A spiking neural network implementation of sound localisation

The focus of this paper is the implementation of a spiking neural network to achieve sound localization; the model is based on the influential short paper by Jeffress in 1948. The SNN has a two-layer topology which can accommodate a limited number of angles in the azimuthal plane. The model accommodates multiple inter-neuron connections with associated delays, and a supervised STDP algorithm is applied to select the optimal pathway for sound localization. Also an analysis of previous relevant work in the area of auditory modelling supports this research.

Coupling of magnetic, strain and electric polarization fields in the structure of multiferroic material

The comprehensive study on the coupling of magnetism, electrical polarization and the crystalline lattice with the off-stoichiometric effects in self-doped multiferroic hexagonal h-LuMnxO3±δ (0.92≤x≤1.12) ceramic oxides was carried out for the PhD work. There is a complex coupling of the three ferroic degrees. The cancelation of the magnetic moments of ions in the antiferromagnetic order, electric polarization with specific vortex/antivortex topology and lattice properties have pushed researchers to find out ways to disclose the underlying physics and chemistry of magneto-electric and magneto-elastic couplings of h-RMnO3 multiferroic materials. In this research work, self-doping of Lu-sites or Mn-sites of h-LuMnxO3±δ ceramics prepared via solid state route was done to pave a way for deeper understanding of the antiferromagnetic transition, the weak ferromagnetism often reported in the same crystalline lattices and the ferroelectric properties coupled to the imposed lattice changes. Accordingly to the aim of the PhD thesis, the objectives set for the sintering study in the first chapter on experimental results were two. First, study of sintering off-stoichiometric samples within conditions reported in the bibliography and also extracted from the phase diagrams of the LuMnxO3±δ, with a multiple firings ending with a last high temperature step at 1300ºC for 24 hours. Second, explore longer annealing times of up to 240 hours at the fixed temperature of 1300 ºC in a search for improving the properties of the solid solution under study. All series of LuMnxO3±δ ceramics for each annealing time were characterized to tentatively build a framework enabling comparison of measured properties with results of others available in literature. XRD and Rietveld refinement of data give the evolution the lattice parameters as a function to x. Shrinkage of the lattice parameters with increasing x values was observed, the stability limit of the solid solution being determined by analysis of lattice parameters. The evolution of grain size and presence of secondary phases have been investigated by means of TEM, SEM, EDS and EBSD techniques. The dependencies of grain growth and regression of secondary phases on composition x and time were further characterized. Magnetic susceptibility of samples and magnetic irreversibility were extensively examined in the present work. The dependency of magnetic susceptibility, Neel ordering transition and important magnetic parameters are determined and compared to observation in other multiferroics in the following chapter of the thesis. As a tool of high sensitivity to detect minor traces of the secondary phase hausmannite, magnetic measurements are suggested for cross-checking of phase diagrams. Difficulty of previous studies on interpreting the magnetic anomaly below 43 K in h-RMnO3 oxides was discussed and assigned to the Mn3O4 phase, with supported of the electron microscopy. Magneto-electric coupling where AFM ordering is coupled to dielectric polarization is investigated as a function of x and of sintering condition via frequency and temperature dependent complex dielectric constant measurements in the final chapter of the thesis. Within the limits of solid solubility, the crystalline lattice of off-stoichiometric ceramics was shown to preserve the magneto-electric coupling at TN. It represents the first research work on magneto-electric coupling modified by vacancy doping to author’s knowledge. Studied lattices would reveal distortions at the atomic scale imposed by local changes of x dependent on sintering conditions which were widely inspected by using TEM/STEM methods, complemented with EDS and EELS spectroscopy all together to provide comprehensive information on cross coupling of distortions, inhomogeneity and electronic structure assembled and discussed in a specific chapter. Internal interfaces inside crystalline grains were examined. Qualitative explanations of the measured magnetic and ferroelectric properties were established in relation to observed nanoscale features of h-LuMnxO3±δ ceramics. Ferroelectric domains and topological defects are displayed both in TEM and AFM/PFM images, the later technique being used to look at size, distribution and switching of ferroelectric domains influenced by vacancy doping at the micron scale bridging to complementary TEM studies on the atomic structure of ferroelectric domains. In support to experimental study, DFT simulations using Wien2K code have been carried out in order to interpret the results of EELS spectra of O K-edge and to obtain information on the cation hybridization to oxygen ions. The L3,2 edges of Mn is used to access the oxidation state of the Mn ions inside crystalline grains. In addition, rehybridization driven ferroelectricity is also evaluated by comparing the partial density of states of the orbitals of all ions of the samples, also the polarization was calculated and correlated to the off-stoichiometric effect.

Energy Management of a Battery-Ultracapacitor Hybrid Energy Storage System in Electric Vehicles

Electric vehicle (EV) batteries tend to have accelerated degradation due to high peak power and harsh charging/discharging cycles during acceleration and deceleration periods, particularly in urban driving conditions. An oversized energy storage system (ESS) can meet the high power demands; however, it suffers from increased size, volume and cost. In order to reduce the overall ESS size and extend battery cycle life, a battery-ultracapacitor (UC) hybrid energy storage system (HESS) has been considered as an alternative solution. In this work, we investigate the optimized configuration, design, and energy management of a battery-UC HESS. One of the major challenges in a HESS is to design an energy management controller for real-time implementation that can yield good power split performance. We present the methodologies and solutions to this problem in a battery-UC HESS with a DC-DC converter interfacing with the UC and the battery. In particular, a multi-objective optimization problem is formulated to optimize the power split in order to prolong the battery lifetime and to reduce the HESS power losses. This optimization problem is numerically solved for standard drive cycle datasets using Dynamic Programming (DP). Trained using the DP optimal results, an effective real-time implementation of the optimal power split is realized based on Neural Network (NN). This proposed online energy management controller is applied to a midsize EV model with a 360V/34kWh battery pack and a 270V/203Wh UC pack. The proposed online energy management controller effectively splits the load demand with high power efficiency and also effectively reduces the battery peak current. More importantly, a 38V-385Wh battery and a 16V-2.06Wh UC HESS hardware prototype and a real-time experiment platform has been developed. The real-time experiment results have successfully validated the real-time implementation feasibility and effectiveness of the real-time controller design for the battery-UC HESS. A battery State-of-Health (SoH) estimation model is developed as a performance metric to evaluate the battery cycle life extension effect. It is estimated that the proposed online energy management controller can extend the battery cycle life by over 60%.

Quantifying the Resilience of an Urban Traffic-Electric Power Coupled System

Transportation system resilience has been the subject of several recent studies. To assess the resilience of a transportation network, however, it is essential to model its interactions with and reliance on other lifelines. In this work, a bi-level, mixed-integer, stochastic program is presented for quantifying the resilience of a coupled traffic-power network under a host of potential natural or anthropogenic hazard-impact scenarios. A two-layer network representation is employed that includes details of both systems. Interdependencies between the urban traffic and electric power distribution systems are captured through linking variables and logical constraints. The modeling approach was applied on a case study developed on a portion of the signalized traffic-power distribution system in southern Minneapolis. The results of the case study show the importance of explicitly considering interdependencies between critical infrastructures in transportation resilience estimation. The results also provide insights on lifeline performance from an alternative power perspective.

Real-time load-side control of electric power systems

Two trends are emerging from modern electric power systems: the growth of renewable (e.g., solar and wind) generation, and the integration of information technologies and advanced power electronics. The former introduces large, rapid, and random fluctuations in power supply, demand, frequency, and voltage, which become a major challenge for real-time operation of power systems. The latter creates a tremendous number of controllable intelligent endpoints such as smart buildings and appliances, electric vehicles, energy storage devices, and power electronic devices that can sense, compute, communicate, and actuate. Most of these endpoints are distributed on the load side of power systems, in contrast to traditional control resources such as centralized bulk generators. This thesis focuses on controlling power systems in real time, using these load side resources. Specifically, it studies two problems.

(1) Distributed load-side frequency control: We establish a mathematical framework to design distributed frequency control algorithms for flexible electric loads. In this framework, we formulate a category of optimization problems, called optimal load control (OLC), to incorporate the goals of frequency control, such as balancing power supply and demand, restoring frequency to its nominal value, restoring inter-area power flows, etc., in a way that minimizes total disutility for the loads to participate in frequency control by deviating from their nominal power usage. By exploiting distributed algorithms to solve OLC and analyzing convergence of these algorithms, we design distributed load-side controllers and prove stability of closed-loop power systems governed by these controllers. This general framework is adapted and applied to different types of power systems described by different models, or to achieve different levels of control goals under different operation scenarios. We first consider a dynamically coherent power system which can be equivalently modeled with a single synchronous machine. We then extend our framework to a multi-machine power network, where we consider primary and secondary frequency controls, linear and nonlinear power flow models, and the interactions between generator dynamics and load control.

(2) Two-timescale voltage control: The voltage of a power distribution system must be maintained closely around its nominal value in real time, even in the presence of highly volatile power supply or demand. For this purpose, we jointly control two types of reactive power sources: a capacitor operating at a slow timescale, and a power electronic device, such as a smart inverter or a D-STATCOM, operating at a fast timescale. Their control actions are solved from optimal power flow problems at two timescales. Specifically, the slow-timescale problem is a chance-constrained optimization, which minimizes power loss and regulates the voltage at the current time instant while limiting the probability of future voltage violations due to stochastic changes in power supply or demand. This control framework forms the basis of an optimal sizing problem, which determines the installation capacities of the control devices by minimizing the sum of power loss and capital cost. We develop computationally efficient heuristics to solve the optimal sizing problem and implement real-time control. Numerical experiments show that the proposed sizing and control schemes significantly improve the reliability of voltage control with a moderate increase in cost.