Transportation is one of the sectors that directly touches the major challenges that energy utilities are faced with, namely, the significant increase in energy demand and environmental issues. In view of these concerns and the problems with the supply of oil, the pursuit of alternative fuels for meeting the future energy demand of the transport sector has gained much attention. The future of transportation is believed to be based on electric drives in fuel cell vehicles (FCVs) or plug-in electric vehicles (PEVs). There are compelling reasons for this to happen: the efficiency of electric drive is at least three times greater than that of combustion processes and these vehicles produce almost zero emissions, which can help relieve many environmental concerns. The future of PEVs is even more promising because of the availability of electricity infrastructure. Furthermore, governments around the world are showing interest in this technology by investing billions of dollars in battery technology and supportive incentive programs for the customers to buy these vehicles. In view of all these considerations, power systems specialists must be prepared for the possible impacts of these new types of loads on the system and plan for the optimal transition to these new types of vehicles by considering the electricity grid constraints. Electricity infrastructure is designed to meet the highest expected demand, which only occurs a few hundred hours per year. For the remaining time, in particular during off-peak hours, the system is underutilized and could generate and deliver a substantial amount of energy to other sectors such as transport by generating hydrogen for FCVs or charging the batteries in PEVs. This thesis investigates the technical and economic feasibility of improving the utilization of electricity system during off-peak hours through alternative-fuel vehicles (AFVs) and develops optimization planning models for the transition to these types of vehicles. These planning models are based on decomposing the region under study into different zones, where the main power generation and electricity load centers are located, and considering the major transmission corridors among them. An emission cost model of generation is first developed to account for the environmental impacts of the extra load on the electricity grid due to the introduction of AFVs. This is followed by developing a hydrogen transportation model and, consequently, a comprehensive optimization model for transition to FCVs in the context of an integrated electricity and hydrogen system. This model can determine the optimal size of the hydrogen production plants to be developed in different zones in each year, optimal hydrogen transportation routes and ultimately bring about hydrogen economy penetration. This model is also extended to account for optimal transition to plug-in hybrid electric vehicles (PHEVs). Different aspects of the proposed transition models are discussed on a developed 3-zone test system. The practical application of the proposed models is demonstrated by applying them to Ontario, Canada, with the purpose of finding the maximum potential penetrations of AFVs into Ontario’s transport sector by 2025, without jeopardizing the reliability of the grid or developing new infrastructure. Applying the models to this real-case problem requires the development of models for Ontario’s transmission network, generation capacity and base-load demand during the planning study. Thus, a zone-based model for Ontario’s transmission network is developed relying on major 500 and 230 kV transmission corridors. Also, based on Ontario’s Integrated Power System Plan (IPSP) and a variety of information provided by the Ontario Power Authority (OPA) and Ontario’s Independent Electricity System Operator (IESO), a zonal pattern of base-load generation capacity is proposed. The optimization models developed in this study involve many parameters that must be estimated; however, estimation errors may substantially influence the optimal solution. In order to resolve this problem, this thesis proposes the application of robust optimization for planning the transition to AFVs. Thus, a comprehensive sensitivity analysis using Monte Carlo simulation is performed to find the impact of estimation errors in the parameters of the planning models; the results of this study reveals the most influential parameters on the optimal solution. Having a knowledge of the most affecting parameters, a new robust optimization approach is applied to develop robust counterpart problems for planning models. These models address the shortcoming of the classical robust optimization approach where robustness is ensured at the cost of significantly losing optimality. The results of the robust models demonstrate that with a reasonable trade-off between optimality and conservatism, at least 170,000 FCVs and 900,000 PHEVs with 30 km all-electric range (AER) can be supported by Ontario’s grid by 2025 without any additional grid investments.

The purpose of study is to research the development of electric vehicle technology in the United States. This study describes the United States public policies towards electric vehicle technology and system of innovation approaches. The government roles with the help of national system of innovation have been also covered in this study. The point of departure was the study of available literature and U.S energy policy acts which illustrates that the break-through in electric vehicles still not only depended on better battery technology and infrastructure for charging stations but also on social, economic and political factors. The important actors involved in the process are both at local and international level are private firms, governmental departments, research and development (R&D) institutes, nongovernment organizations (NGO’s) and environmental organizations etc. The arguments which are put forward in the background of development of such technologies are to reduce dependence on foreign oil and to reduce emissions of harmful gasses.

This article explores the potential of using real-world driving patterns to derive PHEV and BEV utility factors and evaluates how different travel and recharging behaviours affect the calculation of the standard SAE J2841 utility factor. The study relies on six datasets of driving data collected monitoring 508,607 conventional fuel vehicles in six European areas and a dataset of synthetic data from 700,000 vehicles in a seventh European area. Sources representing the actual driving behaviour of PHEV together with the WLTP European utility factor are adopted as term of comparison. The results show that different datasets of driving data can yield to different estimates of the utility factor. The SAE J2841 standard method results to be representative of a large variety of behaviours of PHEVs and BEVs' drivers, characterised by a fully-charged battery at the beginning of the trip sequence, thus being representative for fuel economy and emission estimates in the early phase deployment of EVs, charged at home and overnight. However the results show that the SAE J2841 utility factor might need to be revised to account for more complex future scenarios, such as necessity-driven recharge behaviour with less than one recharge per day or a fully deployed recharge infrastructure with more than one recharge per day.

The automotive industry is undertaking major technological steps in an effort to reduce emissions and fight climate change. To reduce the reliability on fossil fuels a lot of research is invested into electric motors (EM) and their applications. One such application is plug-in hybrid electric vehicles (PHEV), in which internal combustion engines (ICE) and EM are used in combination, and take turns to propel the vehicle based on driving conditions. The main optimization problem of PHEV is to decide when to use which motor. If this optimization is done with respect to emissions, the entire electric charge should be used up before the end of the trip. But if the charge is used up too early, latter driving segments for which the optimal choice would have been to use the EM will have to be done using the ICE. To address this optimization problem, we studied the fuel consumption during different driving conditions. These driving conditions are characterized by hundreds of sensors which collect data about the state of the vehicle continuously when driving. From these data, we constructed 150 seconds segments, including e.g. vehicle speed, before new descriptive features were engineered for each segment, e.g. max vehicle speed. By using the characteristics of typical driving conditions specified by the Worldwide Harmonized Light Vehicles Test Cycle (WLTC), segments were labelled as a highway or city road segments. To reduce the dimensions without losing information, principle component analysis was conducted, and a Gaussian mixture model was used to uncover hidden structures in the data. Three machine learning regression models were trained and tested: a linear mixed model, a kernel ridge regression model with linear kernel function, and lastly a kernel ridge regression model with an RBF kernel function. By splitting the data into a training set and a test set the models were evaluated on data which they have not been trained on. The model performance and explanation rate obtained for each model, such as R2, Mean Absolute Error and Mean Squared Error, were compared to find the best model. The study shows that the fuel consumption can be modelled by the sensor data of a PHEV test fleet where 6 features contributes to an explanation ratio of 0.5, thus having highest impact on the fuel consumption. One needs to keep in mind the data were collected during the Covid-19 outbreak where travel patterns were not considered to be normal. No regression model can explain the real world better than what the underlying data does. / Fordonsindustrin vidtar stora tekniska steg för att minska utsläppen och bekämpa klimatförändringar. För att minska tillförlitligheten på fossila bränslen investeras en hel del forskning i elmotorer (EM) och deras tillämpningar. En sådan applikation är laddhybrider (PHEV), där förbränningsmotorer (ICE) och EM används i kombination, och turas om för att driva fordonet baserat på rådande körförhållanden. PHEV: s huvudoptimeringsproblem är att bestämma när man ska använda vilken motor. Om denna optimering görs med avseende på utsläpp bör hela den elektriska laddningen användas innan resan är slut. Men om laddningen används för tidigt måste senare delar av resan, för vilka det optimala valet hade varit att använda EM, göras med ICE. För att ta itu med detta optimeringsproblem, studerade vi bränsleförbrukningen under olika körförhållanden. Dessa körförhållanden kännetecknas av hundratals sensorer som samlar in data om fordonets tillstånd kontinuerligt vid körning. Från dessa data konstruerade vi 150 sekunder segment, inkluderandes exempelvis fordonshastighet, innan nya beskrivande attribut konstruerades för varje segment, exempelvis högsta fordonshastighet. Genom att använda egenskaperna för typiska körförhållanden som specificerats av Worldwide Harmonized Light Vehicles Test Cycle (WLTC), märktes segment som motorvägs- eller stadsvägsegment. För att minska dimensioner på data utan att förlora information, användes principal component analysis och en Gaussian Mixture model för att avslöja dolda strukturer i data. Tre maskininlärnings regressionsmodeller skapades och testades: en linjär blandad modell, en kernel ridge regression modell med linjär kernel funktion och slutligen en en kernel ridge regression modell med RBF kernel funktion. Genom att dela upp informationen i ett tränings set och ett test set utvärderades de tre modellerna på data som de inte har tränats på. För utvärdering och förklaringsgrad av varje modell användes, R2, Mean Absolute Error och Mean Squared Error. Studien visar att bränsleförbrukningen kan modelleras av sensordata för en PHEV-testflotta där 6 stycken attribut har en förklaringsgrad av 0.5 och därmed har störst inflytande på bränsleförbrukningen . Man måste komma ihåg att all data samlades in under Covid-19-utbrottet där resmönster inte ansågs vara normala och att ingen regressionsmodell kan förklara den verkliga världen bättre än vad underliggande data gör.

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.

This thesis presents two transient models for a prototype integrated charger for use in a plug-in hybrid-electrical vehicle application. The models can be useful in order to develop control algorithms for the system or to recommend improvements to the machine design. A flux map based method, obtaining input data from simulations using the finite element method (FEM) is used to model the grid synchronization process. The grid side voltage can then be predicted by incorporating spatial flux linkage harmonics. The model is implemented in Matlab/Simulink and compared to stand alone FEM simulations with good agreement. The charging process is modeled using an inductance based model also requiring FEM simulations as input data. Since the flux linkages in the grid and inverter side windings are dependent on each other, the presented transient model is linearized around a specific operating point. This model is also implemented in a Matlab/Simulink environment. Sensorless control of a PMSynRel drive is also studied in this thesis. Focus is put on operating limits due to magnetic saturation when operating at low speeds. The rotating and pulsating voltage vector injection methods for sensorless control are studied in detail. A technique to map the feasible sensorless control region is proposed which utilizes the resulting position error signal rather than data of differential inductances. This technique is implemented experimentally and compared to corresponding FEM simulations with good agreement. The impact of spatial inductance harmonics on the quality of the position estimates is also studied. A method to predict the maximum position estimation error due to the inductance harmonics is proposed based on simplified analytical models. A technique is presented and experimentally verified which can compensate for this effect by injecting a modified rotating voltage carrier. Lastly, the impact of saturation in the rotor structure on the initial magnet polarity detection is investigated. The experimental results, in good agreement with the corresponding FEM simulations, indicate that the impact of saturation in the magnet bridges of rotor is the dominant phenomenon at lower peak current magnitudes. / QC 20110928

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The well-to-wheel (WTW) environmental performance of plug-in hybrid electric vehicles (PHEVs) is sensitive to driving patterns, which vary within and across regions. This thesis develops and applies a novel approach for estimating specific regional driving patterns. The approach employs a macroscopic traffic assignment model linked with a vehicle motion model to construct driving cycles, which is done for a wide range of driving patterns. For each driving cycle, the tank-to-wheel energy use of two PHEVs and comparable non-plug-in alternatives is estimated. These estimates are then employed within a WTW analysis to investigate implications of driving patterns on the energy use and greenhouse gas emission of PHEVs, and the WTW performance of PHEVs relative to non-plug-in alternatives for various electricity generation scenarios. The results of the WTW analysis demonstrate that driving patterns and the electricity generation supply interact to substantially impact the WTW performance of PHEVs.

The well-to-wheel (WTW) environmental performance of plug-in hybrid electric vehicles (PHEVs) is sensitive to driving patterns, which vary within and across regions. This thesis develops and applies a novel approach for estimating specific regional driving patterns. The approach employs a macroscopic traffic assignment model linked with a vehicle motion model to construct driving cycles, which is done for a wide range of driving patterns. For each driving cycle, the tank-to-wheel energy use of two PHEVs and comparable non-plug-in alternatives is estimated. These estimates are then employed within a WTW analysis to investigate implications of driving patterns on the energy use and greenhouse gas emission of PHEVs, and the WTW performance of PHEVs relative to non-plug-in alternatives for various electricity generation scenarios. The results of the WTW analysis demonstrate that driving patterns and the electricity generation supply interact to substantially impact the WTW performance of PHEVs.

L’apparition massive des véhicules électriques (VEs) sur les marchés peut avoir un impact important sur le fonctionnement des réseaux d’électricité actuels qui devront ajuster leur fonctionnement à la nouvelle demande massive d'électricité provenant des VEs. Par contre, les VEs peuvent aussi être vus comme une nouvelle opportunité dans le futur marché d’électricité. En effet, une décharge/recharge intelligente peut permettre aux VEs d’être un support de stockage d’électricité important, valable et permanent dont la capacité croit en fonction du nombre des VEs. Ce projet a comme objectifs de : (1) proposer un schéma d’interaction V2G intégrant des techniques permettant de : (a) adapter le fonctionnement de la grille aux contraintes temporelles et spatiales relatives au processus de recharge des VEs dans un milieu résidentiel. (b) optimiser les opérations de chargement/déchargement entre les VEs et la grille dans les deux sens. (2) Proposer de nouveaux schémas de communication sans fil, entre les VEs et la grille intelligente loin des bornes de recharge, qui soient basés sur les standards de communications véhiculaires (VANET) ainsi que sur d’autres standards de communication à grande échelle. On introduira des techniques d’accès à la grille intelligente pour négocier le coût de recharge/décharge des batteries et aussi pour planifier la motivation du consommateur afin de favoriser la stabilité de la grille / In the next years, electric vehicles (EVs) will make their appearance on the market. This even will have significant impact on the operation of the existing electricity networks which have to be updated to reach the new massive electricity demand. Moreover, EVs can also be seen as a new opportunity in the future electricity market. Indeed, a smart EV discharge / charge process can be enable a large power, valuable, and permanent storage media. The project's objectives are to: (1) propose a scheme integrating V2G interaction techniques: (a) adapt the grid functionality to the temporal and spatial constraints to the EV charging process in a residential setting. We seek to satisfy different power demands of EVs connected to the mains without stressing too smart grid, (b) optimizing the loading / unloading between EVs and the grid in both directions. (2) To propose new patterns of wireless communication between EVs and smart grid away from the charging stations, which are based on the standards of vehicle communications (VANET), as well as other communication standards on a large scale. Access to smart grid technologies will be introduced to negotiate the cost of charge / discharge, the waiting time of service, locations and also to plan consumer motivation to promote the grid stability