Modelling and Optimization of Energy Management Strategies for Hybrid Vehicles

In the last decades the automotive sector has seen a technological revolution, due mainly to the more restrictive regulation, the newly introduced technologies and, as last, to the poor resources of fossil fuels remaining on Earth. Promising solution in vehicles’ propulsion are represented by alternative architectures and energy sources, for example fuel-cells and pure electric vehicles. The automotive transition to new and green vehicles is passing through the development of hybrid vehicles, that usually combine positive aspects of each technology. To fully exploit the powerful of hybrid vehicles, however, it is important to manage the powertrain’s degrees of freedom in the smartest way possible, otherwise hybridization would be worthless. To this aim, this dissertation is focused on the development of energy management strategies and predictive control functions. Such algorithms have the goal of increasing the powertrain overall efficiency and contextually increasing the driver safety. Such control algorithms have been applied to an axle-split Plug-in Hybrid Electric Vehicle with a complex architecture that allows more than one driving modes, including the pure electric one. The different energy management strategies investigated are mainly three: the vehicle baseline heuristic controller, in the following mentioned as rule-based controller, a sub-optimal controller that can include also predictive functionalities, referred to as Equivalent Consumption Minimization Strategy, and a vehicle global optimum control technique, called Dynamic Programming, also including the high-voltage battery thermal management. During this project, different modelling approaches have been applied to the powertrain, including Hardware-in-the-loop, and diverse powertrain high-level controllers have been developed and implemented, increasing at each step their complexity. It has been proven the potential of using sophisticated powertrain control techniques, and that the gainable benefits in terms of fuel economy are largely influenced by the chose energy management strategy, even considering the powerful vehicle investigated.

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.

Automotive Radars:towards smarter vehicles

Radars are expected to become the main sensors in various civilian applications, especially for autonomous driving. Their success is mainly due to the availability of low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. This thesis focuses on the study and the development of different deterministic and learning based techniques for colocated multiple-input multiple-output (MIMO) radars. In particular, after providing an overview on the architecture of these devices, the problem of detecting and estimating multiple targets in stepped frequency continuous wave (SFCW) MIMO radar systems is investigated and different deterministic techniques solving it are illustrated. Moreover, novel solutions, based on an approximate maximum likelihood approach, are developed. The accuracy achieved by all the considered algorithms is assessed on the basis of the raw data acquired from low power wideband radar devices. The results demonstrate that the developed algorithms achieve reasonable accuracies, but at the price of different computational efforts. Another important technical problem investigated in this thesis concerns the exploitation of machine learning and deep learning techniques in the field of colocated MIMO radars. In this thesis, after providing a comprehensive overview of the machine learning and deep learning techniques currently being considered for use in MIMO radar systems, their performance in two different applications is assessed on the basis of synthetically generated and experimental datasets acquired through a commercial frequency modulated continuous wave (FMCW) MIMO radar. Finally, the application of colocated MIMO radars to autonomous driving in smart agriculture is illustrated.

Development of predictive energy management strategies for hybrid electric vehicles supported by connectivity

Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of the global warming. In this context, the transportation sector plays a vital role, since it is responsible for a large part of carbon dioxide production. In order to address these issues, the present thesis deals with the development of advanced control strategies for the energy efficiency optimization of plug-in hybrid electric vehicles (PHEVs), supported by the prediction of future working conditions of the powertrain. In particular, a Dynamic Programming algorithm has been developed for the combined optimization of vehicle energy and battery thermal management. At this aim, the battery temperature and the battery cooling circuit control signal have been considered as an additional state and control variables, respectively. Moreover, an adaptive equivalent consumption minimization strategy (A-ECMS) has been modified to handle zero-emission zones, where engine propulsion is not allowed. Navigation data represent an essential element in the achievement of these tasks. With this aim, a novel simulation and testing environment has been developed during the PhD research activity, as an effective tool to retrieve routing information from map service providers via vehicle-to-everything connectivity. Comparisons between the developed and the reference strategies are made, as well, in order to assess their impact on the vehicle energy consumption. All the activities presented in this doctoral dissertation have been carried out at the Green Mobility Research Lab} (GMRL), a research center resulting from the partnership between the University of Bologna and FEV Italia s.r.l., which represents the industrial partner of the research project.

Performance assessment of vehicular connectivity in hybrid electric vehicles for fuel and emissions reduction

Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of global warming. Recently, several metropolitan cities introduced Zero-Emissions Zones where the use of the Internal Combustion Engine is forbidden to reduce localized pollutants emissions. This is particularly problematic for Plug-in Hybrid Electric Vehicles, which usually work in depleting mode. In order to address these issues, the present thesis presents a viable solution by exploiting vehicular connectivity to retrieve navigation data of the urban event along a selected route. The battery energy needed, in the form of a minimum State of Charge (SoC), is calculated by a Speed Profile Prediction algorithm and a Backward Vehicle Model. That value is then fed to both a Rule-Based Strategy, developed specifically for this application, and an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS). The effectiveness of this approach has been tested with a Connected Hardware-in-the-Loop (C-HiL) on a driving cycle measured on-road, stimulating the predictions with multiple re-routings. However, even if hybrid electric vehicles have been recognized as a valid solution in response to increasingly tight regulations, the reduced engine load and the repeated engine starts and stops may reduce substantially the temperature of the exhaust after-treatment system (EATS), leading to relevant issues related to pollutant emission control. In this context, electrically heated catalysts (EHCs) represent a promising solution to ensure high pollutant conversion efficiency without affecting engine efficiency and performance. This work aims at studying the advantages provided by the introduction of a predictive EHC control function for a light-duty Diesel plug-in hybrid electric vehicle (PHEV) equipped with a Euro 7-oriented EATS. Based on the knowledge of future driving scenarios provided by vehicular connectivity, engine first start can be predicted and therefore an EATS pre-heating phase can be planned.

On the Role of Automatic Guided Vehicles in the 4th Industrial Revolution

Elaborate presents automated guided vehicle state-of-art, describing AGVs' types and employed technologies. AGVs' applications is going to be exposed by means of performed work during Toyota's internship. It will be presented the acquired experience on automatic forklifts' implementation and tools employed in a realization of an AGV system. Morover, it will be presented the development of a python program able to retrieve data, stored in a database, and elaborate them to produce heatmaps on vehicles' errors. The said program has been tested live on customer's sites and obtained result will be explained. Finally, it is going to be presented the analysis on natural navigation technology applied to Toyota's AGVs. Tests on natural navigation have been run in warehouses to estimate capabilities and possible application in logistic field.

Optimal energy management of smart households with electric vehicles, energy storage and distributed generation

In this thesis, the optimal operation of a neighborhood of smart households in terms of minimizing the total energy cost is analyzed. Each household may comprise several assets such as electric vehicles, controllable appliances, energy storage and distributed generation. Bi-directional power flow is considered for each household . Apart from the distributed generation unit, technological options such as vehicle-to-home and vehicle-to-grid are available to provide energy to cover self-consumption needs and to export excessive energy to other households, respectively.

Application of the Extended Kalman filter for speed sensorless control of PM synchronous machines

The increasing interest in the decarbonization process led to a rapidly growing trend of electrification strategies in the automotive industry. In particular, OEMs are pushing towards the development and production of efficient electric vehicles. Moreover, research on electric motors and their control are exploding in popularity. The increase of computational power in embedded control hardware is allowing the development of new control algorithm, such as sensorless control strategy. Such control strategy allows the reduction of the number of sensors, which implies reduced costs and increased system reliability. The thesis objective is to realize a sensorless control for high-performance automotive motors. Several algorithms for rotor angle observers are implemented in the MATLAB and Simulink environment, with emphasis on the Kalman observer. One of the Kalman algorithms already available in the literature has been selected, implemented and benchmarked, with emphasis on its comparison with the Sliding Mode observer. Different models characterized by increasing levels of complexity are simulated. A simplified synchronous motor with ”constant parameters”, controlled by an ideal inverter is first analyzed; followed by a complete model defined by real motor maps, and controlled by a switching inverter. Finally, it was possible to test the developed algorithm on a real electric motor mounted on a test bench. A wide range of different electric motors have been simulated, which led to an exhaustive review of the sensorless control algorithm. The final results underline the capability of the Kalman observer to effectively control the motor on a real test bench.

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.

Methodologies for attitude determination from MEMS sensors in vehicles

The focus of the thesis is the application of different attitude’s determination algorithms on data evaluated with MEMS sensor using a board provided by University of Bologna. MEMS sensors are a very cheap options to obtain acceleration, and angular velocity. The use of magnetometers based on Hall effect can provide further data. The disadvantage is that they have a lot of noise and drift which can affects the results. The different algorithms that have been used are: pitch and roll from accelerometer, yaw from magnetometer, attitude from gyroscope, TRIAD, QUEST, Magdwick, Mahony, Extended Kalman filter, Kalman GPS aided INS. In this work the algorithms have been rewritten to fit perfectly with the data provided from the MEMS sensor. The data collected by the board are acceleration on the three axis, angular velocity on the three axis, magnetic fields on the three axis, and latitude, longitude, and altitude from the GPS. Several tests and comparisons have been carried out installing the electric board on different vehicles operating in the air and on ground. The conclusion that can be drawn from this study is that the Magdwich filter is the best trade-off between computational capabilities required and results obtained. If attitude angles are obtained from accelerometers, gyroscopes, and magnetometer, inconsistent data are obtained for cases where high vibrations levels are noticed. On the other hand, Kalman filter based algorithms requires a high computational burden. TRIAD and QUEST algorithms doesn’t perform as well as filters.

Propylthiouracil Quantification In Human Plasma By High-performance Liquid Chromatography Coupled With Electrospray Tandem Mass Spectrometry: Application In A Bioequivalence Study.

A rapid, sensitive and specific method for quantifying propylthiouracil in human plasma using methylthiouracil as the internal standard (IS) is described. The analyte and the IS were extracted from plasma by liquid-liquid extraction using an organic solvent (ethyl acetate). The extracts were analyzed by high performance liquid chromatography coupled with electrospray tandem mass spectrometry (HPLC-MS/MS) in negative mode (ES-). Chromatography was performed using a Phenomenex Gemini C18 5μm analytical column (4.6mm×150mm i.d.) and a mobile phase consisting of methanol/water/acetonitrile (40/40/20, v/v/v)+0.1% of formic acid. For propylthiouracil and I.S., the optimized parameters of the declustering potential, collision energy and collision exit potential were -60 (V), -26 (eV) and -5 (V), respectively. The method had a chromatographic run time of 2.5min and a linear calibration curve over the range 20-5000ng/mL. The limit of quantification was 20ng/mL. The stability tests indicated no significant degradation. This HPLC-MS/MS procedure was used to assess the bioequivalence of two propylthiouracil 100mg tablet formulations in healthy volunteers of both sexes in fasted and fed state. The geometric mean and 90% confidence interval CI of Test/Reference percent ratios were, without and with food, respectively: 109.28% (103.63-115.25%) and 115.60% (109.03-122.58%) for Cmax, 103.31% (100.74-105.96%) and 103.40% (101.03-105.84) for AUClast. This method offers advantages over those previously reported, in terms of both a simple liquid-liquid extraction without clean-up procedures, as well as a faster run time (2.5min). The LOQ of 20ng/mL is well suited for pharmacokinetic studies. The assay performance results indicate that the method is precise and accurate enough for the routine determination of the propylthiouracil in human plasma. The test formulation with and without food was bioequivalent to reference formulation. Food administration increased the Tmax and decreased the bioavailability (Cmax and AUC).

Effect Of Aloe Vera Application On The Content And Molecular Arrangement Of Glycosaminoglycans During Calcaneal Tendon Healing.

Although several treatments for tendon lesions have been proposed, successful tendon repair remains a great challenge for orthopedics, especially considering the high incidence of re-rupture of injured tendons. Our aim was to evaluate the pharmacological potential of Aloe vera on the content and arrangement of glycosaminoglycans (GAGs) during tendon healing, which was based on the effectiveness of A. vera on collagen organization previously observed by our group. In rats, a partial calcaneal tendon transection was performed with subsequent topical A. vera application at the injury site. The tendons were treated with A. vera ointment for 7 days and excised on the 7(th) , 14(th) , or 21(st) day post-surgery. Control rats received ointment without A. vera. A higher content of GAGs and a lower amount of dermatan sulfate were detected in the A. vera-treated group on the 14(th) day compared with the control. Also at 14 days post-surgery, a lower dichroic ratio in toluidine blue stained sections was observed in A. vera-treated tendons compared with the control. No differences were observed in the chondroitin-6-sulfate and TGF-β1 levels between the groups, and higher amount of non-collagenous proteins was detected in the A. vera-treated group on the 21(st) day, compared with the control group. No differences were observed in the number of fibroblasts, inflammatory cells and blood vessels between the groups. The application of A. vera during tendon healing modified the arrangement of GAGs and increased the content of GAGs and non-collagenous proteins.

Chemo-, Regio- And Stereoselective Heck Arylation Of Allylated Malonates: Mechanistic Insights By Esi-ms And Synthetic Application Toward 5-arylmethyl-γ-lactones.

We describe herein a general method for the controlled Heck arylation of allylated malonates. Both electron-rich and electron-poor aryldiazonium salts were readily employed as the aryl-transfer agents in good yields and in high chemo-, regio-, and stereoselectivity without formation of decarboxylated byproducts. Reaction monitoring via ESI-MS was used to support the formation of chelated Pd species through the catalytic cycle. Additionally, some Heck adducts were successfully used in the total synthesis of pharmacologically active γ-lactones.

Recolonization Of Mutans Streptococci After Application Of Chlorhexidine Gel.

Streptococcus mutans is specifically suppressed by intensive treatment with chlorhexidine gel, but the time for recolonization and the effect on other oral bacteria are not totally clear. In this study, recolonization of mutans streptococci was evaluated in nine healthy adult volunteers, who were highly colonized with this microorganism. Stimulated saliva was collected before (baseline) and at 1, 7, 14, 21 and 28 days after application of 1% chlorhexidine gel on volunteers' teeth for two consecutive days. On each day, the gel was applied using disposable trays for 3 x 5 min with intervals of 5 min between each application. Saliva was plated on blood agar to determine total microorganisms (TM); on mitis salivarius agar to determine total streptococci (TS) and on mitis salivarius agar plus bacitracin to determine mutans streptococci (MS). Chlorhexidine was capable of reducing the counts of MS and the proportion of MS with regard to total microorganisms (%MS/TM) (p<0.05), but these values did not differ statistically from baseline (p>0.05) after 14 days for MS and 21 days for %MS/TM. The counts of TM and TS and the proportion of MS to total streptococci did not differ statistically from baseline (p>0.05) after chlorhexidine treatment. The results suggest that the effect of chlorhexidine gel treatment on suppression of mutans streptococci is limited to less than a month in highly colonized individuals.

Application Of A Semi-empirical Model For The Evaluation Of Transmission Properties Of Barite Mortar.

The aim of this study was to estimate barite mortar attenuation curves using X-ray spectra weighted by a workload distribution. A semi-empirical model was used for the evaluation of transmission properties of this material. Since ambient dose equivalent, H(⁎)(10), is the radiation quantity adopted by IAEA for dose assessment, the variation of the H(⁎)(10) as a function of barite mortar thickness was calculated using primary experimental spectra. A CdTe detector was used for the measurement of these spectra. The resulting spectra were adopted for estimating the optimized thickness of protective barrier needed for shielding an area in an X-ray imaging facility.