A NOVEL MODELING AND STATE OF CHARGE ESTIMATION SCHEME FOR LITHIUM-ION BATTERIES

Lithium Ion (Li-Ion) batteries have got attention in recent decades because of their undisputable advantages over other types of batteries. They are used in so many our devices which we need in our daily life such as cell phones, lap top computers, cameras, and so many electronic devices. They also are being used in smart grids technology, stand-alone wind and solar systems, Hybrid Electric Vehicles (HEV), and Plug in Hybrid Electric Vehicles (PHEV). Despite the rapid increase in the use of Lit-ion batteries, the existence of limited battery models also inadequate and very complex models developed by chemists is the lack of useful models a significant matter. A battery management system (BMS) aims to optimize the use of the battery, making the whole system more reliable, durable and cost effective. Perhaps the most important function of the BMS is to provide an estimate of the State of Charge (SOC). SOC is the ratio of available ampere-hour (Ah) in the battery to the total Ah of a fully charged battery. The Open Circuit Voltage (OCV) of a fully relaxed battery has an approximate one-to-one relationship with the SOC. Therefore, if this voltage is known, the SOC can be found. However, the relaxed OCV can only be measured when the battery is relaxed and the internal battery chemistry has reached equilibrium. This thesis focuses on Li-ion battery cell modelling and SOC estimation. In particular, the thesis, introduces a simple but comprehensive model for the battery and a novel on-line, accurate and fast SOC estimation algorithm for the primary purpose of use in electric and hybrid-electric vehicles, and microgrid systems. The thesis aims to (i) form a baseline characterization for dynamic modeling; (ii) provide a tool for use in state-of-charge estimation. The proposed modelling and SOC estimation schemes are validated through comprehensive simulation and experimental results.

Modeling chemistry in massive star forming regions with internal PDRs

Over the past decades star formation has been a very attractive field because knowledge of star formation leads to a better understanding of the formation of planets and thus of our solar system but also of the evolution of galaxies. Conditions leading to the formation of high-mass stars are still under investigation but an evolutionary scenario has been proposed: As a cold pre-stellar core collapses under gravitational force, the medium warms up until it reaches a temperature of 100 K and enters the hot molecular core (HMC) phase. The forming central proto-star accretes materials, increasing its mass and luminosity and eventually it becomes sufficiently evolved to emit UV photons which irradiate the surrounding environment forming a hyper compact (HC) and then a ultracompact (UC) HII region. At this stage, a very dense and very thin internal photon-dominated region (PDR) forms between the HII region and the molecular core. Information on the chemistry allows to trace the physical processes occurring in these different phases of star formation. Formation and destruction routes of molecules are influenced by the environment as reaction rates depend on the temperature and radiation field. Therefore, chemistry also allows the determination of the evolutionary stage of astrophysical objects through the use of chemical models including the time evolution of the temperature and radiation field. Because HMCs host a very rich chemistry with high abundances of complex organic molecules (COMs), several astrochemical models have been developed to study the gas phase chemistry as well as grain chemistry in these regions. In addition to HMCs models, models of PDRs have also been developed to study in particular photo-chemistry. So far, few studies have investigated internal PDRs and only in the presence of outflows cavities. Thus, these unique regions around HC/UCHII regions remain to be examined thoroughly. My PhD thesis focuses on the spatio-temporal chemical evolution in HC/UC HII regions with internal PDRs as well as in HMCs. The purpose of this study is first to understand the impact and effects of the radiation field, usually very strong in these regions, on the chemistry. Secondly, the goal is to study the emission of various tracers of HC/UCHII regions and compare it with HMCs models, where the UV radiation field does not impact the region as it is immediately attenuated by the medium. Ultimately we want to determine the age of a given region using chemistry in combination with radiative transfer.

Experimental and modeling studies of forced convection storage and drying systems for sweet potatoes

Sweet potato is an important strategic agricultural crop grown in many countries around the world. The roots and aerial vine components of the crop are used for both human consumption and, to some extent as a cheap source of animal feed. In spite of its economic value and growing contribution to health and nutrition, harvested sweet potato roots and aerial vine components has limited shelf-life and is easily susceptible to post-harvest losses. Although post-harvest losses of both sweet potato roots and aerial vine components is significant, there is no information available that will support the design and development of appropriate storage and preservation systems. In this context, the present study was initiated to improve scientific knowledge about sweet potato post-harvest handling. Additionally, the study also seeks to develop a PV ventilated mud storehouse for storage of sweet potato roots under tropical conditions. In study one, airflow resistance of sweet potato aerial vine components was investigated. The influence of different operating parameters such as airflow rate, moisture content and bulk depth at different levels on airflow resistance was analyzed. All the operating parameters were observed to have significant (P < 0.01) effect on airflow resistance. Prediction models were developed and were found to adequately describe the experimental pressure drop data. In study two, the resistance of airflow through unwashed and clean sweet potato roots was investigated. The effect of sweet potato roots shape factor, surface roughness, orientation to airflow, and presence of soil fraction on airflow resistance was also assessed. The pressure drop through unwashed and clean sweet potato roots was observed to increase with higher airflow, bed depth, root grade composition, and presence of soil fraction. The physical properties of the roots were incorporated into a modified Ergun model and compared with a modified Shedd’s model. The modified Ergun model provided the best fit to the experimental data when compared with the modified Shedd’s model. In study three, the effect of sweet potato root size (medium and large), different air velocity and temperature on the cooling/or heating rate and time of individual sweet potato roots were investigated. Also, a simulation model which is based on the fundamental solution of the transient equations was proposed for estimating the cooling and heating time at the centre of sweet potato roots. The results showed that increasing air velocity during cooling and heating significantly (P < 0.05) affects the cooling and heating times. Furthermore, the cooling and heating times were significantly different (P < 0.05) among medium and large size sweet potato roots. Comparison of the simulation results with experimental data confirmed that the transient simulation model can be used to accurately estimate the cooling and heating times of whole sweet potato roots under forced convection conditions. In study four, the performance of charcoal evaporative cooling pad configurations for integration into sweet potato roots storage systems was investigated. The experiments were carried out at different levels of air velocity, water flow rates, and three pad configurations: single layer pad (SLP), double layers pad (DLP) and triple layers pad (TLP) made out of small and large size charcoal particles. The results showed that higher air velocity has tremendous effect on pressure drop. Increasing the water flow rate above the range tested had no practical benefits in terms of cooling. It was observed that DLP and TLD configurations with larger wet surface area for both types of pads provided high cooling efficiencies. In study five, CFD technique in the ANSYS Fluent software was used to simulate airflow distribution in a low-cost mud storehouse. By theoretically investigating different geometries of air inlet, plenum chamber, and outlet as well as its placement using ANSYS Fluent software, an acceptable geometry with uniform air distribution was selected and constructed. Experimental measurements validated the selected design. In study six, the performance of the developed PV ventilated system was investigated. Field measurements showed satisfactory results of the directly coupled PV ventilated system. Furthermore, the option of integrating a low-cost evaporative cooling system into the mud storage structure was also investigated. The results showed a reduction of ambient temperature inside the mud storehouse while relative humidity was enhanced. The ability of the developed storage system to provide and maintain airflow, temperature and relative humidity which are the key parameters for shelf-life extension of sweet potato roots highlight its ability to reduce post-harvest losses at the farmer level, particularly under tropical climate conditions.

Extending the Simple Knowledge Organization System for Concept Management in Vocabulary Development Applications

In this article, we describe the development of an exten- sion to the Simple Knowledge Organization System (SKOS) to accommodate the needs of vocabulary devel- opment applications (VDA) managing metadata schemes and requiring close tracking of change to both those schemes and their member concepts. We take a neo- pragmatic epistemic stance in asserting the need for an entity in SKOS modeling to mediate between the abstract concept and the concrete scheme. While the SKOS model sufficiently describes entities for modeling the current state of a scheme in support of indexing and search on the Semantic Web, it lacks the expressive power to serve the needs of VDA needing to maintain scheme historical continuity. We demonstrate prelimi- narily that conceptualizations drawn from empirical work in modeling entities in the bibliographic universe, such as works, texts, and exemplars, can provide the basis for SKOS extension in ways that support more rig- orous demands of capturing concept evolution in VDA.

Otter (Lutra lutra) distribution modeling at two resolution scales suited to conservation planning in the Iberian Peninsula

We used the results of the Spanish Otter Survey of 1994–1996, a Geographic Information System and stepwise multiple logistic regression to model otter presence/absence data in the continental Spanish UTM 10 10-km squares. Geographic situation, indicators of human activity such as highways and major urban centers, and environmental variables related with productivity, water availability, altitude, and environmental energy were included in a logistic model that correctly classified about 73% of otter presences and absences. We extrapolated the model to the adjacent territory of Portugal, and increased the model’s spatial resolution by extrapolating it to 1 1-km squares in the whole Iberian Peninsula. The model turned out to be rather flexible, predicting, for instance, the species to be very restricted to the courses of rivers in some areas, and more widespread in others. This allowed us to determine areas where otter populations may be more vulnerable to habitat changes or harmful human interventions. # 2003 Elsevier Ltd. All rights reserved.

Matlab simulink modeling of photovoltaic cells for understanding shadow effect

Irradiation is the main component for producing the electricity from solar energy. When obstacles come in between the sun and the PV cell then it doesn’t get sufficient irradiance to produce enough electricity. Shadowing has a great impact on photovoltaic cell. The main fuel of PV cell is solar radiation. Using solar radiation, a photovoltaic cell produces electricity. The shadow on a PV cell decreases the output of the photovoltaic cell. It has been already shown in different papers that shadow effect decreases the output of the PV cell. There are different kinds of shadow effects which are observed, some minimize the PV cell output and some reduce the output to zero. There are different types of shadow based on their effects on the photovoltaic cell. The shadow has also effects depending on whether the PV cells are connected in series connection or in parallel connection. In series when one cell is out of order then the whole series of the PV cells will not work but in parallel connection if one cell is damaged, the others will work because they work independently. According to the output requirement the arrangement of the PV cells are made in series or parallel. Simulink modeling is made for series and parallel connection between two PV cells and the shadow effect is analyzed on one of the PV cells. Using SIMULINK, the shadowing is simulated on the two PV cells, where in one system they are in series and in another system they are in parallel. Slowly the irradiance is decreased to simulate the shadow effect. Simulation of the shadow effect gives an idea about the output of the PV cell system when system has shadow on the PV cells. Here the shadow effect on the two PV cells using series and parallel combinations are simulated and analyzed for understanding the effects on output.

Dynamic Modeling and Seasonal Performance Evaluation of Air-to-Water Heat Pump Systems

In this Thesis a series of numerical models for the evaluation of the seasonal performance of reversible air-to-water heat pump systems coupled to residential and non-residential buildings are presented. The exploitation of the energy saving potential linked to the adoption of heat pumps is a hard task for designers due to the influence on their energy performance of several factors, like the external climate variability, the heat pump modulation capacity, the system control strategy and the hydronic loop configuration. The aim of this work is to study in detail all these aspects. In the first part of this Thesis a series of models which use a temperature class approach for the prediction of the seasonal performance of reversible air source heat pumps are shown. An innovative methodology for the calculation of the seasonal performance of an air-to-water heat pump has been proposed as an extension of the procedure reported by the European standard EN 14825. This methodology can be applied not only to air-to-water single-stage heat pumps (On-off HPs) but also to multi-stage (MSHPs) and inverter-driven units (IDHPs). In the second part, dynamic simulation has been used with the aim to optimize the control systems of the heat pump and of the HVAC plant. A series of dynamic models, developed by means of TRNSYS, are presented to study the behavior of On-off HPs, MSHPs and IDHPs. The main goal of these dynamic simulations is to show the influence of the heat pump control strategies and of the lay-out of the hydronic loop used to couple the heat pump to the emitters on the seasonal performance of the system. A particular focus is given to the modeling of the energy losses linked to on-off cycling.

Modeling, Component Selection and Optimization of Servo-controlled Automatic Machinery

A servo-controlled automatic machine can perform tasks that involve synchronized actuation of a significant number of servo-axes, namely one degree-of-freedom (DoF) electromechanical actuators. Each servo-axis comprises a servo-motor, a mechanical transmission and an end-effector, and is responsible for generating the desired motion profile and providing the power required to achieve the overall task. The design of a such a machine must involve a detailed study from a mechatronic viewpoint, due to its electric and mechanical nature. The first objective of this thesis is the development of an overarching electromechanical model for a servo-axis. Every loss source is taken into account, be it mechanical or electrical. The mechanical transmission is modeled by means of a sequence of lumped-parameter blocks. The electric model of the motor and the inverter takes into account winding losses, iron losses and controller switching losses. No experimental characterizations are needed to implement the electric model, since the parameters are inferred from the data available in commercial catalogs. With the global model at disposal, a second objective of this work is to perform the optimization analysis, in particular, the selection of the motor-reducer unit. The optimal transmission ratios that minimize several objective functions are found. An optimization process is carried out and repeated for each candidate motor. Then, we present a novel method where the discrete set of available motor is extended to a continuous domain, by fitting manufacturer data. The problem becomes a two-dimensional nonlinear optimization subject to nonlinear constraints, and the solution gives the optimal choice for the motor-reducer system. The presented electromechanical model, along with the implementation of optimization algorithms, forms a complete and powerful simulation tool for servo-controlled automatic machines. The tool allows for determining a wide range of electric and mechanical parameters and the behavior of the system in different operating conditions.

3D Surveying and Data Management towards the Realization of a Knowledge System for Cultural Heritage

The research activities involved the application of the Geomatic techniques in the Cultural Heritage field, following the development of two themes: Firstly, the application of high precision surveying techniques for the restoration and interpretation of relevant monuments and archaeological finds. The main case regards the activities for the generation of a high-fidelity 3D model of the Fountain of Neptune in Bologna. In this work, aimed to the restoration of the manufacture, both the geometrical and radiometrical aspects were crucial. The final product was the base of a 3D information system representing a shared tool where the different figures involved in the restoration activities shared their contribution in a multidisciplinary approach. Secondly, the arrangement of 3D databases for a Building Information Modeling (BIM) approach, in a process which involves the generation and management of digital representations of physical and functional characteristics of historical buildings, towards a so-called Historical Building Information Model (HBIM). A first application was conducted for the San Michele in Acerboli’s church in Santarcangelo di Romagna. The survey was performed by the integration of the classical and modern Geomatic techniques and the point cloud representing the church was used for the development of a HBIM model, where the relevant information connected to the building could be stored and georeferenced. A second application regards the domus of Obellio Firmo in Pompeii, surveyed by the integration of the classical and modern Geomatic techniques. An historical analysis permitted the definitions of phases and the organization of a database of materials and constructive elements. The goal is the obtaining of a federate model able to manage the different aspects: documental, analytic and reconstructive ones.

Modeling of Electric Hybrid Vehicles based on Innovative Energy Storage Technologies for Super Sports Cars Applications

Today, the contribution of the transportation sector on greenhouse gases is evident. The fast consumption of fossil fuels and its impact on the environment has given a strong impetus to the development of vehicles with better fuel economy. Hybrid electric vehicles fit into this context with different targets, starting from the reduction of emissions and fuel consumption, but also for performance and comfort enhancement. Vehicles exist with various missions; super sport cars usually aim to reach peak performance and to guarantee a great driving experience to the driver, but great attention must also be paid to fuel consumption. According to the vehicle mission, hybrid vehicles can differ in the powertrain configuration and the choice of the energy storage system. Lamborghini has recently invested in the development of hybrid super sport cars, due to performance and comfort reasons, with the possibility to reduce fuel consumption. This research activity has been conducted as a joint collaboration between the University of Bologna and the sportscar manufacturer, to analyze the impact of innovative energy storage solutions on the hybrid vehicle performance. Capacitors have been studied and modeled to analyze the pros and cons of such solution with respect to batteries. To this aim, a full simulation environment has been developed and validated to provide a concept design tool capable of precise results and able to foresee the longitudinal performance on regulated emission cycles and real driving conditions, with a focus on fuel consumption. In addition, the target of the research activity is to deepen the study of hybrid electric super sports cars in the concept development phase, focusing on defining the control strategies and the energy storage system’s technology that best suits the needs of the vehicles. This dissertation covers the key steps that have been carried out in the research project.

Advanced numerical modeling of masonry vaulted structures based on BIM parametric geometry generation

Vaults are an architectural element which during construction history have been built with a great variety of different materials, shapes, and sizes. The shape of these structural elements was often dependent by the necessity to cover complex spaces, by the needed loading capacity, or by architectural aesthetics. Within this complex scenario masonry patterns generates also different effects on loading capacity, load percolation and stiffness of the structure. These effects were been extensively investigated, both with empirical observations and with modern numerical methods. While most of them focus on analyzing the load bearing capacity or the texture effect on vaulted structures, the aim of this analysis is to investigate on the effects of the variation of a single structural characteristic on the load percolation in the vault. Moreover, an additional purpose of the work is related to the coding of a parametrical model aiming at generating different masonry vaulted structures. Nevertheless, proposed script can generate different typology of vaulted structure basing on some structural characteristics, such as the span and the length to cover and the dimensions of the blocks.

Urban air quality: new high-resolution modeling approaches and forecasting tools for citizens

Air pollution is one of the greatest health risks in the world. At the same time, the strong correlation with climate change, as well as with Urban Heat Island and Heat Waves, make more intense the effects of all these phenomena. A good air quality and high levels of thermal comfort are the big goals to be reached in urban areas in coming years. Air quality forecast help decision makers to improve air quality and public health strategies, mitigating the occurrence of acute air pollution episodes. Air quality forecasting approaches combine an ensemble of models to provide forecasts from global to regional air pollution and downscaling for selected countries and regions. The development of models dedicated to urban air quality issues requires a good set of data regarding the urban morphology and building material characteristics. Only few examples of air quality forecast system at urban scale exist in the literature and often they are limited to selected cities. This thesis develops by setting up a methodology for the development of a forecasting tool. The forecasting tool can be adapted to all cities and uses a new parametrization for vegetated areas. The parametrization method, based on aerodynamic parameters, produce the urban spatially varying roughness. At the core of the forecasting tool there is a dispersion model (urban scale) used in forecasting mode, and the meteorological and background concentration forecasts provided by two regional numerical weather forecasting models. The tool produces the 1-day spatial forecast of NO2, PM10, O3 concentration, the air temperature, the air humidity and BLQ-Air index values. The tool is automatized to run every day, the maps produced are displayed on the e-Globus platform, updated every day. The results obtained indicate that the forecasting output were in good agreement with the observed measurements.

Data assimilation for advanced cross-scale unstructured-grid ocean modeling.

The coastal ocean is a complex environment with extremely dynamic processes that require a high-resolution and cross-scale modeling approach in which all hydrodynamic fields and scales are considered integral parts of the overall system. In the last decade, unstructured-grid models have been used to advance in seamless modeling between scales. On the other hand, the data assimilation methodologies to improve the unstructured-grid models in the coastal seas have been developed only recently and need significant advancements. Here, we link the unstructured-grid ocean modeling to the variational data assimilation methods. In particular, we show results from the modeling system SANIFS based on SHYFEM fully-baroclinic unstructured-grid model interfaced with OceanVar, a state-of-art variational data assimilation scheme adopted for several systems based on a structured grid. OceanVar implements a 3DVar DA scheme. The combination of three linear operators models the background error covariance matrix. The vertical part is represented using multivariate EOFs for temperature, salinity, and sea level anomaly. The horizontal part is assumed to be Gaussian isotropic and is modeled using a first-order recursive filter algorithm designed for structured and regular grids. Here we introduced a novel recursive filter algorithm for unstructured grids. A local hydrostatic adjustment scheme models the rapidly evolving part of the background error covariance. We designed two data assimilation experiments using SANIFS implementation interfaced with OceanVar over the period 2017-2018, one with only temperature and salinity assimilation by Argo profiles and the second also including sea level anomaly. The results showed a successful implementation of the approach and the added value of the assimilation for the active tracer fields. While looking at the broad basin, no significant improvements are highlighted for the sea level, requiring future investigations. Furthermore, a Machine Learning methodology based on an LSTM network has been used to predict the model SST increments.

Thermal and air quality modeling of an electric vehicle cabin with low-cost sensors and reinforcement learning

The design process of any electric vehicle system has to be oriented towards the best energy efficiency, together with the constraint of maintaining comfort in the vehicle cabin. Main aim of this study is to research the best thermal management solution in terms of HVAC efficiency without compromising occupant’s comfort and internal air quality. An Arduino controlled Low Cost System of Sensors was developed and compared against reference instrumentation (average R-squared of 0.92) and then used to characterise the vehicle cabin in real parking and driving conditions trials. Data on the energy use of the HVAC was retrieved from the car On-Board Diagnostic port. Energy savings using recirculation can reach 30 %, but pollutants concentration in the cabin builds up in this operating mode. Moreover, the temperature profile appeared strongly nonuniform with air temperature differences up to 10° C. Optimisation methods often require a high number of runs to find the optimal configuration of the system. Fast models proved to be beneficial for these task, while CFD-1D model are usually slower despite the higher level of detail provided. In this work, the collected dataset was used to train a fast ML model of both cabin and HVAC using linear regression. Average scaled RMSE over all trials is 0.4 %, while computation time is 0.0077 ms for each second of simulated time on a laptop computer. Finally, a reinforcement learning environment was built in OpenAI and Stable-Baselines3 using the built-in Proximal Policy Optimisation algorithm to update the policy and seek for the best compromise between comfort, air quality and energy reward terms. The learning curves show an oscillating behaviour overall, with only 2 experiments behaving as expected even if too slow. This result leaves large room for improvement, ranging from the reward function engineering to the expansion of the ML model.

Computational modeling of human atrial cardiomyocytes: integration of electro-mechanical & mechano-electric feedback pathways

The cardiomyocytes are very complex consisting of many interlinked non-linear regulatory mechanisms between electrical excitation and mechanical contraction. Thus given a integrated electromechanically coupled system it becomes hard to understand the individual contributor of cardiac electrics and mechanics under both physiological and pathological conditions. Hence, to identify the causal relationship or to predict the responses in a integrated system the use of computational modeling can be beneficial. Computational modeling is a powerful tool that provides complete control of parameters along with the visibility of all the individual components of the integrated system. The advancement of computational power has made it possible to simulate the models in a short timeframe, providing the possibility of increased predictive power of the integrated system. My doctoral thesis is focused on the development of electromechanically integrated human atrial cardiomyocyte model with proper consideration of feedforward and feedback pathways.