Transportation is one of the areas where Sweden could not yet manage to reduce the CO2 emissions. One solution that has been suggested to reduce the CO2 emissions in this sector is through the mass adoption of electric vehicles (EVs). However, mass EV adoption brings complications with it. Drivers behavior is a critical aspect since people often charge their car at home after work. This could negatively affect the evening load peak and thus cause a high impact on the electricity system. A survey was sent out to current private EV owners in Sweden, to learn about their charging schedules, driving patterns and battery capacity. 226 of 403 replied to the survey which gave a survey reply rate of 56 %. The goal of this work was to estimate the future adoption of EVs, based on the current trends and national targets in order to develop different scenarios. With the scenarios in mind, the projected consumption of EVs for different periods of the day, the magnitude and time of the peak load as well as the overall consumption and CO2 reduction per year were calculated. Three scenarios were analyzed with 96 000, 650 000 and 1 000 000 electric vehicles where 25 % are defined to be running entirely on electricity in the middle and high penetration scenario since even plug-in hybrid electric vehicles, PHEV where included. The scenarios are estimated as the possible situation in 2030 and a simulation is done in MATLAB for summer and winter cases as well as weekdays and weekends. Results showed that the charging pattern of the EV drivers would cause a peak load at around 20.00 where the peak load from the overall household consumptions also takes place. The highest consumption takes place during the weekend cases but there were no significant difference between summer and winter. For example the peak consumption of the EVs was 150 MWh during winter and weekends at 20.00. The annual consumption of the EVs would be 238 GWh, 342 GWh and 616 GWh for the low, middle and high penetration scenario. By analyzing the current installed power of renewable energy sources in Sweden, it was found that the demand for EVs could be met by renewables entirely today. It was also found that using EVs instead of conventional fossil fueled cars can save up to 264 Mton CO2 for the low penetration scenario, 447 Mton for the middle penetration scenario and 688 Mton for the high penetration scenario. Different assumptions could have caused deviation from the actual result and it was found during the implementation of the simulation that the survey questions could be improved for future surveys. It was concluded that mass adoption of EVs is possible in terms of electricity production and installed power. However, increase in the evening peak led to the conclusion that balancing of the grid is necessary for example through Vehicle-to-grid (V2G), controlled charging or energy storage. Keywords: MATLAB, electricity consumption, EV, CO2 emissions, simulation, 2030, Scenario, penetration level

Stricter regulations and evolving environmental concerns have been exerting ever-increasing pressure on the automotive industry to produce low carbon vehicles that reduce emissions. As a result, increasing numbers of alternative powertrain architectures have been released into the marketplace to address this need. However, with a myriad of possible alternative powertrain configurations, which is the most appropriate type for a given vehicle class and duty cycle? To that end, comparative analyses of powertrain configurations have been widely carried out in literature; though such analyses only considered limited types of powertrain architectures at a time. Collating the results from these literature often produced findings that were discontinuous, which made it difficult for drawing conclusions when comparing multiple types of powertrains. The aim of this research is to propose a novel methodology that can be used by practitioners to improve the methods for comparative analyses of different types of powertrain architectures. Contrary to what has been done so far, the proposed methodology combines an optimisation algorithm with a Modular Powertrain Structure that facilitates the simultaneous approach to optimising multiple types of powertrain architectures. The contribution to science is two-folds; presenting a methodology to simultaneously select a powertrain architecture and optimise its component sizes for a given cost function, and demonstrating the use of multi-objective optimisation for identifying trade-offs between cost functions by powertrain architecture selection. Based on the results, the sizing of the powertrain components were influenced by the power and energy requirements of the drivecycle, whereas the powertrain architecture selection was mainly driven by the autonomy range requirements, vehicle mass constraints, CO2 emissions, and powertrain costs. For multi-objective optimisation, the creation of a 3-dimentional Pareto front showed multiple solution points for the different powertrain architectures, which was inherent from the ability of the methodology to concurrently evaluate those architectures. A diverging trend was observed on this front with the increase in the autonomy range, driven primarily by variation in powertrain cost per kilometre. Additionally, there appeared to be a trade-off in terms of electric powertrain sizing between CO2 emissions and lowest mass. This was more evident at lower autonomy ranges, where the battery efficiency was a deciding factor for CO2 emissions. The results have demonstrated the contribution of the proposed methodology in the area of multi-objective powertrain architecture optimisation, thus addressing the aims of this research.

Very little information is known about the impact electrification has on driving behavior, or how drivers charge their electrified vehicles. The recent influx of electrified vehicles presents a new market of vehicles which allow drivers the option between electrical or conventional gasoline energy sources. The current battery capacity in full battery electric vehicles requires planning of routes not required of conventional vehicles, due to the limited range, extended charging times, and limited charging infrastructure. There is currently little information on how drivers react to these limitations. A number of current models of fully electric and plug-in hybrid electric vehicles, transmit data wirelessly on key-on, key-off, and charging events. The data includes battery state of charge, distance of miles driven on gasoline and electric, energy consumed, and many other parameters associated to driving and charging behavior. In this thesis, this data was then processed and analyzed to benchmark the performance and characteristics of driving and charging patterns. Vehicles were analyzed and contrasted based on model type, geographic location, length of ownership and other variables. This data was able to show benchmarks and parameters in aggregate for 56 weeks of electrified vehicle tracking. These parameters were compared to the EV Project, a large scale electrified vehicle study performed by Idaho National Labs, to confirm patterns of expected behavior. New parameters which were not present in the EV Project were analyzed and provided insight to charging and driving behavior not examined in any previous study on a large scale. This study provides benchmarks and conclusions on this new driving behavior, such as large scale analysis of brake regeneration performance and degradation of range anxiety. Analysis of the differences on charging and driving behavior between geographic regions and experience were examined, providing insight to how these variables affect performance and driving and charging patterns. Comparison of parameters established by the EV Project and new parameters analyzed in this report will help build a benchmark for future studies of electrified vehicles.

This thesis is dedicated to a new method of investment evaluation -- real options. The main goal is to simplify the method of real options. This objective is composed of three sub-secondary objectives: the classification of the various valuation methods, focusing on their benefits and pitfalls of practical application, simplifying the mathematical tools for calculating real options and applying the method of real options in the Czech Republic. The authors made use of both printed and electronic sources of information. The benefit of this work is that these resources come from three languages - Russian, Czech and English. The main scientific methods which were used in this thesis are analysis, synthesis, and mathematical-statistical methods. Work is intended for a narrow circle of readers dealing with the evaluation of investments.

The gradual decline in global oil reserves and the presence of ever so stringent emissions rules around the world have created an urgent need for the production of automobiles with improved fuel economy. HEVs (hybrid electric vehicles) have proved a viable option to guaranteeing improved fuel economy and reduced emissions. The fuel consumption benefits which can be realised when utilising HEV architecture are dependent on how much braking energy is regenerated, and how well the regenerated energy is utilised. The challenge in developing a real-time HEV control strategy lies in the satisfaction of often conflicting control constraints involving fuel consumption, emissions and driveability without over-depleting the battery state of charge at the end of the defined driving cycle. Reviewed literature indicates some research gaps and hence exploitable study areas for which this thesis intends to address. For example, despite the research advances made, HEV energy management is still lacking in several key areas: optimisation of braking energy regeneration; real-time sub-optimal control of HEV for robustness, charge sustenance and fuel reduction; and real-time vehicle speed control. Consequently, this thesis aims to primarily develop novel real-time near-optimal control strategies for a parallel HEV, with a view to achieving robustness, fuel savings and charge sustenance simultaneously, under various levels of obtainable driving information (no route preview information, partial route preview information). Using a validated HEV dynamic simulation model, the following novel formulations are proposed in this thesis and subsequently evaluated in real time: 1. A simple grouping system useful for classifying standard and real-world driving cycles on the basis of aggressivity and road type. 2. A simple and effective near-optimal heuristic control strategy with no access to route preview information. 3. A dynamic programming-inspired real-time near-optimal control strategy with no access to route preview information. 4. An ECMS (Equivalent Consumption Minimisation Strategy) inspired real-time near-optimal control strategy with no access to route preview information. 5. An ECMS-inspired real-time near-optimal control strategy with partial access to route preview information. 6. A dynamic programming based route-optimal vehicle speed control strategy which accounts for real-time dynamic effects like engine braking, while solving an optimisation problem involving the maximisation of fuel savings with little or no penalty to trip time. 7. A real-time vehicle speed control approach, which is based on smoothing the speed trajectory of the lead vehicle, consequently reducing the acceleration and deceleration events that the intelligent vehicle (follower vehicle) will undergo. This smoothing effect translates into reduced fuel consumption, which tends to increase with increasing traffic preview window. Among other studies performed in this thesis, the fuel savings potential of the proposed near-optimal controllers was investigated in real time over standard driving cycles and real-world driving profiles. Results from these analyses show that, over standard driving cycles, properly formulated near-optimal real-time controllers are able to achieve a fuel savings potential within 0.03% to 3.71% of the global optimal performance, without requiring any access to route preview information. It was also shown that as much as 2.44% extra fuel savings could be achieved over a driving route, through the incorporation of route preview information into a real-time controller. Investigations were also made into the real-time fuel savings that could be realised over a driving route, through vehicle speed control. Results from these analyses show that, compared to an HEV technology which comes at a bigger cost, far higher fuel savings, as much as 45.96%, could be achieved through a simple real-time vehicle speed control approach.

No description available.

No description available.

No description available.

Innovative Business Models (BMs) are essential in commercialising new technologies that are initially seen as inferior. Battery second use (B2U) brings used batteries from an electric vehicle (EV) into a secondary storage application and holds the potential to improve the sustainability of EVs while generating value for stakeholders across the automotive and energy sectors, as well as for the environment and society (Gohla-Neudecker et al. 2015; Neubauer et al. 2015). However, important knowledge gaps exist as the potential value of second-life batteries and how to better extract that value are still poorly understood by both practitioners and researchers. To fill the knowledge gap, this study explores the BMs of repurposing a second life for the retired EV batteries through rich empirical case studies. The main outcomes of the research are firstly, a deeper understanding of the sustainable value of second-life batteries as is currently being achieved by industry, which also provides a comprehensive view of the potential value of B2U. Secondly, the critical B2U challenges are identified from a multi-stakeholder’s perspective across the value chain that present a fresh overview of the key factors that might impair the potential value of B2U. Thirdly, an empirically-generated typology of existing B2U business models is proposed that shows how B2U stakeholders are interacting in different ways to create and capture value from B2U. Fourthly, three critical BM design elements, namely, lifecycle thinking, system-level design and the shift to services are proposed as helpful aspects for B2U stakeholders to consider to better design their B2U business models. Fifthly, Business Model of a Technology (BMoT) is proposed as a new perspective to understand the value potential of second-life batteries and how to maximise the total value creation from B2U at the system level. The research has filled a literature gap, has met an industrial need, and has made contributions to knowledge on sustainability and BMs in the specific context of B2U. Practically, the findings have the potential to inspire practitioners toward better understanding the potential value of second-life batteries and improve their BMs to better extract value from B2U.

In the effort to reduce the release of harmful gases associated with the transportation sector, Plug-in Electric Vehicles (PEV) have been deployed on the account of zero-tail pipe emissions. With electrification of transport it is imperative to address the electrical grid emissions during vehicle charging by motivating the use of distributed generation. This thesis employs optimal charging strategies based on solar availability and electrical grid tariffs to minimize the cost of retrofitting an existing parking lot with photovoltaic (PV) and PEV infrastructure. The optimization is cast as a unit-commitment problem using the CPLEX optimization tool to determine the optimal charge scheduling. The model determines the optimal capacity of system components and assesses the techno-economic feasibility of PV infrastructure in the microgrid by minimizing the net present cost (NPC) in two case studies: Victoria, BC and Los Angeles, CA. It was determined that due to a relatively low grid tariff and scarcity of solar irradiation, it is not economically feasible to install solar panels and coordination of charging reduces the operating cost by 11% in Victoria. Alternatively, with a high grid tariff and abundance of solar radiation, it shown that Los Angeles is a promising candidate for PV installations. With the implementation of a charging coordination scheme in this region, NPC savings of 8-16% are simulated with the current prices of solar infrastructure. Additionally, coordinated charging was assessed in conjunction with various commercial buildings posing as a base load and it was determined that the effects of coordination were more prominent with smaller base loads. / Graduate