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.

Molecular simulations and modeling of pharmaceutically relevant RNA-processing enzymes

The two-metal-ion architecture is a structural feature found in a variety of RNA processing metalloenzymes or ribozymes (RNA-based enzymes), which control the biogenesis and the metabolism of vital RNAs, including non-coding RNAs (ncRNAs). Notably, such ncRNAs are emerging as key players for the regulation of cellular homeostasis, and their altered expression has been often linked to the development of severe human pathologies, from cancer to mental disorders. Accordingly, understanding the biological processing of ncRNAs is foundational for the development of novel therapeutic strategies and tools. Here, we use state-of the-art molecular simulations, complemented with X-ray crystallography and biochemical experiments, to characterize the RNA processing cycle as catalyzed by two two-metal-ion enzymes: the group II intron ribozymes and the RNase H1. We show that multiple and diverse cations are strategically recruited at and timely released from the enzymes’ active site during catalysis. Such a controlled cations’ trafficking leads to the recursive formation and disruption of an extended two-metal ion architecture that is functional for RNA-hydrolysis – from substrate recruitment to product release. Importantly, we found that these cations’ binding sites are conserved among other RNA-processing machineries, including the human spliceosome and CRISPR-Cas systems, suggesting that an evolutionarily-converged catalytic strategy is adopted by these enzymes to process RNA molecules. Thus, our findings corroborate and sensibly extend the current knowledge of two-metal-ion enzymes, and support the design of novel drugs targeting RNA-processing metalloenzymes or ribozymes as well as the rational engineering of novel programmable gene-therapy tools.

Modeling of clear cell sarcoma of the kidney of the pediatric age

Clear cell sarcoma of the kidney (CCSK) is the second most common pediatric renal tumor, characterized in 90% of cases by the presence of internal tandem duplications (ITDs) localized at the last exon of BCOR gene. BCOR protein constitute a core component of the non-canonical Polycomb Repressive Complex1 (PRC1.1), which performs a fundamental silencing activity. ITDs in the last BCOR exon at the level of PUFD domain have been identified in many tumor subtypes and could affect PCGF1 binding and the subsequent PRC1.1 activity, although the exact oncogenic mechanism of ITD remains poorly understood. This project has the objective of investigating the molecular mechanisms underlying the oncogenesis of CCSK, approaching the study with different methodologies. A first model in HEK-293 allowed to obtain important informations about BCOR functionality, suggesting that the presence of ITD generates an altered activity which is very different from a loss-of-function. It has also been observed that BCOR function within the PRC1.1 complex varies with different ITDs. Moreover, it allowed the identification of molecular signatures evoked by the presence of BCOR-ITD, including its role in extracellular matrix interactions and invasiveness promotion. The parallel analysis of WTS data from 8 CCSK cases permitted the identification of a peculiar signature for metastatic CCSKs, highlighting a 20-fold overexpression of FGF3. This factor promoted a significant increase in invasive ability in the cellular model. In order to study BCOR-ITD effects over cell stemness and differentiation, an inducible model is being obtained in H1 cells. This way, it will be possible to study the functionality of BCOR-ITD in a context more similar to the origin of CCSKs, evaluating both the specific interactome and phenotypic consequences caused by the mutation.

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.

Multiscale modeling of light-harvesting systems based on rhodamine B aggregates

Rhodamine B (RB) has been successfully exploited in the synthesis of light harvesting systems, but since RB is prone to form dimers acting as quenchers for the fluorescence, high energy transfer efficiencies can be reached only when using bulky and hydrophobic counterions acting as spacers between RBs. In this PhD thesis, a multiscale theoretical study aimed at providing insights into the structural, photophysical and optical properties of RB and its aggregates is presented. At the macroscopic level (no atomistic details) a phenomenological model describing the fluorescence decay of RB networks in presence of both quenching from dimers and exciton-exciton annihiliation is presented and analysed, showing that the quenching from dimers affects the decay only at long times, a feature that can be exploited in global fitting analysis to determine relevant chemical and photophysical information. At the mesoscopic level (atomistic details but no electronic structure) the RB aggregation in water in presence of different counterions is studied with molecular dynamics (MD) simulations. A new force field has been parametrized for describing the RB flexibility and the RB-RB interaction driving the dimerization. Simulations correctly predict the RB/counterion aggregation only in presence of bulky and hydrophobic counterion and its ability to prevent the dimerization. Finally, at the microscopic level, DFT calculations are performed to demonstrate the spacing action of bulky counterions, but standard TDDFT calculations are showed to fail in correctly describing the excited states of RB and its dimers. Moreover, also standard procedures proposed in literature for obtaining ad hoc functionals are showed to not work properly. A detailed analysis on the effect of the exact exchange shows that its short-range contribution is the crucial quantity for ameliorating results, and a new functional containing a proper amount of such an exchange is proposed and successfully tested.

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.

Navigation design and flight dynamics operations of the ArgoMoon cis-lunar CubeSat.

On November 16, 2022, the NASA’s Space Launch System (SLS) has been launched for the first time in the context of Artemis-1 mission where, together with the Orion Multi-Purpose Crew Vehicle, a set of 10 CubeSats have been delivered into a translunar trajectory. Among the small satellites deployed during Artemis-1 there is ArgoMoon, a 6U CubeSat built by the Italian company Argotec and coordinated by Italian Space Agency (ASI). The primary goal of ArgoMoon is to capture images of the Interim Cryogenic Propulsion Stage. The ArgoMoon trajectory has been designed as a highly elliptical geocentric orbit, with several encounters with the Moon. In order to successfully fly ArgoMoon along the designed cis-lunar trajectory, a ground-based navigation system has been developed exploiting the guidance techniques also used for regular deep space missions. The navigation process is subdivided into Orbit Determi- nation (OD) and a Flight Path Control (FPC), and it is designed to follow the reference trajectory, prevent impacts with the Earth and the Moon, intensively test the navigation techniques, and guarantee the spacecraft disposal at the end of the mission. The work done in this thesis has accomplished the navigation of ArgoMoon, covering all aspects of the project life, from pre-launch design and analysis to actual operations. Firstly, the designed navigation process and the pre-mission assessment of its performance will be presented. Then, the results of the ArgoMoon navigation operations performed after the launch in November 2022 will be described in detail by discussing the main encountered challenges and the adopted solutions. The results of the operations confirmed the robustness of the designed navigation which allowed to accurately estimate the trajectory of ArgoMoon despite a series of complex events.