The focus of this thesis is to find, visualize and analyze the optimal flow of autonomous electric vehicles with charge constraints in urban traffic with respect to energy consumption. The traffic has been formulated as a static multi-commodity network flow problem, for which two different models have been implemented to handle the charge constraints. The first model uses a recursive algorithm to find the optimal solution fulfilling the charge constraints, while the second model discretizes the commodities’ battery to predetermined battery levels. An implementation of both methods is provided through simulations on scenarios of three different sizes. The results show that both methods are capable of representing the traffic flow with charge constraints, with limitations given by the size of the problem. In particular, the recursive model has the advantage of considering the charge as a continuous quantity. On the other hand the discretization of battery levels allows to handle charge constraint setups with higher complexity, that is when longer detours are needed to fulfill the charge constraints.

Due à l’augmentation de la force des changements climatiques, il devient critique d’éliminer les combustibles fossiles. Les véhicules électriques sont un bon moyen de réduire notre dépendance à ces matières polluantes, mais leur adoption est généralement limitée par le manque d’accessibilité à des stations de recharge. Dans cet article, notre but est d’agrandir l’infrastrucure liée aux stations de recharge pour fournir une meilleure qualité de service aux usagers (et une meilleure accessibilité aux stations). Nous nous attaquons spéficiquement au context urbain. Nous proposons de représenter un modèle d’assignation de demande de recharge à des stations sous la forme d’un problème de flux maximum. Ce modèle nous sert de base pour évaluer la satisfaction des usagers étant donné l’infrastruture disponible. Par la suite, nous incorporons le model de flux maximum à un programme en nombre entier mixte qui a pour but d’évaluer l’installation de nouvelles stations et d’étendre leur disponibilité en ajoutant plus de bornes de recharge. Nous présentons notre méthodologie dans le cas de la ville de Montréal et montrons que notre approche est en mesure résoudre des instances réalistes. Nous concluons en montrant l’importance de la variation dans le temps et l’espace de la demande de recharge lorsque l’on résout des instances de taille réelle. / With the increasing effects of climate change, the urgency to step away from fossil fuels is greater than ever before. Electric vehicles (EVs) are one way to diminish these effects, but their widespread adoption is often limited by the insufficient availability of charging stations. In this work, our goal is to expand the infrastructure of EV charging stations, in order to provide a better quality of service in terms of user satisfaction (and availability of charging stations). Specifically, our focus is directed towards urban areas. We first propose a model for the assignment of EV charging demand to stations, framing it as a maximum flow problem. This model is the basis for the evaluation of the user satisfaction by a given charging infrastructure. Secondly, we incorporate the maximum flow model into a mixedinteger linear program, where decisions on the opening of new stations and on the expansion of their capacity through additional outlets is accounted for. We showcase our methodology for the city of Montreal, demonstrating the scalability of our approach to handle real-world scenarios. We conclude that considering both spacial and temporal variations in charging demand is meaningful when solving realistic instances.

Hela världen står inför nya utmaningar till följd av klimatförändringar. I syfte att mitigera människans påverkan på klimatet har den Europeiska unionen och Sverige som EU-land beslutat att fasa ut användningen av fossila drivmedel. Omställningen från fossildrivna fordon till eldrivna har påbörjats men Sverige är i dagsläget långt ifrån en fullständig omställning. Förutsättningen för att en hundraprocentig omställning skall vara möjlig är att en fungerande laddningsinfrastruktur är på plats och kan hantera det ökande behovet av laddning. I denna rapport studeras den nuvarande svenska laddningsinfrastrukturen och de faktorer som hämmar en fortsatt utveckling presenteras. Avslutningsvis redovisas det att satsning på publik- och hemmaladdning är viktiga faktorer i alla nivåer av omställning, insyn och samverkar är något som saknas idag och är något som måste finnas för att fullständig omställning skall vara möjlig. / The entire world is confronting new challenges as a result of climate change. To mitigate human impact on the climate, the European Union and Sweden, as an EU member state, have decided to phase out the use of fossil fuels. The transition from fossil-fueled vehicles to electric ones has commenced, but Sweden is currently far from achieving a complete transition. The prerequisite for a complete transition is the establishment of a functional charging infrastructure capable of handling the increasing demand for charging. This report examines the current state of the Swedish charging infrastructure, highlights the factors hindering further development, and concludes by outlining the necessary steps for the Swedish charging infrastructure to facilitate transitions of 50-, 75-, and 100-percent from fossil-fueled to electric vehicles. Based on this analysis, it is evident that investments in public and home charging are critical factors at all levels of transition. Transparency and collaboration are currently lacking but are essential elements that must be in place for a successful and complete transition.

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.

All over the world, raising concerns about energy conservation and the environmental impacts of greenhouse gas emissions has promoted the development of a sustainable mobility transition. Successful electric vehicle (EV) deployment plays a vital role in this manner but is still facing obstacles, where public charging infrastructure is one of them. Additionally, digitization is transforming and introducing new industries worldwide, contributing with new constructs to be used in the evolving transition. Simultaneously, technology is surpassing the competition, and is one of the most potent transformational force affecting customer relations in the energy sector, leading to customers anticipating more from the power utility companies. To attain a long-termsustainable competitive advantage, firms have to retain, sustain, and nurture their customer base. To do so, corporations have comprehended the value of embracing customer-centric incentives, enabling them to capture more indirect business values. Furthermore, this thesis was done in collaboration with a power utility company, referred to as ‘Org X’ or ‘the CPO’. Influenced by the reasoning above, it investigated the opportunity to create indirect business values through a demand-driven roll-out of the national charging infrastructure with the use of crowdfunding. This was achieved by adopting an exploratory methodology approach, where a mixed inductive-deductive design was used. A multi-method qualitative data collection was made; consisting mainly of semi-structured-, and unstructured interviews with experts in the field. Thus, a profound perspective of the EV-charging market landscape was attained, which enabled adequate reasoning when proposing a strategy approach for the cause. Additionally, quantitative secondary data was used to develop a tool for an initial location evaluation, that is part of the recommended approach. This tool was also used to enhance the understanding of the national EV-charging market landscape, the customer segments, as well the potential market for a co-creating platform. The findings suggest that the perceived readiness level of crowdfunding charging infrastructure varies depending on what aspect that is being accommodated. A platform that connects stakeholders is encouraged by actors in the field, but crowdfunding through solely end-users is questioned as close proximity to the end-user’s location is a key-factor regarding motivation to fund a charging point. A ‘Tier based framework’, that facilitates this transition was therefore developed and evaluated. Additionally, the framework was considered in the market analysis case study, which further included a recommended implementation and communication approach. If used accordingly, this framework could bring both indirect- and direct business values to the power utility company in question, as well as the involved stakeholders. / Oro över energieffektiviseringar och miljökonsekvenser från utsläpp av växthusgaser världen över har främjat utvecklingen av ett hållbart energisystem. En framgångsrik marknadspenetration av elbilar (EVs) har en viktig roll i denna aspekt, men står fortfarande inför hinder där publik laddningsinfrastruktur är en del av problemet. Digitaliseringen leder till transformation och nya industrier vilket bidrar med ytterligare konstruktioner som används i övergången till ett mer hållbart samhälle. Samtidigt driver teknikutvecklingen till ökad konkurrens och har en kraftfull påverkan på vad kunderna förväntar sig från företagen. För att uppnå mer långsiktigt hållbara konkurrensfördelar måste företag sträva efter att behålla, upprätthålla och ta hand sin kundbas. Företag börjar förstå det ökade värde som finns i att utöva mer kundcentrerade incitament och strategier, vilket potentiellt bidrar till mer indirekta affärsvärden. Denna uppsats är i samarbete med ett Svenskt energiföretag, följaktligen refererat till som 'Org X' eller 'CPO'. Baserat på resonemanget ovan, har möjligheten att skapa indirekta affärsvärden genom verkställandet av en mer efterfrågedriven utveckling av den nationella elbilsladdning infrastruktur med hjälp av crowdfunding undersökts. Detta uppnåddes genom att använda ett utforskat tillvägagångssätt, där en blandad induktiv-deduktiv design användes. En kvalitativ datainsamling gjordes på flera sätt; huvudsakligen bestående av semistrukturerade och ostrukturerade intervjuer med experter inom området. Således uppnåddes ett djupgående perspektiv på av elbilsladdning marknaden vilket möjliggjorde sakliga resonemang kring den presenterade och rekommenderade strategin. Ytterligare användes kvantitativ sekundärdata för att utveckla ett verktyg för en initial plats bedömning, vilket är en del av den rekommenderade strategin. Detta verktyg användes dessutom för att öka förståelsen för den nationella elbilsladdning marknaden, kundsegmenten, liksom den potentiella marknaden för en samskapande plattform. Resultaten tyder på att den upplevda beredskapsnivån att crowdfunda laddningsinfrastruktur varierar beroende på plats och kundgrupp. En plattform som ansluter intressenter uppmuntras av aktörer på marknaden, men crowdfunding genom enbart slutanvändare ifrågasätts då närhet till slutanvändarens läge är en nyckelfaktor när det gäller motivationen att medverka i finansieringen. Därav har ett tier based framework utvecklats och presenterats, som bör underlätta transformationen mot en mer kunddriven affärsmöjlighet. Dessutom beaktades ramverket i fallstudien för marknadsanalysen, som ytterligare inkluderade en rekommenderad strategi för implementering och kommunikation. Om den används i enlighet bör ramverket ge både indirekta och direkta affärsvärden till det aktuella energi företaget, liksom till berörda intressenter.

Due to the intermittent nature of renewable energy production, maintaining the stability of the power supply system is becoming a significant challenge of the energy transition. Besides, the penetration of Electric Vehicles (EVs) and the development of a large network of charging stations will inevitably increase the pressure on the electrical grid. However, this network and the batteries that are connected to it also constitute a significant resource to provide ancillary services and therefore a new opportunity to stabilize the power grid. This requires to be able to produce accurate short term forecasts of the power consumption of charging stations at distribution scale. This work proposes a full forecasting framework, from the transformation of discrete charging sessions logs into a continuous aggregated load profile, to the pre-processing of the time series and the generation of predictions. This framework is used to identify the most appropriate model to provide two days ahead predictions of the hourly load profile of large charging stations networks. Using three years of data collected at Amsterdam’s public stations, the performance of several state-of-the-art forecasting models, including Gradient Boosted Trees (GBTs) and Recurrent Neural Networks (RNNs) is evaluated and compared to a classical time series model (Auto Regressive Integrated Moving Average (ARIMA)). The best performances are obtained with an Extreme Gradient Boosting (XGBoost) model using harmonic terms, past consumption values, calendar information and temperature forecasts as prediction features. This study also highlights periodical patterns in charging behaviors, as well as strong calendar effects and an influence of temperature on EV usage. / På grund av den intermittenta karaktären av förnybar energiproduktion, blir upprätthållandet av elnäts stabilitet en betydande utmaning. Dessutom kommer penetrationen av elbilar och utvecklingen av ett stort nät av laddstationer att öka trycket på elnätet. Men detta laddnät och batterierna som är anslutna till det utgör också en betydande resurs för att tillhandahålla kompletterande tjänster och därför en ny möjlighet att stabilisera elnätet. För att göra sådant bör man kunna producera korrekta kortsiktiga prognoser för laddstationens strömförbrukning i distributions skala. Detta arbete föreslår ett fullständigt prognos protokoll, från omvandlingen av diskreta laddnings sessioner till en kontinuerlig förbrukningsprofil, till förbehandling av tidsserier och generering av förutsägelser. Protokollet används för att identifiera den mest lämpliga metoden för att ge två dagars förutsägelser av timförbrukning profilen för ett stort laddstation nät. Med hjälp av tre års data som samlats in på Amsterdams publika stationer utvärderas prestanda för flera avancerade prognosmodeller som är gradient boosting och återkommande neurala nätverk, och jämförs med en klassisk tidsseriemodell (ARIMA). De bästa resultaten uppnås med en XGBoost modell med harmoniska termer, tidigare förbrukningsvärden, kalenderinformation och temperatur prognoser som förutsägelse funktioner. Denna studie belyser också periodiska mönster i laddningsbeteenden, liksom starka kalendereffekter och temperaturpåverkan på elbilar-användning.

Switching from fossil-fuel-powered vehicles to electric vehicles has become an international focus in the pursuit of combatting climate change. Regardless, the adoption of electric vehicles has been slow, in part, due to range anxiety. One solution to mitigating range anxiety is to provide a more accurate state of charge (SOC) and range estimation. SOC estimation of lithium-ion batteries for electric vehicle application is a well-researched topic, yet minimal tools and code exist online for researchers and students alike. To that end, a publicly available Kalman filter-based SOC estimation function is presented. The MATLAB function utilizes a second-order resistor-capacitor equivalent circuit model. It requires the SOC-OCV (open circuit voltage) curve, internal resistance, and equivalent circuit model battery parameters. Users can use an extended Kalman filter (EKF) or adaptive extended Kalman filter (AEKF) algorithm and temperature-dependent battery data. A practical example is illustrated using the LA92 driving cycle of a Turnigy battery at multiple temperatures ranging from -10C to 40C. Current range estimation methods suffer from inaccuracy as factors including temperature, wind, driver behaviour, battery voltage, current, SOC, route/terrain, and much more make it difficult to model accurately. One of the most critical factors in range estimation is the battery. However, most models thus far are represented using equivalent circuit models as they are more widely researched. Another limitation is that any machine learning-based range estimation is typically based on historical driving data that require odometer readings for training. A range estimation algorithm using a machine learning-based voltage estimation model is presented. Specifically, the long short-term memory cell in a recurrent neural network is used for the battery model. The model is trained with two datasets, classic and whole, from the experimental data of four Tesla/Panasonic 2170 battery cells. All network training is completed on SHARCNET, a resource provided by Canada Compute to researchers. The classically trained network achieved an average root mean squared error (RMSE) of 44 mV compared to 34 mV achieved by the network trained on the whole dataset. Based on the whole dataset, all test cases achieve an end range estimation of less than 5 km with an average of 0.29 km. / Thesis / Master of Applied Science (MASc)

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This study delves into the comparative operational effectiveness of non-coordinated, bottom-up, and top-down coordinated control models within Distributed Energy Storage Systems (DESS) and Electric Vehicle (EV) networks. Employing meticulous data analysis, this research evaluates power demand and supply dynamics within the infrastructure and buildings, aiming to optimize energy usage and storage. The analysis involves comprehensive steps: descriptive statistical breakdown, understanding energy patterns across buildings, and a comparative assessment of the control models. Visual representations and graphs aid in depicting energy patterns, emphasizing the distinctive characteristics and effectiveness of each control model. The findings reinforce the superiority of the top-down coordinated control model in managing supply-demand imbalances, echoing established literature.