Design of alternative energy storage systems for hybrid vehicles based on statistical processing of driving cycles information

Hybrid vehicles represent the future for automakers, since they allow to improve the fuel economy and to reduce the pollutant emissions. A key component of the hybrid powertrain is the Energy Storage System, that determines the ability of the vehicle to store and reuse energy. Though electrified Energy Storage Systems (ESS), based on batteries and ultracapacitors, are a proven technology, Alternative Energy Storage Systems (AESS), based on mechanical, hydraulic and pneumatic devices, are gaining interest because they give the possibility of realizing low-cost mild-hybrid vehicles. Currently, most literature of design methodologies focuses on electric ESS, which are not suitable for AESS design. In this contest, The Ohio State University has developed an Alternative Energy Storage System design methodology. This work focuses on the development of driving cycle analysis methodology that is a key component of Alternative Energy Storage System design procedure. The proposed methodology is based on a statistical approach to analyzing driving schedules that represent the vehicle typical use. Driving data are broken up into power events sequence, namely traction and braking events, and for each of them, energy-related and dynamic metrics are calculated. By means of a clustering process and statistical synthesis methods, statistically-relevant metrics are determined. These metrics define cycle representative braking events. By using these events as inputs for the Alternative Energy Storage System design methodology, different system designs are obtained. Each of them is characterized by attributes, namely system volume and weight. In the last part the work, the designs are evaluated in simulation by introducing and calculating a metric related to the energy conversion efficiency. Finally, the designs are compared accounting for attributes and efficiency values. In order to automate the driving data extraction and synthesis process, a specific script Matlab based has been developed. Results show that the driving cycle analysis methodology, based on the statistical approach, allows to extract and synthesize cycle representative data. The designs based on cycle statistically-relevant metrics are properly sized and have satisfying efficiency values with respect to the expectations. An exception is the design based on the cycle worst-case scenario, corresponding to same approach adopted by the conventional electric ESS design methodologies. In this case, a heavy system with poor efficiency is produced. The proposed new methodology seems to be a valid and consistent support for Alternative Energy Storage System design.

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.

Influence of microstructure variability on short crack growth behavior

Fatigue life in metals is predicted utilizing regression analysis of large sets of experimental data, thus representing the material’s macroscopic response. Furthermore, a high variability in the short crack growth (SCG) rate has been observed in polycrystalline materials, in which the evolution and distributionof local plasticity is strongly influenced by the microstructure features. The present work serves to (a) identify the relationship between the crack driving force based on the local microstructure in the proximity of the crack-tip and (b) defines the correlation between scatter observed in the SCG rates to variability in the microstructure. A crystal plasticity model based on the fast Fourier transform formulation of the elasto-viscoplastic problem (CP-EVP-FFT) is used, since the ability to account for the both elastic and plastic regime is critical in fatigue. Fatigue is governed by slip irreversibility, resulting in crack growth, which starts to occur during local elasto-plastic transition. To investigate the effects of microstructure variability on the SCG rate, sets of different microstructure realizations are constructed, in which cracks of different length are introduced to mimic quasi-static SCG in engineering alloys. From these results, the behavior of the characteristic variables of different length scale are analyzed: (i) Von Mises stress fields (ii) resolved shear stress/strain in the pertinent slip systems, and (iii) slip accumulation/irreversibilities. Through fatigue indicator parameters (FIP), scatter within the SCG rates is related to variability in the microstructural features; the results demonstrate that this relationship between microstructure variability and uncertainty in fatigue behavior is critical for accurate fatigue life prediction.

Influence of cladding on robustness of multi-storey buildings

In this dissertation the influence of a precast concrete cladding system on structural robustness of a multi-storey steel-composite building is studied. The analysis follows the well-established framework developed at Imperial College London for the appraisal of robustness of multi-storey buildings. For this scope a simplified nonlinear model of a typical precast concrete façade-system is developed. Particular attention is given to the connection system between structural frame and panel, recognised as the driving component of the nonlinear behaviour of the façade-system. Only connections involved in the gravity load path are evaluated (bearing connections). Together with standard connection, a newly proposed system (Slotted Bearing Connection) is designed to achieve a more ductile behaviour of the panel-connection system. A parametric study involving the dimensions of panel-connection components is developed to search for an optimal configuration of the bearing connection. From the appraisal of structural robustness of the panelised frame it is found that the standard connection systems may reduce the robustness of a multi-storey frame due to a poor ductile behaviour while the newly proposed connection is able to guarantee an enhanced response to the panelised multi-storey frame thanks to a higher ductility.

Context sensitive design of provincial road Galliera SP4.

In the past centuries and before the invention of automobile, roads consisted mainly of unpaved paths connecting only few cities. Later, in the beginning of the twentieth century, the automobile was introduced and a new type of the transportation system was born. Therefore, it was necessary to change the condition of roads to fit with the automobiles. With the spread and the development of the automobiles, roads also have developed and increased all over the world. That caused negative effects on the environment and humans’ life quality. Thus, highways associations and communities had to take some steps to reduce these effects and care about environmental and cultural issues with the traditional commitment to safety and mobility, and that is known as context sensitive design. The aim of this thesis is to use the concepts of context sensitive design to reduce the negative environmental impacts of provincial road Galliera, which connects via Colombo in city of Bologna to provincial road 3 in Argelato city. Some solutions were proposed in this thesis to reduce traffic noise, fragmentation, fauna mortality and to improve the aesthetics of the road.

A study of air-sea interaction processes on water mass formation and upwelling in the Mediterranean sea

Air-sea interactions are a key process in the forcing of the ocean circulation and the climate. Water Mass Formation is a phenomenon related to extreme air-sea exchanges and heavy heat losses by the water column, being capable to transfer water properties from the surface to great depth and constituting a fundamental component of the thermohaline circulation of the ocean. Wind-driven Coastal Upwelling, on the other hand, is capable to induce intense heat gain in the water column, making this phenomenon important for climate change; further, it can have a noticeable influence on many biological pelagic ecosystems mechanisms. To study some of the fundamental characteristics of Water Mass Formation and Coastal Upwelling phenomena in the Mediterranean Sea, physical reanalysis obtained from the Mediterranean Forecating System model have been used for the period ranging from 1987 to 2012. The first chapter of this dissertation gives the basic description of the Mediterranean Sea circulation, the MFS model implementation, and the air-sea interaction physics. In the second chapter, the problem of Water Mass Formation in the Mediterranean Sea is approached, also performing ad-hoc numerical simulations to study heat balance components. The third chapter considers the study of Mediterranean Coastal Upwelling in some particular areas (Sicily, Gulf of Lion, Aegean Sea) of the Mediterranean Basin, together with the introduction of a new Upwelling Index to characterize and predict upwelling features using only surface estimates of air-sea fluxes. Our conclusions are that latent heat flux is the driving air-sea heat balance component in the Water Mass Formation phenomenon, while sensible heat exchanges are fundamental in Coastal Upwelling process. It is shown that our upwelling index is capable to reproduce the vertical velocity patterns in Coastal Upwelling areas. Nondimensional Marshall numbers evaluations for the open-ocean convection process in the Gulf of Lion show that it is a fully turbulent, three-dimensional phenomenon.

Formula Zero: Traduzione trilingue inglese-italiano-spagnolo di un policy statement sulla sicurezza stradale della Federazione Internazionale dell'Automobile (FIA)

The aim of this dissertation is to provide a trilingual translation from English into Italian and from Italian into Spanish of a policy statement from the Fédération Internationale de l’Automobile (FIA) regarding road safety. The document, named “Formula Zero: a strategy for reducing fatalities and injuries on track and road”, was published in June 2000 and involves an approach about road safety inspired by another approach introduced in Sweden called ‘Vision Zero’. This work consists of six sections. The first chapter introduces the main purposes and activities of the Federation, as well as the institutions related to it and Vision Zero. The second chapter presents the main lexical, morphosyntactic and stylistic features of the institutional texts and special languages. In particular, the text contains technical nomenclature of transports and elements of sport language, especially regarding motor sport and Formula One. In the third chapter, the methodology is explained, with all the resources used during the preliminary phase and the translation, including corpora, glossaries, expert consultancy and specialised sites. The fourth chapter focuses on the morphosyntactic and terminology features contained in the text, while the fifth chapter presents the source text and the target texts. The final chapter deals with all the translation strategies that are applied, alongside with all the challenging elements detected. Therefore, the dissertation concludes with some theoretical and practical considerations about the role of inverse translation and English as Lingua Franca (ELF), by comparing the text translated into Spanish to the original in English, using Italian as a lingua franca.

Assessment of marine litter in the Western Adriatic Sea and its impact on marine biota

Due to its environmental, safety, health and socio-economic impacts, marine litter has been recognized as a 21st century global challenge, so that it has been included in Descriptor 10 of the EU MSFD. For its morphological features and anthropogenic pressures, the Adriatic Sea is very sensitive to the accumulation of debris, but data are inconsistent and fragmented. This thesis, in the framework of DeFishGear project, intents to assess marine litter on beaches and on seafloor in the Western Adriatic sea, and test if debris ingestion by fish occurs. Three beaches were sampled during two surveys in 2015. Benthic litter monitoring was carried out in the FAO GSA17 during fall 2014, using a rapido trawl. Litter ingestion was investigated through gut contents analysis of 260 fish belonging to 8 commercial species collected in Western Gulf of Venice. Average litter density on beaches was 1.5 items/m2 during spring, and decreased to 0.8 items/m2 in summer. Litter composition was heterogeneous and varied among sites, even if no significant differences were found. Most of debris consisted of plastic sheets, fragments, polystyrene pieces, mussels nets and cottons bud sticks, showing that sources are many and include aquaculture, land-based activities and local users of beaches. Average density of benthic litter was 913 items/Km2 (82 Kg/Km2). Plastic dominated in terms of numbers and weight, and consisted mainly of bags, sheets and mussel nets. The highest density was found close to the coast, and sources driving the major differences in litter distribution were mussel farms and shipping lanes. Litter ingestion occurred in 47% of examined fish, mainly consisting of fibers. Among species, S. pilchardus swallowed almost all debris categories. Findinds may provide a baseline to set the necessary measures to manage and minimize marine litter in the Western Adriatic region and to protect aquatic life from plastic pollution, even accounting the possible implications on human health.

Gyrokinetic modelling of plasma transport and investigation on test particle dynamics

Turbulent plasmas inside tokamaks are modeled and studied using guiding center theory, applied to charged test particles, in a Hamiltonian framework. The equations of motion for the guiding center dynamics, under the conditions of a constant and uniform magnetic field and turbulent electrostatic field are derived by averaging over the fast gyroangle, for the first and second order in the guiding center potential, using invertible changes of coordinates such as Lie transforms. The equations of motion are then made dimensionless, exploiting temporal and spatial periodicities of the model chosen for the electrostatic potential. They are implemented numerically in Python. Fast Fourier Transform and its inverse are used. Improvements to the original Python scripts are made, notably the introduction of a power-law curve fitting to account for anomalous diffusion, the possibility to integrate the equations in two steps to save computational time by removing trapped trajectories, and the implementation of multicolored stroboscopic plots to distinguish between trapped and untrapped guiding centers. The post-processing of the results is made in MATLAB. The values and ranges of the parameters chosen for the simulations are selected based on numerous simulations used as feedback tools. In particular, a recurring value for the threshold to detect trapped trajectories is evidenced. Effects of the Larmor radius, the amplitude of the guiding center potential and the intensity of its second order term are studied by analyzing their diffusive regimes, their stroboscopic plots and the shape of guiding center potentials. The main result is the identification of cases anomalous diffusion depending on the values of the parameters (mostly the Larmor radius). The transitions between diffusive regimes are identified. The presence of highways for the super-diffusive trajectories are unveiled. The influence of the charge on these transitions from diffusive to ballistic behaviors is analyzed.

Design and implementation of an optimal motion cueing algorithm for the Stuttgart Driving Simulator

Driving simulators emulate a real vehicle drive in a virtual environment. One of the most challenging problems in this field is to create a simulated drive as real as possible to deceive the driver's senses and cause the believing to be in a real vehicle. This thesis first provides an overview of the Stuttgart driving simulator with a description of the overall system, followed by a theoretical presentation of the commonly used motion cueing algorithms. The second and predominant part of the work presents the implementation of the classical and optimal washout algorithms in a Simulink environment. The project aims to create a new optimal washout algorithm and compare the obtained results with the results of the classical washout. The classical washout algorithm, already implemented in the Stuttgart driving simulator, is the most used in the motion control of the simulator. This classical algorithm is based on a sequence of filters in which each parameter has a clear physical meaning and a unique assignment to a single degree of freedom. However, the effects on human perception are not exploited, and each parameter must be tuned online by an engineer in the control room, depending on the driver's feeling. To overcome this problem and also consider the driver's sensations, the optimal washout motion cueing algorithm was implemented. This optimal control-base algorithm treats motion cueing as a tracking problem, forcing the accelerations perceived in the simulator to track the accelerations that would have been perceived in a real vehicle, by minimizing the perception error within the constraints of the motion platform. The last chapter presents a comparison between the two algorithms, based on the driver's feelings after the test drive. Firstly it was implemented an off-line test with a step signal as an input acceleration to verify the behaviour of the simulator. Secondly, the algorithms were executed in the simulator during a test drive on several tracks.

Smart Data Analysis on Lamborghini Connected Vehicles

The thesis is the result of work conducted during a period of six months at the Strategy department of Automobili Lamborghini S.p.A. in Sant'Agata Bolognese (BO) and concerns the study and analysis of Big Data relating to Lamborghini's connected cars. The Big Data is a project of Connected Car Project House, that is an inter-departmental team which works toward the definition of the Lamborghini corporate connectivity strategy and its implementation in the product portfolio. The Data of the connected cars is one of the hottest topics right now in the automotive industry; in fact, all the largest automotive companies are investi,ng a lot in this direction, in order to derive the greatest advantages both from a purely economic point of view, because from these data you can understand a lot the behaviors and habits of each driver, and from a technological point of view because it will increasingly promote the development of 5G that will be an important enabler for the future of connectivity. The main purpose of the work by Lamborghini prospective is to analyze the data of the connected cars, in particular a data-set referred to connected Huracans that had been already placed on the market, and, starting from that point, derive valuable Key Performance Indicators (KPIs) on which the company could partly base the decisions to be made in the near future. The key result that we have obtained at the end of this period was the creation of a Dashboard, in which is possible to visualize many parameters and indicators both related to driving habits and the use of the vehicle itself, which has brought great insights on the huge potential and value that is present behind the study of these data. The final Demo of the project has received great interest, not only from the whole strategy department but also from all the other business areas of Lamborghini, making mostly a great awareness that this will be the road to follow in the coming years.

Copy-paste data augmentation for domain transfer on traffic signs

City streets carry a lot of information that can be exploited to improve the quality of the services the citizens receive. For example, autonomous vehicles need to act accordingly to all the element that are nearby the vehicle itself, like pedestrians, traffic signs and other vehicles. It is also possible to use such information for smart city applications, for example to predict and analyze the traffic or pedestrian flows. Among all the objects that it is possible to find in a street, traffic signs are very important because of the information they carry. This information can in fact be exploited both for autonomous driving and for smart city applications. Deep learning and, more generally, machine learning models however need huge quantities to learn. Even though modern models are very good at gener- alizing, the more samples the model has, the better it can generalize between different samples. Creating these datasets organically, namely with real pictures, is a very tedious task because of the wide variety of signs available in the whole world and especially because of all the possible light, orientation conditions and con- ditions in general in which they can appear. In addition to that, it may not be easy to collect enough samples for all the possible traffic signs available, cause some of them may be very rare to find. Instead of collecting pictures manually, it is possible to exploit data aug- mentation techniques to create synthetic datasets containing the signs that are needed. Creating this data synthetically allows to control the distribution and the conditions of the signs in the datasets, improving the quality and quantity of training data that is going to be used. This thesis work is about using copy-paste data augmentation to create synthetic data for the traffic sign recognition task.