UA cloudflare authentication

Theses and Dissertations - Department of Electrical & Computer Engineering (ECE)

Permanent URI for this collection

https://ir.ua.edu/handle/123456789/103

Browse

Browsing Theses and Dissertations - Department of Electrical & Computer Engineering (ECE) by Author "Balasubramanian, Bharat"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Approximate dynamic programming and artificial neural network control of electric vehicles: from motor drives to grid integration
    (University of Alabama Libraries, 2019) Sun, Yang; Li, Shuhui; University of Alabama Tuscaloosa
    The drive system of an electric vehicle (EV) includes two major parts- the powertrain and charging system. This dissertation investigates the implementation of the approximate dynamics programming (ADP) based artificial neural network (ANN) control on these two parts to increase the efficiency, stability and reliability of EVs. The major challenge of the powertrain control is to control the EV motor, which is usually an interior mounted permanent magnetic motor(IPM). By using the conventional vector controller, the IPM encounters high current distortion and speed oscillation especially when working in overmodulation area, due to the decoupling inaccuracy issue. The ADP-ANN controller resolves the decoupling issue and guarantees better speed and current tracking performance. For industrial implementation, the motor control algorithm is normally achieved by a digital signal processor (DSP), which has limited computational resources. As ADP-ANN has more complex structure than the conventional controller, whether it can be put into a DSP need to be tested. This dissertation optimized the ADP-ANN algortithm and make it successfully running in a TMS320F28335 DSP platform. To control a gird-connected solar based EV charging system, the dc-bus voltage stability of the solar inverter need to be maintained to acquire high charging efficiency and reduce the grid current distortion. This will become a challenge to conventional vector controller when the solar irradiation level changing rapidly. The implementation of the proposed controller allows the solar inverter improve the dc-bus voltage stability, energy capture efficiency, adaptivity, power conversion efficiency and power quality. Multiple EVs can be used to supply reactive power to the grid when connected with the charging system. But, a great challenge is that grid integration inverters would fight each other when operated autonomously in participating grid voltage control using the conventional control methods. The ADP-ANN control is able to properly handle the inverter constraints in achieving Voltage/Var control objectives at the grid edge and overcomes the challenges of conventional DER inverter control techniques.
  • Thumbnail Image
    Item
    High-density high-efficiency power magnetics
    (University of Alabama Libraries, 2016) Dang, Zhigang; Abu Qahouq, Jaber A.; University of Alabama Tuscaloosa
    This dissertation presents several concepts and techniques in order to (1) increase the inductance density and power density of power inductors (PIs) with high power efficiency and (2) achieve magnetically coupled wireless power transfer (WPT) systems with higher efficiency and longer transmission distances under varying conditions. Chapter 1 provides an overview and introduction on applications of power magnetic devices and systems along with the challenges facing the state-of-the-art PIs and WPT systems. Chapter 2 develops a concept which results in doubling the saturation current of a high current PI with NdFeB permanent magnet (PMPI). By adding a well-designed small piece of fabricated NdFeB magnet in the air gap of the PI, the saturation current of the PMPI is doubled with the same size and inductance value. Chapter 3 presents a two-phase coupled power inductor (CPI) that utilizes a PM in order to achieve almost doubled saturation current with the same size compared to the CPI and more than 70% core size reduction compared to the single-phase non-coupled PIs. Both the PMPI and PMCI concepts are experimentally verified in DC-DC power converter prototypes. Chapter 4 and 5 present a two-coil and a four-coil reconfigurable WPT system topology, respectively, in order to optimize transmission efficiency under different distance and misalignment conditions. The two-coil reconfigurable WPT system achieves re-configurability by switching between different values of series and shunt capacitors at Tx side and/or Rx side. The four-coil reconfigurable WPT system achieves re-configurability by switching between different sizes of drive loops and load loops. Experimental results verified effectiveness of developed reconfiguration methods. Chapter 6 presents a method to achieve wired power conversion and WPT using a hybrid “Power Converter-WPT system”. By achieving WPT using AC switching ripple of power converter, the system eliminates the need for a transmitter stage of conventional WPT system, which could be beneficial for system size and cost reduction. The method is verified and demonstrated using Buck-WPT system as an example. The last chapter summarizes this work and provides conclusions before discussing some possible future research directions related to the dissertation work.
  • Thumbnail Image
    Item
    Machine learning enhanced 5G vehicle-to-everything (V2X) communication networks with millimeter-waves and terahertz links
    (University of Alabama Libraries, 2020) Rasheed, Iftikhar; Hu, Fei; University of Alabama Tuscaloosa
    With the incoming of 5G communications, Vehicular Networks have the hope to achieve ultra-high data transmission rate with extremely low end-to-end delay. However, the dynamic nature of transportation traffic and increased data bandwidth demands are the major obstacles to achieve high transmission rate in Vehicular-to-Anything (V2X) Networks. To overcome these obstacles, this work presents a novel Software Defined Networking(SDN)-controlled and Cognitive Radio (CR)-enabled V2X routing approach to achieve ultra-high data rate, by using predictive V2X routing that supports the intelligent switching between two 5G technologies: millimeter-wave (mmWave) and terahertz (THz). To improve the network management, Road Side units (RSUs) are used to segregate the V2X network into different clusters. Stability-aware clustering (SAC) scheme is also used for cluster formations. The proposed intelligent V2X network is based on three features enabled machine learning approach: (1) To predict future 3D positions of the vehicles in the Cluster Heads (CHs) using Deep Neural Network with Extended Kalman Filter (DNN-EKF) algorithm for real-time, high-resolution prediction. (2) For THz communications, 0.3 THz to 3 THz band is selected for short-distance super-fast data transmissions. The THz band detection is performed by the CR-enabled Road Side Units (cRSUs). A Genetic Algorithm (GA)-based Improved Fruit Fly (GA-IFF) scheme is proposed to achieve an optimal route selection in THz communications. (3) In mmWave based V2X communications, optimal beam selection is performed by the multi-type2 fuzzy inference system (M-T2FIS). By using these three intelligent designs approaches, we are able to achieve ultrahigh data rate and minimized transmission delay for short-range (in THz bands) and middle-range (in mmWave) communications. Finally, the proposed SDN-controlled, CR-enabled V2X Network is modeled and tested for performance evaluations with the metrics of delivery ratio, routing delay, protocol overhead, and data rate. This work consists of effective cluster formation, intelligent switching, optimal path selection, and optimal beam selection. And it provides high data rate with lower latency and better reliability which is very much necessary for V2X communications.
  • Thumbnail Image
    Item
    Modeling and simulation of radiated emissions and susceptibility
    (University of Alabama Libraries, 2021) Wallner, Tripp Christian; Lemmon, Andrew; University of Alabama Tuscaloosa
    The ongoing development of finite element analysis (FEA) software has made it possible for high validity evaluations to be conducted in simulated environments. In the realm of emissions and susceptibility analysis, rigorous field and lab testing of shielding effectiveness is an expensive and resource-intensive necessity to ensure accordance with electrical standards. The use of FEA software can reduce the cost of achieving compliance by enabling system optimization prior to the investment of significant resources incompliance testing. However, this simulated compliance approach must be carefully calibrated to ensure relevance to real-world results. The development of a consistent methodology for achieving this relevance can satiate a demand for confidence in meeting electrical standards prior to field and lab testing. This thesis aims to establish a procedure for simulating radiated susceptibility in simulated environments analogous to those used in field and lab tests. Frequency domain simulation provides a means for the exploration of frequency-dependent effects as a result of shielding choices. Furthermore, time domain simulation provides predictions that can be readily compared to physical measurements. Finally, FEA software permits evaluation of ideal conditions, such as perfect conduction and lossless mediums, in order to expose designs to conditions more severe than actuality. Using these ideal simulation conditions in conjunction with the aforementioned domains of study can provide results for a worst-case scenario, in turn instilling confidence in shielding effectiveness prior to real-world field testing.
  • Thumbnail Image
    Item
    Optimization of multi-port dc fast charging stations operating with power cap policy
    (University of Alabama Libraries, 2020) Buckreus, Ramona; Kisacikoglu, Mithat C.; University of Alabama Tuscaloosa
    Since many DC fast charging (DCFC) stations are currently low utilized, they suffer financially from high demand charges and are difficult to operate as a business case. However, DCFC stations are necessary to provide coverage for long range travel and promote wide adoption of electric vehicles (EV). This study investigates a new method to design and operate a multi-port DCFC station with power cap in order to mitigate high operation costs while providing sufficient service quality to the customers. Different policies which distribute available power capacity between multiple charging ports in a fair manner are simulated and compared. The impact of the power cap policy on the quality of service (QoS) and operation costs is quantified. A method is introduced which penalizes insufficient QoS, and thus, allows to determine the optimal power cap level at which the QoS is still sufficient but the operations costs are reduced. The impact of other parameters such as the demand and the station architecture design on the optimal power cap is simulated and analyzed. Finally, the station design is optimized for maximum profit or minimum unit cost over the lifetime of the station operation. In summary, the thesis presents a new comprehensive approach on how to design and operate a DCFC station in order to achieve a good business case.