrain cooling system with appropriate control strategy and active actuators allows greater flexibility in managing engine temperatures and operating near constraints. An organized controls development process is necessary to allow comparison of multiple configurations to select the best way forward. In this work, we formulate, calibrate and validate a Model Predictive Controller (MPC) for temperature regulation and constraint handling in an advanced cooling system. A model-based development process was followed; where the system model was used to develop and calibrate a gain scheduled linear MPC. The implementation of MPC for continuous systems and the modification related to implementing switching systems has been described. Multiple hardware configurations were compared with their corresponding control system in simulations. The system level requirements were translated into MPC calibration parameters for consistent comparison between multiple configurations.

With the rapid development of artificial intelligence, the autonomous vehicles (AV) have attracted considerable attention in the automotive industry. However, different factors negatively impact the adoption of the AVs, delaying their successful commercialization. Accretion of atmospheric icing, especially wet snow, on AV sensors causes blockage on their lenses, making them prone to lose their sight, in turn, increasing potential chances of accidents. In this study, two different designs are proposed in order to prevent snow accretion on the lenses of AVs via air flow across the lens surface. In both designs, lenses made of plain glass and superhydrophobic coated glass surfaces are tested. While some researchers have shown promise of water repellency on superhydrophobic surfaces, more snow accretion is observed on the superhydrophobic surfaces, when compared to the plain glass lenses.

An assessment tool was developed for rollover CAE signals evaluation to assess primarily the qualities of CAE generated sensor waveforms. This is a key tool to be used to assess CAE results as to whether they can be used for algorithm calibration and identify areas for further improvement of sensor. Currently, the method is developed using error estimates on mean, peak and standard deviation. More metrics, if necessary, can be added to the assessment tool in the future. This method has been applied to various simulated signals for laboratory-based rollover test modes with rigid-body-based MADYDO models.

Optical based sensor systems for vehicle based detection and warning systems are under development to reduce accidents and limit injuries caused by accidents. (1, 2, 3) In order to validate these types of detection systems, it is necessary to perform real world tests. In the case of pedestrian detection systems, this is very difficult in the field for safety reasons. Instead, simulated tests are more desirable. This paper describes work to understand the effectiveness of using virtual pedestrians as surrogates for real world pedestrian detection.

Understanding the variation in the deployment times for crash sensor systems is important to ensure robust performance of a crash sensor system. Increases in both the numbers of crash modes and deployable devices have reduced the margins for the decisions about when to deploy any given device. Currently, the industry practice is to run a sweep over the potential sources of variation, recording the minimum and maximum deployment time. Questions such as: “How often do the extremes occur?” or “Are there multiple peaks in the deployment time?” can not be answered. This work uses numerical analysis methods to build on the current sweep methodology to obtain information on the distribution of the deployment times so that questions such as these can be answered when evaluating sensor calibrations. The end result is better informed engineering decisions during the calibration development.

Successful demonstrations of fully autonomous vehicle operation in controlled situations are leading to increased research investment and activity. This has already resulted in significant advancements in the underlying technologies necessary to make it a practical reality someday. Not only are these idealized events sparking imaginations with the potential benefits for safety, convenience, fuel economy and emissions, they also embolden some to make somewhat surprising and sometimes astonishing projections for their appearance on public roads in the near future. Are we now ready for a giant leap forward to the self-driving car with all its complexity and inter-dependencies? Humans will need to grow with and adapt to the technological advancements of the machine and we'll deeply challenge our social and political paradigms before we're done. Even if we as engineers are ready, is the driving public ready?

Presented in this paper is an adaptive steady state fueling control system for spark ignition-internal combustion engines. Since the fueling control system is model based, the engine maps currently used in engine fueling control are eliminated. This proposed fueling control system is modular and can therefore accommodate changes in the engine sensor set such as replacing the mass-air flow sensor with a manifold air pressure sensor. The fueling algorithm can operate with either a switching type O2 sensor or a linear O2 sensor. The steady state fueling compensation utilizes a feedforward controller which determines the necessary fuel pulsewidth after a throttle transient to achieve stoichiometry. This feedforward controller is comprised of two nonlinear models capturing the steady state characteristics of the fueling process. These models are identified from an input-output testing procedure where the inputs are fuel pulsewidth and mass-air flow signal and the output is a lambda signal.

CAE has played a key role in development of the rollover safety technology by reducing the required number of prototypes. CAE-led Design Of Experiments (DOE) studies have helped in developing the process to minimize the number of CAE runs and to optimize use of the prototypes. This paper demonstrates the use of CAE/DOE for the design and optimization of rollover vehicle prototypes and also investigates effects of various factors in the selection of vehicle configuration for rollover sensor calibration testing. The process described herein has been successfully applied to vehicle programs. Modeling and analysis guidelines are also presented for CAE engineers to help in optimizing vehicle prototypes at program level.

In this paper recent control developments for an electromechanical valve actuator will be presented. The model-based control methodology utilizes position feedback, a nonlinear observer that provides virtual sensing of the armature velocity and current, and cycle-to-cycle learning. The controller is based on a nonlinear state-space description of the actuator that is derived based on physical principles and parameter identification. A bench-top experimental setup and a rapid control prototyping system are used to quantify the actuator performance. Experiments are conducted to measure valve release timing, transition times, and contact velocities for open- and closed-loop control schemes. Simulation results are presented for a feed-forward cycle-to-cycle learning controller.