Automotive manufacturers are facing increasing pressure from legislative bodies and consumers to reduce fuel consumption and greenhouse gas emissions of vehicles. This has led to many automotive manufacturers starting production of Plug-in Hybrid Electric Vehicles (PHEV's) and Battery Electric Vehicles (BEV's). Another method that helps to reduce the environmental effect of transportation is EcoRouting. The standard Global Positioning System (GPS) navigation offers route alternatives between user specified origin and destination. This technology provides multiple routes to the user and focuses on reducing the travel time to reach to the destination. EcoRouting is the method to determine a route that minimizes vehicle energy consumption, unlike traditional routing methods that minimize travel time. An EcoRouting system has been developed as a part of this thesis that takes in information such as speed limits, the number of stop lights, and the road grade to calculate the energy consumption of a vehicle along a route. A synthesis methodology is introduced that takes into consideration the distance between the origin and destination, the acceleration rate of the vehicle, cruise speed and jerk rate as inputs to simulate driver behavior on a given route. A new approach is presented in this thesis that weighs the energy consumption for different routes and chooses the route with the least energy consumption, subject to a constraint on travel time. A cost function for quantifying the effect of travel time is introduced that assists in choosing the EcoRoute with an acceptable limit on the travel time required to reach the destination. The analysis of the EcoRouting system with minimum number of conditional stops and maximum number of conditional stops is done in this thesis. The effect on energy consumption with the presence and absence of road-grade information along a route is also studied. A sensitivity study is performed to observe the change in energy consumption of the vehicle with a change in acceleration rates and road grade. Three routing scenarios are presented in this thesis to demonstrate the functionality of EcoRouting. The EcoRouting model presented in this thesis is also validated against an external EcoRouting research paper and the energy consumption along three routes is calculated. The EcoRoute solution is found to vary with the information given to the variable acceleration rate model. The synthesis and the results that are obtained show that parameters such as acceleration, deceleration, and road grade affect the overall energy consumption of a vehicle and are helpful in determining the EcoRoute. / Master of Science / The automotive industry is undergoing a major transformation throughout the world in terms of regulations on greenhouse gas emissions and fuel consumption. There is a significant amount of research being done on reducing emissions of cars while maintaining safety, performance and consumer acceptability of vehicles with an emphasis on cost and innovation. Vehicle manufacturers have started manufacturing Plug-In Hybrid Electric Vehicles (PHEV’s) and Battery Electric Vehicles (BEV’s) with a focus on reducing petroleum use. While a lot of work is being done on manufacturing cars that help reduce emissions, significant research is also being conducted to help navigate cars in an energy efficient manner. EcoRouting is defined as the method that helps to route cars efficiently and conserve energy. EcoRouting helps to increase fuel efficiency without any modifications to the vehicle powertrain and can be customized to any vehicle. A simulation study to analyze the effects of EcoRouting in different driving conditions with an emphasis on the effects of road grade and stop lights on energy consumption is presented. The EcoRoute solution is found to vary with the road grade, the maximum allowed acceleration, and the number of conditional traffic lights. The synthesis and the results that are obtained show that external parameters such as road grades, speed limits, and stop lights affect the overall energy consumption of a vehicle and that EcoRouting can significantly reduce vehicle energy consumption. The EcoRouting research done in this thesis focuses mainly on analyzing the effect of changes in road grade and accelerations on the energy consumption of a vehicle. A sensitivity study is performed to study the change in energy consumption of a vehicle with a change in road grade and acceleration. It is found that the net difference in elevation between the origin and the destination plays a significant role in determining the energy consumption of a vehicle. This thesis also focuses on formulating a cost function for the maximum permissible travel time required to reach the destination and shows how travel time is an important metric to determine an EcoRoute. Three case studies are presented which provide a demonstration of the discussed methods and typify a working EcoRouting model.

With the advent of smart sensor devices and Internet of Things (IoT) in the rapid urbanizing cities, data is being generated, collected and analyzed to solve urban problems in the areas of transportation, epidemiology, emergency management, economics, and sustainability etc. The work in this area basically involves analyzing one or more types of data to identify and characterize their impact on other urban phenomena like traffic speed and ride-sharing, spread of diseases, emergency evacuation, share market and electricity demand etc. In this work, we perform spatio-temporal analysis of various urban datasets collected from different urban application areas. We start with presenting a framework for predicting traffic demand around a location of interest and explain how it can be used to analyze other urban activities. We use a similar method to characterize and analyze spatio-temporal criminal activity in an urban city. At the end, we analyze the impact of nearby traffic volume on the electric vehicle charging demand at a charging station. / Master of Science / Because of the ubiquity of the Internet and smart devices, a tremendous amount of data has been collected from multiple sources like vehicles, purchasing details, online searches etc., which is being used to develop innovative applications. These applications aim to improve economic, social and personal lives of people through new start-of-the-art techniques like machine learning and data analytics. With this motivation in mind, we present three applications leveraging the data collected from urban cities to improve the life of people living in such cities. First, we start by using taxi trip data, collected around a given location, and use it to develop a model that can predict taxi demand for next half hour. This model can be used to schedule advertisements or dispatch taxis depending upon the demand. Second, using a similar mathematical approach, we propose a strategy to predict the number of crimes that can happen at a given location on the next day. This helps in maintaining law and order in the city. As our third and last application, we use the traffic and historical charging data to predict electric vehicle charging demand for the next day. Electricity generating power plants can use this model to prepare themselves for the higher demand emerged because of the increasing use of electric vehicles.

[pt] Sistemas condicionadores de ar automotivos têm sido extensivamente estudados, buscando melhor eficiência de resfriamento e redução do consumo de combustível. O presente trabalho tem como objetivo o estudo de um sistema condicionador de ar automotivo operando nos modos de resfriamento e aquecimento, este último atendendo às necessidades de conforto em dias frios nos veículos elétricos, os quais não apresentam calor de rejeito do motor, como nos veículos convencionais. Para tal foi projetado e montado, no Laboratório de Refrigeração, Condicionamento de Ar e Criogenia da PUC-Rio, um aparato experimental composto por duas câmaras de temperatura e umidade controladas, uma simulando o compartimento de passageiros e a outra, o ambiente externo. Um típico sistema condicionador de ar automotivo, composto por componentes comercialmente disponíveis e utilizados nos veículos atuais, foi dotado de válvulas direcionais, permitindo a inversão do ciclo de compressão de vapor do modo de resfriamento para o modo de aquecimento, operando neste último como bomba de calor. Dados experimentais foram levantados sob operação em regime permanente e transiente (período de partida), com temperaturas entre – 5 graus Celcius e 45 graus Celcius. Para o modo de resfriamento, seguiu-se a norma SAE J2765 e, para o de aquecimento, na ausência de normas, foram cobertas as operações em modos de recirculação do ar da cabine e de renovação com ar externo, entre as temperaturas de -5 graus Celcius e 10 graus Celcius. Foi também realizada uma simulação numérica, validada pelos dados experimentais, utilizando as equações fundamentais da termodinâmica e transferência de calor. O sistema testado mostrou-se viável na aplicação em veículos elétricos, uma vez que nestes o calor de rejeito previsto (regeneração de frenagem e efeito Joule na eletrônica de potência) não é suficiente para o conforto térmico em dias frios. Demonstrou-se que a bomba de calor consome menos energia que resistências as elétricas atualmente utilizadas. / [en] Automotive air conditioning systems have been extensively studied, searching for better cooling efficiency and reduced fuel consumption. The present work aims to study a system of automotive air conditioner operating in cooling and heating modes, the latter satisfies the needs of comfort on cold days in electrical vehicles, which do not include waste heat from the engine as the conventional vehicles. To this was designed and assembled in the Refrigeration, Air Conditioning and Cryogenics Laboratory, in Puc-Rio, an experimental apparatus consists of two chambers with temperature and humidity controlled, one, simulating the passenger compartment and the other, the external environment. A typical automotive air conditioning system, composed of commercially available components used in current vehicles is provided with a directional valve, allowing the inversion of vapor compression cooling mode to the heating mode cycle, the latter operating as a heat pump.

The increasing penetration of electric vehicles (EVs) and other distributed energy resources (DERs) offers enhanced flexibility and resilience. During extreme conditions, grid-connected EVs and DERs can provide electricity service and restore critical loads when the utility system is unavailable. On the other hand, during normal operation, these proactive devices can provide ancillary services to alleviate voltage fluctuations and support frequency regulation. In comparison with other DERs, EVs are more flexible in providing ancillary services due to their mobile nature. However, the proliferation of EVs and DERs also introduces operational challenges to the distribution grid. For instance, EVs primarily fulfill their transportation needs. Uncoordinated charging of a large number of EVs can increase the burden on the distribution system. Due to the limited charging rate and battery size, it is generally impractical for a single EV to directly participate in the ancillary service market. A conventional distribution system is designed for unidirectional flow of electric energy. With the growing installation of DERs on the distribution system, the flow of electric energy is bi-directional and, therefore, there is a higher risk of protection miscoordination due to the fault currents resulting from DERs. With limited communication capability, these undetected protective device (PD) actuations can cause uncertainties and delay the service restoration process. This dissertation makes contributions to the coordination of EVs and DERs. It introduces four innovative models for EV coordination: 1) A transactive energy (TE) trading mechanism is proposed to coordinate EVs and aggregators. 2) Optimal tools are provided to assist EVs and aggregators in optimal decision making while participating in TE. 3) A charging station model is developed to allow EVs to provide ancillary service aligned with their mobile nature. 4) A utility function model is presented to capture the EV owners' behaviors for providing ancillary services and charging vehicles. Charging stations can estimate the electric energy demand and optimize ancillary service provision to meet their goals. Simulation cases validated that the proposed optimization tools can align EV owners' preferences in providing ancillary service to enhance distribution system operation flexibility. To enhance the resilience of distribution systems, two novel optimization strategies are presented: 1) An advanced outage management (AOM) is proposed to utilize smart meters and fault indicators (FIs) to identify the most credible outage scenario and fault locations. 2) An advanced feeder restoration (AFR) is developed to provide an optimal restoration strategy to enhance system resilience. The proposed optimization models have been validated with realistic simulation cases. / Doctor of Philosophy / As Electric Vehicles (EVs) and other Distributed Energy Resources (DERs) become more common, they are changing how our distribution systems work. For example, during power outages, grid-connected DERs and EVs can be deployed to sustain essential electricity services such as hospitals and communications. On the other hand, during a normal operating condition, they can help maintain the stability of our electricity systems. It is a technical challenge to integrate these new EV and DER devices into the existing power grid. For example, EVs are mainly designed for transportation. Their clustered charging patterns can significantly increase the electrical demand if they are not managed properly. Also, the limited battery capacity and charging speed make it difficult for a single vehicle to provide meaningful support to the grid operation. For the EV management side, this research is concerned with how to better integrate EVs and similar technologies into the power grid. Four key contributions of this dissertation are: 1) Developing a trading mechanism for EVs and aggregators of EVs to exchange energy and ancillary services efficiently; 2) Creating computational technologies to help these entities optimize their decisions while meeting their requirements; 3) Structuring charging station operations that cater to the preferences of EV owners while supporting grid operation; and 4) Modeling EV owners' decision-making to set optimal pricing and service strategies at charging stations. These mechanisms and strategies will allow EV owners to support the power grid while meeting their transportation needs. Moreover, the study addresses the issue of enhancement of the distribution system's capability to restore services under extreme conditions. It provides an advanced outage management method that utilizes remote monitoring and control technologies, including smart meters and fault indicators, to identify the location of electrical faults and reduce the outage areas. The advanced feeder restoration method determines an optimal strategy to restore the electricity service efficiently while keeping the distribution grid stable.

Exploring the integration of multi-speed gearboxes in electric vehicle (EV) drivetrains, this research presents a comprehensive analysis through detailed gearbox modelling, empirical traction machine testing, and analytical drive unit loss evaluations. The study utilizes two distinct automotive-grade electric machines – an axial-flux permanent magnet synchronous machine and an interior permanent magnet machine, the latter coupled with a single-speed gearbox – to demonstrate how multi-speed gearboxes can enhance drivetrain efficiency and performance for a subcompact EV. Extensive dynamometer testing, incorporating a variety of electrical and thermal conditions, characterizes both traction machines. Findings reveal that despite the incremental churning losses from additional gear pairs, two-speed gearboxes facilitate a more efficient operation of the electric machine, inverter, and gearbox, particularly when optimized through strategic gear ratio selection. Dynamometer testing under no-load conditions and at different temperatures underscores the impact of gearbox churning and bearing drag losses and the potential for their reduction. Detailed examinations of load-dependent and independent losses within the drive unit elucidate the interactions among drivetrain components across various gear ratios. Optimized two-speed gearboxes are shown to reduce vehicle energy consumption by up to 9% and increase driving range compared to conventional single-speed configurations, supported by strategic gear ratio selections and optimizations aimed at achieving vehicle performance targets, such as acceleration, gradeability, and top speed. This research contributes to advancing the field of electric vehicle technology by illustrating the complex trade-offs and potential enhancements achievable with multi-speed drivetrains, setting a precedent for future studies to refine gearbox performance and explore novel technologies to optimize powertrain performance across diverse operational landscapes. / Thesis / Doctor of Philosophy (PhD)

During the past two decades desperate need for energy-efficient vehicles which has less emission have led to a great attention to and development of electrified vehicles like pure electric, Hybrid Electric Vehicle (HEV) and Plug-in Hybrid Electric Vehicles (PHEVs). Resultantly, a great amount of research efforts have been dedicated to development of control strategies for this type of vehicles including PHEV which is the case study in this thesis. This thesis presents a real-time control scheme to improve the fuel economy of plug-in hybrid electric vehicles (PHEVs) by accounting for the instantaneous states of the system as well as the future trip information. To design the mentioned parametric real-time power management policies, we use dynamic programming (DP). First, a representative power-split PHEV powertrain model is introduced, followed by a DP formulation for obtaining the optimal powertrain trajectories from the energy cost point of view for a given drive cycle. The state and decision variables in the DP algorithm are selected in a way that provides the best tradeoff between the computational time and accuracy which is the first contribution of this research effort. These trajectories are then used to train a set of linear maps for the powertrain control variables such as the engine and electric motor/generator torque inputs, through a least-squares optimization process. The DP results indicate that the trip length (distance from the start of the trip to the next charging station) is a key factor in determining the optimal control decisions. To account for this factor, an additional input variable pertaining to the remaining length of the trip is considered during the training of the real-time control policies. The proposed controller receives the demanded propulsion force and the powertrain variables as inputs, and generates the torque commands for the engine and the electric drivetrain system. Numerical simulations indicate that the proposed control policy is able to approximate the optimal trajectories with a good accuracy using the real-time information for the same drive cycles as trained and drive cycle out of training set. To maintain the battery state-of-charge (SOC) above a certain lower bound, two logics have been introduced a switching logic is implemented to transition to a conservative control policy when the battery SOC drops below a certain threshold. Simulation results indicate the effectiveness of the proposed approach in achieving near-optimal performance while maintaining the SOC within the desired range. / MS / During the past two decades desperate need for energy-efficient vehicles which has less emission have led to a great attention to and development of electrified vehicles like pure electric, Hybrid Electric Vehicle (HEV) and Plug-in Hybrid Electric Vehicles (PHEVs). Resultantly, a great amount of research efforts have been dedicated to development of control strategies for this type of vehicles including PHEV which is the case study in this thesis. This thesis presents a real-time control scheme to improve the fuel economy of plug-in hybrid electric vehicles (PHEVs) by accounting for the instantaneous states of the system as well as the future trip information. To design the mentioned parametric real-time power management policies, we use dynamic programming (DP). First, a representative power-split PHEV powertrain model is introduced, followed by a DP formulation for obtaining the optimal powertrain trajectories from the energy cost point of view for a given drive cycle. The state and decision variables in the DP algorithm are selected in a way that provides the best tradeoff between the computational time and accuracy which is the first contribution of this research effort. These trajectories are then used to train a set of linear maps for the powertrain control variables such as the engine and electric motor/generator torque inputs, through a least-squares optimization process. The DP results indicate that the trip length (distance from the start of the trip to the next charging station) is a key factor in determining the optimal control decisions. To account for this iv factor, an additional input variable pertaining to the remaining length of the trip is considered during the training of the real-time control policies. The proposed controller receives the demanded propulsion force and the powertrain variables as inputs, and generates the torque commands for the engine and the electric drivetrain system. Numerical simulations indicate that the proposed control policy is able to approximate the optimal trajectories with a good accuracy using the real-time information for the same drive cycles as trained and drive cycle out of training set. To maintain the battery state-of-charge (SOC) above a certain lower bound, two logics have been introduced a switching logic is implemented to transition to a conservative control policy when the battery SOC drops below a certain threshold. Simulation results indicate the effectiveness of the proposed approach in achieving near-optimal performance while maintaining the SOC within the desired range.

Automotive vehicle manufacturers have been facing increased pressures from legislative bodies and consumers to reduce the fuel consumption and harmful emissions of their newly produced vehicles as a result of new research showing the detrimental effects these emissions have on the environment. These pressures are encouraging manufactures and researchers to invest billions of dollars into the development of new advanced vehicle technologies. Some of these investments have resulted in substantial progress in powertrain technologies that have led to the preliminary adoption of electrified powertrain vehicles. Other areas of research are actively working to reduce the energy consumption of a vehicle, regardless of its powertrain, by influencing driver behavior and by optimizing the way a vehicle travels between an origin and destination. This intelligent vehicle routing is done by analyzing a range of possible routes and selecting the route that consumes the least amount of fuel. An accurate method for predetermining vehicle energy expenditure along a given route before it is driven is needed to effectively implement intelligent vehicle routing systems. One common method is the generation of a road network-wide database with energy use figures for each section of road. This method requires expensive experimentation trials or network simulation software. Individual-level vehicle predictive energy estimation eliminates the need for costly fuel use generation by utilizing vehicle velocity generation techniques and vehicle powertrain models. Estimation of individual vehicle energy consumption along a route is done by identifying an origin-destination pair, detecting required full-stops along the path, and synthesizing multiple stop-to-stop velocity modes between each set of stops. The resulting velocity profile is paired with a specific vehicle powertrain model to determine fuel consumption. A drawback of this route generation technique is that the vehicle path is assumed to be one-dimensional and lacks inclusion of road curves and their associated velocity changes to maintain passenger comfort. This thesis evaluates the merit of discounting road curves in predictive vehicle energy consumption analyses and presents a technique for modeling common road corners that require velocity changes to limit passenger discomfort. The resulting corner synthesis method is combined with a validated vehicle powertrain model to complete full route consumption modeling. Two routes, an urban and highway, are modeled and driven to evaluate the accuracy of the full simulation model when compared with on-road data. The results show that corners can largely be ignored during energy consumption analysis for highways. The cornering effects on a vehicle during urban driving, however, should be included in urban route analyses with multiple road curves. Inclusion of the cornering effects during an example urban route analysis decreased the error between the on-road consumption data and the simulation results. / Master of Science / Automotive vehicle manufacturers have been facing increased pressures from legislative bodies and consumers to reduce the fuel consumption and harmful emissions of their newly produced vehicles as a result of new research showing the detrimental effects these emissions have on the environment. These pressures are encouraging manufactures and researchers to invest billions of dollars into the development of new advanced vehicle technologies. Some of these investments have resulted in substantial progress in powertrain technologies that have led to the preliminary adoption of electrified powertrain vehicles. Other areas of research are actively working to reduce the energy consumption of a vehicle, regardless of its powertrain, by influencing driver behavior and by optimizing the way a vehicle travels between an origin and destination. This intelligent vehicle routing is done by analyzing a range of possible routes and selecting the route that consumes the least amount of fuel. An accurate method for predetermining vehicle energy expenditure along a given route before it is driven is needed to effectively implement intelligent vehicle routing systems. One common method is the generation of a road network-wide database with energy use figures for each section of road. This method requires expensive experimentation trials or network simulation software. Individual-level vehicle predictive energy estimation eliminates the need for costly fuel use generation by utilizing vehicle velocity generation techniques and vehicle powertrain models. Estimation of individual vehicle energy consumption along a route is done by identifying an origin-destination pair, detecting required full-stops along the path, and synthesizing multiple stop-to-stop velocity modes between each set of stops. The resulting velocity profile is paired with a specific vehicle powertrain model to determine fuel consumption. A drawback of this route generation technique is that the vehicle path is assumed to be one-dimensional and lacks inclusion of road curves and their associated velocity changes to maintain passenger comfort. This thesis evaluates the merit of discounting road curves in predictive vehicle energy consumption analyses and presents a technique for modeling common road corners that require velocity changes to limit passenger discomfort. The resulting corner synthesis method is combined with a validated vehicle powertrain model to complete full route consumption modeling. Two routes, an urban and highway, are modeled and driven to evaluate the accuracy of the full simulation model when compared with on-road data. The results show that corners can largely be ignored during energy consumption analysis for highways. The cornering effects on a vehicle during urban driving, however, should be included in urban route analyses with multiple road curves. Inclusion of the cornering effects during an example urban route analysis decreased the error between the on-road consumption data and the simulation results.

The popularity of the internal combustion engine (ICE) vehicles has contributed to global warming problem and degradation of air quality around the world. Furthermore, the vehicles??? massive demand on gas has played a role in the depletion of fossil fuel reserves and the considerable rise in the gas price over the past twenty years. Those existing challenges force the auto-industry to move towards the technology development of vehicle electrification. An electrified vehicle is driven by one or more electric motors. And the electricity comes from the onboard energy storage system (ESS). Currently, no single type of green energy source could meet all the requirements to drive a vehicle. A hybrid energy storage system (HESS), as a combination of battery and ultra-capacitor units, is expected to improve the overall performance of vehicles??? ESS. This thesis focuses on the design of HESS and the development of a HESS prototype for electric vehicles (EVs) and hybrid electric vehicles (HEVs). Battery unit (BU), ultra-capacitor unit (UC) and a DC/DC converter interfacing BU and UC are the three main components of HESS. The research work first reviews literatures regarding characteristics of BU, UC and power electronic converters. HESS design is then conducted based on the considerations of power capability, energy efficiency, size and cost optimization. Besides theoretical analysis, a HESS prototype is developed to prove the principles of operation as well. The results from experiment are compared with those from simulation.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-04-24T18:25:05Z No. of bitstreams: 1 rodolfolacerdavalle.pdf: 17326163 bytes, checksum: b5bfc03806c064bdf98c9890ed322936 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-04-25T12:14:13Z (GMT) No. of bitstreams: 1 rodolfolacerdavalle.pdf: 17326163 bytes, checksum: b5bfc03806c064bdf98c9890ed322936 (MD5) / Made available in DSpace on 2017-04-25T12:14:13Z (GMT). No. of bitstreams: 1 rodolfolacerdavalle.pdf: 17326163 bytes, checksum: b5bfc03806c064bdf98c9890ed322936 (MD5) Previous issue date: 2013-03-07 / Esta dissertação apresenta as etapas de desenvolvimento de um sistema de acionamento de um motor CC de ímãs permanentes sem escovas para aplicação em veículos elétricos de pequeno porte. O motor é acionado por um inversor fonte de tensão trifásico (VSI, Voltage Source Inverter) baseado em interruptores IGBT (Insulated Gate Bipolar Transistor). Os sinais de comutação dos interruptores são gerados por uma estratégia de modulação vetorial. Sinais gerados por três sensores de efeito Hall são usados para detectar a posição angular do rotor. O motor é controlado a partir de duas malhas. Uma malha externa controla a velocidade enquanto a malha interna controla as correntes de fase do motor. O desempenho de dois tipos de controladores foram investigados para o controle das correntes sintetizadas pelo VSI: (i) controlador proporcional– integral no sistema de coordenadas síncrono e (ii) controlador proporcional–integral com integradores em múltiplos sistemas de coordenadas rotativas. Além das malhas de controle, o sistema de acionamento foi projetado com uma estratégia de desacoplamento das correntes de controle e uma ação preditiva para compensar o efeito das tensões contra–eletromotriz trapezoidais geradas por fase pelo motor. Um protótipo experimental do sistema de 5 kW foi montado em laboratório. Os algoritmos de controle do motor e do VSI, juntamente com o algoritmo de deteção de posição do rotor foi implementado em um controlador digital de sinais TMS320F28335. Resultados experimentais são apresentados, discutidos e utilizados para validar as estratégias de controle. / This dissertation presents the development of a permanent magnet brushless DC motor drive (PMBLDC) for small electric vehicles applications. A three–phase voltage source inverter (VSI) based on IGBT (insulated gate bipolar transistor) is used to drive the electric motor. The semiconductor switches are controlled by space vector modulation (SVM). Signals from three Hall effect sensors are used to detect the rotor angular position. Two closed–loops are used to control the PMBLDC motor. The outer loop controls the rotor velocity while the inner loop controls the motor phase currents. Two types of controllers were investigated to synthesize the currents: (i) proportional– integral controller in synchronous reference frame and (ii) proportional–integral controller with multiple rotating synchronous reference frame integrators. Besides the current control loops, the drive system was designed considering the decoupling of the synthesized phase currents and a feed-forward electromotive force waveform compensation. An experimental prototype of 5 kW was built in laboratory. The motor and VSI control algorithms and the algorithm used to detect the rotor position were implemented in a TMS320F28335, digital signal controller. Experimental results are presented, discussed along with the validation of the control strategy.

The thesis deals with the use of solar energy for electromobility. First, the potential of electromobility on the current market is theoretically discussed. This issue includes mainly the producers of electrically powered vehicles, the issue of electric energy storage and the real applications of fast charging and photovoltaic charging stations. In the next part, electric cars are analyzed, which use solar energy for their function and their efficiency is compared with the effiency of combustion engines. The main part of this thesis is the design of photovoltaic charging station for electric vehicles. The final part deals with the economic evaluation of the proposed charging station.