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.

Analysis of the impact of stationary energy storage systems in trolleybus grids using Simulink-based modelling

The voltage profile of the catenary between traction substations (TSSs) is affected by the trolleybus current intake and by its position with respect to the TSSs: the higher the current requested by the bus and the further the bus from the TSSs, the deeper the voltage drop. When the voltage drops below 500V, the trolleybus is forced to decrease its consumption by reducing its input current. This thesis deals with the analysis of the improvements that the installation of an BESS produces in the operation of a particularly loaded FS of the DC trolleybus network of the city of Bologna. The stationary BESS is charged by the TSSs during off-peak times and delivers the stored energy when the catenary is overloaded alleviating the load on the TSSs and reducing the voltage drops. Only IMC buses are considered in the prospect of a future disposal of all internal combustion engine vehicles. These trolleybuses cause deeper voltage drops because they absorb enough current to power their traction motor and recharge the on board battery. The control of the BESS aims to keep the catenary voltage within the admissible voltage range and makes sure that all physical limitations are met. A model of FS Marconi Trento Trieste is implemented in Simulink environment to simulate its daily operation and compare the behavior of the trolleybus network with and without BESS. From the simulation without BESS, the best location of the energy storage system is deduced, and the battery control is tuned. Furthermore, from the knowledge of the load curve and the battery control trans-characteristic, it is formulated a prediction of the voltage distribution at BESS connection point. The prediction is then compared with the simulation results to validate the Simulink model. The BESS allows to decrease the voltage drops along the catenary, the Joule losses and the current delivered by the TSSs, indicating that the BESS can be a solution to improve the operation of the trolleybus network.

Analysis of Lead-Acid batteries with a third-order dynamical model

Electrical energy storage is a really important issue nowadays. As electricity is not easy to be directly stored, it can be stored in other forms and converted back to electricity when needed. As a consequence, storage technologies for electricity can be classified by the form of storage, and in particular we focus on electrochemical energy storage systems, better known as electrochemical batteries. Largely the more widespread batteries are the Lead-Acid ones, in the two main types known as flooded and valve-regulated. Batteries need to be present in many important applications such as in renewable energy systems and in motor vehicles. Consequently, in order to simulate these complex electrical systems, reliable battery models are needed. Although there exist some models developed by experts of chemistry, they are too complex and not expressed in terms of electrical networks. Thus, they are not convenient for a practical use by electrical engineers, who need to interface these models with other electrical systems models, usually described by means of electrical circuits. There are many techniques available in literature by which a battery can be modeled. Starting from the Thevenin based electrical model, it can be adapted to be more reliable for Lead-Acid battery type, with the addition of a parasitic reaction branch and a parallel network. The third-order formulation of this model can be chosen, being a trustworthy general-purpose model, characterized by a good ratio between accuracy and complexity. Considering the equivalent circuit network, all the useful equations describing the battery model are discussed, and then implemented one by one in Matlab/Simulink. The model has been finally validated, and then used to simulate the battery behaviour in different typical conditions.

Studies of CMS data access patterns with machine learning techniques

This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy over time and different access patterns, and ultimately to extract suggested actions based on this information (e.g. targetted disk clean-up and/or data replication). In this sense, the application of Machine Learning techniques allows to learn from past data and to gain predictability potential for the future CMS data access patterns. Chapter 1 provides an introduction to High Energy Physics at the LHC. Chapter 2 describes the CMS Computing Model, with special focus on the data management sector, also discussing the concept of dataset popularity. Chapter 3 describes the study of CMS data access patterns with different depth levels. Chapter 4 offers a brief introduction to basic machine learning concepts and gives an introduction to its application in CMS and discuss the results obtained by using this approach in the context of this thesis.

Experimental and numerical characterization of flame instability in hydrogen-containing flames

In the framework of the energy transition, the acquisition of proper knowledge of fundamental aspects characterizing the use of alternative fuels is paramount as well as the development of optimized know-how and technologies. In this sense, the use of hydrogen has been indicated as a promising route for decarbonization at the end-users stage in the energy supply chain. However, the elevated reactivity and the low-density at atmospheric conditions of hydrogen pose new challenges. Among the others, the dilution of hydrogen with carbon dioxide from carbon capture and storage systems represents a possible route. However, the interactions between these species have been poorly studied so far. For these reasons, this thesis, in collaboration between the University of Bologna and Technische Universität Bergakademie of Freiberg in Saxony (Germany), investigates the laminar flame of hydrogen-based premixed gas with the dilution of carbon dioxide. An experimental system, called a heat flux burner, was adopted ad different operating conditions. The presence of the cellularity phenomenon, forming the so-called cellular flame, was observed and analysed. Theoretical and visual methods have allowed for the characterization of the investigated flames, opening new alternatives for sustainable energy production via hydrogen transformation.

A framework for the environmental risk assessment of offshore carbon storage in CCS systems

Carbon capture and storage (CCS) represents an interesting climate mitigation option, however, as for any other human activity, there is the impelling need to assess and manage the associated risks. This study specifically addresses the marine environmental risk posed by CO2 leakages associated to CCS subsea engineering system, meant as offshore pipelines and injection / plugged and abandoned wells. The aim of this thesis work is to start approaching the development of a complete and standardized practical procedure to perform a quantified environmental risk assessment for CCS, with reference to the specific activities mentioned above. Such an effort would be of extreme relevance not only for companies willing to implement CCS, as a methodological guidance, but also, by uniformizing the ERA procedure, to begin changing people’s perception about CCS, that happens to be often discredited due to the evident lack of comprehensive and systematic methods to assess the impacts on the marine environment. The backbone structure of the framework developed consists on the integration of ERA’s main steps and those belonging to the quantified risk assessment (QRA), in the aim of quantitatively characterizing risk and describing it as a combination of magnitude of the consequences and their frequency. The framework developed by this work is, however, at a high level, as not every single aspect has been dealt with in the required detail. Thus, several alternative options are presented to be considered for use depending on the situation. Further specific studies should address their accuracy and efficiency and solve the knowledge gaps emerged, in order to establish and validate a final and complete procedure. Regardless of the knowledge gaps and uncertainties, that surely need to be addressed, this preliminary framework already finds some relevance in on field applications, as a non-stringent guidance to perform CCS ERA, and it constitutes the foundation of the final framework.

Qualitatitive and quantitative analysis of fish spoilage processes for development of new food monitoring systems

This study investigates the growth and metabolite production of microorganisms causing spoilage of Atlantic cod (Gadus morhua) fillets packaged under air and modified atmosphere (60 % CO2, 40 % O2). Samples were provided by two different retailers (A and B). Storage of packaged fillets occurred at 4 °C and 8 °C. Microbiological quality and metabolite production of cod fillets stored in MAP 4 °C, MAP 8 °C and air were monitored during 13 days, 7 days and 3 days of storage, respectively. Volatile compounds concentration in the headspace were quantified by Selective ion flow tube mass spectrometry and a correlation with microbiological spoilage was studied. The onset of volatile compounds detection was observed to be mostly around 7 log cfu/g of total psychrotrophic count. Trimethylamine and dimethyl sulfide were found to be the dominant volatiles in all of the tested storage conditions, nevertheless there was no close correlation between concentrations of each main VOC and percentages of rejection based on sensory evaluation. According to results it was concluded that they cannot be considered as only indicators of the quality of cod fillets stored in modified atmosphere and air.  

Application and optimization of a thermal energy storage on a cruise ship

The thesis, developed in collaboration between the team Systems and Equipment for Energy and Environment of Bologna University and Chalmers University of Technology in Goteborg, aims to study the benefits resulting from the adoption of a thermal storage system for marine application. To that purpose a chruis ship has been considered. To reach the purpose has been used the software EGO (Energy Greed Optimization) developed by University of Bologna.

Deployment of a data analysis workflow of the ATLAS experiment on HPC systems

LHC experiments produce an enormous amount of data, estimated of the order of a few PetaBytes per year. Data management takes place using the Worldwide LHC Computing Grid (WLCG) grid infrastructure, both for storage and processing operations. However, in recent years, many more resources are available on High Performance Computing (HPC) farms, which generally have many computing nodes with a high number of processors. Large collaborations are working to use these resources in the most efficient way, compatibly with the constraints imposed by computing models (data distributed on the Grid, authentication, software dependencies, etc.). The aim of this thesis project is to develop a software framework that allows users to process a typical data analysis workflow of the ATLAS experiment on HPC systems. The developed analysis framework shall be deployed on the computing resources of the Open Physics Hub project and on the CINECA Marconi100 cluster, in view of the switch-on of the Leonardo supercomputer, foreseen in 2023.

Keyword-based multimedia data lookup in decentralized systems

I sistemi decentralizzati hanno permesso agli utenti di condividere informazioni senza la presenza di un intermediario centralizzato che possiede la sovranità sui dati scambiati, rischi di sicurezza e la possibilità di colli di bottiglia. Tuttavia, sono rari i sistemi pratici per il recupero delle informazioni salvate su di essi che non includano una componente centralizzata. In questo lavoro di tesi viene presentato lo sviluppo di un'applicazione il cui scopo è quello di consentire agli utenti di caricare immagini in un'architettura totalmente decentralizzata, grazie ai Decentralized File Storage e alla successiva ricerca e recupero di tali oggetti attraverso una Distributed Hash Table (DHT) in cui sono memorizzati i necessari Content IDentifiers (CID).\\ L'obiettivo principale è stato quello di trovare una migliore allocazione delle immagini all'interno del DHT attraverso l'uso dell'International Standard Content Code (ISCC), ovvero uno standard ISO che, attraverso funzioni hash content-driven, locality-sensitive e similarity-preserving, assegna i CID IPFS delle immagini ai nodi del DHT in modo efficiente, per ridurre il più possibile i salti tra i nodi e recuperare immagini coerenti con la query eseguita. Verranno, poi, analizzati i risultati ottenuti dall'allocazione dei CID delle immagini nei nodi mettendo a confronto ISCC e hash crittografico SHA-256, per verificare se ISCC rappresenti meglio la somiglianza tra le immagini allocando le immagini simili in nodi vicini tra loro.