Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge

Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That’s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.

Development of Optimal Energy Management Strategies for a Hybrid Boat

The present work proposes different approaches to extend the mathematical methods of supervisory energy management used in terrestrial environments to the maritime sector, that diverges in constraints, variables and disturbances. The aim is to find the optimal real-time solution that includes the minimization of a defined track time, while maintaining the classical energetic approach. Starting from analyzing and modelling the powertrain and boat dynamics, the energy economy problem formulation is done, following the mathematical principles behind the optimal control theory. Then, an adaptation aimed in finding a winning strategy for the Monaco Energy Boat Challenge endurance trial is performed via ECMS and A-ECMS control strategies, which lead to a more accurate knowledge of energy sources and boat’s behaviour. The simulations show that the algorithm accomplishes fuel economy and time optimization targets, but the latter adds huge tuning and calculation complexity. In order to assess a practical implementation on real hardware, the knowledge of the previous approaches has been translated into a rule-based algorithm, that let it be run on an embedded CPU. Finally, the algorithm has been tuned and tested in a real-world race scenario, showing promising results.

Microfluidic microbial fuel cell fabrication and rapid screening of electrochemically microbes

The demand for novel renewable energy sources, together with the new findings on bacterial electron transport mechanisms and the progress in microbial fuel cell design, have raised a noticeable interest in microbial power generation. Microbial fuel cell (MFC) is an electrochemical device that converts organic substrates into electricity via catalytic conversion by microorganism. It has represented a continuously growing research field during the past few years. The great advantage of this device is the direct conversion of the substrate into electricity and in the future, MFC may be linked to municipal waste streams or sources of agricultural and animal waste, providing a sustainable system for waste treatment and energy production. However, these novel green technologies have not yet been used for practical applications due to their low power outputs and challenges associated with scale-up, so in-depth studies are highly necessary to significantly improve and optimize the device working conditions. For the time being, the micro-scale MFCs show great potential in the rapid screening of electrochemically active microbes. This thesis presents how it will be possible to optimize the properties and design of the micro-size microbial fuel cell for maximum efficiency by understanding the MFC system. So it will involve designing, building and testing a miniature microbial fuel cell using a new species of microorganisms that promises high efficiency and long lifetime. The new device offer unique advantages of fast start-up, high sensitivity and superior microfluidic control over the measured microenvironment, which makes them good candidates for rapid screening of electrode materials, bacterial strains and growth media. It will be made in the Centre of Hybrid Biodevices (Faculty of Physical Sciences and Engineering, University of Southampton) from polymer materials like PDMS. The eventual aim is to develop a system with the optimum combination of microorganism, ion exchange membrane and growth medium. After fabricating the cell, different bacteria and plankton species will be grown in the device and the microbial fuel cell characterized for open circuit voltage and power. It will also use photo-sensitive organisms and characterize the power produced by the device in response to optical illumination.

A study of the turbulence of the neutral medium in two nearby spiral galaxies

The width of the 21 cm line (HI) emitted by spiral galaxies depends on the physical processes that release energy in the Interstellar Medium (ISM). This quantity is called velocity dispersion (σ) and it is proportional first of all to the thermal kinetic energy of the gas. The accepted theoretical picture predicts that the neutral hydrogen component (HI) exists in the ISM in two stable phases: a cold one (CNM, with σ~0.8 km/s) and a warm one (WNM, with σ~8 km/s). However, this is called into question by the observation that the HI gas has usually larger velocity dispersions. This suggests the presence of turbulence in the ISM, although the energy sources remain unknown. In this thesis we want to shed new light on this topic. We have studied the HI line emission of two nearby galaxies: NGC6946 and M101. For the latter we used new deep observations obtained with the Westerbork radio interferometer. Through a gaussian fitting procedure, we produced dispersion maps of the two galaxies. For both of them, we compared the σ values measured in the spiral arms with those in the interarms. In NGC6946 we found that, in both arms and interarms, σ grows with the column density, while we obtained the opposite for M 101. Using a statistical analysis we did not find a significant difference between arm and interarm dispersion distributions. Producing star formation rate density maps (SFRD) of the galaxies, we studied their global and local relations with the HI kinetic energy, as inferred from the measured dispersions. For NGC6946 we obtained a good log-log correlation, in agreement with a simple model of supernova feedback driven turbulence. This shows that in this galaxy turbulent motions are mainly induced by the stellar activity. For M 101 we did not find an analogous correlation, since the gas kinetic energy appears constant with the SFRD. We think that this may indicate that in this galaxy turbulence is driven also by accretion of extragalactic material.

Development and optimization of fibre-shaped dye-sensitized solar cells employing an innovative fully organic sensitizer

The quality of human life depends to a large degree on the availability of energy. In recent years, photovoltaic technology has been growing extraordinarily as a suitable source of energy, as a consequence of the increasing concern over the impact of fossil fuels on climate change. Developing affordable and highly efficiently photovoltaic technologies is the ultimate goal in this direction. Dye-sensitized solar cells (DSSCs) offer an efficient and easily implementing technology for future energy supply. Compared to conventional silicon solar cells, they provide comparable power conversion efficiency at low material and manufacturing costs. In addition, DSSCs are able to harvest low-intensity light in diffuse illumination conditions and then represent one of the most promising alternatives to the traditional photovoltaic technology, even more when trying to move towards flexible and transparent portable devices. Among these, considering the increasing demand of modern electronics for small, portable and wearable integrated optoelectronic devices, Fibre Dye-Sensitized Solar Cells (FDSSCs) have gained increasing interest as suitable energy provision systems for the development of the next-generation of smart products, namely “electronic textiles” or “e-textiles”. In this thesis, several key parameters towards the optimization of FDSSCs based on inexpensive and abundant TiO2 as photoanode and a new innovative fully organic sensitizer were studied. In particular, the effect of various FDSSCs components on the device properties pertaining to the cell architecture in terms of photoanode oxide layer thickness, electrolytic system, cell length and electrodes substrates were examined. The photovoltaic performances of the as obtained FDSSCs were fully characterized. Finally, the metal part of the devices (wire substrate) was substituted with substrates suitable for the textile industry as a fundamental step towards commercial exploitation.

Sintesi e caratterizzazione di materiali polimerici water-soluble per applicazioni nel campo del fotovoltaico organico

The need to use renewable energy sources, due to the massive production of pollution for the energy production, has led to the development of new technologies for the use of solar energy. The purpose of this thesis project is to synthesize and characterize new thiophene-based polymeric materials processable in water, a green solvent, for the construction of organic solar cells, promising and versatile devices used for the production of electric energy. For this, a highly regioregular polymer was synthesized through GRIM polymerization (Grignard Metathesis Polymerization) on which a study was performed to identify the optimal reaction time.

Progetto e valutazione di una piattaforma sperimentale di Internet of Energy per veicoli elettrici

This dissertation deals with the development of a project concerning a demonstration in the scope of the Supply Chain 6 of the Internet of Energy (IoE) project: the Remote Monitoring Emulator, which bears my personal contribution in several sections. IoE is a project of international relevance, that means to establish an interoperability standard as regards the electric power production and utilization infrastructure, using Smart Space platforms. The future perspectives of IoE have to do with a platform for electrical power trade-of, the Smart Grid, whose energy is produced by decentralized renewable sources and whose services are exploited primarily according to the Internet of Things philosophy. The main consumers of this kind of smart technology will be Smart Houses (that is to say, buildings controlled by an autonomous system for electrical energy management that is interoperable with the Smart Grid) and Electric Mobility, that is a smart and automated management regarding movement and, overall, recharging of electrical vehicles. It is precisely in the latter case study that the project Remote Monitoring Emulator takes place. It consists in the development of a simulated platform for the management of an electrical vehicle recharging in a city. My personal contribution to this project lies in development and modeling of the simulation platform, of its counterpart in a mobile application and implementation of a city service prototype. This platform shall, ultimately, make up a demonstrator system exploiting the same device which a real user, inside his vehicle, would use. The main requirements that this platform shall satisfy will be interoperability, expandability and relevance to standards, as it needs to communicate with other development groups and to effectively respond to internal changes that can affect IoE.

Properties of cosmic voids in dark energy simulations

The last decade has witnessed the establishment of a Standard Cosmological Model, which is based on two fundamental assumptions: the first one is the existence of a new non relativistic kind of particles, i. e. the Dark Matter (DM) that provides the potential wells in which structures create, while the second one is presence of the Dark Energy (DE), the simplest form of which is represented by the Cosmological Constant Λ, that sources the acceleration in the expansion of our Universe. These two features are summarized by the acronym ΛCDM, which is an abbreviation used to refer to the present Standard Cosmological Model. Although the Standard Cosmological Model shows a remarkably successful agreement with most of the available observations, it presents some longstanding unsolved problems. A possible way to solve these problems is represented by the introduction of a dynamical Dark Energy, in the form of the scalar field ϕ. In the coupled DE models, the scalar field ϕ features a direct interaction with matter in different regimes. Cosmic voids are large under-dense regions in the Universe devoided of matter. Being nearby empty of matter their dynamics is supposed to be dominated by DE, to the nature of which the properties of cosmic voids should be very sensitive. This thesis work is devoted to the statistical and geometrical analysis of cosmic voids in large N-body simulations of structure formation in the context of alternative competing cosmological models. In particular we used the ZOBOV code (see ref. Neyrinck 2008), a publicly available void finder algorithm, to identify voids in the Halos catalogues extraxted from CoDECS simulations (see ref. Baldi 2012 ). The CoDECS are the largest N-body simulations to date of interacting Dark Energy (DE) models. We identify suitable criteria to produce voids catalogues with the aim of comparing the properties of these objects in interacting DE scenarios to the standard ΛCDM model, at different redshifts. This thesis work is organized as follows: in chapter 1, the Standard Cosmological Model as well as the main properties of cosmic voids are intro- duced. In chapter 2, we will present the scalar field scenario. In chapter 3 the tools, the methods and the criteria by which a voids catalogue is created are described while in chapter 4 we discuss the statistical properties of cosmic voids included in our catalogues. In chapter 5 the geometrical properties of the catalogued cosmic voids are presented by means of their stacked profiles. In chapter 6 we summarized our results and we propose further developments of this work.

Machine learning "as a service" for high energy physics (MLaaS4HEP): evolution of a framework for ML-based physics analyses

The scientific success of the LHC experiments at CERN highly depends on the availability of computing resources which efficiently store, process, and analyse the amount of data collected every year. This is ensured by the Worldwide LHC Computing Grid infrastructure that connect computing centres distributed all over the world with high performance network. LHC has an ambitious experimental program for the coming years, which includes large investments and improvements both for the hardware of the detectors and for the software and computing systems, in order to deal with the huge increase in the event rate expected from the High Luminosity LHC (HL-LHC) phase and consequently with the huge amount of data that will be produced. Since few years the role of Artificial Intelligence has become relevant in the High Energy Physics (HEP) world. Machine Learning (ML) and Deep Learning algorithms have been successfully used in many areas of HEP, like online and offline reconstruction programs, detector simulation, object reconstruction, identification, Monte Carlo generation, and surely they will be crucial in the HL-LHC phase. This thesis aims at contributing to a CMS R&D project, regarding a ML "as a Service" solution for HEP needs (MLaaS4HEP). It consists in a data-service able to perform an entire ML pipeline (in terms of reading data, processing data, training ML models, serving predictions) in a completely model-agnostic fashion, directly using ROOT files of arbitrary size from local or distributed data sources. This framework has been updated adding new features in the data preprocessing phase, allowing more flexibility to the user. Since the MLaaS4HEP framework is experiment agnostic, the ATLAS Higgs Boson ML challenge has been chosen as physics use case, with the aim to test MLaaS4HEP and the contribution done with this work.