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Description
The efficiency of spacecraft’s solar cells reduces over the course of their operation. Traditionally, they are configured to extract maximum power at the end of their life and not have a system which dynamically extracts the maximum power over their entire life. This work demonstrates the benefit of dynamic re-configuration of spacecraft’s solar arrays to access the full power available from the solar panels throughout their lifetime. This dynamic re-configuration is achieved using enhancement mode GaN devices as the switches due to their low Ron and small footprint.
This work discusses hardware Implementation challenges and a prototype board is designed using components-off-the-shelf (COTS) to study the behavior of photovoltaic (PV) panels with different configurations of switches between 5 PV cells. The measurement results from the board proves the feasibility of the idea, showing the power improvements of having the switch structure. The measurement results are used to simulate a 1kW satellite system and understand practical trade-offs of this idea in actual satellite power systems.
Additionally, this work also presents the implementation of CMOS controller integrated circuit (IC) in 0.18um technology. The CMOS controller IC includes switched-capacitor converters in open loop to provide the floating voltages required to drive the GaN switches. Each CMOS controller IC can drive 10 switches in series and parallel combination. Furthermore, the designed controller IC is expected to operate under 300MRad of total dose radiation, thus enabling the controller modules to be placed on the solar cell wings of the satellites.
This work discusses hardware Implementation challenges and a prototype board is designed using components-off-the-shelf (COTS) to study the behavior of photovoltaic (PV) panels with different configurations of switches between 5 PV cells. The measurement results from the board proves the feasibility of the idea, showing the power improvements of having the switch structure. The measurement results are used to simulate a 1kW satellite system and understand practical trade-offs of this idea in actual satellite power systems.
Additionally, this work also presents the implementation of CMOS controller integrated circuit (IC) in 0.18um technology. The CMOS controller IC includes switched-capacitor converters in open loop to provide the floating voltages required to drive the GaN switches. Each CMOS controller IC can drive 10 switches in series and parallel combination. Furthermore, the designed controller IC is expected to operate under 300MRad of total dose radiation, thus enabling the controller modules to be placed on the solar cell wings of the satellites.
ContributorsHeblikar, Anand N (Author) / Kitchen, Jennifer (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Ozev, Sule (Committee member) / Arizona State University (Publisher)
Created2019
Description
The objectives of this research project were to develop a model of real power demand from a dc fast charging station both with and without an integrated battery energy storage system (BESS). An optimal deterministic control strategy was devel-oped to perform load-shaping under various scenarios with various load-shaping goals in mind to establish the value for BESS’s with various power and energy capacities.
To achieve these objectives, first a statistical model of electric vehicle drivers’ charging behaviors (home charging and dc fast charging) was constructed and simu-lated according to empirical charging data and several key findings about people’s charging habits in the literature.
Data of private vehicles’ driving records was extracted from the National Household Travel Survey (NHTS), derived 42 statistical distributions that mathe-matically modeled people’s driving behaviors. From this start, two algorithms were developed to simulate driver behavior: one using a database sampling method (DSM) and another using probability distribution sampling method (PDSM) to simulate the electric vehicles’ driving cycles. Both methods used data and statistical distributions derived from NHTS. Next, a model of the EV drivers’ charging behavior was incor-porated into the simulation of the electric vehicles’ driving cycles, and then the ve-hicles’ charging behaviors were simulated. From these simulations, one can forecast the real-power demand of a typical dc fast charging station with six dc 50 kW fast chargers serving a population of 700 EVs. (The ratio of six dc fast chargers to 700 EVs was selected based on the current value of this ratio in the US.) Next, a BESS was integrated into the dc fast charging station demand model and the size and charging behavior was optimized to account for different criteria which were based on the goals of the different potential owners: SRP or a third-party owner. It was established when a BESS would become economically feasible using a simplified economic model.
It was observed that the real-power demand shape is a function of the size of the BESS and the owner’s objective, i.e., flattening the demand curve or minimizing the cost of electricity.
To achieve these objectives, first a statistical model of electric vehicle drivers’ charging behaviors (home charging and dc fast charging) was constructed and simu-lated according to empirical charging data and several key findings about people’s charging habits in the literature.
Data of private vehicles’ driving records was extracted from the National Household Travel Survey (NHTS), derived 42 statistical distributions that mathe-matically modeled people’s driving behaviors. From this start, two algorithms were developed to simulate driver behavior: one using a database sampling method (DSM) and another using probability distribution sampling method (PDSM) to simulate the electric vehicles’ driving cycles. Both methods used data and statistical distributions derived from NHTS. Next, a model of the EV drivers’ charging behavior was incor-porated into the simulation of the electric vehicles’ driving cycles, and then the ve-hicles’ charging behaviors were simulated. From these simulations, one can forecast the real-power demand of a typical dc fast charging station with six dc 50 kW fast chargers serving a population of 700 EVs. (The ratio of six dc fast chargers to 700 EVs was selected based on the current value of this ratio in the US.) Next, a BESS was integrated into the dc fast charging station demand model and the size and charging behavior was optimized to account for different criteria which were based on the goals of the different potential owners: SRP or a third-party owner. It was established when a BESS would become economically feasible using a simplified economic model.
It was observed that the real-power demand shape is a function of the size of the BESS and the owner’s objective, i.e., flattening the demand curve or minimizing the cost of electricity.
ContributorsDeng, Qian (Author) / Tylavsky, Daniel J (Thesis advisor) / Wu, Meng (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2019
Description
Point of Load (POL) DC-DC converters are increasingly used in space applications, data centres, electric vehicles, portable computers and devices and medical electronics. Heavy computing and processing capabilities of the modern devices have ushered the use of higher battery supply voltage to increase power storage. The need to address this consumer experience driven requirement has propelled the evolution of the next generation of small form-factor power converters which can operate with higher step down ratios while supplying heavy continuous load currents without sacrificing efficiency. Constant On-Time (COT) converter topology is capable of achieving stable operation at high conversion ratio with minimum off-chip components and small silicon area. This work proposes a Constant On-Time buck dc-dc converter for a wide dynamic input range and load currents from 100mA to 10A. Accuracy of this ripple based converter is improved by a unique voltage positioning technique which modulates the reference voltage to lower the average ripple profile close to the nominal output. Adaptive On-time block features a transient enhancement scheme to assist in faster voltage droop recovery when the output voltage dips below a defined threshold. UtilizingGallium Nitride (GaN) power switches enable the proposed converter to achieve very high efficiency while using smaller size inductor-capacitor (LC) power-stage. Use of novel Superjunction devices with higher drain-source blocking voltage simplifies the complex driver design and enables faster frequency of operation. It allows 1.8VComplementary Metal-Oxide Semiconductor (CMOS) devices to effectively drive GaNpower FETs which require 5V gate signal swing. The presented controller circuit uses internal ripple generation which reduces reliance on output cap equivalent series resistance (ESR) for loop stability and facilitates ripples reduction at the output. The ripple generation network is designed to provide ai
optimally stable performance while maintaining load regulation and line regulation accuracy withing specified margin. The chip with ts external Power FET package is proposed to be integrated on a printed circuit board for testing. The designed power converter is expected to operate under 200 MRad of a total ionising dose of radiation enabling it to function within large hadron collider at CERN and space satellite and probe missions.
optimally stable performance while maintaining load regulation and line regulation accuracy withing specified margin. The chip with ts external Power FET package is proposed to be integrated on a printed circuit board for testing. The designed power converter is expected to operate under 200 MRad of a total ionising dose of radiation enabling it to function within large hadron collider at CERN and space satellite and probe missions.
ContributorsJoshi, Omkar (Author) / Bakkaloglu, Bertan (Thesis advisor) / Kitchen, Jennifer (Committee member) / Long, Yu (Committee member) / Arizona State University (Publisher)
Created2019
Description
Robotic lower limb prostheses provide new opportunities to help transfemoral amputees regain mobility. However, their application is impeded by that the impedance control parameters need to be tuned and optimized manually by prosthetists for each individual user in different task environments. Reinforcement learning (RL) is capable of automatically learning from interacting with the environment. It becomes a natural candidate to replace human prosthetists to customize the control parameters. However, neither traditional RL approaches nor the popular deep RL approaches are readily suitable for learning with limited number of samples and samples with large variations. This dissertation aims to explore new RL based adaptive solutions that are data-efficient for controlling robotic prostheses.
This dissertation begins by proposing a new flexible policy iteration (FPI) framework. To improve sample efficiency, FPI can utilize either on-policy or off-policy learning strategy, can learn from either online or offline data, and can even adopt exiting knowledge of an external critic. Approximate convergence to Bellman optimal solutions are guaranteed under mild conditions. Simulation studies validated that FPI was data efficient compared to several established RL methods. Furthermore, a simplified version of FPI was implemented to learn from offline data, and then the learned policy was successfully tested for tuning the control parameters online on a human subject.
Next, the dissertation discusses RL control with information transfer (RL-IT), or knowledge-guided RL (KG-RL), which is motivated to benefit from transferring knowledge acquired from one subject to another. To explore its feasibility, knowledge was extracted from data measurements of able-bodied (AB) subjects, and transferred to guide Q-learning control for an amputee in OpenSim simulations. This result again demonstrated that data and time efficiency were improved using previous knowledge.
While the present study is new and promising, there are still many open questions to be addressed in future research. To account for human adaption, the learning control objective function may be designed to incorporate human-prosthesis performance feedback such as symmetry, user comfort level and satisfaction, and user energy consumption. To make the RL based control parameter tuning practical in real life, it should be further developed and tested in different use environments, such as from level ground walking to stair ascending or descending, and from walking to running.
This dissertation begins by proposing a new flexible policy iteration (FPI) framework. To improve sample efficiency, FPI can utilize either on-policy or off-policy learning strategy, can learn from either online or offline data, and can even adopt exiting knowledge of an external critic. Approximate convergence to Bellman optimal solutions are guaranteed under mild conditions. Simulation studies validated that FPI was data efficient compared to several established RL methods. Furthermore, a simplified version of FPI was implemented to learn from offline data, and then the learned policy was successfully tested for tuning the control parameters online on a human subject.
Next, the dissertation discusses RL control with information transfer (RL-IT), or knowledge-guided RL (KG-RL), which is motivated to benefit from transferring knowledge acquired from one subject to another. To explore its feasibility, knowledge was extracted from data measurements of able-bodied (AB) subjects, and transferred to guide Q-learning control for an amputee in OpenSim simulations. This result again demonstrated that data and time efficiency were improved using previous knowledge.
While the present study is new and promising, there are still many open questions to be addressed in future research. To account for human adaption, the learning control objective function may be designed to incorporate human-prosthesis performance feedback such as symmetry, user comfort level and satisfaction, and user energy consumption. To make the RL based control parameter tuning practical in real life, it should be further developed and tested in different use environments, such as from level ground walking to stair ascending or descending, and from walking to running.
ContributorsGao, Xiang (Author) / Si, Jennie (Thesis advisor) / Huang, He Helen (Committee member) / Santello, Marco (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2020
Description
Presented is a design approach and test of a novel compact waveguide that demonstrated the outer dimensions of a rectangular waveguide through the introduction of parallel raised strips, or flanges, which run the length of the rectangular waveguide along the direction of wave propagation. A 10GHz waveguide was created with outer dimensions of a=9.0mm and b=3.6mm compared to a WR-90 rectangular waveguide with outer dimensions of a=22.86mm and b=10.16mm which the area is over 7 times the area. The first operating bandwidth for a hollow waveguide of dimensions a=9.0mm and b=3.6mm starts at 16.6GHz a 40% reduction in cutoff frequency.
The prototyped and tested compact waveguide demonstrated an operating close to the predicted 2GHz with predicted vs measured injection loss generally within 0.25dB and an overall measured injection loss of approximately 4.67dB/m within the operating bandwidth.
The prototyped and tested compact waveguide demonstrated an operating close to the predicted 2GHz with predicted vs measured injection loss generally within 0.25dB and an overall measured injection loss of approximately 4.67dB/m within the operating bandwidth.
ContributorsJones, Jimmy, Ph.D (Author) / Pan, George (Thesis advisor) / Palais, Joseph (Committee member) / Aberle, James T., 1961- (Committee member) / Young, William (Committee member) / Arizona State University (Publisher)
Created2019
Description
The goal of any solar photovoltaic (PV) system is to generate maximum energy throughout its lifetime. The parameters that can affect PV module power output include: solar irradiance, temperature, soil accumulation, shading, encapsulant browning, encapsulant delamination, series resistance increase due to solder bond degradation and corrosion and shunt resistance decrease due to potential induced degradation, etc. Several PV modules together in series makes up a string, and in a power plant there are a number of these strings in parallel which can be referred to as an array. Ideally, PV modules in a string should be identically matched to attain maximum power output from the entire string. Any underperforming module or mismatch among modules within a string can reduce the power output. The goal of this project is to quickly identify and quantitatively determine the underperforming module(s) in an operating string without the use of an I-V curve tracer, irradiance sensor or temperature sensor. This goal was achieved by utilizing Radiovoltmeters (RVM). In this project, it is demonstrated that the voltages at maximum power point (Vmax) of all the individual modules in a string can be simultaneously and quantitatively obtained using RVMs at a single irradiance, single module operating temperature, single spectrum and single angle of incidence. By combining these individual module voltages (Vmax) with the string current (Imax) using a Hall sensor, the power output of individual modules can be obtained, quickly and quantitatively.
ContributorsTahghighi, Arash (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2019
Description
With the steady advancement of neural network research, new applications are continuously emerging. As a tool for test time reduction, neural networks provide a reliable method of identifying and applying correlations in datasets to speed data processing. By leveraging the power of a deep neural net, it is possible to record the motion of an accelerometer in response to an electrical stimulus and correlate the response with a trim code to reduce the total test time for such sensors. This reduction can be achieved by replacing traditional trimming methods such as physical shaking or mathematical models with a neural net that is able to process raw sensor data collected with the help of a microcontroller. With enough data, the neural net can process the raw responses in real time to predict the correct trim codes without requiring any additional information. Though not yet a complete replacement, the method shows promise given more extensive datasets and industry-level testing and has the potential to disrupt the current state of testing.
ContributorsDebeurre, Nicholas (Author) / Ozev, Sule (Thesis advisor) / Vrudhula, Sarma (Thesis advisor) / Kniffin, Margaret (Committee member) / Arizona State University (Publisher)
Created2019
Description
This dissertation explores thermal effects and electrical characteristics in metal-oxide-semiconductor field effect transistor (MOSFET) devices and circuits using a multiscale dual-carrier approach. Simulating electron and hole transport with carrier-phonon interactions for thermal transport allows for the study of complementary logic circuits with device level accuracy in electrical characteristics and thermal effects. The electrical model is comprised of an ensemble Monte Carlo solution to the Boltzmann Transport Equation coupled with an iterative solution to two-dimensional (2D) Poisson’s equation. The thermal model solves the energy balance equations accounting for carrier-phonon and phonon-phonon interactions. Modeling of circuit behavior uses parametric iteration to ensure current and voltage continuity. This allows for modeling of device behavior, analyzing circuit performance, and understanding thermal effects.
The coupled electro-thermal approach, initially developed for individual n-channel MOSFET (NMOS) devices, now allows multiple devices in tandem providing a platform for better comparison with heater-sensor experiments. The latest electro-thermal solver allows simulation of multiple NMOS and p-channel MOSFET (PMOS) devices, providing a platform for the study of complementary MOSFET (CMOS) circuit behavior. Modeling PMOS devices necessitates the inclusion of hole transport and hole-phonon interactions. The analysis of CMOS circuits uses the electro-thermal device simulation methodology alongside parametric iteration to ensure current continuity. Simulating a CMOS inverter and analyzing the extracted voltage transfer characteristics verifies the efficacy of this methodology. This work demonstrates the effectiveness of the dual-carrier electro-thermal solver in simulating thermal effects in CMOS circuits.
The coupled electro-thermal approach, initially developed for individual n-channel MOSFET (NMOS) devices, now allows multiple devices in tandem providing a platform for better comparison with heater-sensor experiments. The latest electro-thermal solver allows simulation of multiple NMOS and p-channel MOSFET (PMOS) devices, providing a platform for the study of complementary MOSFET (CMOS) circuit behavior. Modeling PMOS devices necessitates the inclusion of hole transport and hole-phonon interactions. The analysis of CMOS circuits uses the electro-thermal device simulation methodology alongside parametric iteration to ensure current continuity. Simulating a CMOS inverter and analyzing the extracted voltage transfer characteristics verifies the efficacy of this methodology. This work demonstrates the effectiveness of the dual-carrier electro-thermal solver in simulating thermal effects in CMOS circuits.
ContributorsDaugherty, Robin (Author) / Vasileska, Dragica (Thesis advisor) / Aberle, James T., 1961- (Committee member) / Ferry, David (Committee member) / Goodnick, Stephen (Committee member) / Arizona State University (Publisher)
Created2019
Description
Over the last decade, deep neural networks also known as deep learning, combined with large databases and specialized hardware for computation, have made major strides in important areas such as computer vision, computational imaging and natural language processing. However, such frameworks currently suffer from some drawbacks. For example, it is generally not clear how the architectures are to be designed for different applications, or how the neural networks behave under different input perturbations and it is not easy to make the internal representations and parameters more interpretable. In this dissertation, I propose building constraints into feature maps, parameters and and design of algorithms involving neural networks for applications in low-level vision problems such as compressive imaging and multi-spectral image fusion, and high-level inference problems including activity and face recognition. Depending on the application, such constraints can be used to design architectures which are invariant/robust to certain nuisance factors, more efficient and, in some cases, more interpretable. Through extensive experiments on real-world datasets, I demonstrate these advantages of the proposed methods over conventional frameworks.
ContributorsLohit, Suhas Anand (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Li, Baoxin (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2019
Description
Disentangling latent spaces is an important research direction in the interpretability of unsupervised machine learning. Several recent works using deep learning are very effective at producing disentangled representations. However, in the unsupervised setting, there is no way to pre-specify which part of the latent space captures specific factors of variations. While this is generally a hard problem because of the non-existence of analytical expressions to capture these variations, there are certain factors like geometric
transforms that can be expressed analytically. Furthermore, in existing frameworks, the disentangled values are also not interpretable. The focus of this work is to disentangle these geometric factors of variations (which turn out to be nuisance factors for many applications) from the semantic content of the signal in an interpretable manner which in turn makes the features more discriminative. Experiments are designed to show the modularity of the approach with other disentangling strategies as well as on multiple one-dimensional (1D) and two-dimensional (2D) datasets, clearly indicating the efficacy of the proposed approach.
transforms that can be expressed analytically. Furthermore, in existing frameworks, the disentangled values are also not interpretable. The focus of this work is to disentangle these geometric factors of variations (which turn out to be nuisance factors for many applications) from the semantic content of the signal in an interpretable manner which in turn makes the features more discriminative. Experiments are designed to show the modularity of the approach with other disentangling strategies as well as on multiple one-dimensional (1D) and two-dimensional (2D) datasets, clearly indicating the efficacy of the proposed approach.
ContributorsKoneripalli Seetharam, Kaushik (Author) / Turaga, Pavan (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2019