Automobility refers to the continued, self-perpetuating dominance of privately-owned, gasoline-powered vehicles used primarily by single occupants—a system which clearly has broad environmental and societal impacts. Despite increasing societal interest in transitions to more sustainable transportation technologies, there has been little consideration of how such innovations might challenge, maintain or support different aspects of automobility, and what that means for technology deployment, transport policy, and user practices. To bring attention to the complexity and apparent durability of the automobility system, in this paper we develop a conceptual framework that explores automobility through a categorization of frames, or shared cultural meanings. This framework moves beyond the typical focus on private, functional considerations of user choice, financial costs and time use to also consider symbolic and societal frames of automobility that exist among users, non-users, industry, policymakers and other relevant social groups. We illustrate this framework with eight particular frames of automobility that fall into four broad categories: private-functional frames such as (1) cocooning and fortressing and (2) mobile digital offices; private-symbolic frames such as (3) gender identity and (4) social status; societal-functional frames such as (5) environmental stewardship and (6) suburbanization; and societal-symbolic frames such as (7) self-sufficiency and (8) innovativeness. Finally, we start the process of discussing several transportation innovations in light of these automobility frames, namely electrified, autonomous and shared mobility—examining early evidence for which frames would be challenged or supported by such transitions. We believe that appreciation of the complex and varied frames of automobility can enrich discussion of transitions and policy relating to sustainable transportation.

The main goal of this thesis is to evaluate different machine learning models in order to classify buyers of an electric or a hybrid vehicle and to identify the characteristics of the buyers in the European Union. Machine learning algorithms and techniques were adopted to analyze the dataset and to create models that could predict, with a certain accuracy, the customer’s willingness to buy an EV. Identification of the characteristics of the buyers were based on the identified most important features from the machine learning models and statistical analysis. The research consisted of exploratory and explanatory methods (mixed method) with quantitative and qualitative techniques. Quantitative technique was applied to convert categorical values to ordinal and nominal numeric values, to establish cause and effect relationship between the variables by using statistical analysis and to apply machine learning methods on the dataset. The quantitative results were then analyzed by using quantitative and qualitative techniques in order to identify the characteristics of the buyers. The data analytics part relied on a publicly available large dataset from the EU containing transport and mobility related data. From the experiments with logistics regression, support vector machine, random forest, gradient boosting classifier and the artificial neural network it was found that ANN is the best model to identify who won’t buy an EV and gradient boosting classifier is the best model to identify who would like to buy and EV. ML based feature importance identification methods (MDI, permutation feature importance) were used to analyze the characteristics of the buyers. The major buyer’s characteristics found in this thesis are environmental concern, knowledge on car sharing, country of residence, education, control traffic, gender, incentive, education and location of residence. Authors have recommended green marketing as the potential enablers towards a faster and larger adoption of electrical vehicles in the market as environmental impact was found as the most significant behavior of the buyer. Finally, for the future researchers, the authors have recommended fine-tuning the algorithms extensively in order to achieve better accuracy and to collect primary data based on the most important features identified in this thesis.

The rollout of electric vehicles (EV) occurring in parallel with the decarbonisation of the power sector can bring uncontested environmental benefits, in terms of CO2 emission reduction and air quality. This roll out, however, poses challenges to power systems, as additional power demand is injected in context of increasingly volatile supply from renewable energy sources. Smart EV charging services can provide a solution to such challenges. The development of effective smart charging services requires evaluating pre-emptively EV drivers’ response. The current practice in the appraisal of smart charging strategies largely relies on simplistic or theoretical representation of drivers’ charging and travel behaviour. We propose a random utility model for joint EV drivers’ activity-travel scheduling and charging choices. Our model easily integrates in activity-based demand modelling systems for the analyses of integrated transport and energy systems. However, unlike previous charging behaviour models used in integrated transport and energy system analyses, our model empirically captures the behavioural nuances of tactical charging choices in smart grid context, using empirically estimated charging preferences. We present model estimation results that provide insights into the value placed by individuals on the main attributes of the charging choice and draw implications charging service providers

Research that addresses policy measures to increase the adoption of electric vehicles (EVs) has discussed government regulations such as California’s Zero Emission Vehicle (ZEV) or penalties on petroleum-based fuels. Relatively few articles have addressed policy measures designed to increase the adoption of EVs by incentives to influence car buyers’ voluntary behavior. This article examines the effects of such policy measures. Two of these attributes are monetary measures, two others are traffic regulations, and the other three are related to investments in charging infrastructure. Consumer preferences were assessed using a choice-based conjoint analysis on an individual basis by applying the hierarchical Bayes method. In addition, the Kano method was used to elicit consumer satisfaction. This not only enabled the identification of preferences but also why preferences were based on either features that were “must-haves” or on attributes that were not expected but were highly attractive and, thus, led to high satisfaction. The results of surveys conducted in 20 countries in 5 continents showed that the installation of a charging network on freeways is an absolute necessity. This was completely independent from the average mileage driven per day. High cash grants were appreciated as attractive; however, combinations of lower grants with charging facilities resulted in similar preference shares in market simulations for each country. The results may serve as initial guidance for policymakers and practitioners in improving their incentive programs for electric mobility.

In spite of the purported positive environmental consequences of electrifying the light duty vehicle fleet, the number of electric vehicles (EVs) in use is still insignificant. One reason for the modest adoption figures is that the mass acceptance of EVs to a large extent is reliant on consumers’ perception of EVs. This paper presents a comprehensive overview of the drivers for and barriers against consumer adoption of plug-in EVs, as well as an overview of the theoretical perspectives that have been utilized for understanding consumer intentions and adoption behavior towards EVs. In addition, we identify gaps and limitations in existing research and suggest areas in which future research would be able to contribute.

Electric Vehicles (EVs) reduce dependency on oil and carbon emissions. An upsurge in demand for EVs could lead to negative impacts on the grid. However, charging strategies, such as supporting the grid using vehicle-to-grid (V2G) and smart charging technology, can go a long way to reducing the impacts on the electrical load curve. The thesis presents a number of aspects which relate to the interconnection between EVs and the electric grid for achieving an optimized integration. An adaptive algorithm has been developed to perform load peak shaving by V2G and smart charging, while a hypothetical case study containing several types of EVs in a local grid has been conducted. The aim is to examine the developed algorithm. In conclusion, by using the adaptive algorithm, written in C++, an optimum status has been achieved concerning the electric grid and EV batteries.

Wireless power transfer is a method of transferring electric power from a transmitter to a receiver without requiring any physical connection between the two. Dynamic Wireless Power Transfer (DWPT) entails having the transmitters buried under the roadway and the receiver unit being installed on the Electric Vehicle (EV). In this method, EVs are charged while driving over the transmitters as they receive bursts of electric energy at the time of significant alignment between transmitters and receivers. Compared to the stationary charging method which involves parking the EV for long hours for a full charge, the dynamic charging method (i.e., DWPT) offers convenience as the vehicle gets charged while driving. It also facilitates extended driving range of EVs. Despite offering these advantages, DWPT causes a few significant issues. DWPT charging results in a transient power profile both at grid side and EV side, which not only hampers grid-side regulation but also affects EV-battery longevity. To address these two issues, both grid-side and EV-side energy management are needed to be employed to protect the grid and the vehicle from sudden exposure to harmful power transients. In this dissertation, the grid-side and EV-side energy management methods have been investigated. Firstly, a detection system to safely detect the vehicle on charging lane is proposed. This detection system is used to facilitate safe and efficient operation of DWPT chargers on EV roadways. Secondly, A novel DWPT system is proposed, which reduces the grid-side power transients with minimal additional hardware requirements. Finally, an EV-side energy management system is proposed which reduces the exposure of EV batteries to pulsating DPWT-power, thereby helping batteries to last longer.

Les ventes de Véhicules Électriques (VE) ont fortement augmenté ces dernières années. Si les processus de charge de ces VE ne sont pas gérés de manière intelligente, ils risquent de surcharger les réseaux électriques. Inversement, les VE pourraient représenter une opportunité pour ces réseaux en tant qu'unités de stockage distribuées.Cette thèse se propose d’étudier l’intégration intelligente des véhicules rechargeables dans les réseaux électriques d’un point de vue technique, réglementaire et économique. Dans un premier temps, le cadre général nécessaire au développement de ces solutions est passé en revue : les domaines d’application et scenarios de référence sont décrits, les acteurs principaux listés, et les défis principaux analysés.Ensuite, l’accent est mis sur les services système, et plus particulièrement sur le réglage de fréquence. Les conditions règlementaires permettant la participation d’une flotte de véhicules électriques à ce service sont étudiées à partir d’une revue des règles de gestionnaires de réseau de transport existants. De nombreuses simulations techniques et économiques sont réalisées, pour différentes règles de marché.Les services réseau locaux sont ensuite considères. Un éco-quartier est modélisé : il comprend différentes unités de consommation et des sources de production distribuées. Un gestionnaire énergétique local est proposé : son rôle est de contrôler les taux de charge / décharge des véhicules électriques de l’éco-quartier dans l’objectif de limiter les surcharges subies par le transformateur électrique de l’éco-quartier. Des conséquences économiques sont tirées des résultats techniques.Enfin, des résultats expérimentaux sont présentés. Le comportement de deux VE est analysé, dont un dispose de capacités bidirectionnelles. Les preuves de concept expérimentales confirment les capacités théoriques des véhicules électriques : il s’agit d’unités à temps de réponse très court (même en considérant l’architecture TIC complète) et ils sont capables de réagir à des signaux réseau très précisément. / Electric vehicles (EVs) penetration has been rapidly increasing during the last few years. If not managed properly, the charging process of EVs could jeopardize electric grid operations. On the other hand, Grid Integrated Vehicles (GIVs), i.e. vehicles whose charging and discharging patterns are smartly controlled, could turn into valuable assets for the electrical grids as distributed storage units.In this thesis, GIVs are studied from a technical, regulatory, and economics perspectives. First, the general framework for a smart grid integration of EVs is reviewed: application areas and benchmark scenarios are described, the main actors are listed, and the most important challenges are analyzed.Then, the emphasis is put on system wide services, and more particularly on frequency control mechanisms. The regulatory conditions enabling the participation of GIV fleets to this service are studied based on an intensive survey of existing transmission system operator rules. Several economics and technical simulations are performed for various market designs.Then, local grid services are investigated. A representative eco-district is modeled, considering various consumption units and distributed generation. A local energy management system is proposed; it is responsible for controlling the charging / discharging patterns of the GIVs which are located in the district in order to mitigate the overloading conditions of the eco-district transformer. Economic consequences are derived from this technical analysis.At last, some experimental results are presented. They show the behavior of two GIVs, including one with bidirectional capabilities. The experimental proof of concepts confirm the theoretical abilities of GIVs: they are very fast responding units (even considering the complete required IT architecture) and are able to follow grid signals very accurately.

Les ventes de Véhicules Electriques ont été en constante augmentation ces dix dernières années, stimulées par l’adoption de politique publique favorisant la décarbonation du secteur automobile. Dans un contexte d’accroissement des énergies renouvelables dans le mix énergétique, entraînant des besoins plus important en flexibilité, la diffusion massive des véhicules électriques pourrait constituer une nouvelle source de contrainte pour les gestionnaire de réseaux d'électricité si la recharge n’est pas gérée de manière intelligente.La gestion de la recharge des flottes de Véhicules Electriques peut aussi constituer une opportunité pour apporter cette flexibilité, en particulier si les véhicules sont équipés de chargeurs bidirectionnels, capables de réinjecter de l’électricité dans le système pour équilibrer les réseaux.La recherche s’est principalement intéressée à la conception d’algorithmes permettant cette recharge « intelligente », qui prennent en compte les besoins en mobilité des utilisateurs, tout en fournissant différents services de flexibilité.Cette thèse s’attache à aller au-delà de l’aspect algorithmique, en balayant l’ensemble des aspects qui permettraient d’aboutir à un modèle d’affaire viable, et en se focalisant sur la fourniture d’un type de service : la réserve primaire (Frequency Containment Reserve), qui constitue le service identifié comme ayant la plus forte valeur pour des flottes de Véhicules équipés de chargeurs bidirectionnels. / Transport industry being one the first CO2 emitters, there is an urgent need to decarbonize this sector, which could be achieved by the conjunction of the electrification of the vehicles and decarbonization of the electricity generation mix. In conjunction with increasing flexibility needs to support the introduction of Renewable Energy Sources, the development of Electric Vehicles could add new constraints for System Operators if charging process is not managed in a smart way.However, considering mobility requirements, there is a flexibility in the charging pattern of the vehicles, which could be used to offer flexibility services to System Operators, using smart-charging algorithms. Moreover, this flexibility could increase with the possibility to have reverse flow from the battery to the grid.Research focused mainly, during last years, on the design of algorithms to provide services with electric vehicles, taking into account mobility needs of users. In this thesis, we try to go beyond this design of algorithms, going through the different steps to elaborate a viable business model. We focus on the provision of one service – Frequency Containment Reserve – identified as the most valuable for Electric Vehicles equipped with bidirectional chargers.

abstract: Lithium titanium oxide (LTO), is a crystalline (spinel) anode material that has recently been considered as an alternative to carbon anodes in conventional lithium-ion batteries (LIB), mainly due to the inherent safety and durability of this material. In this paper commercial LTO anode 18650 cells with lithium cobalt oxide (LCO) cathodes have been cycled to simulate EV operating condition (temperature and drive profiles) in Arizona. The capacity fade of battery packs (pack #1 and pack#2), each consisting 6 such cells in parallel was studied. While capacity fades faster at the higher temperature (40°C), fading is significantly reduced at the lower temperature limit (0°C). Non-invasive techniques such as Electrochemical Impedance Spectroscopy (EIS) show a steady increase in the high-frequency resistance along with capacity fade indicating Loss of Active Material (LAM) and formation of co-intercalation products like Solid Electrolyte Interface (SEI). A two-stage capacity fade can be observed as previously reported and can be proved by differential voltage curves. The first stage is gradual and marks the slow degradation of the anode while the second stage is marked by a drastic capacity fade and can be attributed to the fading cathode. After an effective capacity fading of ~20%, the battery packs were disassembled, sorted and repackaged into smaller packs of 3 cells each for second-life testing. No major changes were seen in the crystal structure of LTO, establishing its electrochemical stability. However, the poor built of the 18650-cell appears to have resulted in failures like gradual electrolytic decomposition causing prominent swelling and failure in a few cells and LAM from the cathode along with cation dissolution. This result is important to understand how LTO batteries fail to better utilize the batteries for specific secondary-life applications. / Dissertation/Thesis / Masters Thesis Materials Science and Engineering 2019