The new generation of cars are so-called Plug-in Hybrid Electric Vehicles (PHEVs) which has the grid connection capability. By the introduction of these vehicles, the grid issues will be connected to the private car transportation sector for the first time. The cars from the gird perspective can be considered as a regular load with certain power factor. The effects of this type of new load in distribution grid are studied in this thesis. By modelling the cars as regular load, the effects of the cars in three distinct areas in Stockholm are investigated. The car number in each area is estimated based on the population and commercial density of electricity consumption in the three areas. Afterward, the average electricity consumption by the cars in one day is distributed among 24 hours of the day with peak load in the studied year. This distribution is done by two regulated and unregulated methods. The regulated method is based on the desired pattern of electricity consumption of PHEVs by vehicle owners. On the other hand, the regulated pattern is designed based on encouragement of the car owners to consume electricity for charging their car batteries at low-power hours of day (usually midnight hours). The power system from high voltage lines in Sweden down to 11 kV substations in Stockholm simulated in PSS/E software has been used in this study. The automation program (written in Python) is run in order to get the output report (voltage variation and losses) of the load flow calculations for different hours of day by adding the required power for PHEVs both by regulated and unregulated patterns. The results show the possibility of introducing growing number of cars till year 2050 in each area with existing grid infrastructures. Moreover, the number of cars, yearly and daily electric consumption for PHEVs in pure electric mode are shown in this project and the effects of regulated electricity consumption are investigated. It is concluded that since the car number is estimated based on the population, the areas with higher residential characteristics are more problematic for integration of PHEVs from capacity point of view. Moreover, by regulating the charging pattern of PHEVs, the higher number of PHEVs can be integrated to the grid with the existing infrastructures. In addition, the losses have been decreased in regulated pattern in comparison with unregulated pattern with the same power consumption. The voltage in different substations is within the standard boundaries by adding 100 percent of PHEVs load for both regulated and unregulated patterns in all three areas.

Increase in the awareness environmental conservation is leading the automotive industry into the adaptation of alternatively fueled vehicles. Electric, Fuel-Cell as well as Hybrid-Electric vehicles focus on this research area with aim to efficiently utilize vehicle powertrain as the first step. Energy and Power Management System control strategies play vital role in improving efficiency of any hybrid propulsion system. However, these control strategies are sensitive to the dynamics of the powertrain components used in the given system. A kinematic mathematical model for Plug-in Hybrid Electric Vehicle (PHEV) has been developed in this study and is further optimized by determining optimal power management strategy for minimal fuel consumption as well as NOx emissions while executing a set drive cycle. A multi-objective optimization using weighted sum formulation is needed in order to observe the trade-off between the optimized objectives. Particle Swarm Optimization (PSO) algorithm has been used in this research, to determine the trade-off curve between fuel and NOx. In performing these optimizations, the control signal consisting of engine speed and reference battery SOC trajectory for a 2-hour cycle is used as the controllable decision parameter input directly from the optimizer. Each element of the control signal was split into 50 distinct points representing the full 2 hours, giving slightly less than 2.5 minutes per point, noting that the values used in the model are interpolated between the points for each time step. With the control signal consisting of 2 distinct signals, speed and SOC trajectory, as 50 element time variant signals, a multidimensional problem was formulated for the optimizer. Novel approaches to balance the optimizer exploration and convergence, as well as seeding techniques are suggested to solve the optimal control problem. The optimization of each involved individual runs at 5 different weight levels with the resulting cost populations being compiled together to visually represent with the help of Pareto front development. The obtained results of simulations and optimization are presented involving performances of individual components of the PHEV powertrain as well as the optimized PMS strategy to follow for given drive cycle. Observations of the trade-off is discussed in the case of Multi-Objective Optimizations.

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Progressing constraints on green house gas emissions lead to a sustainability trend,which greatly a↵ects the transport sector. Nowadays, companies show increasing interest indeveloping sustainable solutions.This thesis has been started thanks to a project given by Sj¨or¨addningss¨allskapet, themost relevant association that performs sea rescue operations in Swedish waters.Sj¨or¨addningss¨allskapet would like to explore the possibility of making their rescue boat fleetentirely carbon-free, hence more sustainable.What may provide a suitable solution is an electric drive train with hybrid energy storage,composed by a battery pack and a fuel cell stack. The research question is whether it wouldbe feasible to combine fuel cell stacks and battery packs to provide power to a fast smallboat.From a sketch of a rescue boat, the drive train design for such boat is studied in itsintegrity, from the water jet pump to the battery and fuel cell systems.The required power has been calculated empirically, using data from online tests on waterjet boats. Di↵erent tests have been considered, resulting in a mean power curve and a meanconsumption curve and allowing comparison between the hybrid electric drive train withan internal combustion engine drive train.Three profiles of speed, power and consumption have been assumed for the calculation ofthe required energy and hence rate the energy storage system. A design has been proposedin terms of fuel cell capacity and battery capacity.The propulsion unit, composed by the electric machine and water jet, has been studied,focusing on di↵erent electric drive technologies. Few conclusions on both the weight andsustainability requirements are discussed.A sustainability analysis is carried out in terms of CO2 emissions, through a life cycleassessment accounting for the environmental impact of the system during the whole lifecycle, from cradle to grave. / Ökande begränsningar för utsläpp av växthusgaser leder till en hållbarhetsutveckling, vilket påverkar transportsektorn kraftigt. Nuförtiden visar företag ett ökande intresse för att utveckla hållbara lösningar.‌Denna avhandling har startats tack vare ett projekt som ges av Sjöräddningssällskapet, den viktigaste föreningen som utför havsräddningsinsatser i svenska vatten.Sjöräddningssällskapet vill undersöka möjligheten att göra deras räddningsbåtflotta helt emissionfri, och därmed mer hållbar.Det som kan ge en lämplig lösning är ett elektriskt drivsystem med hybrid energilagring, sammansatt av ett batteripaket och en bränslecell-stapel. Forskningsfrågan är om det skulle vara möjligt att kombinera bränslecellstaplar och batteripaket för att driva en snabb liten båt.Från en skiss av en räddningsbåt studeras designen för en sådan båt i dess integritet, från vattenstrålpumpen till batteri och bränslecellsystem.Den erforderliga kraften har beräknats empiriskt med hjälp av data från onlinetest av vattenstrålbåtar. Olika tester har beaktats, vilket resulterar i en genomsnittlig e↵ektkurva och en genomsnittlig förbrukningskurva och möjliggör en jämförelse mellan det hybridelektriska drivsystemet med ett förbränningsmotordrivsystem.Tre profiler av hastighet, e↵ekt och förbrukning har antagits för beräkning av den erforderliga energin och därmed för energilagringssystemet. En design har föreslagits vad gäller bränslecellkapacitet och batterikapacitet.Framdrivningsenheten, sammansatt av den elektriska maskinen och vattenstrålen, har studerats med fokus på olika elektriska drivtekniker. Några slutsatser om både vikten och hållbarhetskraven diskuteras.En hållbarhetsanalys utförs med avseende på koldioxidutsläpp genom en livscykelbedömningsom redovisar systemets miljöpåverkan under hela livscykeln, från vagga till grav.

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This thesis aims to uncover preliminary design relationships for an inlet of a split-wing electric distributed propulsion regional airliner. Several aspects of the inlet design were investigated, including: the overall thickness of the airfoil section with respect to the chord, inlet throat area, and lip radius. These parameters were investigated using several angles of attack and mass flow rates through the fan. Computational fluid dynamics, with a 2nd Order turbulence model was used and validated against World War II era data from NACA, as those studies were the most pertinent wind tunnel data available. Additionally, other works by Boeing, Empirical Systems Aerospace (ESAero), Rolling Hills Research, and the Air Force Research Laboratories (AFRL) were considered as part of this design tool tradespace. Future work considerations include utilizing an airfoil section designed for M = 0.6 or 0.65 cruise conditions as opposed to a symmetrical airfoil section, extruding the 2-D airfoil section discussed in this thesis for 3-D effects, and incorporating fan rotational physics into the simulations to better account for inlet Mach number effects.

Hybrid electric vehicles significantly reduce exhaust emissions and increase fuel economy. Power packs are the most fundamental components in a series powertrain configuration of a hybrid vehicle, which produce the necessary power to run the vehicle. The aim of this project is to design a compact power pack for a series hybrid vehicle, using virtual prototyping. The hybrid electric vehicle characteristics and configurations are analysed, followed by an explanation of the principles of induction machines. A new type of rotary induction machine with an outer rotor construction is designed to be coupled with the novel rotary internal combustion engine with rotating crankcase in order to form the compact power unit for the hybrid vehicle. The starting and generation performance of the designed machine is analysed by an electric machine simulator, called JMAG. ADVISOR software is studied and utilised to simulate the overall vehicle performance, employing different categories of power packs in the powertrain. Results show that the proposed compact power pack has the best performance in terms of fuel economy, emissions and battery charging compared to the existing power unit options. Over the city cycle, fuel economy is increased by up to 47 % with emission reduced by up to 36 % and over the highway cycle, fuel economy is increased by up to 69 % with emission reduced by up to 42 %.

Mobility is a topic of great interest in research and engineering since critical aspects such as safety, traffic efficiency, and environmental sustainability still represent wide open challenges for researchers and engineers. In this thesis, at first, we address the cooperative driving safety problem both from a centralized and decentralized perspective. Then we address the problem of optimal energy management of hybrid vehicles to improve environmental sustainability, and finally, we develop an intersection management systems for Connected Autonomous Vehicle to maximize the traffic efficiency at an intersection. To address the first two topics, we define a common framework. Both the cooperative safety and the energy management for Hybrid Electric Vehicle requires to model the driver behavior. In the first case, we are interested in evaluating the safety of the driver’s intentions, while in the second case, we are interested in predicting the future velocity profile to optimize energy management in a fixed time horizon. The framework is the Co-Driver, which is, in short, a bio-inspired agent able both to model and to imitate a human driver. It is based on a layered control structure based on the generation of atomic human-like longitudinal maneuvers that compete with each other like affordances. To address driving safety, the Co-Driver behaves like a safe driver, and its behavior is compared to the actual driver to understand if he/she is acting safely and providing warnings if not. In the energy management problem, the Co-Driver aims at imitating the driver to predict the future velocity. The Co-Driver generates a set of possible maneuvers and selects one of them, imitating the action selection process of the driver. At first, we address the problem of safety by developing and investigating a framework for Advanced Driving Assistance Systems (ADAS) built on the Co-Driver. We developed and investigated this framework in an innovative context of new intelligent road infrastructure, where vehicles and roads communicate. The infrastructure that allows the roads to interact with vehicles and the environment is the topic of a research project called SAFESTRIP. This project is about deploying innovative sensors and communication devices on the road that communicate with all vehicles. Including vehicles that are equipped with Vehicle-To-Everything (V2X) technology and vehicles that are not, using an interface (HMI) on smart-phones. Co-Driver-based ADAS systems exploit connections between vehicles and (smart) roads provided by SAFESTRIP to cover several safety-critical use cases: pedestrian protection, wrong-way vehicles on-ramps, work-zones on roads and intersections. The ADAS provide personalized warning messages that account for the adaptive driver behavior to maximize the acceptance of the system. The ability of the framework to predict human drivers’ intention is exploited in a second application to improve environmental sustainability. We employ it to feed with the estimated speed profile a novel online Model Predictive Control (MPC) approach for Hybrid Electric Vehicles, introducing a state-of-the-art electrochemical model of the battery. Such control aims at preserving battery life and fuel consumption through equivalent costs. We validated the approach with actual driving data used to simulate vehicles and the power-train dynamics. At last, we address the traffic efficiency problem in the context of autonomous vehicles crossing an intersection. We propose an intersection management system for Connected Autonomous Vehicles based on a bi-level optimization framework. The motion planning of the vehicle is provided by a simplified optimal control problem, while we formulate the intersection management problem (in terms of order and timing) as a Mixed Integer Non-Linear Programming. The latter approximates a linear problem with a powerful piecewise linearization technique. Therefore, thanks to this technique, we can bound the error and employ commercial solvers to solve the problem (fast enough). Finally, this framework is validated in simulation and compared with the "Fist-Arrived First-Served" approach to show the impact of the proposed algorithm.