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Scalable Simulation Environment for Adaptive Cruise Controller Development

The University Of Alabama-David Barnes, Jared Folden, Hwan-Sik Yoon, Paulius Puzinauskas
  • Technical Paper
  • 2020-01-1359
To be published on 2020-04-14 by SAE International in United States
In the development of an Adaptive Cruise Control (ACC) system, a model-based design process uses a simulation environment with models for sensor data, sensor fusion, ACC, and vehicle dynamics. Previous work has sought to control the dynamics between two vehicles both in simulation and in empirical testing environments. This paper outlines a new modular simulation framework for full model-based design integration, to iteratively design ACC systems. The simulation framework uses physics-based vehicle models to test ACC systems in three ways. The first two are Model-in-the-Loop (MIL) testing, using scripted scenarios or Driver-in-the-Loop (DIL) control of a target vehicle. The third testing method uses collected test data replayed as inputs to the simulation to additionally test sensor fusion algorithms. The simulation framework uses 3D visualization of the vehicles and implements mathematical driver comfortability models to better understand the perspectives of the driver or passenger. The addition of a high-fidelity vehicle plant model provides energy consumption and emissions predictions for autonomous, conventional vehicles or hybrid electric vehicles (HEV) in realistic driving scenarios. Finally, the simulations are run…
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A Study of Using a Reinforcement Learning Method to Improve Fuel Consumption of a Connected Vehicle with Signal Phase and Timing Data

The University of Alabama-Ashley Phan, Hwan-Sik Yoon
  • Technical Paper
  • 2020-01-0888
To be published on 2020-04-14 by SAE International in United States
Connected and automated vehicles (CAVs) promise to reshape two areas of the mobility industry: the transportation and driving experience. The connected feature of the vehicle uses communication protocols to provide awareness of the surrounding world while the automated feature uses technology to minimize driver dependency. Constituting a subset of connected technologies, vehicle-to-infrastructure (V2I) technologies provide vehicles with real-time traffic light information, or Signal Phase and Timing (SPaT) data. In this paper, the vehicle and SPaT data are combined with a reinforcement learning (RL) method as an effort to minimize the vehicle’s energy consumption. Specifically, this paper explores the implementation of the deep deterministic policy gradient (DDPG) algorithm. As an off-policy approach, DDPG utilizes the maximum Q-value for the state regardless of the previous action performed. In this research, the SPaT data collected from dedicated short-range communication (DSRC) hardware installed at 16 real traffic lights is utilized in a simulated road modeled after a road in Tuscaloosa, Alabama. The vehicle is trained using DDPG and the SPaT data to determine the optimal action to take in…
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A Comparative Study on Engine Thermal Management System

Hyundai-Kia America Technical Center, Inc.-Yong Sun, Jason Hoon Lee, Jinho Ha, EunKyung Lee
The University of Alabama-Samuel Wilson, Hwan-Sik Yoon
  • Technical Paper
  • 2020-01-0946
To be published on 2020-04-14 by SAE International in United States
As the automotive industry faces tighter fuel economy and emission regulations, it is becoming increasingly important to improve powertrain system efficiency. One of the areas to improve powertrain efficiency is the thermal management system. By controlling how to distribute the heat rejected by the engine, especially during the warm-up stage under cold temperatures, an engine thermal management system can improve the overall energy efficiency of the powertrain system. Conventionally, engine thermal management systems have been operated by a mechanical water pump and a thermostat. However, the recent introduction of electric water pumps and electrically-controlled flow valves allow for more sophisticated control of the thermal management system. In this study, these two different thermal management system architectures are investigated by conducting simulations. Specifically, a vehicle model with a high-fidelity thermal management system is developed in GT-SUITE and a simple rule-based control algorithm is applied to control the system. Using the system model, multiple drive cycle simulations are conducted and their performances are compared and discussed.
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Control Strategy for Hybrid Electric Vehicle Based on Online Driving Pattern Classification

SAE International Journal of Alternative Powertrains

University of Alabama, USA-Zhengyu Yao, Hwan-Sik Yoon
  • Journal Article
  • 08-08-02-0006
Published 2019-12-04 by SAE International in United States
Hybrid Electric Vehicles (HEVs) are gaining popularity these days mainly due to their high fuel economy. While conventional HEV controllers can be classified into rule-based control and optimization-based control, most of the production vehicles employ rule-based control due to their reliability. However, once the rule is optimized for a given driving pattern, it is not necessarily optimal for other driving patterns. In order to further improve fuel economy for HEVs, this article investigates the feasibility of optimizing control algorithm for different driving patterns so that the vehicle maintains a high level of optimality regardless of the driving patterns. For this purpose, a two-level supervisory control algorithm is developed where the top-level algorithm classifies the current driving pattern to select optimal control parameters, and the lower level algorithm controls the vehicle power flow using the selected control parameters in a similar way to conventional supervisory controllers. To study the effectiveness of the proposed algorithm, a HEV model with a rule-based control algorithm is modified such that the control parameters are optimized for different driving patterns, and…
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Development of a Catalytic Converter Cool-Down Model to Investigate Intermittent Engine Operation in HEVs

SAE International Journal of Alternative Powertrains

The University of Alabama, USA-Karissa Young, Ryan Jones, A.J. Hamley, Josh Stoddard, Travis Foust, Paul Puzinauskas, Hwan-Sik Yoon
  • Journal Article
  • 08-07-02-0009
Published 2018-10-29 by SAE International in United States
Catalytic converters, a primary component in most automotive emissions control systems, do not function well until they are heated substantially above ambient temperature. As the primary energy for catalyst heating comes from engine exhaust gases, plug-in hybrid electric vehicles (PHEVs) that have the potential for short and infrequent use of their onboard engine may have limited energy available for catalytic converter heating. This article presents a comparison of multiple hybrid supervisory control strategies to determine the ability to avoid engine cold starts during a blended charge-depleting propulsion mode. Full vehicle and catalytic converter simulations are performed in parallel with engine dynamometer testing in order to examine catalyst temperature variations during the course of the US06 City drive cycle. Emissions and energy consumption (E&EC) calculations are also performed to determine the effective number of engine starts during the drive cycle.
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Sliding Mode Control of Hydraulic Excavator for Automated Grading Operation

SAE International Journal of Commercial Vehicles

The University of Alabama-Jiaqi Xu, Hwan-Sik Yoon
  • Journal Article
  • 02-11-02-0010
Published 2018-06-07 by SAE International in United States
Although ground grading is one of the most common tasks that hydraulic excavators perform in typical work sites, proper grading is not easy for less-skilled operators as it requires coordinated manipulation of multiple hydraulic cylinders. In order to help alleviate this difficulty, automated grading systems are considered as an effective alternative to manual operations of hydraulic excavators. In this article, a sliding mode controller design is presented for automated grading control of a hydraulic excavator. First, an excavator manipulator model is developed in Simulink by using SimMechanics and SimHydraulics toolboxes. Then, a sliding mode controller is designed to control the manipulator to trace a predefined trajectory for a grading task. For a comparison study, a PI controller is used to control the manipulator to perform a grading task following the same desired trajectory and the performance is compared with those obtained by the sliding mode controller. The simulation results show that the sliding mode controller can control the grading operation with less tracking errors than the PI controller.
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Estimation of Excavator Manipulator Position Using Neural Network-Based Vision System

The University of Alabama-Jiaqi Xu, Hwan-Sik Yoon
Volvo Construction Equipment-Jae Y. Lee, Seonggon Kim
Published 2016-09-27 by SAE International in United States
A neural network-based computer vision system is developed to estimate position of an excavator manipulator in real time. A camera is used to capture images of a manipulator, and the images are down-sampled and used to train a neural network. Then, the trained neural network can estimate the position of the excavator manipulator in real time. To study the feasibility of the proposed system, a webcam is used to capture images of an excavator simulation model and the captured images are used to train a neural network. The simulation results show that the developed neural network-based computer vision system can estimate the position of the excavator manipulator with an acceptable accuracy.
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Continued Development of a High-Fidelity 1D Physics-Based Engine Simulation model in MATLAB/Simulink

University of Alabama-Bradley Thompson, Hwan-Sik Yoon
Published 2015-04-14 by SAE International in United States
Engine and drivetrain simulation has become an integral part of the automotive industry. By creating a virtual representation of a physical system, engineers can design controllers and optimize components without producing a prototype, thus reducing design costs. Among the numerous simulation approaches, 1D physics-based models are frequently implemented due to balanced performance between accuracy and computational speed. Several 1D physics-based simulation software packages currently exist but cannot be directly implemented in MALAB/Simulink. To leverage MATLAB/Simulink's powerful controller design and simulation capabilities, a 1D physics-based engine simulation tool is currently being developed at The University of Alabama. Previously presented work allowed the user to connect engine components in a physically representative manner within the Simulink environment using a standard block connection scheme and embedded MATLAB functions. The current model improves usability, simulation time, and flexibility by running the model from a single S-function. Component models, new features, and overall structure of the new model are presented in this paper.
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Swing Energy Recuperation Scheme for Hydraulic Excavators

The University of Alabama-Bradley Thompson, Hwan-Sik Yoon
Volvo Construction Equipment-Jaehong Kim, Jae Lee
Published 2014-09-30 by SAE International in United States
Due to the high demand of fuel efficient construction equipment, significant research effort has been dedicated to improving excavator efficiency. Among various possibilities, methods to recuperate energy during cab swing motion have been widely examined. Electric and hydraulic hybrids designs have shown to greatly improve fuel efficiency but require drastic design changes. The redesigned systems thus require many hours of operation to offset the manufacturing costs with fuel savings. In this research, a relatively simple swing energy recuperation system is presented using an additional accumulator, fixed displacement hydraulic motor, and control valves. With the system, hydraulic fluid is stored in an accumulator, and a simple controller opens a valve to allow the stored energy to assist the engine in running the main pumps. Using various accumulator capacity and hydraulic motor displacement combinations, the recuperation system was simulated for six cycles of a digging and dumping operation. The simulation results show that an optimum configuration reduces the swing energy consumption by 48% and the total excavator energy by 17% during digging and dumping operations.
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Automated Grading Operation for Hydraulic Excavators

The University of Alabama-Jiaqi Xu, Bradley Thompson, Hwan-Sik Yoon
Published 2014-09-30 by SAE International in United States
Hydraulic excavators perform numerous tasks in the construction and mining industry. Although ground grading is a common task, proper grading cannot easily be achieved. Grading requires an experienced operator to control the boom, arm, and bucket cylinders in a rapid and coordinated manner. Due to this reason, automated grade control is being considered as an effective alternative to conventional human-operated ground grading. In this paper, a path-planning method based on a 2D kinematic model and inverse kinematics is used to determine the desired trajectory of an excavator's boom, arm, and bucket cylinders. Then, the developed path planning method and PI control algorithms for the three cylinders are verified by a simple excavator model developed in Simulink®. The simulation results show that the automated grade control algorithm can grade level or with reduced operation time and error.
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