Traction axial flux motor-generator for hybrid electric bus application

Tato dizertační práce se zabývá návrhem původního motor-generátoru s axiálním tokem a buzením permanetními magnety, zkonstruovaným specificky pro hybridní elektrický autobus. Návrhové zadání pro tento stroj přineslo požadavky, které vedly k této unikátní topologii tak, aby byl dosažen výkon, účinnost a rozměry stroje. Tato partikulární topologie motor-generátoru s axiálním tokem je výsledkem literární rešerše, kterou následoval výběr koncepce stroje s představeným návrhem jako výsledkem těchto procesů. Přístup k návrhu stroje s axiálním tokem sledoval „multi-fyzikální“ koncepci, která pracuje s návrhem elektromagnetickým, tepelným, mechanickým, včetně návrhu řízení, v jedné iteraci. Tím je v konečném návrhu zajištěna rovnováha mezi těmito inženýrskými disciplínami. Pro samotný návrh stroje byla vyvinuta sada výpočtových a analytických nástrojů, které byly podloženy metodou konečných prvků tak, aby samotný návrh stroje byl přesnější a spolehlivější. Modelování somtného elektrického stroje a celého pohonu poskytlo představu o výkonnosti a účinnosti celého subsytému v rozmanitých operačních podmínkách. Rovněž poukázal na optimizační potenciál pro návrh řízení subsystému ve smyslu maximalizace účinnosti celého pohonu. Bylo postaveno několik prototypů tohoto stroje, které prošly intensivním testováním jak na úrovni sybsytému, tak systému. Samotné výsledky testů jsou diskutovány a porovnány s analytickými výpočty parametrů stroje. Poznatky získané z prvního prototypu stroje pak sloužily k představení možností, jak zjednodušit výrobu a montáž stroje v příští generaci. Tato práce zaznamenává jednotlivé kroky během všech fází vývoje elektrického stroje s axiálním tokem, počínaje výběrem konceptu stroje, konče sumarizací zkušeností získaných z první generace prototypu tohoto stroje.This thesis deals with a design of a novel Axial-Flux Permanent Magnet Motor-Generator for a hybrid electric bus application. Thus, the design specification represents a set of requirements, which leads toward a concept of a unique topology meeting performance, efficiency and dimensional targets. The particular topology of the Axial-Flux Permanent Magnet Motor-Generator discussed in this work is an outcome of deep literature survey, followed by the concept selection stage with the layout of the machine as an outcome of this processes. The design approach behind this so-called Spoke Axial-Flux Machine follows an idea of multiphysics iterations, including electromagnetic, thermal, mechanical and controls design. Such a process behind the eventually proposed design ensured a right balance in between all of these engineering disciplines. A set of bespoke design and analysis tools was developed for that reason, and was backed up by extensive use of Finite-Element Analysis and Computational Fluid Dynamics. Therefore, the actual machine design gained higher level of confidence and fidelity. Modelling of the machine and its drive provided understanding of performance and efficiency of the whole subsystem at various operational conditions. Moreover, it has illustrated an optimization potential for the controls design, so that efficiency of the machine and power electronics might be maximized. Several prototypes of this machine have been built and passed through extensive testing both on the subsystem and system level. Actual test results are discussed, and compared to analytical predictions in terms of the machine's parameters. As a lesson learned from the first prototype of this machine, a set of redesign proposals aiming for simplification of manufacturing and assembly processes, are introduced. This work records steps behind all phases of development of the Axial Flux Machine from a basic idea as an outcome of concept selection stage, up to testing and wrap-up of experience gained from the first generation of the machine.

Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization

This study delves into the shift from centralized to decentralized approaches in the electricity industry, with a particular focus on how machine learning (ML) advancements play a crucial role in empowering renewable energy sources and improving grid management. ML models have become increasingly important in predicting renewable energy generation and consumption, utilizing various techniques like artificial neural networks, support vector machines, and decision trees. Furthermore, data preprocessing methods, such as data splitting, normalization, decomposition, and discretization, are employed to enhance prediction accuracy. The incorporation of big data and ML into smart grids offers several advantages, including heightened energy efficiency, more effective responses to demand, and better integration of renewable energy sources. Nevertheless, challenges like handling large data volumes, ensuring cybersecurity, and obtaining specialized expertise must be addressed. The research investigates various ML applications within the realms of solar energy, wind energy, and electric distribution and storage, illustrating their potential to optimize energy systems. To sum up, this research demonstrates the evolving landscape of the electricity sector as it shifts from centralized to decentralized solutions through the application of ML innovations and distributed decision-making, ultimately shaping a more efficient and sustainable energy future

Design Simulation and Experiments on Electrical Machines for Integrated Starter-Generator Applications

This thesis presents two different non-permanent magnet machine designs for belt-driven integrated starter-generator (B-ISG) applications. The goal of this project is to improve the machine performance over a benchmark classical switched reluctance machine (SRM) in terms of efficiency, control complexity, torque ripple level and power factor. The cost penalty due to the necessity of a specially designed H-bridge machine inverter is also taken into consideration by implementation of a conventional AC inverter. The first design changes the classical SRM winding configuration to utilise both self-inductance and mutual-inductance in torque production. This allows the use of AC sinusoidal current with lower cost and comparable or even increased torque density. Torque density can be further increased by using a bipolar square current drive with optimum conduction angle. A reduction in control difficulty is also achieved by adoption of standard AC machine control theory. Despite these merits, the inherently low power factor and poor field weakening capability makes these machines unfavourable in B-ISG applications. The second design is a wound rotor synchronous machine (WRSM). From FE analysis, a six pole geometry presents a lower loss level over four pole geometry. Torque ripple and iron loss are effectively reduced by the use of an eccentric rotor pole. To determine the minimum copper loss criteria, a novel algorithm is proposed over the conventional Lagrange method, where the deviation is lowered from ± 10% to ± 1%, and the simulation time is reduced from hours to minutes on standard desktop PC hardware. With the proposed design and control strategies, the WRSM delivers a comparable field weakening capability and a higher efficiency compared with the benchmark SRM under the New European Driving Cycle, where a reduction in machine losses of 40% is possible. Nevertheless, the wound rotor structure brings mechanical and thermal challenges. A speed limit of 11,000 rpm is imposed by centrifugal forces. A maximum continuous motoring power of 3.8 kW is imposed by rotor coil temperature performance, which is extended to 5 kW by a proposed temperature balancing method. A prototype machine is then constructed, where the minimum copper loss criteria is experimentally validated. A discrepancy of no more than 10% is shown in back-EMF, phase voltage, average torque and loss from FE simulation

An assessment of flywheel storage for efficient provision of reliable power for residential premises in islanded operation

Energy storage systems (ESS) are key devices for improving power quality, electrical system stability and system efficiency by contributing to the balance of supply and demand. They can enhance the flexibility of electrical systems by mitigating supply intermittency, which has recently become problematic due to the increased penetration of renewable generation. The subject of this thesis is flywheel energy storage system (FESS), a technology that is gathering great interest due to benefits offered over alternative energy storage solutions, including high cycle life, long calendar life, high round‐trip efficiency, high power density, operation at high ambient temperatures and low negative environmental impact. This thesis describes the modelling and assessment of small scale energy system incorporating FESS with solar photovoltaic (PV) and a diesel generator for use in islanded residential premises with highly intermittent or non‐existent grid infrastructure. In this application, incorporation of FESS is shown to be beneficial in comparison to a system without storage or one with the alternative storage technology, Li‐Ion batteries. The thesis begins with a description of flywheel storage systems configured for electrical storage which comprises of a mechanical part; flywheel rotor, bearings and containment, and an electric drive part; motor‐generator and associated power electronics. Each of these components is described in the thesis along with the equations and modelling, itself carried out in the MATLAB/Simulink environment. Finally, the flywheel model is combined with a model of an islanded residential power system incorporating a solar PV system with a diesel generator. Such a system would be particularly useful for offgrid applications or those with weak grids as occurs in developing countries

Methods for Optimal Microgrid Management

Abstract During the last years, the number of distributed generators has grown significantly and it is expected to become higher in the future. Several new technologies are being de-veloped for this type of generation (including microturbines, photovoltaic plants, wind turbines and electrical storage systems) and have to be integrated in the electrical grid. In this framework, active loads (i.e., shiftable demands like electrical vehicles, intelligent buildings, etc.) and storage systems are crucial to make more flexible and smart the dis-tribution system. This thesis deals with the development and application of system engi-neering methods to solve real-world problems within the specific framework of microgrid control and management. The typical kind of problems that is considered when dealing with the manage-ment and control of Microgrids is generally related to optimal scheduling of the flows of energy among the various components in the systems, within a limited area. The general objective is to schedule the energy consumptions to maximize the expected system utility under energy consumption and energy generation constraints. Three different issues related to microgrid management will be considered in detail in this thesis: 1. The problem of Nowcasting and Forecasting of the photovoltaic power production (PV). This problem has been approached by means of several data-driven techniques. 2. The integration of stations to charge electric vehicles in the smart grids. The impact of this integration on the grid processes and on the demand satisfaction costs have been analysed. In particular, two different models have been developed for the optimal integration of microgrids with renewable sources, smart buildings, and the electrical vehicles (EVs), taking into account two different technologies. The first model is based on a discrete-time representation of the dynamics of the system, whereas the second one adopts a discrete-event representation. 3. The problem of the energy optimization for a set of interconnencted buildings. In ths connection, an architecture, structured as a two-level control scheme has been developed. More precisely, an upper decision maker solves an optimization problem to minimize its own costs and power losses, and provides references (as 3 regars the power flows) to local controllers, associated to buildings. Then, lower level (local) controllers, on the basis of a more detailed representation of each specific subsystem (the building associated to the controller), have the objective of managing local storage systems and devices in order to follow the reference values (provided by the upper level), to contain costs, and to achieve comfort requirements

Multiple Objective Co-Optimization of Switched Reluctance Machine Design and Control

This dissertation includes a review of various motor types, a motivation for selecting the switched reluctance motor (SRM) as a focus of this work, a review of SRM design and control optimization methods in literature, a proposed co-optimization approach, and empirical evaluations to validate the models and proposed co-optimization methods. The switched reluctance motor (SRM) was chosen as a focus of research based on its low cost, easy manufacturability, moderate performance and efficiency, and its potential for improvement through advanced design and control optimization. After a review of SRM design and control optimization methods in the literature, it was found that co-optimization of both SRM design and controls is not common, and key areas for improvement in methods for optimizing SRM design and control were identified. Among many things, this includes the need for computationally efficient transient models with the accuracy of FEA simulations and the need for co-optimization of both machine geometry and control methods throughout the entire operation range with multiple objectives such as torque ripple, efficiency, etc. A modeling and optimization framework with multiple stages is proposed that includes robust transient simulators that use mappings from FEA in order to optimize SRM geometry, windings, and control conditions throughout the entire operation region with multiple objectives. These unique methods include the use of particle swarm optimization to determine current profiles for low to moderate speeds and other optimization methods to determine optimal control conditions throughout the entire operation range with consideration of various characteristics and boundary conditions such as voltage and current constraints. This multi-stage optimization process includes down-selections in two previous stages based on performance and operational characteristics at zero and maximum speed. Co-optimization of SRM design and control conditions is demonstrated as a final design is selected based on a fitness function evaluating various operational characteristics including torque ripple and efficiency throughout the torque-speed operation range. The final design was scaled, fabricated, and tested to demonstrate the viability of the proposed framework and co-optimization method. Accuracy of the models was confirmed by comparing simulated and empirical results. Test results from operation at various torques and speeds demonstrates the effectiveness of the optimization approach throughout the entire operating range. Furthermore, test results confirm the feasibility of the proposed torque ripple minimization and efficiency maximization control schemes. A key benefit of the overall proposed approach is that a wide range of machine design parameters and control conditions can be swept, and based on the needs of an application, the designer can select the appropriate geometry, winding, and control approach based on various performance functions that consider torque ripple, efficiency, and other metrics

Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations

Load forecast on a Micro Grid level through Machine Learning algorithms

As Micro Redes constituem um sector em crescimento da indústria energética, representando uma mudança de paradigma, desde as remotas centrais de geração até à produção mais localizada e distribuída. A capacidade de isolamento das principais redes elétricas e atuar de forma independente tornam as Micro Redes em sistemas resilientes, capazes de conduzir operações flexíveis em paralelo com a prestação de serviços que tornam a rede mais competitiva. Como tal, as Micro Redes fornecem energia limpa eficiente de baixo custo, aprimoram a coordenação dos ativos e melhoram a operação e estabilidade da rede regional de eletricidade, através da capacidade de resposta dinâmica aos recursos energéticos. Para isso, necessitam de uma coordenação de gestão inteligente que equilibre todas as tecnologias ao seu dispor. Daqui surge a necessidade de recorrer a modelos de previsão de carga e de produção robustos e de confiança, que interligam a alocação dos recursos da rede perante as necessidades emergentes. Sendo assim, foi desenvolvida a metodologia HALOFMI, que tem como principal objetivo a criação de um modelo de previsão de carga para 24 horas. A metodologia desenvolvida é constituída, numa primeira fase, por uma abordagem híbrida de multinível para a criação e escolha de atributos, que alimenta uma rede neuronal (Multi-Layer Perceptron) sujeita a um ajuste de híper-parâmetros. Posto isto, numa segunda fase são testados dois modos de aplicação e gestão de dados para a Micro Rede. A metodologia desenvolvida é aplicada em dois casos de estudo: o primeiro é composto por perfis de carga agregados correspondentes a dados de clientes em Baixa Tensão Normal e de Unidades de Produção e Autoconsumo (UPAC). Este caso de estudo apresenta-se como um perfil de carga elétrica regular e com contornos muito suaves. O segundo caso de estudo diz respeito a uma ilha turística e representa um perfil irregular de carga, com variações bruscas e difíceis de prever e apresenta um desafio maior em termos de previsão a 24-horas A partir dos resultados obtidos, é avaliado o impacto da integração de uma seleção recursiva inteligente de atributos, seguido por uma viabilização do processo de redução da dimensão de dados para o operador da Micro Rede, e por fim uma comparação de estimadores usados no modelo de previsão, através de medidores de erros na performance do algoritmo.Micro Grids constitute a growing sector of the energetic industry, representing a paradigm shift from the central power generation plans to a more distributed generation. The capacity to work isolated from the main electric grid make the MG resilient system, capable of conducting flexible operations while providing services that make the network more competitive. Additionally, Micro Grids supply clean and efficient low-cost energy, enhance the flexible assets coordination and improve the operation and stability of the of the local electric grid, through the capability of providing a dynamic response to the energetic resources. For that, it is required an intelligent coordination which balances all the available technologies. With this, rises the need to integrate accurate and robust load and production forecasting models into the MG management platform, thus allowing a more precise coordination of the flexible resource according to the emerging demand needs. For these reasons, the HALOFMI methodology was developed, which focus on the creation of a precise 24-hour load forecast model. This methodology includes firstly, a hybrid multi-level approach for the creation and selection of features. Then, these inputs are fed to a Neural Network (Multi-Layer Perceptron) with hyper-parameters tuning. In a second phase, two ways of data operation are compared and assessed, which results in the viability of the network operating with a reduced number of training days without compromising the model's performance. Such process is attained through a sliding window application. Furthermore, the developed methodology is applied in two case studies, both with 15-minute timesteps: the first one is composed by aggregated load profiles of Standard Low Voltage clients, including production and self-consumption units. This case study presents regular and very smooth load profile curves. The second case study concerns a touristic island and represents an irregular load curve with high granularity with abrupt variations. From the attained results, it is evaluated the impact of integrating a recursive intelligent feature selection routine, followed by an assessment on the sliding window application and at last, a comparison on the errors coming from different estimators for the model, through several well-defined performance metrics

Space transportation systems, launch systems, and propulsion for the Space Exploration Initiative: Results from Project Outreach

A number of transportation and propulsion options for Mars exploration missions are analyzed. As part of Project Outreach, RAND received and evaluated 350 submissions in the launch vehicle, space transportation, and propulsion areas. After screening submissions, aggregating those that proposed identical or nearly identical concepts, and eliminating from further consideration those that violated known physical princples, we had reduced the total number of viable submissions to 213. In order to avoid comparing such disparate things as launch vehicles and electric propulsion systems, six broad technical areas were selected to categorize the submissions: space transportation systems; earth-to-orbit (ETO) launch systems; chemical propulsion; nuclear propulsion; low-thrust propulsion; and other. To provide an appropriate background for analyzing the submissions, an extensive survey was made of the various technologies relevant to the six broad areas listed above. We discuss these technologies with the intent of providing the reader with an indication of the current state of the art, as well as the advances that might be expected within the next 10 to 20 years