Your Selections

Clemson University
Show Only

Collections

File Formats

Content Types

Dates

Sectors

Topics

Authors

Publishers

Affiliations

Events

   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Integrated Engine States Estimation using Extended Kalman Filter and Disturbance Observer

Clemson University-Qilun Zhu, Robert Prucka
  • Technical Paper
  • 2019-01-2603
To be published on 2019-10-22 by SAE International in United States
Accurate estimation of engine state(s) is vital for engine control systems to achieve their designated objectives. Fusion of sensors can significantly improve the estimation results in terms of accuracy and precision. This paper investigates using an Extended Kalman Filter (EKF) to estimate engine state(s) for Spark Ignited (SI) engines with the external EGR system. The EKF combines air path sensors with cylinder pressure feedback through a control-oriented engine cycle domain model. The model integrates air path dynamics, torque generation, exhaust gas temperature, and residual gas mass. The EKF generates a cycle-based estimation of engine state(s) for model-based control algorithms which is not the focus of this paper. The sensor and noise dynamics are analyzed and integrated into the EKF formulation. To account for ‘none-white’ disturbances including modeling errors and sensor/actuator offset, the EKF engine state(s) observer is augmented with disturbance state(s) estimation. Case studies demonstrate that the disturbance augmented EKF can identify the sources of estimation errors and mitigates these errors automatically within several engine cycles. This paper concludes that the number of disturbance states…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.
new

Quantification of Linear Approximation Error for Model Predictive Control of Spark-Ignited Turbocharged Engines

Clemson University-Rohit Koli, Daniel Egan, Qilun Zhu, Robert Prucka
  • Technical Paper
  • 2019-24-0014
Published 2019-09-09 by SAE International in United States
Modern turbocharged spark-ignition engines are being equipped with an increasing number of control actuators to meet fuel economy, emissions, and performance targets. The response time variations between engine control actuators tend to be significant during transients and necessitate highly complex actuator scheduling routines. Model Predictive Control (MPC) has the potential to significantly reduce control calibration effort as compared to the current methodologies that are based on decentralized feedback control strategies. MPC strategies simultaneously generate all actuator responses by using a combination of current engine conditions and optimization of a control-oriented plant model. To achieve real-time control, the engine model and optimization processes must be computationally efficient without sacrificing effectiveness. Most MPC systems intended for real-time control utilize a linearized model that can be quickly evaluated using a sub-optimal optimization methodology. Online linearization of the engine model is computationally expensive so it should be performed as infrequently as possible. Since engine dynamics are non-linear, a local linearity approximation error occurs during this process. This research presents a method of evaluating the impact of local linear approximation…
This content contains downloadable datasets
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.
new

A Review of Spark-Assisted Compression Ignition (SACI) Research in the Context of Realizing Production Control Strategies

Clemson University-Dennis Robertson, Robert Prucka
  • Technical Paper
  • 2019-24-0027
Published 2019-09-09 by SAE International in United States
This paper seeks to identify key input parameters needed to achieve a production-viable control strategy for spark-assisted compression ignition (SACI) engines. SACI is a combustion strategy that uses a spark plug to initiate a deflagration flame that generates sufficient ignition energy to trigger autoignition in the remaining charge. The flame propagation phase limits the rate of cylinder pressure rise, while autoignition rapidly completes combustion. High dilution within the autoignited charge is generally required to maintain reaction rates feasible for production. However, this high dilution may not be reliably ignited by the spark plug. These competing constraints demand novel mixture preparation strategies for SACI to be feasible in production. SACI with charge stratification has demonstrated sufficiently stable flame propagation to reliably trigger autoignition across much of the engine operating map. A key controls challenge of SACI is the two regimes of combustion are near several constraints that may be competing. This work summarizes key findings from decades of research that can help enable production control strategies for SACI engines. A summary and analysis of the broad…
This content contains downloadable datasets
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Prediction of Human Actions in Assembly Process by a Spatial-Temporal End-to-End Learning Model

Clemson University-Zhujun Zhang, Weitian Wang, Yi Chen, Yunyi Jia
Harbin Institute of Technology-Zhujun Zhang, Gaoliang Peng
Published 2019-04-02 by SAE International in United States
It’s important to predict human actions in the industry assembly process. Foreseeing future actions before they happened is an essential part for flexible human-robot collaboration and crucial to safety issues. Vision-based human action prediction from videos provides intuitive and adequate knowledge for many complex applications. This problem can be interpreted as deducing the next action of people from a short video clip. The history information needs to be considered to learn these relations among time steps for predicting the future steps. However, it is difficult to extract the history information and use it to infer the future situation with traditional methods. In this scenario, a model is needed to handle the spatial and temporal details stored in the past human motions and construct the future action based on limited accessible human demonstrations. In this paper, we apply an autoencoder-based deep learning framework for human action construction, merging into the RNN pipeline for human action prediction. This contrasts with traditional approaches which use hand-crafted features and different domain outputs. We implement the proposed framework on a…
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Trust-Based Control and Scheduling for UGV Platoon under Cyber Attacks

Clemson University-Fangjian Li, John R. Wagner, Yue Wang
U.S. Army TARDEC-Dariusz Mikulski
Published 2019-04-02 by SAE International in United States
Unmanned ground vehicles (UGVs) may encounter difficulties accommodating environmental uncertainties and system degradations during harsh conditions. However, human experience and onboard intelligence can may help mitigate such cases. Unfortunately, human operators have cognition limits when directly supervising multiple UGVs. Ideally, an automated decision aid can be designed that empowers the human operator to supervise the UGVs. In this paper, we consider a connected UGV platoon under cyber attacks that may disrupt safety and degrade performance. An observer-based resilient control strategy is designed to mitigate the effects of vehicle-to-vehicle (V2V) cyber attacks. In addition, each UGV generates both internal and external evaluations based on the platoons performance metrics. A cloud-based trust-based information management system collects these evaluations to detect abnormal UGV platoon behaviors. To deal with inaccurate information due to a V2C cyber attack, a RoboTrust algorithm is designed to analyze vehicle trustworthiness and eliminate information with low credit. Finally, a human operator scheduling algorithm is proposed when the number of abnormal UGVs exceeds the limit of what human operators can handle concurrently. Representative simulation results…
This content contains downloadable datasets
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Detection of Presence and Posture of Vehicle Occupants Using a Capacitance Sensing Mat

Clemson University-Rahul Prasanna Kumar, Yunyi Jia
Ford Motor Company-David Melcher, Pietro Buttolo
Published 2019-04-02 by SAE International in United States
Capacitance sensing is the technology that detects the presence of nearby objects by measuring the change in capacitance. A change in capacitance is triggered either by a change in dielectric constant, area of overlap or distance of separation between the electrodes of the capacitor. It is a technology that finds wide use in applications such as touch screens, proximity sensing etc. Drawing motivation from such applications, this paper investigates how capacitive sensing can be employed to detect the presence and posture of occupants inside vehicles. Compared to existing solutions, the proposed approach is low-cost, easy to deploy and highly efficient. The sensing system consists of a capacitance-sensing mat that is embedded with copper foils and an associated sensing circuitry. Inside the mat the foils are arranged in rows and columns to form several touch-nodes across the surface of the mat. The system segregates row and column capacitances from each other and computes their tensor product to generate grayscale capacitance-sensing images. The images are real-time pictorial representation of the capacitance of each touch-node. When the mat…
This content contains downloadable datasets
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

A Systems Approach in Developing an Ultralightweight Outside Mounted Rearview Mirror Using Discontinuous Fiber Reinforced Thermoplastics

Clemson University-Sai Aditya Pradeep, Srikanth Pilla
Clemson University - ICAR-Senthil Raj Ramesh, Veera Aditya Yerra
Published 2019-04-02 by SAE International in United States
Fuel efficiency improvement in automobiles has been a topic of great interest over the past few years, especially with the introduction of the new CAFE 2025 standards. Although there are multiple ways of improving the fuel efficiency of an automobile, lightweighting is one of the most common approaches taken by many automotive manufacturers. Lightweighting is even more significant in electric vehicles as it directly affects the range of the vehicle. Amidst this context of lightweighting, the use of composite materials as alternatives to metals has been proven in the past to help achieve substantial weight reduction. The focus of using composites for weight reduction has however been typically limited to major structural components, such as BiW and closures, due to high material costs. Secondary structural components which contribute approximately 30% of the vehicle weight are usually neglected by these weight reduction studies. This work is an attempt to prove that composites can also be used effectively in the weight reduction of secondary structural components, while meeting the desired standards on mechanical performance, cost, and scalability.…
This content contains downloadable datasets
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

A Look-Ahead Model Predictive Optimal Control Strategy of a Waste Heat Recovery-Organic Rankine Cycle for Automotive Application

Clemson University-Dhruvang Rathod, Bin Xu, Adamu Yebi, Ardalan Vahidi, Zoran Filipi
Auburn University-Mark Hoffman
Published 2019-04-02 by SAE International in United States
The Organic Rankine Cycle (ORC) has proven to be a promising technology for Waste Heat Recovery (WHR) systems in heavy duty diesel engine applications. However, due to the highly transient heat source, controlling the working fluid flow through the ORC system is a challenge for real time application. With advanced knowledge of the heat source dynamics, there is potential to enhance power optimization from the WHR system through predictive optimal control. This paper proposes a look-ahead control strategy to explore the potential of increased power recovery from a simulated WHR system. In the look-ahead control, the future vehicle speed is predicted utilizing road topography and V2V connectivity. The forecasted vehicle speed is utilized to predict the engine speed and torque, which facilitates estimation of the engine exhaust conditions used in the ORC control model. In the simulation study, a reference tracking controller is designed based on the Model Predictive Control (MPC) methodology. Two variants of Non-linear MPC (NMPC) are evaluated: an NMPC with look-ahead exhaust conditions and a baseline NMPC without knowledge of future exhaust…
This content contains downloadable datasets
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

An Immersive Vehicle-in-the-Loop VR Platform for Evaluating Human-to-Autonomous Vehicle Interactions

Clemson University-Roberto Merco, Manveen Kaur, Anjan Rayamajhi, Gianluca Papa, Pierluigi Pisu, Sabarish Babu, Andrew Robb, Jim Martin
Maserati-Marco Gavelli
Published 2019-04-02 by SAE International in United States
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.
This content contains downloadable datasets
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Strain Rate Effect on Martensitic Transformation in a TRIP Steel Containing Carbide-Free Bainite

Clemson University-Rakan Alturk
General Motors LLC-Charles Enloe, Vesna Savic, Whitney Poling, Louis Hector
Published 2019-04-02 by SAE International in United States
Adiabatic heating during plastic straining can slow the diffusionless shear transformation of austenite to martensite in steels that exhibit transformation induced plasticity (TRIP). However, the extent to which the transformation is affected over a strain rate range of relevance to automotive stamping and vehicle impact events is unclear for most third-generation advanced high strength TRIP steels. In this study, an 1180MPa minimum tensile strength TRIP steel with carbide-free bainite is evaluated by measuring the variation of retained austenite volume fraction (RAVF) in fractured tensile specimens with position and strain. This requires a combination of servo-hydraulic load frame instrumented with high speed stereo digital image correlation for measurement of strains and ex-situ synchrotron x-ray diffraction for determination of RAVF in fractured tensile specimens. Specifically, the potentially competing effects of strain rate on austenite transformation to martensite were investigated to determine which predominate at nominal strain rates of 0.5 s-1, 5 s-1, 50 s-1 and 500 s-1. A corresponding decrease in austenite volume fraction at a fixed true strain with strain rate suggests that austenite transformation to…
This content contains downloadable datasets
Annotation ability available