439 research outputs found
Optimization, Control and Operation Strategies for Distributed Energy Systems
No full textIn this dissertation, the problem of optimal operation strategies and control for gas turbine based distributed energy systems (DES) is addressed using mathematical optimization methods and computational intelligence techniques. Detailed mathematical component models for the key components of the DES such as gas turbine power generator, heat exchanger, absorption chiller, photovoltaic and wind turbine, are developed to capture the important behavior of these components and their interactions with the overall DES. These models are used to develop integrated DES models for the purpose of optimization and control design. A multi-objective function considering the total system efficiency and operational cost is formulated for designing optimal operation strategies. Various optimization problems are formulated based on different DES configurations depending on if energy storage units or renewable energy are used, which results in nonlinear programming (NLP), mixed integer nonlinear programming (MINLP) and stochastic programming (SP) problems. A two-stage approach combining the improved particle swarm optimization (PSO) with the sequential quadratic programming (SQP) method is proposed to solve the resulting optimization problems. The simulation results are compared with those using traditional rule-based operation methods under various loads, such as summer, transition season and winter. It is found the proposed optimal strategy for the DES is capable of achieving an improved performance. It is illustrated that by applying renewable energy the operational cost will be reduced and the system efficiency is increased. A model predictive control with is employed for the real-time control implementation of the proposed optimal strategy. It is shown by applying the proposed two-stage method the DESs are able to follow the optimal strategies under all conditions
VR-Based Testing Bed for Pedestrian Behavior Prediction Algorithms
No full textUpon introducing semi- and fully automated vehicles on the road, drivers will be reluctant to focus on the traffic interaction and rely on the vehicles\u27 decision-making. However, encountering pedestrians still poses a significant difficulty for modern automated driving technologies. Considering the high-level complexity in human behavior modeling to solve a real-world problem, deep-learning algorithms trained from naturalistic data have become promising solutions. Nevertheless, although developing such algorithms is achievable based on scene data collection and driver knowledge extraction, evaluation remains challenging due to the potential crash risks and limitations in acquiring ground-truth intention changes.This study proposes a VR-based testing bed to evaluate real-time pedestrian intention algorithms as VR simulators are recognized for their affordability and adaptability in producing a variety of traffic situations, and it is more reliable to conduct human-factor research in autonomous cars. The pedestrian wears the head-mounted headset or uses the keyboard input and makes decisions in accordance with the circumstances. The simulator has added a credible and robust experience, essential for exhibiting the real-time behavior of the pedestrian. While crossing the road, there exists uncertainty associated with pedestrian intention. Our simulator will anticipate the crossing intention with consideration of the ambiguity of the pedestrian behavior. The case study has been performed over multiple subjects in several crossing conditions based on day-today life activities. It can be inferred from the study outcomes that the pedestrian intention can be precisely inferred using this VR-based simulator. However, depending on the speed of the car and the distance between the vehicle and the pedestrian, the accuracy of the prediction can differ considerably in some cases
Performance evaluation of routing protocols using NS-2 and realistic traces on driving simulator
No full textWith the rapid growth in wireless mobile communication technology, Vehicular Ad-hoc Network (VANET) has emerged as a promising method to effectively solve transportation-related issues. So far, most of researches on VANETs have been conducted with simulations as the real-world experiment is expensive. A core problem affecting the fidelity of simulation is the mobility model employed. In this thesis, a sophisticated traffic simulator capable of generating realistic vehicle traces is introduced. Combined with network simulator NS-2, we used this tool to evaluate the general performance of several routing protocols and studied the impact of intersections on simulation results. We show that static nodes near the intersection tend to become more active in packet delivery with higher transferred throughput
Design of a Vehicle Automatic Emergency Pullover System for Automated Driving with Implementation on a Simulator
No full textThis thesis addresses a critical issue of automotive safety. As traffic is increasing on the roads day by day, road safety is also a very important concern. Driving simulators can play an extensive role in the development and testing of advanced safety systems in peculiar traffic environments, respectively. Advanced Driver Assist Systems (ADAS) are getting enormous reputation but there is still need for more improvements. This thesis presents a design of an Automatic Emergency Pullover (AEP) strategy using active safety systems for a semi-autonomous vehicle. The idea for this system is that a moving vehicle equipped with an AEP system can automatically pull over on the roadside safely when the driver is considered incapable of driving. Furthermore, AEP supporting features such as; Lane Keeping Assist, Blind Spot Monitoring, Vehicle and Pedestrian Automatic Emergency Braking, Adaptive Cruise Control are also included in this work. The designs for application of each system have been explained along with its algorithms, model development, component architecture, simulation results, vehicular/pedestrian behavior and trajectory precision on software tools provided by Realtime Technologies, Inc. All major variables which influence the performance of vehicle after AEP activation, have been observed and remodeled according to control algorithms. The implementation of AEP system which can control vehicle dynamics has been verified with the help of simulation results
Design of an Automobile Accelerator/Brake Pedal Robot for Advanced Driver Assistance Systems
No full textThis paper delves into designing an actuator system to control the accelerator and brake pedal to control the speed of an automobile. The actuator system comprises of an electric actuator that controls the accelerator pedal and a parallel linkage that controls the brake pedal. The parallel linkage is connected to the actuator such that it provides an opposite reaction to the brake pedal. This paper compares the speed control with and without the use of a parallel linkage with respect to overshoot and steady state error. A simplified actuator and car model are developed. A PID controller and a Fuzzy controller were designed, simulated, and compared in their ability to control the developed car model. Both controllers were then implemented and tested in two different cars
Modeling and Simulation of Lane Keeping Support System Using Hybrid Petri Nets
No full textIn the past decades, the rapid innovation on technology has greatly affected the automotive industry. However, every innovation has always been paired with safety risks that needs to be quickly addressed. This is where Petri nets (PNs) have come into the picture and have been used to model complex systems for different purposes, such as production management, traffic flow estimation and the introduction of new car features collectively known as, Adaptive Driver Assistance Systems (ADAS). Since most of these systems include both discrete and continuous dynamics, the Hybrid Petri net (HPN) model is an essential tool to model these. The objective of this thesis is to develop, analyze and simulate a lane keeping support system using an HPN model. Chapter 1 includes a brief summary of the specific ADAS used, lane departure warning and lane keeping assist systems and then related work on PNs is mentioned. Chapter 2 provides a background on Petri nets. In chapter 3, we develop a discrete PN model first, then we integrate continuous dynamics to extend it to a HPN model that combines the functionalities of the two independent ADAS systems. Several scenarios are introduced to explain the expected model behavior. Chapter 4 presents the analysis and simulation results obtained on the final model. Chapter 5 provides a summary for the work done and discusses future work
Optimal energy management system of plug-in hybrid electric vehicle
No full textPlug-in Hybrid Electric Vehicles (PHEV) are new generation Hybrid Electric Vehicles (HEV) with larger battery capacity compared to Hybrid Electric Vehicles. They can store electrical energy from a domestic power supply and can drive the vehicle alone in Electric Vehicle (EV) mode. According to the U.S. Department of Transportation 80 % of the American driving public on average drives under 50 miles per day. A PHEV vehicle that can drive up to 50 miles by making maximum use of cheaper electrical energy from a domestic supply can significantly reduce the conventional fuel consumption. This may also help in improving the environment as PHEVs emit less harmful gases. However, the Energy Management System (EMS) of PHEVs would have to be very different from existing EMSs of HEVs. In this thesis, three different Energy Management Systems have been designed specifically for PHEVs using simulated study. For most of the EMS development mathematical vehicle models for powersplit drivetrain configuration are built and later on the results are tested on advanced vehicle modeling tools like ADVISOR or PSAT. The main objective of the study is to design EMSs to reduce fuel consumption by the vehicle. These EMSs are compared with existing EMSs which show overall improvement. In this thesis the final EMS is designed in three intermediate steps. First, a simple rule based EMS was designed to improve the fuel economy for parametric study. Second, an optimized EMS was designed with the main objective to improve fuel economy of the vehicle. Here Particle Swarm Optimization (PSO) technique is used to obtain the optimum parameter values. This EMS has provided optimum parameters which result in optimum blended mode operation of the vehicle. Finally, to obtain optimum charge depletion and charge sustaining mode operation of the vehicle an advanced PSO EMS is designed which provides optimal results for the vehicle to operate in charge depletion and charge sustaining modes. Furthermore, to implement the developed advanced PSO EMS in real-time a possible real time implementation technique is designed using neural networks. This neural network implementation provides sub-optimal results as compared to advanced PSO EMS results but it can be implemented in real time in a vehicle. These EMSs can be used to obtain optimal results for the vehicle driving conditions such that fuel economy is improved. Moreover, the optimal designed EMS can also be implemented in real-time using the neural network procedure described
Lithium ion battery failure detection using temperature difference between internal point and surface
No full textLithium-ion batteries are widely used for portable electronics due to high energy density, mature processing technology and reduced cost. However, their applications are somewhat limited by safety concerns. The lithium-ion battery users will take risks in burn or explosion which results from some internal components failure. So, a practical method is required urgently to find out the failures in early time. In this thesis, a new method based on temperature difference between internal point and surface (TDIS) of the battery is developed to detect the thermal failure especially the thermal runaway in early time. A lumped simple thermal model of a lithium-ion battery is developed based on TDIS. Heat transfer coefficients and heat capacity are determined from simultaneous measurements of the surface temperature and the internal temperature in cyclic constant current charging/discharging test. A look-up table of heating power in lithium ion battery is developed based on the lumped model and cyclic charging/discharging experimental results in normal operating condition. A failure detector is also built based on TDIS and reference heating power curve from the look-up table to detect aberrant heating power and bad parameters in transfer function of the lumped model. The TDIS method and TDIS detector is validated to be effective in thermal runaway detection in a thermal runway experiment. In the validation of thermal runway test, the system can find the abnormal heat generation before thermal runaway happens by detecting both abnormal heating power generation and parameter change in transfer function of thermal model of lithium ion batteries. The result of validation is compatible with the expectation of detector design. A simple and applicable detector is developed for lithium ion battery catastrophic failure detection
Electric utility planning methods for the design of one shot stability controls
No full textReliability of the wide-area power system is becoming a greater concern as the power grid is growing. Delivering electric power from the most economical source through fewest and shortest transmission lines to customers frequently increases the stress on the system and prevents it from maintaining its stability. Events like loss of transmission equipment and phase to ground faults can force the system to cross its stability limits by causing the generators to lose their synchronism. Therefore, a helpful solution is detection of these dynamic events and prediction of instability. Decision Trees (DTs) were used as a pattern recognition tool in this thesis. Based on training data, DT generated rules for detecting event, predicting loss of synchronism, and selecting stabilizing control. To evaluate the accuracy of these rules, they were applied to testing data sets. To train DTs of this thesis, direct system measurements like generator rotor angles and bus voltage angles as well as calculated indices such as the rate of change of bus angles, the Integral Square Bus Angle (ISBA) and the gradient of ISBA were used. The initial method of this thesis included a response based DT only for instability prediction. In this method, time and location of the events were unknown and the one shot control was applied when the instability was predicted. The control applied was in the form of fast power changes on four different buses. Further, an event detection DT was combined with the instability prediction such that the data samples of each case was checked with event detection DT rules. In cases that an event was detected, control was applied upon prediction of instability. Later in the research, it was investigated that different control cases could behave differently in terms of the number of cases they stabilize. Therefore, a third DT was trained to select between two different control cases to improve the effectiveness of the methodology. It was learned through internship at Midwest Independent Transmission Operators (MISO) that post-event steady-state analysis is necessary for better understanding the effect of the faults on the power system. Hence, this study was included in this research
Deep Brain Dynamics and Images Mining for Tumor Detection and Precision Medicine
No full textIUPUIAutomatic brain tumor segmentation in Magnetic Resonance Imaging scans is essential for the diagnosis, treatment, and surgery of cancerous tumors. However, identifying the hardly detectable tumors poses a considerable challenge, which are usually of different sizes, irregular shapes, and vague invasion areas. Current advancements have not yet fully leveraged the dynamics in the multiple modalities of MRI, since they usually treat multi-modality as multi-channel, and the early channel merging may not fully reveal inter-modal couplings and complementary patterns. In this thesis, we propose a novel deep cross-attention learning algorithm that maximizes the subtle dynamics mining from each of the input modalities and then boosts feature fusion capability. More specifically, we have designed a Multimodal Cross-Attention Module (MM-CAM), equipped with a 3D Multimodal Feature Rectification and Feature Fusion Module. Extensive experiments have shown that the proposed novel deep learning architecture, empowered by the innovative MM-CAM, produces higher-quality segmentation masks of the tumor subregions. Further, we have enhanced the algorithm with image matting refinement techniques. We propose to integrate a Progressive Refinement Module (PRM) and perform Cross-Subregion Refinement (CSR) for the precise identification of tumor boundaries. A Multiscale Dice Loss was also successfully employed to enforce additional supervision for the auxiliary segmentation outputs. This enhancement will facilitate effectively matting-based refinement for medical image segmentation applications. Overall, this thesis, with deep learning, transformer-empowered pattern mining, and sophisticated architecture designs, will greatly advance deep brain dynamics and images mining for tumor detection and precision medicine
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