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abstract: Heterogeneous multiprocessor systems-on-chip (MPSoCs) powering mobile platforms integrate multiple asymmetric CPU cores, a GPU, and many specialized processors. When the MPSoC operates close to its peak performance, power dissipation easily increases the temperature, hence adversely impacts reliability. Since using a fan is not a viable solution for hand-held devices, there is a strong need for dynamic thermal and power management (DTPM) algorithms that can regulate temperature with minimal performance impact. This abstract presents a DTPM algorithm based on a practical temperature prediction methodology using system identification. The DTPM algorithm dynamically computes a power budget using the predicted temperature, and controls the types and number of active processors as well as their frequencies. Experiments on an octa-core big.LITTLE processor and common Android apps demonstrate that the proposed technique predicts temperature within 3% accuracy, while the DTPM algorithm provides around 6x reduction in temperature variance, and as large as 16% reduction in total platform power compared to using a fan. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015

abstract: Recent trends in the electric power industry have led to more attention to optimal operation of power transformers. In a deregulated environment, optimal operation means minimizing the maintenance and extending the life of this critical and costly equipment for the purpose of maximizing profits. Optimal utilization of a transformer can be achieved through the use of dynamic loading. A benefit of dynamic loading is that it allows better utilization of the transformer capacity, thus increasing the flexibility and reliability of the power system. This document presents the progress on a software application which can estimate the maximum time-varying loading capability of transformers. This information can be used to load devices closer to their limits without exceeding the manufacturer specified operating limits. The maximally efficient dynamic loading of transformers requires a model that can accurately predict both top-oil temperatures (TOTs) and hottest-spot temperatures (HSTs). In the previous work, two kinds of thermal TOT and HST models have been studied and used in the application: the IEEE TOT/HST models and the ASU TOT/HST models. And, several metrics have been applied to evaluate the model acceptability and determine the most appropriate models for using in the dynamic loading calculations. In this work, an investigation to improve the existing transformer thermal models performance is presented. Some factors that may affect the model performance such as improper fan status and the error caused by the poor performance of IEEE models are discussed. Additional methods to determine the reliability of transformer thermal models using metrics such as time constant and the model parameters are also provided. A new production grade application for real-time dynamic loading operating purpose is introduced. This application is developed by using an existing planning application, TTeMP, as a start point, which is designed for the dispatchers and load specialists. To overcome the limitations of TTeMP, the new application can perform dynamic loading under emergency conditions, such as loss-of transformer loading. It also has the capability to determine the emergency rating of the transformers for a real-time estimation. / Dissertation/Thesis / M.S. Electrical Engineering 2013

Small scale unmanned aircraft, such as quadrotors, that are quickly emerging as versatile tools for a wide range of applications including search and rescue, hazardous environment exploration, or just shooting great video, are known as micro air vehicles (MAVs). However, for millimeter scale vehicles with weights under 10 grams, conventional flight technologies become greatly inefficient and instead inspiration is drawn from biology. Flapping wing MAVs (FWMAVs) have been created based on insects and hummingbirds in an effort to emulate their extreme agility and ability to hover in place. FWMAVs possess unique capabilities in terms of maneuverability, small size, and ability to operate in dynamic environments that make them particularly well suited for environmental monitoring and swarm applications such as artificial crop pollination. Despite their advantages, significant challenges in fabrication, power, and control must be overcome in order to make FWMAVs a reliable platform. Current designs suffer from high mechanical complexity and often rely on off-board power, sensing, and control, which compromises their autonomy and limits practical applications. The goal of my research is to develop a simple FWMAV design that provides high efficiency and controllability. An efficient, simple, and controllable vehicle design is developed utilizing the principles of resonance, emulation of biological flight control, and under-actuation. A highly efficient, resonant actuator is achieved by attaching a spring in parallel to the output shaft of a commercial geared DC micro-motor. This actuator directly drives the wings of the vehicle, allowing them to be controlled precisely and independently. This direct control strategy emulates biology and differs from other FWMAV designs that utilize complicated transmissions to generate flapping from rotary motor output. Direct control of the wings allows for emulation of biological wing kinematics, resulting in control based on wing motion alone. Furthermore, under-actuation is employed to mimic the rotational motion of insect wings. A rotational joint is added between the motor and wing membrane such that the wing rotates passively in response to aerodynamic forces that are generated as the wing is driven. This design is realized in several stages, initial prototyping, simulation and development of the actuator and wings, then finally a control system is developed. First the system was modeled and improved experimentally in order to achieve lift off. Improvements to the actuator were realized through component variation and custom fabrication increasing torque and power density by 161.1% and 666.8% respectively compared to the gearmotor alone and increased the resonant operating frequency of the vehicle from 4 Hz to 23 Hz. Advances in wing fabrication allowed for flexible wings that increased translational lift production by 35.3%, aerodynamic efficiency by 41.3%, and the effective lift coefficient by 63.7% with dynamic twisting. A robust control architecture was then developed iteratively based on a date driven system model in order to increase flight time from 1 second (10 wing strokes) to over 10 seconds (230 wing strokes). The resulting design improves lift to weight by 166%, allowing for a payload capacity of approximately 8.7 g and offers the potential for fully autonomous operation with all necessary components included on-board. A thermal model for micro-motors was developed and tuned to accurately predict an upper limit of system operation of 41 seconds as well as to optimize a heatsink that increases operating time by 102.4%.

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The tyre is an essential part of a road vehicle. It is in the contact between road and tyre that the forces that create the possibility for the driver to control the vehicle are generated. Tyres, however, wear down, which leads to both unhealthy wear particles and disposal of old tyres, both of which are harmful to the environment. If one could learn more about what causes wear, it might be possible to reduce tyre wear, which would be beneficial from both an economic and an ecological point of view. The aim of this thesis work is to develop a tyre model that can simulate tyre wear and take temperature, pressure and vehicle settings into account. Based on tyre brush theory, a tyre wear model has been developed which includes a thermal model, a pressure model and a friction model. Simulations and analysis of different cases has been performed. From the results, one can conclude the following: the tyre temperature and inflation pressure change with the distance the vehicle travels at the beginning and later become steady; higher external temperature will decrease tyre wear rate since the inflation pressure increases with the external temperature and the sliding friction decreases; higher vehicle speed leads to a higher tyre wear rate; the tyre temperature increases with increasing vehicle speed; the amount of tyre wear increases linearly with the normal load on the tyre; the tyre wear increases with the slip ratio exponentially due to both the siding distance and the sliding friction increasing with the slip ratio; the tyre wear increases exponentially with the slip angle. The complete model can estimate the tyre wear with different vehicle settings and external factors. More experiments are needed in the future to validate the complete model. In addition, since the heat transfer coefficient is changeable with temperature, the thermal model can be improved by introducing dynamic heat transfer coefficients. The Savkoor friction model used in the report can also be improved by tuning its parameters using more experimental data.

Laser drilling of microvias for organic packaging applications is studied in present research. Thermal model is essential to understand the laser-materials interactions and to control laser drilling of blind micro holes through polymeric dielectrics in multilayer electronic substrates. In order to understand the profile of the drilling front irradiated with different laser beam profiles, a transient heat conduction model including vaporization parameters is constructed. The absorption length in the dielectric is also considered in this model. Therefore, the volumetric heating source criteria are applied in the model and the equations are solved analytically. The microvia drilling speed, temperature distribution in the dielectric and the thickness of the residue along the microvia walls and at the bottom of the microvia are studied for different laser irradiation conditions. An overheated metastable state of material is found to exist inside the workpiece. The overheating parameters are calculated for various laser drilling parameters and are used to predict the onset of thermal damage and to minimize the residue. As soon as a small cavity is formed during the drilling process, the concave curvature of the drilling front acts as a concave lens that diverges the incident laser beam. This self-defocusing effect can greatly reduce the drilling speed. This effect makes the refractive index of the substrate at different wavelengths an important parameter for laser drilling. A numerical thermal model is built to study the effect of self-defocusing for laser microvias drilling in multilayer electronic substrates with Nd:YAG and CO2 lasers.. The laser ablation thresholds was calculated with this model for the CO2 and Nd:YAG lasers respectively. Due to the expulsion of materials because of high internal pressures in the case of Nd:YAG laser microvia drilling, the ablation threshold may be far below the calculated value. A particular laser beam shape, such as pitch fork, was found to drill better holes than the Gaussian beam in terms of residue and tapering angle. Laser beam shaping technique is used to produce the desired pitchfork beam. Laser beam shaping allows redistribution of laser power and phase across the cross-section of the beam for drilling perfectly cylindrical holes. An optical system, which is comprised of three lenses, is designed to transform a Gaussian beam into a pitchfork beam. The first two lenses are the phase elements through which a Gaussian laser beam is transformed into a super Gaussian beam. The ray tracing technique of geometrical optics is used to design these phase elements. The third lens is the transform element which produces a pitchfork profile at the focal plane due to the diffraction effect. A pinhole scanning power meter is used to measure the laser beam profile at the focal plane to verify the existence of the pitchfork beam. To account for diffraction effect, the above mentioned laser beam shaping system was optimized by iterative method using Adaptive Additive algorithm. Fresnel diffraction is used in the iterative calculation. The optimization was target to reduce the energy contained in the first order diffraction ring and to increase the depth of focus for the system. Two diffractive optical elements were designed. The result of the optimization was found dependent on the relation between the diameter of the designed beam shape and the airy disk diameter. If the diameter of the designed beam is larger, the optimization can generate better result. Drilling experiment is performed with a Q-switched CO2 laser at wavelength of 9.3 μm. Comparison among the drilling results from Gaussian beam, Bessel beam and Pitchfork beam shows that the pitchfork beam can produce microvias with less tapering angle and less residue at the bottom of the via. Laser parameters were evaluated experimentally to study their influences on the via quality. Laser drilling process was optimized based on the evaluation to give high quality of the via and high throughput rate. Nd:YAG laser at wavelengths of 1.06 μm and 532 nm were also used in this research to do microvias drilling. Experimental result is compared with the model. Experimental results show the formation of convex surfaces during laser irradiation. These surfaces eventually rupture and the material is removed explosively due to high internal pressures. Due to the short wavelength, high power, high efficiency and high repetition rate, these lasers exhibit large potentials for microvias drilling.

Recently, lithium-ion batteries (LIBs) have achieved wide acceptance for various energy storage applications, such as electric vehicles (EVs) and smart grids. As a vital component in EVs, the performance of lithium-ion batteries in the last few decades has made significant progress. The development of a robust battery management system (BMS) has become a necessity to ensure the reliability and safety of battery packs. In addition, state of charge (SOC) estimation and thermal models with high-fidelity are essential to ensure efficient BMS performance. The SOC of a LIB is an essential factor that should be reported to the vehicle’s electronic control unit and the driver. Inaccurate reported SOC impacts the reliability and safety of the lithium-ion battery packs (LIBP) and the vehicle. Different algorithms are used to estimate the SOC of a LIBP, including measurement-based, adaptive filters and observers, and data-driven; however, there is a gap in feasibility studies of running these algorithms for multi-cell LIBP on BMS microprocessors. On the other hand, temperature sensors are utilized to monitor the temperature of the cells in LIBPs. Using a temperature sensor for every cell is often impractical due to cost and wiring complexity. Robust temperature estimation models can replace physical sensors and help the fault detection algorithms by providing a redundant monitoring system. In this thesis, an accurate SOC estimation and thermal modeling for lithium-ion batteries (LIBs) are presented using deep neural networks (DNNs). Firstly, two DNN-based SOC estimation algorithms, including a feedforward neural network (FNN) enhanced with external filters and a recurrent neural network with a long short-term memory layer (LSTM), are developed and benchmarked versus an extended Kalman filter (EKF) and EKF with recursive least squares filter (EKF-RLS) SOC estimation algorithms. The execution time of EKF, EKF-RLS, FNN, and LSTM SOC estimation algorithms with similar accuracy was found to be 0.24 ms, 0.25 ms, 0.14 ms, and 0.71 ms, respectively. The DNN SOC estimation algorithms were also demonstrated to have lower RAM use than the EKFs, with less than 1 kB RAM required to run one estimator. The proposed FNN and LSTM models are also used to predict the surface temperature of different lithium-ion cells. These DNN models are shown to be capable of estimating temperature with less than 2 ⁰C root mean square error for challenging low ambient temperature drive cycles and just 0.3 ⁰C for 4C rate fast charging conditions. In addition, a DNN model which is trained to estimate the temperature of a new battery cell, is found to still have a very low error of just 0.8 ⁰C when tested on an aged cell. Finally, an integrated physics, and neural network-based battery pack thermal model (LP+FNN) is developed and used to detect and identify different thermal faults of a LIBP. The proposed fault detection and identification method is validated using various thermal faults, including fan system failure, airflow lower and higher than setpoint, airflow blockage of submodule and temperature sensor reading faults. The proposed method is able to detect different cooling system faults within 10 to 35 minutes after fault occurrence. In addition, the proposed method demonstrated being capable of detecting temperature sensor reading offset and scale faults of ±3 ⁰C and ±0.15% or more, respectively with 100% accuracy. / Thesis / Doctor of Philosophy (PhD)

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