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Low-pressure cooled EGR (LP-cEGR) systems can provide significant improvements in spark-ignition engine efficiency and knock resistance. However, open-loop control of these systems is challenging due to low pressure differentials and the presence of pulsating flow at the EGR valve. This research describes a control structure for Low-pressure cooled EGR systems using closed loop feedback control along with internal model control. A Smith Predictor based PID controller is utilized in combination with an intake oxygen sensor for feedback control of EGR fraction. Gas transport delays are considered as dead-time delays and a Smith Predictor is one of the conventional methods to address stability concerns of such systems. However, this approach requires a plant model of the air-path from the EGR valve to the sensor.

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

As engines are equipped with an increased number of control actuators to meet fuel economy targets they become more difficult to control and calibrate. The large number of control actuators encourages the investigation of physics-based control strategies to reduce calibration time and complexity. Of particular interest is spark timing control and calibration since it has a significant influence on engine efficiency, emissions, vibration and durability. Spark timing determination to achieve a desired combustion phasing is currently an empirical process that occurs during the calibration phase of engine development. This process utilizes a large number of stored surfaces and corrections to account for the wide range of operating environments and conditions that a given engine will experience. An obstacle to realizing feedforward physics-based combustion phasing control is the requirement for an accurate and fast combustion model.

Accurate in-cylinder air charge estimation is important for engine torque determination, controlling air-to-fuel ratio, and ensuring high after-treatment efficiency. Spark ignition (SI) engine technologies like variable valve timing (VVT) and exhaust gas recirculation (EGR) are applied to improve fuel economy and reduce pollutant emissions, but they increase the complexity of air charge estimation. Increased air-path complexity drives the need for cost effective solutions that produce high air mass prediction accuracy while minimizing sensor cost, computational effort, and calibration time. A large number of air charge estimation techniques have been developed using a range of sensors sets combined with empirical and/or physics-based models. This paper provides a technical review of research in this area, focused on SI engines.

Barriers are commonly used on roadways to separate and to protect against vehicles traveling in opposing directions from possible head-on collisions. However, these barriers may interfere with wildlife passage such that animals become trapped on the road. Typically, small animals cannot find safe passage across all traffic lanes due to the presence of solid barriers and eventually die after being hit by a vehicle. The occurrence of animal-to-vehicle collisions also presents a dangerous scenario for motorists as a driver may intuitively swerve to avoid hitting the animal. In this paper, a redesigned Jersey style barrier, named the Clemson smart portal, will be presented and discussed. This roadway barrier features a portal for small animal travel, along with a mechatronic-based warning system to notify drivers of animal passage.

Autonomous vehicles have the potential to provide mobility to individuals who experience transportation disadvantages due to the inability to drive as a result of physical, cognitive or visual limitations/impairments as well as able-bodied individuals with no/limited desire to drive. Individuals who do not have easy access to transportation have social, academic, health, and career disadvantages in comparison to their peers. Fully autonomous vehicles have the potential to offer mobility solutions to these individuals. A user-centered design approach was utilized by a multidisciplinary team of engineers, human factors specialists, and designers to develop future vehicle features for a broad range of users.

Novice drivers are often ill-equipped to safely operate a motor vehicle due to their limited repertoire of skills and experiences. However, automotive simulation tools can be applied to better educate young drivers for a number of common driving scenarios. In this paper, the Clemson Automotive Training System (CATS) will be presented to educate and train novice drivers to safely operate four wheel passenger vehicles on paved roadways. A portable automotive simulator can be programmed to emulate a variety of high-crash rate scenarios and roadway geometries. Drivers receive instructions regarding proper driving techniques and behaviors with an opportunity to practice the given vehicle maneuver. An on-line evaluation methodology has been designed to analyze the drivers' capabilities at handling these roadway events. First, a pre-simulation questionnaire evaluates their basic understanding of everyday driving situations.

This paper describes the development and simulation of a voice and pointing gesture interaction system for on-route update of autonomous vehicles’ path. The objective of this research is to provide users of autonomous vehicles a human vehicle interaction mode that enables them to make and communicate spontaneous decisions to the autonomous car, modifying its pre-defined autonomous route in real-time. For example, similar to giving directions to a taxi driver, a user will be able to tell the car «Stop there» or «Take that exit». In this way, the user control/spontaneity vs interaction flexibility dilemma that current autonomous vehicle concepts have, could be solved, potentially increasing the user acceptance of this technology. The system was designed following a level structured state machine approach. The simulations were developed using MATLAB and VREP, a robotics simulation platform, which has accurate vehicle and sensor models.

This paper describes a computational and experimental effort to document the detailed flow field around a pickup truck. The major objective was to benchmark several different computational approaches through a series of validation simulations performed at Clemson University (CU) and overseen by those performing the experiments at the GM R&D Center. Consequently, no experimental results were shared until after the simulations were completed. This flow represented an excellent test case for turbulence modeling capabilities developed at CU. Computationally, three different turbulence models were employed. One steady simulation used the realizable k-ε model. The second approach was an unsteady RANS simulation, which included a turbulence closure model developed in-house. This simulation captured the unsteady shear layer rollup and breakdown over the front of the hood that was expected and seen in the experiments but unattainable with other off-the-shelf turbulence models.

The deployment of autonomous vehicles in real-world scenarios requires thorough testing to ensure sufficient safety levels. Driving simulators have proven to be useful testbeds for assisted and autonomous driving functionalities but may fail to capture all the nuances of real-world conditions. In this paper, we present a snapshot of the design and evaluation using a Cooperative Adaptive Cruise Control application of virtual reality platform currently in development at our institution. The platform is designed so to: allow for incorporating live real-world driving data into the simulation, enabling Vehicle-in-the-Loop testing of autonomous driving behaviors and providing us with a useful mean to evaluate the human factor in the autonomous vehicle context.

Enhanced electric motor performance in transportation vehicles can improve system reliability and durability over rigorous operating cycles. The design of innovative heat rejection strategies in electric motors can minimize cooling power consumption and associated noise generation while offering configuration flexibility. This study investigates an innovative electric motor cooling strategy through bench top thermal testing on an emulated electric motor. The system design includes passive (e.g., heat pipes) cooling as the primary heat rejection pathway with supplemental conventional cooling using a variable speed coolant pump and radiator fan(s). The integrated thermal structure, “cradle”, transfers heat from the motor shell towards an end plate for heat dissipation to the ambient surroundings or transmission to an external thermal bus to remote heat exchanger.

High compression ratios are critical to increasing the efficiency of spark ignition engines, but the trend in downsized and down sped configurations has brought attention to the nominally low compression ratios used to avoid knock. Knock is an abnormal combustion event defined by the acoustic sound caused by end-gas auto-ignition ahead of the flame front. In order to avoid engine-damaging levels of knock, low compression ratios and retarded combustion phasing at high loads are used, both of which lower efficiency. Low carbon alternative fuels such as ethanol or water-based alcohol fuels combine strong chemical auto-ignition resistance with large charge cooling characteristics that can suppress knock and enable optimal combustion phasing, thus allowing an increase in the compression ratio.

Thermal barrier coatings (TBCs) have been of interest since the 1970s for application in internal combustion (IC) engines. Thin TBCs exhibit a temperature swing phenomenon wherein wall temperatures dynamically respond to the transient working-gas temperature throughout the engine cycle, thus reducing the temperature difference driving the heat transfer. Determining these varying wall temperatures is necessary to evaluate and study the effect of coatings on wall heat transfer. This study focuses on developing a 3D computational fluid dynamics (CFD)-finite element analysis (FEA) coupled simulation, or co-simulation, routine to determine the wall temperatures of a piston coated with a thin TBC layer subject to spark ignition combustion heat flux. A CONVERGE 3D-CFD model was used to simulate the combustion process in a single-cylinder, light-duty experimental spark ignition (SI) engine.

A safe driver program has been established through a public-private partnership. This program targets novice drivers and uses a combination of classroom and in-vehicle training exercises to address critical driver errors known to lead to crashes. Students participate in four modules: braking to learn proper stopping technique, obstacle avoidance / reaction time to facilitate proper lane selection and collision avoidance, tailgating to learn about following distances, and loss of control to react appropriately when a vehicle is about to become laterally unstable. Knowledge pre and posttests are also administered at the start and end of the program. Students' in-vehicle driving performance are evaluated by instructors as well as recorded by onboard data acquisition units. The data has been evaluated with objective and subjective grading rubrics. The 70 participants in three classes used as a case study achieved an average skill score of 83.93/100.

Automotive simulators offer an immersive environment to operate vehicle systems in a safe and repeatable manner. A fundamental question exists regarding their effectiveness for an identified task. For instance, driving simulators can play a significant role in evaluating vehicle designs, developing safety regulations, supporting human factors engineering research, administering driver training and education, and offering individual entertainment. Some of the driving simulator technology users include automotive manufacturers and suppliers, research laboratories at universities and government agencies, driver education and training programs, and motorsports and racing entertainment venues. In each case, the simulator capabilities and functionality must encompass the expectations of the driver to permit their perception of realistic scenarios for evaluation. This paper investigates three driving simulators in terms of their hardware and software, as well as their applications.

Collaborative robots are more and more used in smart manufacturing because of their capability to work beside and collaborate with human workers. With the deployment of these robots, manufacturing tasks are more inclined to be accomplished by multiple humans and multiple robots (MH-MR) through teaming effort. In such MH-MR collaboration scenarios, the task distribution among the multiple humans and multiple robots is very critical to efficiency. It is also more challenging due to the heterogeneity of different agents. Existing approaches in task distribution among multiple agents mostly consider humans with assumed or known capabilities. However human capabilities are always changing due to various factors, which may lead to suboptimal efficiency. Although some researches have studied several human factors in manufacturing and applied them to adjust the robot task and behaviors.

Several emerging technologies hold great promise to improve the 360-degree awareness of the heavy vehicle driver. However, current industry-standard evaluation methods do not measure all the comprehensive factors contributing to the overall effectiveness of such systems. As a result, industry is challenged to evaluate new technologies in a way that is objective and allows the comparison of different systems in a consistent manner. This research aims to explore the methods currently in use, identify relevant factors not presently incorporated in standard procedures, and recommend best practices to accomplish an overall measurement system that can quantify performance beyond simply the field of view of a driver visibility system. We introduce a new metric, “Clarity of View,” that incorporates several important factors for visibility systems including: gap acceptance accuracy, image detection time, and distortion.

In this paper, a reinforcement learning-based method is proposed to adapt autonomous vehicle passengers’ expectation of comfort through in-situ human-vehicle interaction. Ride comfort has a significant influence on the user’s experience and thus acceptance of autonomous vehicles. There is plenty of research about the motion planning and control of autonomous vehicles. However, limited studies have explicitly considered the comfort of passengers in autonomous vehicles. This paper studies the comfort of humans in autonomous vehicles longitudinal autonomous driving. The paper models and then improves passengers’ feelings about autonomous driving behaviors. This proposed approach builds a control and adaptation strategy based on reinforcement learning using human’s in-situ feedback on autonomous driving. It also proposes an adaptation of humans to autonomous vehicles to account for improper human driving expectations.

In the ever-evolving landscape of automotive technology, the need for robust security measures and dependable vehicle performance has become paramount with connected vehicles and autonomous driving. The Unified Diagnostic Services (UDS) protocol is the diagnostic communication layer between various vehicle components which serves as a critical interface for vehicle servicing and for software updates. Fuzz testing is a dynamic software testing technique that involves the barrage of unexpected and invalid inputs to uncover vulnerabilities and erratic behavior. This paper presents the implementation of fuzz testing methodologies on the UDS layer, revealing the potential vulnerabilities that could be exploited by malicious entities. By employing both open-source and commercial fuzzing tools and techniques, this paper simulates real-world scenarios to assess the UDS layer’s resilience against anomalous data inputs.

This paper discusses a compliant link suspension concept developed for use on a high performance automobile. This suspension uses compliant or flexible members to integrate energy storage and kinematic guidance functions. The goal of the design was to achieve similar elasto-kinematic performance compared to a benchmark OEM suspension, while employing fewer components and having reduced mass and complexity, and potentially providing packaging advantages. The proposed suspension system replaces a control arm in the existing suspension with a ternary supported compliant link that stores energy in bending during suspension vertical motion. The design was refined iteratively by using a computational model to simulate the elasto-kinematic performance as the dimensions and attachment point locations of the compliant link were varied, until the predicted performance closely matched the performance of the benchmark suspension.