A Ride-Sharing System with High Capacity Vehicles and Flexible Meeting Points

In many major cities, fixed route transit systems such as bus and rail serve millions of trips per day. These systems have people collect at common locations (the station or stop), and board at common times (for example according to a predetermined schedule or headway). By using common service locations and times, these modes can consolidate many trips that have similar origins and destinations or overlapping routes. However, the routes are not sensitive to changing travel patterns, and have no way of identifying which trips are going unserved, or are poorly served, by the existing routes. On the opposite end of the spectrum, personal modes of transportation, such as a private vehicle or taxi, offer service to and from the exact origin and destination of a rider, at close to exactly the time they desire to travel. Despite the apparent increased convenience to users, the presence of a large number of small vehicles results in a disorganized, and potentially congested road network during high demand periods. The focus of the research presented in this paper is to develop a system that possesses both the on-demand nature of a personal mode, with the efficiency of shared modes. In this system, users submit their request for travel, but are asked to make small compromises in their origin and destination location by walking to a nearby meeting point, as well as slightly modifying their time of travel, in order to accommodate other passengers. Because the origin and destination location of the request can be adjusted, this is a more general case of the Dial-a-Ride problem with time windows. The solution methodology uses a graph clustering algorithm coupled with a greedy insertion technique. A case study is presented using actual requests for taxi trips in Washington DC, and shows a significant decrease in the number of vehicles required to serve the demand.

MODELING AND HARDWARE-IN-THE-LOOP SIMULATION OF POWER-SPLIT HYBRID ELECTRIC VEHICLES

Conventional vehicles are creating pollution problems, global warming and the extinction of high density fuels. To address these problems, automotive companies and universities are researching on hybrid electric vehicles where two different power devices are used to propel a vehicle. This research studies the development and testing of a dynamic model for Prius 2010 Hybrid Synergy Drive (HSD), a power-split device. The device was modeled and integrated with a hybrid vehicle model. To add an electric only mode for vehicle propulsion, the hybrid synergy drive was modified by adding a clutch to carrier 1. The performance of the integrated vehicle model was tested with UDDS drive cycle using rule-based control strategy. The dSPACE Hardware-In-the-Loop (HIL) simulator was used for HIL simulation test. The HIL simulation result shows that the integration of developed HSD dynamic model with a hybrid vehicle model was successful. The HSD model was able to split power and isolate engine speed from vehicle speed in hybrid mode.

IMPACT OF PLUG-IN ELECTRIC VEHICLES AND WIND GENERATORS ON HARMONIC DISTORTION OF ELECTRIC DISTRIBUTION SYSTEMS

Harmonic distortion on voltages and currents increases with the increased penetration of Plug-in Electric Vehicle (PEV) loads in distribution systems. Wind Generators (WGs), which are source of harmonic currents, have some common harmonic profiles with PEVs. Thus, WGs can be utilized in careful ways to subside the effect of PEVs on harmonic distortion. This work studies the impact of PEVs on harmonic distortions and integration of WGs to reduce it. A decoupled harmonic three-phase unbalanced distribution system model is developed in OpenDSS, where PEVs and WGs are represented by harmonic current loads and sources respectively. The developed model is first used to solve harmonic power flow on IEEE 34-bus distribution system with low, moderate, and high penetration of PEVs, and its impact on current/voltage Total Harmonic Distortions (THDs) is studied. This study shows that the voltage and current THDs could be increased upto 9.5% and 50% respectively, in case of distribution systems with high PEV penetration and these THD values are significantly larger than the limits prescribed by the IEEE standards. Next, carefully sized WGs are selected at different locations in the 34-bus distribution system to demonstrate reduction in the current/voltage THDs. In this work, a framework is also developed to find optimal size of WGs to reduce THDs below prescribed operational limits in distribution circuits with PEV loads. The optimization framework is implemented in MATLAB using Genetic Algorithm, which is interfaced with the harmonic power flow model developed in OpenDSS. The developed framework is used to find optimal size of WGs on the 34-bus distribution system with low, moderate, and high penetration of PEVs, with an objective to reduce voltage/current THD deviations throughout the distribution circuits. With the optimal size of WGs in distribution systems with PEV loads, the current and voltage THDs are reduced below 5% and 7% respectively, which are within the limits prescribed by IEEE.

Hybrid power system intelligent operation and protection involving plug-in electric vehicles

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. ^ The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. ^ The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.^

Comparison of small remotely operated vehicles and diver-operated video of circalittoral benthos

Underwater video transect methods using small remotely operated vehicles (ROVs) and diveroperated video (DOV) are commonly used in benthic biodiversity assessments. Constraints posed by deeper waters have made surveys of the circalittoral zone ([30 m depth), a particularly challenging problem. Here we compare benthic diversity metrics and cluster analyses obtained with ROV and DOV between 45 and 65 m depth off southwest Iberia, across local (tens to hundreds of meters) and regional scales (tens of kilometers). Results showed no difference between methods in terms of the benthic species richness, taxonomic distinctness, and beta diversity, but only minor differences in the spatial structure depicted at the regional level. At the local scale, DOV performed better at discriminating patterns likely because of the divers visual acuity. We found that small ROV and DOV are reliable and comparable methods for the study of circalittoral benthic assemblages and can be used in a complimentary way to detect the greatest amount of variation in benthic ecosystems. Our study facilitates the understanding of capabilities and limitations of two underwater video methods and provides important insight into choice of the most appropriate technique.

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.

Motion Control of an Advanced Electric Implement for Agricultural Autonomous Vehicles

Nowadays, the development of intelligent and autonomous vehicles used to perform agricultural activities is essential to improve quantity and quality of agricultural productions. Moreover, with automation techniques it is possible to reduce the usage of agrochemicals and minimize the pollution. The University of Bologna is developing an innovative system for orchard management called ORTO (Orchard Rapid Transportation System). This system involves an autonomous electric vehicle capable to perform agricultural activities inside an orchard structure. The vehicle is equipped with an implement capable to perform different tasks. The purpose of this thesis project is to control the vehicle and the implement to perform an inter-row grass mowing. This kind of task requires a synchronized motion between the traction motors and the implement motors. A motion control system has been developed to generate trajectories and manage their synchronization. Two main trajectories type have been used: a five order polynomial trajectory and a trapezoidal trajectory. These two kinds of trajectories have been chosen in order to perform a uniform grass mowing, paying a particular attention to the constrains of the system. To synchronize the motions, the electronic cams approach has been adopted. A master profile has been generated and all the trajectories have been linked to the master motion. Moreover, a safety system has been developed. The aim of this system is firstly to improve the safety during the motion, furthermore it allows to manage obstacle detection and avoidance. Using some particular techniques obstacles can be detected and recovery action can be performed to overcome the problem. Once the measured force reaches the predefined force threshold, then the vehicle stops immediately its motion. The whole project has been developed by employing Matlab and Simulink. Eventually, the software has been translated into C code and executed on the TI Lauchpad XL board.

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.

Risk assessment of liquid hydrogen tanks for heavy duty vehicles

The advent of the hydrogen economy has already been predicted but it does not represent a tangible reality yet. However, decarbonizing the global economy and particularly the energy sector is vital to limit global warming and reduce the incumbent environmental problems. Hydrogen is a promising zero-emission fuel that could replace traditional fossil fuels, playing a key role in the transition towards a more sustainable economy. At present, hydrogen-powered cars are already spread worldwide and the deployment of hydrogen buses seems to be the next goal in the decarbonization process of the transportation sector. In contrast with the undeniable benefits introduced by the use of this alternative fuel, given its hazardous properties, safety is a topic of high concern. The present study concerns the evaluation of the risks linked to the on board storage of hydrogen on hydrogen-powered buses in case of road accident. Currently, hydrogen can be stored on board as a high-pressure gas, as a cryogenic liquid or in cryo-compressed form. Those solutions are compared from a safety point of view. First, the final accidental scenarios that could follow the release of the fuel in case of a road crash are pointed out. Secondly, threshold values for the hazardous effects of each scenario are fixed and the corresponding damage distances are calculated thanks to the use of the software PHAST 8.4. Finally, indicators are quantified to compare the different options. Results are discussed to find out the safer solution and to evaluate whether the replacement of fossil fuels with hydrogen introduces new safety issues.

Crowd-shipping and autonomous vehicles: an optimization model for the last mile delivery

Il seguente elaborato propone un modello innovativo per la gestione della logistica distributiva nell’ultimo miglio, congiungendo l’attività di crowd-shipping con la presenza di Autonomous Vehicles, per il trasporto di prodotti all’interno della città. Il crowd-shipping utilizza conducenti occasionali, i quali deviano il loro tragitto in cambio di una ricompensa per il completamento dell’attività. Dall’altro lato, gli Autonomous Vehicles sono veicoli elettrici a guida autonoma, in grado di trasportare un numero limitato di pacchi e dotati di un sistema di sicurezza avanzato per garantire la fiducia nel trasporto. In primo luogo, nel seguente elaborato verrà mostrato il modello di ottimizzazione che congiunge i due attori principali in un unico ambiente, dove sono presenti un numero determinato di prodotti da muovere. Successivamente, poiché il problema di ottimizzazione è molto complesso e il numero di istanze valutabili è molto basso, verranno presentate due soluzioni differenti. La prima riguarda la metaeuristica chiamata Ant System, che cerca di avvicinarsi alle soluzioni ottime del precedente modello, mentre la seconda riguarda l’utilizzo di operatori di Local Search, i quali permettono di valutare soluzioni per istanze molto più grandi rispetto alla metaeuristica. Infine, i due modelli euristici verranno utilizzati per analizzare uno scenario che cerca di riprodurre una situazione reale. Tale scenario tenta di allocare strategicamente le risorse presenti e permette di dimostrare che gli Autonomous Vehicles riescono a supportare gli Occasional Drivers anche quando il numero di prodotti trasportabili è elevato. Inoltre, le due entità proposte riescono a soddisfare la domanda, garantendo un servizio che nel futuro potrebbe sostituire il tradizionale sistema di logistica distributiva last mile.